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This model is a finetune of jondurbin's excellent [bagel](https://huggingface.co/jondurbin/bagel-34b-v0.2) model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
This model has not utilised any form of merging.
### Evaluation Results
| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| --- | --- | --- | --- | --- | --- | --- |
| 77.29 | 74.23 | 86.76 | 76.66 | 70.22 | 83.66 | 72.18 |
### Contamination Results
With reference model jondurbin/bagel-34b-v0.2:
| ARC | TruthfulQA | GSM8K |
| --- | --- | --- |
| 0.08| 0.38| 0.88| | {"license": "other", "license_name": "yi-license", "license_link": "https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE", "base_model": "jondurbin/bagel-34b-v0.2"} | text-generation | LoneStriker/Smaug-34B-v0.1-6.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:jondurbin/bagel-34b-v0.2",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T21:15:30+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #base_model-jondurbin/bagel-34b-v0.2 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| !image/png
!image/png
This model is a finetune of jondurbin's excellent bagel model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
This model has not utilised any form of merging.
### Evaluation Results
### Contamination Results
With reference model jondurbin/bagel-34b-v0.2:
ARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88
| [
"### Evaluation Results",
"### Contamination Results\n\n\nWith reference model jondurbin/bagel-34b-v0.2:\n\n\nARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #base_model-jondurbin/bagel-34b-v0.2 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Evaluation Results",
"### Contamination Results\n\n\nWith reference model jondurbin/bagel-34b-v0.2:\n\n\nARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88"
] | [
72,
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #base_model-jondurbin/bagel-34b-v0.2 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Evaluation Results### Contamination Results\n\n\nWith reference model jondurbin/bagel-34b-v0.2:\n\n\nARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88"
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null | null | null |
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="OscarGalavizC/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.50 +/- 2.73", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | OscarGalavizC/q-Taxi-v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-14T21:18:02+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
32,
33
] | [
"passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-classification | travelgate/room_environment-es-classifier | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T21:19:35+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #distilbert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
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### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #distilbert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #distilbert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Goodreads_Books_Reviews_med2_50
This model is a fine-tuned version of [prajjwal1/bert-medium](https://huggingface.co/prajjwal1/bert-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8153
- F1: 0.6069
- Accuracy: 0.6534
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:--------:|
| 0.8417 | 1.0 | 12657 | 0.8303 | 0.5971 | 0.6453 |
| 0.7803 | 2.0 | 25314 | 0.8153 | 0.6069 | 0.6534 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "Goodreads_Books_Reviews_med2_50", "results": []}]} | text-classification | lilouuch/Goodreads_Books_Reviews_med2_50 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T21:20:38+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Goodreads\_Books\_Reviews\_med2\_50
===================================
This model is a fine-tuned version of prajjwal1/bert-medium on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8153
* F1: 0.6069
* Accuracy: 0.6534
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.20.1
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
| [
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"### Training results",
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] | [
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"passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.20.1\n* Pytorch 1.11.0\n* Datasets 2.1.0\n* Tokenizers 0.12.1"
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null | null | null |
# Lora of gridley/グリッドレイ/格里德利 (Azur Lane)
## What Is This?
This is the LoRA model of waifu gridley/グリッドレイ/格里德利 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/gridley_azurlane](https://huggingface.co/datasets/CyberHarem/gridley_azurlane), which contains 30 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `gridley_azurlane`.**
* Pruned core tags for this waifu are `blonde_hair, blue_eyes, bangs, hair_between_eyes, ahoge, long_hair, bow, hair_ornament, two_side_up, drill_hair, red_bow, animal_ears, deer_ears, ribbon`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 700, you need to download [`700/gridley_azurlane.pt`](https://huggingface.co/CyberHarem/gridley_azurlane/resolve/main/700/gridley_azurlane.pt) as the embedding and [`700/gridley_azurlane.safetensors`](https://huggingface.co/CyberHarem/gridley_azurlane/resolve/main/700/gridley_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 700.
1480 images (1.66 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:-----------------------------------------------------------------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-------------------------------|:-----------------------------------|:-------------------------------|:---------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:-----------------------------|:---------------------------------|:---------------------------------|:-------------------------------|:-----------------------------------------------|:---------------------------------|:---------------------------------|:-----------------------------|:-------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------|:-------------------------------------|
| 700 | 94 | **0.947** | 0.965 | 0.846 | **0.730** | [Download](https://huggingface.co/CyberHarem/gridley_azurlane/resolve/main/700/gridley_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 320 | 43 | 0.923 | **0.985** | 0.850 | 0.722 | [Download](https://huggingface.co/CyberHarem/gridley_azurlane/resolve/main/320/gridley_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 280 | 38 | 0.891 | 0.980 | 0.857 | 0.711 | [Download](https://huggingface.co/CyberHarem/gridley_azurlane/resolve/main/280/gridley_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 300 | 40 | 0.890 | 0.969 | **0.857** | 0.710 | [Download](https://huggingface.co/CyberHarem/gridley_azurlane/resolve/main/300/gridley_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 600 | 80 | 0.922 | 0.984 | 0.842 | 0.709 | [Download](https://huggingface.co/CyberHarem/gridley_azurlane/resolve/main/600/gridley_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 620 to 800](all/0.md)
* [Steps From 420 to 600](all/1.md)
* [Steps From 220 to 400](all/2.md)
* [Steps From 20 to 200](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/gridley_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/gridley_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/gridley_azurlane",
"license:mit",
"region:us"
] | 2024-02-14T21:23:06+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/gridley_azurlane #license-mit #region-us
| Lora of gridley/グリッドレイ/格里德利 (Azur Lane)
=======================================
What Is This?
-------------
This is the LoRA model of waifu gridley/グリッドレイ/格里德利 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/gridley\_azurlane, which contains 30 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'gridley\_azurlane'.
* Pruned core tags for this waifu are 'blonde\_hair, blue\_eyes, bangs, hair\_between\_eyes, ahoge, long\_hair, bow, hair\_ornament, two\_side\_up, drill\_hair, red\_bow, animal\_ears, deer\_ears, ribbon'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 700, you need to download '700/gridley\_azurlane.pt' as the embedding and '700/gridley\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 700.
1480 images (1.66 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 620 to 800
* Steps From 420 to 600
* Steps From 220 to 400
* Steps From 20 to 200
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 700, you need to download '700/gridley\\_azurlane.pt' as the embedding and '700/gridley\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 700.\n\n\n1480 images (1.66 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/gridley_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 700, you need to download '700/gridley\\_azurlane.pt' as the embedding and '700/gridley\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 700.\n\n\n1480 images (1.66 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
44,
38,
465
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/gridley_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
] | [
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "codeparrot/codeparrot-small"} | null | adalib/sqlmodel-cond-gen-codeparrot-small-prefix | [
"peft",
"safetensors",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-codeparrot/codeparrot-small #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
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- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
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APA:
## Glossary [optional]
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] |
null | null | transformers |
This model is a finetune of jondurbin's excellent [bagel](https://huggingface.co/jondurbin/bagel-34b-v0.2) model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
This model has not utilised any form of merging.
### Evaluation Results
| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| --- | --- | --- | --- | --- | --- | --- |
| 77.29 | 74.23 | 86.76 | 76.66 | 70.22 | 83.66 | 72.18 |
### Contamination Results
With reference model jondurbin/bagel-34b-v0.2:
| ARC | TruthfulQA | GSM8K |
| --- | --- | --- |
| 0.08| 0.38| 0.88| | {"license": "other", "license_name": "yi-license", "license_link": "https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE", "base_model": "jondurbin/bagel-34b-v0.2"} | text-generation | LoneStriker/Smaug-34B-v0.1-8.0bpw-h8-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:jondurbin/bagel-34b-v0.2",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T21:26:51+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #base_model-jondurbin/bagel-34b-v0.2 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| !image/png
!image/png
This model is a finetune of jondurbin's excellent bagel model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
This model has not utilised any form of merging.
### Evaluation Results
### Contamination Results
With reference model jondurbin/bagel-34b-v0.2:
ARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88
| [
"### Evaluation Results",
"### Contamination Results\n\n\nWith reference model jondurbin/bagel-34b-v0.2:\n\n\nARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #base_model-jondurbin/bagel-34b-v0.2 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Evaluation Results",
"### Contamination Results\n\n\nWith reference model jondurbin/bagel-34b-v0.2:\n\n\nARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88"
] | [
72,
5,
40
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #base_model-jondurbin/bagel-34b-v0.2 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Evaluation Results### Contamination Results\n\n\nWith reference model jondurbin/bagel-34b-v0.2:\n\n\nARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# shqip-mms
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4601
- Wer: 0.4095
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5728 | 1.06 | 100 | 0.5361 | 0.4876 |
| 0.5221 | 2.13 | 200 | 0.4970 | 0.4495 |
| 0.5093 | 3.19 | 300 | 0.4925 | 0.4400 |
| 0.4562 | 4.26 | 400 | 0.4858 | 0.4305 |
| 0.4518 | 5.32 | 500 | 0.4740 | 0.4416 |
| 0.471 | 6.38 | 600 | 0.4709 | 0.4198 |
| 0.4316 | 7.45 | 700 | 0.4629 | 0.4128 |
| 0.4175 | 8.51 | 800 | 0.4626 | 0.4116 |
| 0.4296 | 9.57 | 900 | 0.4601 | 0.4095 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.3.0.dev20240212
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "facebook/mms-1b-all", "model-index": [{"name": "shqip-mms", "results": []}]} | automatic-speech-recognition | akadriu/shqip-mms | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/mms-1b-all",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | 2024-02-14T21:27:50+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/mms-1b-all #license-cc-by-nc-4.0 #endpoints_compatible #region-us
| shqip-mms
=========
This model is a fine-tuned version of facebook/mms-1b-all on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4601
* Wer: 0.4095
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.001
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.38.0.dev0
* Pytorch 2.3.0.dev20240212
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.3.0.dev20240212\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/mms-1b-all #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.3.0.dev20240212\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
73,
115,
4,
41
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/mms-1b-all #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.3.0.dev20240212\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# furina_seed42_eng_kin_amh_basic
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0154
- Spearman Corr: 0.8112
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.95 | 200 | 0.0239 | 0.7162 |
| 0.0881 | 3.9 | 400 | 0.0177 | 0.7990 |
| 0.0228 | 5.85 | 600 | 0.0167 | 0.8089 |
| 0.0168 | 7.8 | 800 | 0.0153 | 0.8124 |
| 0.0127 | 9.76 | 1000 | 0.0168 | 0.8121 |
| 0.0102 | 11.71 | 1200 | 0.0157 | 0.8114 |
| 0.0085 | 13.66 | 1400 | 0.0154 | 0.8122 |
| 0.0073 | 15.61 | 1600 | 0.0156 | 0.8132 |
| 0.0068 | 17.56 | 1800 | 0.0152 | 0.8097 |
| 0.0061 | 19.51 | 2000 | 0.0158 | 0.8094 |
| 0.0055 | 21.46 | 2200 | 0.0155 | 0.8098 |
| 0.005 | 23.41 | 2400 | 0.0152 | 0.8113 |
| 0.005 | 25.37 | 2600 | 0.0152 | 0.8099 |
| 0.0046 | 27.32 | 2800 | 0.0153 | 0.8113 |
| 0.0044 | 29.27 | 3000 | 0.0154 | 0.8112 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "yihongLiu/furina", "model-index": [{"name": "furina_seed42_eng_kin_amh_basic", "results": []}]} | text-classification | Shijia/furina_seed42_eng_kin_amh_basic | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:yihongLiu/furina",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T21:28:13+00:00 | [] | [] | TAGS
#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us
| furina\_seed42\_eng\_kin\_amh\_basic
====================================
This model is a fine-tuned version of yihongLiu/furina on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0154
* Spearman Corr: 0.8112
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 128
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
60,
141,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | wndiros/diva_em_leo_mistral_v05 | [
"transformers",
"safetensors",
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"text-generation",
"arxiv:1910.09700",
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#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
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## Uses
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small tr Beta - tgrhn
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2781
- Wer: 159.5487
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1524 | 1.09 | 250 | 0.2564 | 90.9258 |
| 0.0654 | 3.02 | 500 | 0.2527 | 83.9888 |
| 0.0331 | 4.12 | 750 | 0.2706 | 170.4544 |
| 0.0168 | 6.05 | 1000 | 0.2781 | 159.5487 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"language": ["tr"], "license": "apache-2.0", "tags": ["whisper-event", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small tr Beta - tgrhn", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 11.0", "type": "mozilla-foundation/common_voice_11_0", "config": "tr", "split": "test", "args": "tr"}, "metrics": [{"type": "wer", "value": 159.5486638323717, "name": "Wer"}]}]}]} | automatic-speech-recognition | tgrhn/whisper-small-turkish | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"tr",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-14T21:30:51+00:00 | [] | [
"tr"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #tr #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Whisper Small tr Beta - tgrhn
=============================
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2781
* Wer: 159.5487
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 250
* training\_steps: 1000
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 250\n* training\\_steps: 1000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #tr #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 250\n* training\\_steps: 1000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
100,
158,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #tr #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 250\n* training\\_steps: 1000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "codeparrot/codeparrot-small"} | null | adalib/sfepy-cond-gen-codeparrot-small-prefix | [
"peft",
"safetensors",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-codeparrot/codeparrot-small #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Model type:
- Language(s) (NLP):
- License:
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### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
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APA:
## Glossary [optional]
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mnli
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6820
- Accuracy: 0.8222
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.7
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-base-uncased", "model-index": [{"name": "mnli", "results": []}]} | text-classification | dezibelkarate/mnli-biasmodel | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T21:32:19+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# mnli
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6820
- Accuracy: 0.8222
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.7
- Tokenizers 0.14.1
| [
"# mnli\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6820\n- Accuracy: 0.8222",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.14.7\n- Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# mnli\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6820\n- Accuracy: 0.8222",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.14.7\n- Tokenizers 0.14.1"
] | [
64,
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"passage: TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# mnli\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6820\n- Accuracy: 0.8222## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 3.0### Training results### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.14.7\n- Tokenizers 0.14.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# scenario-KD-PO-MSV-EN-CL-D2_data-en-massive_all_1_1
This model is a fine-tuned version of [haryoaw/scenario-TCR-data-cl-massive-all_1.1](https://huggingface.co/haryoaw/scenario-TCR-data-cl-massive-all_1.1) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0528
- Accuracy: 0.6919
- F1: 0.6462
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 47
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| No log | 0.28 | 100 | 3.8138 | 0.6389 | 0.5611 |
| No log | 0.56 | 200 | 3.4524 | 0.6604 | 0.5887 |
| No log | 0.83 | 300 | 3.3073 | 0.6719 | 0.6070 |
| No log | 1.11 | 400 | 3.3540 | 0.6642 | 0.6039 |
| 1.7741 | 1.39 | 500 | 3.2769 | 0.6714 | 0.6147 |
| 1.7741 | 1.67 | 600 | 3.3137 | 0.6690 | 0.6081 |
| 1.7741 | 1.94 | 700 | 3.2334 | 0.6758 | 0.6160 |
| 1.7741 | 2.22 | 800 | 3.4030 | 0.6625 | 0.6051 |
| 1.7741 | 2.5 | 900 | 3.3975 | 0.6681 | 0.6132 |
| 0.888 | 2.78 | 1000 | 3.2058 | 0.6820 | 0.6256 |
| 0.888 | 3.06 | 1100 | 3.2794 | 0.6768 | 0.6219 |
| 0.888 | 3.33 | 1200 | 3.1776 | 0.6826 | 0.6303 |
| 0.888 | 3.61 | 1300 | 3.2248 | 0.6753 | 0.6182 |
| 0.888 | 3.89 | 1400 | 3.1162 | 0.6857 | 0.6289 |
| 0.594 | 4.17 | 1500 | 3.2438 | 0.6794 | 0.6302 |
| 0.594 | 4.44 | 1600 | 3.2544 | 0.6827 | 0.6318 |
| 0.594 | 4.72 | 1700 | 3.2055 | 0.6806 | 0.6262 |
| 0.594 | 5.0 | 1800 | 3.2021 | 0.6812 | 0.6293 |
| 0.594 | 5.28 | 1900 | 3.1766 | 0.6862 | 0.6333 |
| 0.4608 | 5.56 | 2000 | 3.2701 | 0.6798 | 0.6284 |
| 0.4608 | 5.83 | 2100 | 3.2048 | 0.6827 | 0.6336 |
| 0.4608 | 6.11 | 2200 | 3.2235 | 0.6816 | 0.6297 |
| 0.4608 | 6.39 | 2300 | 3.1952 | 0.6814 | 0.6315 |
| 0.4608 | 6.67 | 2400 | 3.2690 | 0.6779 | 0.6257 |
| 0.392 | 6.94 | 2500 | 3.0575 | 0.6934 | 0.6372 |
| 0.392 | 7.22 | 2600 | 3.2603 | 0.6780 | 0.6287 |
| 0.392 | 7.5 | 2700 | 3.1213 | 0.6877 | 0.6360 |
| 0.392 | 7.78 | 2800 | 3.1437 | 0.6851 | 0.6325 |
| 0.392 | 8.06 | 2900 | 3.2481 | 0.6796 | 0.6277 |
| 0.3362 | 8.33 | 3000 | 3.2574 | 0.6805 | 0.6374 |
| 0.3362 | 8.61 | 3100 | 3.1799 | 0.6827 | 0.6353 |
| 0.3362 | 8.89 | 3200 | 3.1619 | 0.6831 | 0.6375 |
| 0.3362 | 9.17 | 3300 | 3.0926 | 0.6890 | 0.6431 |
| 0.3362 | 9.44 | 3400 | 3.2109 | 0.6808 | 0.6315 |
| 0.3053 | 9.72 | 3500 | 3.1810 | 0.6828 | 0.6339 |
| 0.3053 | 10.0 | 3600 | 3.3693 | 0.6738 | 0.6323 |
| 0.3053 | 10.28 | 3700 | 3.2051 | 0.6833 | 0.6330 |
| 0.3053 | 10.56 | 3800 | 3.0777 | 0.6892 | 0.6392 |
| 0.3053 | 10.83 | 3900 | 3.1487 | 0.6847 | 0.6343 |
| 0.2789 | 11.11 | 4000 | 3.1779 | 0.6859 | 0.6361 |
| 0.2789 | 11.39 | 4100 | 3.0622 | 0.6901 | 0.6418 |
| 0.2789 | 11.67 | 4200 | 3.2776 | 0.6778 | 0.6354 |
| 0.2789 | 11.94 | 4300 | 3.1611 | 0.6853 | 0.6401 |
| 0.2789 | 12.22 | 4400 | 3.1556 | 0.6875 | 0.6407 |
| 0.2647 | 12.5 | 4500 | 3.2092 | 0.6812 | 0.6368 |
| 0.2647 | 12.78 | 4600 | 3.3073 | 0.6782 | 0.6325 |
| 0.2647 | 13.06 | 4700 | 3.1741 | 0.6852 | 0.6419 |
| 0.2647 | 13.33 | 4800 | 3.1907 | 0.6847 | 0.6393 |
| 0.2647 | 13.61 | 4900 | 3.1004 | 0.6889 | 0.6454 |
| 0.2493 | 13.89 | 5000 | 3.0599 | 0.6910 | 0.6439 |
| 0.2493 | 14.17 | 5100 | 2.9828 | 0.6947 | 0.6466 |
| 0.2493 | 14.44 | 5200 | 3.0982 | 0.6898 | 0.6447 |
| 0.2493 | 14.72 | 5300 | 3.1316 | 0.6865 | 0.6404 |
| 0.2493 | 15.0 | 5400 | 3.1395 | 0.6867 | 0.6362 |
| 0.2411 | 15.28 | 5500 | 3.1417 | 0.6856 | 0.6400 |
| 0.2411 | 15.56 | 5600 | 3.1353 | 0.6860 | 0.6381 |
| 0.2411 | 15.83 | 5700 | 3.1166 | 0.6879 | 0.6394 |
| 0.2411 | 16.11 | 5800 | 3.0215 | 0.6937 | 0.6437 |
| 0.2411 | 16.39 | 5900 | 3.0632 | 0.6911 | 0.6443 |
| 0.23 | 16.67 | 6000 | 3.0764 | 0.6893 | 0.6406 |
| 0.23 | 16.94 | 6100 | 3.0606 | 0.6913 | 0.6437 |
| 0.23 | 17.22 | 6200 | 3.1023 | 0.6884 | 0.6407 |
| 0.23 | 17.5 | 6300 | 3.0954 | 0.6877 | 0.6393 |
| 0.23 | 17.78 | 6400 | 3.1082 | 0.6897 | 0.6434 |
| 0.2247 | 18.06 | 6500 | 3.2086 | 0.6836 | 0.6410 |
| 0.2247 | 18.33 | 6600 | 3.0756 | 0.6886 | 0.6441 |
| 0.2247 | 18.61 | 6700 | 3.1241 | 0.6869 | 0.6439 |
| 0.2247 | 18.89 | 6800 | 3.1416 | 0.6888 | 0.6433 |
| 0.2247 | 19.17 | 6900 | 3.0201 | 0.6941 | 0.6480 |
| 0.2145 | 19.44 | 7000 | 3.2236 | 0.6817 | 0.6393 |
| 0.2145 | 19.72 | 7100 | 3.1263 | 0.6892 | 0.6451 |
| 0.2145 | 20.0 | 7200 | 3.1594 | 0.6873 | 0.6419 |
| 0.2145 | 20.28 | 7300 | 3.1003 | 0.6880 | 0.6423 |
| 0.2145 | 20.56 | 7400 | 3.0484 | 0.6908 | 0.6449 |
| 0.2086 | 20.83 | 7500 | 3.0228 | 0.6939 | 0.6473 |
| 0.2086 | 21.11 | 7600 | 3.1254 | 0.6892 | 0.6430 |
| 0.2086 | 21.39 | 7700 | 3.0679 | 0.6916 | 0.6441 |
| 0.2086 | 21.67 | 7800 | 3.0992 | 0.6893 | 0.6428 |
| 0.2086 | 21.94 | 7900 | 3.0560 | 0.6906 | 0.6438 |
| 0.2035 | 22.22 | 8000 | 3.0692 | 0.6910 | 0.6461 |
| 0.2035 | 22.5 | 8100 | 3.1023 | 0.6874 | 0.6434 |
| 0.2035 | 22.78 | 8200 | 3.0447 | 0.6910 | 0.6469 |
| 0.2035 | 23.06 | 8300 | 3.0808 | 0.6902 | 0.6463 |
| 0.2035 | 23.33 | 8400 | 3.1031 | 0.6906 | 0.6464 |
| 0.1997 | 23.61 | 8500 | 3.0738 | 0.6901 | 0.6446 |
| 0.1997 | 23.89 | 8600 | 3.1039 | 0.6877 | 0.6409 |
| 0.1997 | 24.17 | 8700 | 3.0955 | 0.6894 | 0.6406 |
| 0.1997 | 24.44 | 8800 | 3.1115 | 0.6900 | 0.6455 |
| 0.1997 | 24.72 | 8900 | 3.1270 | 0.6870 | 0.6450 |
| 0.1946 | 25.0 | 9000 | 3.0710 | 0.6912 | 0.6487 |
| 0.1946 | 25.28 | 9100 | 3.0805 | 0.6904 | 0.6453 |
| 0.1946 | 25.56 | 9200 | 3.0624 | 0.6906 | 0.6466 |
| 0.1946 | 25.83 | 9300 | 3.0539 | 0.6916 | 0.6469 |
| 0.1946 | 26.11 | 9400 | 3.0886 | 0.6907 | 0.6463 |
| 0.1925 | 26.39 | 9500 | 3.0717 | 0.6905 | 0.6452 |
| 0.1925 | 26.67 | 9600 | 3.0957 | 0.6896 | 0.6455 |
| 0.1925 | 26.94 | 9700 | 3.0122 | 0.6945 | 0.6471 |
| 0.1925 | 27.22 | 9800 | 3.1008 | 0.6899 | 0.6471 |
| 0.1925 | 27.5 | 9900 | 3.0804 | 0.6910 | 0.6462 |
| 0.1901 | 27.78 | 10000 | 3.0287 | 0.6930 | 0.6483 |
| 0.1901 | 28.06 | 10100 | 3.0691 | 0.6900 | 0.6452 |
| 0.1901 | 28.33 | 10200 | 3.0444 | 0.6925 | 0.6468 |
| 0.1901 | 28.61 | 10300 | 3.0686 | 0.6909 | 0.6470 |
| 0.1901 | 28.89 | 10400 | 3.0778 | 0.6908 | 0.6462 |
| 0.189 | 29.17 | 10500 | 3.0605 | 0.6912 | 0.6462 |
| 0.189 | 29.44 | 10600 | 3.0580 | 0.6918 | 0.6469 |
| 0.189 | 29.72 | 10700 | 3.0559 | 0.6924 | 0.6471 |
| 0.189 | 30.0 | 10800 | 3.0528 | 0.6919 | 0.6462 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["massive"], "metrics": ["accuracy", "f1"], "base_model": "haryoaw/scenario-TCR-data-cl-massive-all_1.1", "model-index": [{"name": "scenario-KD-PO-MSV-EN-CL-D2_data-en-massive_all_1_1", "results": []}]} | null | haryoaw/scenario-KD-PO-MSV-EN-CL-D2_data-en-massive_all_1_1 | [
"transformers",
"pytorch",
"xlm-roberta",
"generated_from_trainer",
"dataset:massive",
"base_model:haryoaw/scenario-TCR-data-cl-massive-all_1.1",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-14T21:34:13+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #generated_from_trainer #dataset-massive #base_model-haryoaw/scenario-TCR-data-cl-massive-all_1.1 #license-mit #endpoints_compatible #region-us
| scenario-KD-PO-MSV-EN-CL-D2\_data-en-massive\_all\_1\_1
=======================================================
This model is a fine-tuned version of haryoaw/scenario-TCR-data-cl-massive-all\_1.1 on the massive dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0528
* Accuracy: 0.6919
* F1: 0.6462
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 47
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
### Training results
### Framework versions
* Transformers 4.33.3
* Pytorch 2.1.1+cu121
* Datasets 2.14.5
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 47\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.33.3\n* Pytorch 2.1.1+cu121\n* Datasets 2.14.5\n* Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #generated_from_trainer #dataset-massive #base_model-haryoaw/scenario-TCR-data-cl-massive-all_1.1 #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 47\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.33.3\n* Pytorch 2.1.1+cu121\n* Datasets 2.14.5\n* Tokenizers 0.13.3"
] | [
70,
98,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #generated_from_trainer #dataset-massive #base_model-haryoaw/scenario-TCR-data-cl-massive-all_1.1 #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 47\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30### Training results### Framework versions\n\n\n* Transformers 4.33.3\n* Pytorch 2.1.1+cu121\n* Datasets 2.14.5\n* Tokenizers 0.13.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# scenario-KD-SCR-MSV-EN-CL-D2_data-en-massive_all_1_1
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 12.8207
- Accuracy: 0.1087
- F1: 0.0816
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 47
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| No log | 0.28 | 100 | 9.4505 | 0.0729 | 0.0048 |
| No log | 0.56 | 200 | 9.2984 | 0.0890 | 0.0261 |
| No log | 0.83 | 300 | 9.7553 | 0.0848 | 0.0444 |
| No log | 1.11 | 400 | 9.7570 | 0.1069 | 0.0697 |
| 6.4752 | 1.39 | 500 | 10.0436 | 0.1015 | 0.0695 |
| 6.4752 | 1.67 | 600 | 10.1380 | 0.1028 | 0.0676 |
| 6.4752 | 1.94 | 700 | 10.3695 | 0.1066 | 0.0728 |
| 6.4752 | 2.22 | 800 | 10.8331 | 0.0965 | 0.0689 |
| 6.4752 | 2.5 | 900 | 11.1266 | 0.0997 | 0.0749 |
| 3.2372 | 2.78 | 1000 | 10.8630 | 0.0989 | 0.0681 |
| 3.2372 | 3.06 | 1100 | 11.1994 | 0.1067 | 0.0769 |
| 3.2372 | 3.33 | 1200 | 10.9878 | 0.1034 | 0.0724 |
| 3.2372 | 3.61 | 1300 | 11.5239 | 0.1017 | 0.0719 |
| 3.2372 | 3.89 | 1400 | 11.1544 | 0.1120 | 0.0788 |
| 2.2795 | 4.17 | 1500 | 11.3500 | 0.0981 | 0.0717 |
| 2.2795 | 4.44 | 1600 | 11.5489 | 0.1016 | 0.0718 |
| 2.2795 | 4.72 | 1700 | 11.5448 | 0.1075 | 0.0810 |
| 2.2795 | 5.0 | 1800 | 12.4145 | 0.1075 | 0.0787 |
| 2.2795 | 5.28 | 1900 | 11.9629 | 0.1000 | 0.0712 |
| 1.7085 | 5.56 | 2000 | 12.7747 | 0.1037 | 0.0748 |
| 1.7085 | 5.83 | 2100 | 12.2893 | 0.1025 | 0.0742 |
| 1.7085 | 6.11 | 2200 | 13.1726 | 0.1026 | 0.0809 |
| 1.7085 | 6.39 | 2300 | 12.7693 | 0.0951 | 0.0697 |
| 1.7085 | 6.67 | 2400 | 12.2965 | 0.0988 | 0.0700 |
| 1.3908 | 6.94 | 2500 | 12.5426 | 0.0990 | 0.0683 |
| 1.3908 | 7.22 | 2600 | 13.1850 | 0.1011 | 0.0792 |
| 1.3908 | 7.5 | 2700 | 12.6008 | 0.1050 | 0.0745 |
| 1.3908 | 7.78 | 2800 | 13.5513 | 0.1061 | 0.0730 |
| 1.3908 | 8.06 | 2900 | 13.3849 | 0.1029 | 0.0755 |
| 1.1599 | 8.33 | 3000 | 13.0032 | 0.1039 | 0.0747 |
| 1.1599 | 8.61 | 3100 | 13.6328 | 0.1044 | 0.0769 |
| 1.1599 | 8.89 | 3200 | 13.3697 | 0.0955 | 0.0715 |
| 1.1599 | 9.17 | 3300 | 13.3708 | 0.1066 | 0.0766 |
| 1.1599 | 9.44 | 3400 | 13.1751 | 0.1062 | 0.0807 |
| 1.0188 | 9.72 | 3500 | 12.4242 | 0.1046 | 0.0724 |
| 1.0188 | 10.0 | 3600 | 13.0852 | 0.0974 | 0.0730 |
| 1.0188 | 10.28 | 3700 | 12.9191 | 0.1052 | 0.0757 |
| 1.0188 | 10.56 | 3800 | 12.6856 | 0.1036 | 0.0731 |
| 1.0188 | 10.83 | 3900 | 13.3897 | 0.1035 | 0.0762 |
| 0.9195 | 11.11 | 4000 | 13.7717 | 0.1059 | 0.0768 |
| 0.9195 | 11.39 | 4100 | 12.3253 | 0.0935 | 0.0712 |
| 0.9195 | 11.67 | 4200 | 13.7718 | 0.1051 | 0.0793 |
| 0.9195 | 11.94 | 4300 | 12.9579 | 0.1070 | 0.0741 |
| 0.9195 | 12.22 | 4400 | 12.9222 | 0.1019 | 0.0727 |
| 0.8308 | 12.5 | 4500 | 12.8426 | 0.1058 | 0.0748 |
| 0.8308 | 12.78 | 4600 | 13.0151 | 0.1044 | 0.0800 |
| 0.8308 | 13.06 | 4700 | 12.5798 | 0.1100 | 0.0792 |
| 0.8308 | 13.33 | 4800 | 12.3432 | 0.1052 | 0.0758 |
| 0.8308 | 13.61 | 4900 | 12.8850 | 0.1050 | 0.0800 |
| 0.774 | 13.89 | 5000 | 12.5100 | 0.1075 | 0.0803 |
| 0.774 | 14.17 | 5100 | 12.8300 | 0.1031 | 0.0757 |
| 0.774 | 14.44 | 5200 | 12.6709 | 0.1051 | 0.0798 |
| 0.774 | 14.72 | 5300 | 13.2432 | 0.1065 | 0.0783 |
| 0.774 | 15.0 | 5400 | 13.1242 | 0.1080 | 0.0797 |
| 0.7241 | 15.28 | 5500 | 13.3100 | 0.1079 | 0.0815 |
| 0.7241 | 15.56 | 5600 | 12.2066 | 0.1059 | 0.0786 |
| 0.7241 | 15.83 | 5700 | 14.1967 | 0.1033 | 0.0848 |
| 0.7241 | 16.11 | 5800 | 13.3712 | 0.1075 | 0.0831 |
| 0.7241 | 16.39 | 5900 | 13.0712 | 0.1046 | 0.0807 |
| 0.6809 | 16.67 | 6000 | 13.3231 | 0.1055 | 0.0814 |
| 0.6809 | 16.94 | 6100 | 13.5326 | 0.1070 | 0.0812 |
| 0.6809 | 17.22 | 6200 | 13.7487 | 0.1051 | 0.0804 |
| 0.6809 | 17.5 | 6300 | 12.1853 | 0.1050 | 0.0812 |
| 0.6809 | 17.78 | 6400 | 12.3673 | 0.1022 | 0.0779 |
| 0.651 | 18.06 | 6500 | 13.1333 | 0.1022 | 0.0798 |
| 0.651 | 18.33 | 6600 | 13.3400 | 0.1044 | 0.0807 |
| 0.651 | 18.61 | 6700 | 12.7509 | 0.1038 | 0.0797 |
| 0.651 | 18.89 | 6800 | 12.6380 | 0.1045 | 0.0812 |
| 0.651 | 19.17 | 6900 | 13.1808 | 0.1076 | 0.0831 |
| 0.6254 | 19.44 | 7000 | 13.0347 | 0.1082 | 0.0851 |
| 0.6254 | 19.72 | 7100 | 12.6187 | 0.1031 | 0.0774 |
| 0.6254 | 20.0 | 7200 | 13.5187 | 0.1051 | 0.0801 |
| 0.6254 | 20.28 | 7300 | 13.0479 | 0.1082 | 0.0809 |
| 0.6254 | 20.56 | 7400 | 12.7203 | 0.1100 | 0.0793 |
| 0.5961 | 20.83 | 7500 | 12.6789 | 0.1043 | 0.0780 |
| 0.5961 | 21.11 | 7600 | 12.7525 | 0.1045 | 0.0807 |
| 0.5961 | 21.39 | 7700 | 13.4722 | 0.1060 | 0.0794 |
| 0.5961 | 21.67 | 7800 | 13.1929 | 0.1073 | 0.0821 |
| 0.5961 | 21.94 | 7900 | 12.8445 | 0.1041 | 0.0810 |
| 0.5787 | 22.22 | 8000 | 12.3704 | 0.1070 | 0.0807 |
| 0.5787 | 22.5 | 8100 | 13.1430 | 0.1084 | 0.0829 |
| 0.5787 | 22.78 | 8200 | 13.0495 | 0.1076 | 0.0820 |
| 0.5787 | 23.06 | 8300 | 13.1554 | 0.1064 | 0.0822 |
| 0.5787 | 23.33 | 8400 | 13.5845 | 0.1074 | 0.0814 |
| 0.5615 | 23.61 | 8500 | 12.8609 | 0.1100 | 0.0823 |
| 0.5615 | 23.89 | 8600 | 13.0800 | 0.1084 | 0.0810 |
| 0.5615 | 24.17 | 8700 | 12.8942 | 0.1068 | 0.0795 |
| 0.5615 | 24.44 | 8800 | 13.1842 | 0.1098 | 0.0834 |
| 0.5615 | 24.72 | 8900 | 13.3674 | 0.1062 | 0.0818 |
| 0.5483 | 25.0 | 9000 | 12.5537 | 0.1085 | 0.0809 |
| 0.5483 | 25.28 | 9100 | 12.5065 | 0.1094 | 0.0821 |
| 0.5483 | 25.56 | 9200 | 12.9911 | 0.1083 | 0.0842 |
| 0.5483 | 25.83 | 9300 | 12.8359 | 0.1092 | 0.0849 |
| 0.5483 | 26.11 | 9400 | 13.4473 | 0.1091 | 0.0840 |
| 0.5369 | 26.39 | 9500 | 12.6063 | 0.1083 | 0.0826 |
| 0.5369 | 26.67 | 9600 | 12.8707 | 0.1052 | 0.0777 |
| 0.5369 | 26.94 | 9700 | 13.1274 | 0.1088 | 0.0824 |
| 0.5369 | 27.22 | 9800 | 12.8088 | 0.1077 | 0.0815 |
| 0.5369 | 27.5 | 9900 | 13.0837 | 0.1080 | 0.0842 |
| 0.5264 | 27.78 | 10000 | 12.5880 | 0.1078 | 0.0813 |
| 0.5264 | 28.06 | 10100 | 12.8597 | 0.1087 | 0.0835 |
| 0.5264 | 28.33 | 10200 | 12.7099 | 0.1085 | 0.0827 |
| 0.5264 | 28.61 | 10300 | 12.9146 | 0.1070 | 0.0825 |
| 0.5264 | 28.89 | 10400 | 12.8773 | 0.1085 | 0.0831 |
| 0.5172 | 29.17 | 10500 | 12.6886 | 0.1073 | 0.0806 |
| 0.5172 | 29.44 | 10600 | 12.8463 | 0.1076 | 0.0813 |
| 0.5172 | 29.72 | 10700 | 12.7620 | 0.1082 | 0.0824 |
| 0.5172 | 30.0 | 10800 | 12.8207 | 0.1087 | 0.0816 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["massive"], "metrics": ["accuracy", "f1"], "base_model": "FacebookAI/xlm-roberta-base", "model-index": [{"name": "scenario-KD-SCR-MSV-EN-CL-D2_data-en-massive_all_1_1", "results": []}]} | null | haryoaw/scenario-KD-SCR-MSV-EN-CL-D2_data-en-massive_all_1_1 | [
"transformers",
"pytorch",
"xlm-roberta",
"generated_from_trainer",
"dataset:massive",
"base_model:FacebookAI/xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-14T21:34:18+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #generated_from_trainer #dataset-massive #base_model-FacebookAI/xlm-roberta-base #license-mit #endpoints_compatible #region-us
| scenario-KD-SCR-MSV-EN-CL-D2\_data-en-massive\_all\_1\_1
========================================================
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on the massive dataset.
It achieves the following results on the evaluation set:
* Loss: 12.8207
* Accuracy: 0.1087
* F1: 0.0816
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 47
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
### Training results
### Framework versions
* Transformers 4.33.3
* Pytorch 2.1.1+cu121
* Datasets 2.14.5
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 47\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.33.3\n* Pytorch 2.1.1+cu121\n* Datasets 2.14.5\n* Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #generated_from_trainer #dataset-massive #base_model-FacebookAI/xlm-roberta-base #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 47\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.33.3\n* Pytorch 2.1.1+cu121\n* Datasets 2.14.5\n* Tokenizers 0.13.3"
] | [
60,
98,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #generated_from_trainer #dataset-massive #base_model-FacebookAI/xlm-roberta-base #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 47\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30### Training results### Framework versions\n\n\n* Transformers 4.33.3\n* Pytorch 2.1.1+cu121\n* Datasets 2.14.5\n* Tokenizers 0.13.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# scenario-KD-SCR-MSV-EN-EN-D2_data-en-massive_all_1_1
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 14.1819
- Accuracy: 0.1011
- F1: 0.0771
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 47
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| No log | 0.28 | 100 | 9.3069 | 0.0775 | 0.0065 |
| No log | 0.56 | 200 | 9.2256 | 0.0881 | 0.0285 |
| No log | 0.83 | 300 | 9.5054 | 0.0874 | 0.0449 |
| No log | 1.11 | 400 | 9.6152 | 0.0930 | 0.0575 |
| 6.7715 | 1.39 | 500 | 9.8484 | 0.1023 | 0.0669 |
| 6.7715 | 1.67 | 600 | 9.7053 | 0.1054 | 0.0676 |
| 6.7715 | 1.94 | 700 | 10.0033 | 0.1116 | 0.0761 |
| 6.7715 | 2.22 | 800 | 10.3355 | 0.1024 | 0.0758 |
| 6.7715 | 2.5 | 900 | 10.5199 | 0.1051 | 0.0756 |
| 3.5854 | 2.78 | 1000 | 10.4890 | 0.1003 | 0.0730 |
| 3.5854 | 3.06 | 1100 | 11.1371 | 0.1026 | 0.0742 |
| 3.5854 | 3.33 | 1200 | 10.6889 | 0.1114 | 0.0744 |
| 3.5854 | 3.61 | 1300 | 11.2190 | 0.1009 | 0.0723 |
| 3.5854 | 3.89 | 1400 | 11.6248 | 0.1006 | 0.0752 |
| 2.5619 | 4.17 | 1500 | 11.4919 | 0.1041 | 0.0773 |
| 2.5619 | 4.44 | 1600 | 11.9283 | 0.0969 | 0.0709 |
| 2.5619 | 4.72 | 1700 | 11.9070 | 0.0994 | 0.0729 |
| 2.5619 | 5.0 | 1800 | 11.9216 | 0.0989 | 0.0679 |
| 2.5619 | 5.28 | 1900 | 12.1528 | 0.1011 | 0.0714 |
| 1.9218 | 5.56 | 2000 | 12.8701 | 0.0963 | 0.0705 |
| 1.9218 | 5.83 | 2100 | 12.5439 | 0.0992 | 0.0741 |
| 1.9218 | 6.11 | 2200 | 14.2823 | 0.0970 | 0.0706 |
| 1.9218 | 6.39 | 2300 | 12.9570 | 0.0996 | 0.0701 |
| 1.9218 | 6.67 | 2400 | 12.7219 | 0.1098 | 0.0758 |
| 1.5444 | 6.94 | 2500 | 13.2803 | 0.0926 | 0.0704 |
| 1.5444 | 7.22 | 2600 | 13.5962 | 0.0999 | 0.0764 |
| 1.5444 | 7.5 | 2700 | 13.6673 | 0.0997 | 0.0743 |
| 1.5444 | 7.78 | 2800 | 13.8965 | 0.1047 | 0.0724 |
| 1.5444 | 8.06 | 2900 | 13.5849 | 0.0922 | 0.0728 |
| 1.2579 | 8.33 | 3000 | 15.0406 | 0.1019 | 0.0736 |
| 1.2579 | 8.61 | 3100 | 15.6149 | 0.1019 | 0.0738 |
| 1.2579 | 8.89 | 3200 | 14.3982 | 0.0980 | 0.0762 |
| 1.2579 | 9.17 | 3300 | 13.6922 | 0.1017 | 0.0651 |
| 1.2579 | 9.44 | 3400 | 13.9173 | 0.1048 | 0.0700 |
| 1.0753 | 9.72 | 3500 | 14.3425 | 0.1003 | 0.0742 |
| 1.0753 | 10.0 | 3600 | 14.0474 | 0.0955 | 0.0771 |
| 1.0753 | 10.28 | 3700 | 13.7067 | 0.1012 | 0.0756 |
| 1.0753 | 10.56 | 3800 | 13.5322 | 0.1067 | 0.0738 |
| 1.0753 | 10.83 | 3900 | 14.6059 | 0.0988 | 0.0763 |
| 0.9469 | 11.11 | 4000 | 15.2368 | 0.1030 | 0.0811 |
| 0.9469 | 11.39 | 4100 | 14.3134 | 0.0936 | 0.0713 |
| 0.9469 | 11.67 | 4200 | 14.9626 | 0.1018 | 0.0761 |
| 0.9469 | 11.94 | 4300 | 13.3460 | 0.0969 | 0.0697 |
| 0.9469 | 12.22 | 4400 | 13.9374 | 0.0912 | 0.0743 |
| 0.8089 | 12.5 | 4500 | 14.3300 | 0.1032 | 0.0765 |
| 0.8089 | 12.78 | 4600 | 15.1234 | 0.1022 | 0.0778 |
| 0.8089 | 13.06 | 4700 | 14.9212 | 0.0978 | 0.0763 |
| 0.8089 | 13.33 | 4800 | 13.7898 | 0.1019 | 0.0756 |
| 0.8089 | 13.61 | 4900 | 13.8005 | 0.0923 | 0.0752 |
| 0.7474 | 13.89 | 5000 | 13.4644 | 0.0887 | 0.0702 |
| 0.7474 | 14.17 | 5100 | 14.1735 | 0.0990 | 0.0762 |
| 0.7474 | 14.44 | 5200 | 14.0039 | 0.0948 | 0.0732 |
| 0.7474 | 14.72 | 5300 | 15.0009 | 0.0980 | 0.0758 |
| 0.7474 | 15.0 | 5400 | 15.7028 | 0.1025 | 0.0759 |
| 0.6826 | 15.28 | 5500 | 14.9519 | 0.0999 | 0.0759 |
| 0.6826 | 15.56 | 5600 | 14.4389 | 0.1038 | 0.0759 |
| 0.6826 | 15.83 | 5700 | 15.6013 | 0.0999 | 0.0787 |
| 0.6826 | 16.11 | 5800 | 14.1587 | 0.0992 | 0.0766 |
| 0.6826 | 16.39 | 5900 | 16.4603 | 0.0989 | 0.0773 |
| 0.6265 | 16.67 | 6000 | 16.0249 | 0.1035 | 0.0763 |
| 0.6265 | 16.94 | 6100 | 14.4053 | 0.1007 | 0.0780 |
| 0.6265 | 17.22 | 6200 | 13.9288 | 0.0946 | 0.0756 |
| 0.6265 | 17.5 | 6300 | 15.3227 | 0.1016 | 0.0768 |
| 0.6265 | 17.78 | 6400 | 13.8783 | 0.0948 | 0.0729 |
| 0.5821 | 18.06 | 6500 | 14.1279 | 0.0928 | 0.0745 |
| 0.5821 | 18.33 | 6600 | 14.4890 | 0.0961 | 0.0744 |
| 0.5821 | 18.61 | 6700 | 14.8176 | 0.1014 | 0.0760 |
| 0.5821 | 18.89 | 6800 | 14.7877 | 0.1012 | 0.0775 |
| 0.5821 | 19.17 | 6900 | 14.5485 | 0.1013 | 0.0786 |
| 0.5462 | 19.44 | 7000 | 15.0580 | 0.1007 | 0.0818 |
| 0.5462 | 19.72 | 7100 | 13.9213 | 0.0935 | 0.0738 |
| 0.5462 | 20.0 | 7200 | 16.5866 | 0.1005 | 0.0766 |
| 0.5462 | 20.28 | 7300 | 14.1027 | 0.0969 | 0.0748 |
| 0.5462 | 20.56 | 7400 | 14.2922 | 0.1021 | 0.0759 |
| 0.519 | 20.83 | 7500 | 14.2771 | 0.0986 | 0.0772 |
| 0.519 | 21.11 | 7600 | 14.2693 | 0.1027 | 0.0749 |
| 0.519 | 21.39 | 7700 | 13.5863 | 0.1042 | 0.0795 |
| 0.519 | 21.67 | 7800 | 15.6105 | 0.1018 | 0.0820 |
| 0.519 | 21.94 | 7900 | 15.7645 | 0.1024 | 0.0829 |
| 0.499 | 22.22 | 8000 | 14.1501 | 0.0988 | 0.0782 |
| 0.499 | 22.5 | 8100 | 13.9119 | 0.0988 | 0.0771 |
| 0.499 | 22.78 | 8200 | 14.5926 | 0.1031 | 0.0790 |
| 0.499 | 23.06 | 8300 | 14.6785 | 0.1011 | 0.0748 |
| 0.499 | 23.33 | 8400 | 13.7131 | 0.0977 | 0.0772 |
| 0.4814 | 23.61 | 8500 | 13.9351 | 0.1013 | 0.0773 |
| 0.4814 | 23.89 | 8600 | 13.9906 | 0.0979 | 0.0758 |
| 0.4814 | 24.17 | 8700 | 14.1299 | 0.1005 | 0.0758 |
| 0.4814 | 24.44 | 8800 | 13.1678 | 0.1007 | 0.0761 |
| 0.4814 | 24.72 | 8900 | 13.5850 | 0.0967 | 0.0753 |
| 0.4569 | 25.0 | 9000 | 14.9372 | 0.1032 | 0.0787 |
| 0.4569 | 25.28 | 9100 | 14.4079 | 0.1006 | 0.0767 |
| 0.4569 | 25.56 | 9200 | 14.3035 | 0.1016 | 0.0770 |
| 0.4569 | 25.83 | 9300 | 14.0566 | 0.1023 | 0.0773 |
| 0.4569 | 26.11 | 9400 | 14.0034 | 0.0994 | 0.0794 |
| 0.4514 | 26.39 | 9500 | 14.6733 | 0.0985 | 0.0792 |
| 0.4514 | 26.67 | 9600 | 14.3151 | 0.1010 | 0.0772 |
| 0.4514 | 26.94 | 9700 | 14.6124 | 0.1005 | 0.0796 |
| 0.4514 | 27.22 | 9800 | 13.5692 | 0.0985 | 0.0751 |
| 0.4514 | 27.5 | 9900 | 14.7810 | 0.0999 | 0.0790 |
| 0.4355 | 27.78 | 10000 | 14.6610 | 0.0981 | 0.0768 |
| 0.4355 | 28.06 | 10100 | 14.5152 | 0.0999 | 0.0800 |
| 0.4355 | 28.33 | 10200 | 14.1330 | 0.1001 | 0.0779 |
| 0.4355 | 28.61 | 10300 | 14.5362 | 0.0998 | 0.0777 |
| 0.4355 | 28.89 | 10400 | 14.1860 | 0.1004 | 0.0783 |
| 0.4302 | 29.17 | 10500 | 14.6424 | 0.1015 | 0.0789 |
| 0.4302 | 29.44 | 10600 | 14.3844 | 0.1010 | 0.0779 |
| 0.4302 | 29.72 | 10700 | 14.2358 | 0.1006 | 0.0781 |
| 0.4302 | 30.0 | 10800 | 14.1819 | 0.1011 | 0.0771 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["massive"], "metrics": ["accuracy", "f1"], "base_model": "FacebookAI/xlm-roberta-base", "model-index": [{"name": "scenario-KD-SCR-MSV-EN-EN-D2_data-en-massive_all_1_1", "results": []}]} | null | haryoaw/scenario-KD-SCR-MSV-EN-EN-D2_data-en-massive_all_1_1 | [
"transformers",
"pytorch",
"xlm-roberta",
"generated_from_trainer",
"dataset:massive",
"base_model:FacebookAI/xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-14T21:34:42+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #generated_from_trainer #dataset-massive #base_model-FacebookAI/xlm-roberta-base #license-mit #endpoints_compatible #region-us
| scenario-KD-SCR-MSV-EN-EN-D2\_data-en-massive\_all\_1\_1
========================================================
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on the massive dataset.
It achieves the following results on the evaluation set:
* Loss: 14.1819
* Accuracy: 0.1011
* F1: 0.0771
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 47
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
### Training results
### Framework versions
* Transformers 4.33.3
* Pytorch 2.1.1+cu121
* Datasets 2.14.5
* Tokenizers 0.13.3
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"### Training results",
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# scenario-KD-PO-MSV-EN-EN-D2_data-en-massive_all_1_1
This model is a fine-tuned version of [haryoaw/scenario-TCR_data-en-massive_all_1_1](https://huggingface.co/haryoaw/scenario-TCR_data-en-massive_all_1_1) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7908
- Accuracy: 0.5365
- F1: 0.4786
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 47
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| No log | 0.28 | 100 | 5.1614 | 0.4860 | 0.3990 |
| No log | 0.56 | 200 | 5.1590 | 0.4790 | 0.4068 |
| No log | 0.83 | 300 | 4.9490 | 0.5093 | 0.4251 |
| No log | 1.11 | 400 | 5.0823 | 0.5050 | 0.4359 |
| 2.0111 | 1.39 | 500 | 5.9727 | 0.4599 | 0.4095 |
| 2.0111 | 1.67 | 600 | 5.0874 | 0.5192 | 0.4413 |
| 2.0111 | 1.94 | 700 | 5.2264 | 0.5153 | 0.4446 |
| 2.0111 | 2.22 | 800 | 5.8439 | 0.4731 | 0.4246 |
| 2.0111 | 2.5 | 900 | 5.5183 | 0.5102 | 0.4374 |
| 1.0011 | 2.78 | 1000 | 6.6020 | 0.4541 | 0.4114 |
| 1.0011 | 3.06 | 1100 | 6.1540 | 0.4795 | 0.4246 |
| 1.0011 | 3.33 | 1200 | 5.5901 | 0.5168 | 0.4453 |
| 1.0011 | 3.61 | 1300 | 5.9977 | 0.4949 | 0.4239 |
| 1.0011 | 3.89 | 1400 | 5.7634 | 0.5070 | 0.4461 |
| 0.67 | 4.17 | 1500 | 5.6536 | 0.5233 | 0.4470 |
| 0.67 | 4.44 | 1600 | 5.8327 | 0.5184 | 0.4445 |
| 0.67 | 4.72 | 1700 | 5.8242 | 0.5181 | 0.4379 |
| 0.67 | 5.0 | 1800 | 6.5490 | 0.4856 | 0.4429 |
| 0.67 | 5.28 | 1900 | 5.6837 | 0.5270 | 0.4480 |
| 0.4793 | 5.56 | 2000 | 5.7311 | 0.5269 | 0.4600 |
| 0.4793 | 5.83 | 2100 | 5.8170 | 0.5244 | 0.4530 |
| 0.4793 | 6.11 | 2200 | 5.9569 | 0.5190 | 0.4572 |
| 0.4793 | 6.39 | 2300 | 5.7950 | 0.5260 | 0.4610 |
| 0.4793 | 6.67 | 2400 | 6.8529 | 0.4865 | 0.4238 |
| 0.3811 | 6.94 | 2500 | 6.3352 | 0.5007 | 0.4455 |
| 0.3811 | 7.22 | 2600 | 6.1439 | 0.5193 | 0.4511 |
| 0.3811 | 7.5 | 2700 | 5.9568 | 0.5255 | 0.4573 |
| 0.3811 | 7.78 | 2800 | 6.8359 | 0.4888 | 0.4367 |
| 0.3811 | 8.06 | 2900 | 6.1294 | 0.5196 | 0.4600 |
| 0.3163 | 8.33 | 3000 | 6.5122 | 0.5033 | 0.4433 |
| 0.3163 | 8.61 | 3100 | 6.0960 | 0.5186 | 0.4621 |
| 0.3163 | 8.89 | 3200 | 6.4174 | 0.5053 | 0.4457 |
| 0.3163 | 9.17 | 3300 | 6.3298 | 0.5052 | 0.4527 |
| 0.3163 | 9.44 | 3400 | 6.7517 | 0.4944 | 0.4515 |
| 0.2726 | 9.72 | 3500 | 6.6248 | 0.5012 | 0.4521 |
| 0.2726 | 10.0 | 3600 | 6.8595 | 0.4902 | 0.4429 |
| 0.2726 | 10.28 | 3700 | 6.2875 | 0.5141 | 0.4546 |
| 0.2726 | 10.56 | 3800 | 6.0717 | 0.5228 | 0.4625 |
| 0.2726 | 10.83 | 3900 | 5.9079 | 0.5230 | 0.4608 |
| 0.2407 | 11.11 | 4000 | 5.9972 | 0.5251 | 0.4553 |
| 0.2407 | 11.39 | 4100 | 6.0573 | 0.5248 | 0.4521 |
| 0.2407 | 11.67 | 4200 | 6.3872 | 0.5113 | 0.4610 |
| 0.2407 | 11.94 | 4300 | 6.3037 | 0.5159 | 0.4547 |
| 0.2407 | 12.22 | 4400 | 6.2782 | 0.5123 | 0.4532 |
| 0.2201 | 12.5 | 4500 | 6.1318 | 0.5239 | 0.4619 |
| 0.2201 | 12.78 | 4600 | 6.2953 | 0.5150 | 0.4622 |
| 0.2201 | 13.06 | 4700 | 6.2881 | 0.5212 | 0.4625 |
| 0.2201 | 13.33 | 4800 | 6.4247 | 0.5104 | 0.4571 |
| 0.2201 | 13.61 | 4900 | 6.1305 | 0.5250 | 0.4635 |
| 0.2044 | 13.89 | 5000 | 5.8602 | 0.5343 | 0.4711 |
| 0.2044 | 14.17 | 5100 | 5.8894 | 0.5289 | 0.4659 |
| 0.2044 | 14.44 | 5200 | 6.2145 | 0.5211 | 0.4625 |
| 0.2044 | 14.72 | 5300 | 6.1177 | 0.5234 | 0.4620 |
| 0.2044 | 15.0 | 5400 | 5.8386 | 0.5324 | 0.4677 |
| 0.1962 | 15.28 | 5500 | 6.0351 | 0.5254 | 0.4673 |
| 0.1962 | 15.56 | 5600 | 5.9421 | 0.5290 | 0.4679 |
| 0.1962 | 15.83 | 5700 | 5.9836 | 0.5239 | 0.4673 |
| 0.1962 | 16.11 | 5800 | 6.0245 | 0.5304 | 0.4725 |
| 0.1962 | 16.39 | 5900 | 6.3386 | 0.5162 | 0.4641 |
| 0.1876 | 16.67 | 6000 | 6.2542 | 0.5185 | 0.4641 |
| 0.1876 | 16.94 | 6100 | 5.9417 | 0.5308 | 0.4709 |
| 0.1876 | 17.22 | 6200 | 5.7436 | 0.5418 | 0.4799 |
| 0.1876 | 17.5 | 6300 | 6.1042 | 0.5311 | 0.4715 |
| 0.1876 | 17.78 | 6400 | 6.2918 | 0.5155 | 0.4654 |
| 0.179 | 18.06 | 6500 | 6.1913 | 0.5178 | 0.4609 |
| 0.179 | 18.33 | 6600 | 6.0804 | 0.5316 | 0.4724 |
| 0.179 | 18.61 | 6700 | 5.8362 | 0.5330 | 0.4722 |
| 0.179 | 18.89 | 6800 | 6.1539 | 0.5254 | 0.4730 |
| 0.179 | 19.17 | 6900 | 6.2638 | 0.5155 | 0.4655 |
| 0.1738 | 19.44 | 7000 | 5.9394 | 0.5344 | 0.4739 |
| 0.1738 | 19.72 | 7100 | 5.9005 | 0.5341 | 0.4749 |
| 0.1738 | 20.0 | 7200 | 6.4484 | 0.5119 | 0.4635 |
| 0.1738 | 20.28 | 7300 | 5.9396 | 0.5268 | 0.4674 |
| 0.1738 | 20.56 | 7400 | 5.9318 | 0.5286 | 0.4688 |
| 0.1694 | 20.83 | 7500 | 6.0616 | 0.5255 | 0.4723 |
| 0.1694 | 21.11 | 7600 | 5.8511 | 0.5351 | 0.4742 |
| 0.1694 | 21.39 | 7700 | 5.9842 | 0.5282 | 0.4692 |
| 0.1694 | 21.67 | 7800 | 6.2691 | 0.5176 | 0.4647 |
| 0.1694 | 21.94 | 7900 | 6.1831 | 0.5199 | 0.4635 |
| 0.1642 | 22.22 | 8000 | 5.8385 | 0.5363 | 0.4748 |
| 0.1642 | 22.5 | 8100 | 6.1696 | 0.5195 | 0.4643 |
| 0.1642 | 22.78 | 8200 | 5.7393 | 0.5382 | 0.4778 |
| 0.1642 | 23.06 | 8300 | 5.6763 | 0.5385 | 0.4752 |
| 0.1642 | 23.33 | 8400 | 5.7960 | 0.5368 | 0.4756 |
| 0.1635 | 23.61 | 8500 | 5.8562 | 0.5341 | 0.4738 |
| 0.1635 | 23.89 | 8600 | 5.9408 | 0.5322 | 0.4707 |
| 0.1635 | 24.17 | 8700 | 5.6433 | 0.5433 | 0.4774 |
| 0.1635 | 24.44 | 8800 | 5.7061 | 0.5391 | 0.4779 |
| 0.1635 | 24.72 | 8900 | 5.6887 | 0.5407 | 0.4809 |
| 0.1587 | 25.0 | 9000 | 5.8796 | 0.5328 | 0.4742 |
| 0.1587 | 25.28 | 9100 | 5.7879 | 0.5370 | 0.4750 |
| 0.1587 | 25.56 | 9200 | 5.7480 | 0.5348 | 0.4766 |
| 0.1587 | 25.83 | 9300 | 5.9260 | 0.5292 | 0.4738 |
| 0.1587 | 26.11 | 9400 | 5.7467 | 0.5347 | 0.4752 |
| 0.1565 | 26.39 | 9500 | 5.9216 | 0.5324 | 0.4739 |
| 0.1565 | 26.67 | 9600 | 5.7947 | 0.5350 | 0.4769 |
| 0.1565 | 26.94 | 9700 | 5.7898 | 0.5349 | 0.4757 |
| 0.1565 | 27.22 | 9800 | 5.9008 | 0.5298 | 0.4736 |
| 0.1565 | 27.5 | 9900 | 5.6786 | 0.5406 | 0.4779 |
| 0.1542 | 27.78 | 10000 | 5.7844 | 0.5350 | 0.4751 |
| 0.1542 | 28.06 | 10100 | 5.7515 | 0.5365 | 0.4758 |
| 0.1542 | 28.33 | 10200 | 5.8749 | 0.5315 | 0.4739 |
| 0.1542 | 28.61 | 10300 | 5.8789 | 0.5320 | 0.4756 |
| 0.1542 | 28.89 | 10400 | 5.7866 | 0.5351 | 0.4769 |
| 0.1533 | 29.17 | 10500 | 5.7045 | 0.5390 | 0.4799 |
| 0.1533 | 29.44 | 10600 | 5.7853 | 0.5356 | 0.4760 |
| 0.1533 | 29.72 | 10700 | 5.7948 | 0.5354 | 0.4771 |
| 0.1533 | 30.0 | 10800 | 5.7908 | 0.5365 | 0.4786 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["massive"], "metrics": ["accuracy", "f1"], "base_model": "haryoaw/scenario-TCR_data-en-massive_all_1_1", "model-index": [{"name": "scenario-KD-PO-MSV-EN-EN-D2_data-en-massive_all_1_1", "results": []}]} | null | haryoaw/scenario-KD-PO-MSV-EN-EN-D2_data-en-massive_all_1_1 | [
"transformers",
"pytorch",
"xlm-roberta",
"generated_from_trainer",
"dataset:massive",
"base_model:haryoaw/scenario-TCR_data-en-massive_all_1_1",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-14T21:34:50+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #generated_from_trainer #dataset-massive #base_model-haryoaw/scenario-TCR_data-en-massive_all_1_1 #license-mit #endpoints_compatible #region-us
| scenario-KD-PO-MSV-EN-EN-D2\_data-en-massive\_all\_1\_1
=======================================================
This model is a fine-tuned version of haryoaw/scenario-TCR\_data-en-massive\_all\_1\_1 on the massive dataset.
It achieves the following results on the evaluation set:
* Loss: 5.7908
* Accuracy: 0.5365
* F1: 0.4786
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 47
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
### Training results
### Framework versions
* Transformers 4.33.3
* Pytorch 2.1.1+cu121
* Datasets 2.14.5
* Tokenizers 0.13.3
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"### Training results",
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null | null | null | This is the test version for pruning. This model is a base model that will be pruned and quantized for on-device purpose.
I used mergekit for merging two models:
https://github.com/cg123/mergekit
The two models I combined are:
https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v2
https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct-DPO-v2
I used GGUF quantization. | {} | null | invalid-coder/test-GGUF | [
"gguf",
"region:us"
] | 2024-02-14T21:36:42+00:00 | [] | [] | TAGS
#gguf #region-us
| This is the test version for pruning. This model is a base model that will be pruned and quantized for on-device purpose.
I used mergekit for merging two models:
URL
The two models I combined are:
URL
URL
I used GGUF quantization. | [] | [
"TAGS\n#gguf #region-us \n"
] | [
9
] | [
"passage: TAGS\n#gguf #region-us \n"
] | [
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] |
null | null | sample-factory |
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Overgrown7380/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
| {"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_health_gathering_supreme", "type": "doom_health_gathering_supreme"}, "metrics": [{"type": "mean_reward", "value": "10.63 +/- 4.89", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Overgrown7380/rl_course_vizdoom_health_gathering_supreme | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-14T21:36:46+00:00 | [] | [] | TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
A(n) APPO model trained on the doom_health_gathering_supreme environment.
This model was trained using Sample-Factory 2.0: URL
Documentation for how to use Sample-Factory can be found at URL
## Downloading the model
After installing Sample-Factory, download the model with:
## Using the model
To run the model after download, use the 'enjoy' script corresponding to this environment:
You can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.
See URL for more details
## Training with this model
To continue training with this model, use the 'train' script corresponding to this environment:
Note, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at.
| [
"## Downloading the model\n\nAfter installing Sample-Factory, download the model with:",
"## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details",
"## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
] | [
"TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"## Downloading the model\n\nAfter installing Sample-Factory, download the model with:",
"## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details",
"## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
] | [
34,
19,
59,
67
] | [
"passage: TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n## Downloading the model\n\nAfter installing Sample-Factory, download the model with:## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2119
- Accuracy: 0.929
- F1: 0.9292
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8151 | 1.0 | 250 | 0.3052 | 0.911 | 0.9101 |
| 0.2492 | 2.0 | 500 | 0.2119 | 0.929 | 0.9292 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.929, "name": "Accuracy"}, {"type": "f1", "value": 0.9292042228940055, "name": "F1"}]}]}]} | text-classification | saqidr/distilbert-base-uncased-finetuned-emotion | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T21:40:23+00:00 | [] | [] | TAGS
#transformers #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2119
* Accuracy: 0.929
* F1: 0.9292
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
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"passage: TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# pdalbiancoany/distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1927
- Validation Loss: 0.0723
- Train Precision: 0.8979
- Train Recall: 0.9192
- Train F1: 0.9085
- Train Accuracy: 0.9788
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.1927 | 0.0723 | 0.8979 | 0.9192 | 0.9085 | 0.9788 | 0 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "pdalbiancoany/distilbert-base-uncased-finetuned-ner", "results": []}]} | token-classification | pdalbiancoany/distilbert-base-uncased-finetuned-ner | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T21:42:14+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| pdalbiancoany/distilbert-base-uncased-finetuned-ner
===================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1927
* Validation Loss: 0.0723
* Train Precision: 0.8979
* Train Recall: 0.9192
* Train F1: 0.9085
* Train Accuracy: 0.9788
* Epoch: 0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 2631, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.35.2
* TensorFlow 2.15.0
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
75,
227,
4,
31
] | [
"passage: TAGS\n#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers | # Model Card for Zenith-7B-dpo
Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper [Self-Rewarding Language Models](https://arxiv.org/abs/2401.10020).
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Xenon1/Zenith-7B-dpo")
tokenizer = AutoTokenizer.from_pretrained("Xenon1/Zenith-7B-dpo")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer | {"language": ["en"], "license": "apache-2.0", "tags": ["mistral", "Zenith-7B-dpo"], "pipeline_tag": "text-generation"} | text-generation | Xenon1/Zenith-7B-dpo | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"Zenith-7B-dpo",
"conversational",
"en",
"arxiv:2401.10020",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T21:44:27+00:00 | [
"2401.10020"
] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #Zenith-7B-dpo #conversational #en #arxiv-2401.10020 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Model Card for Zenith-7B-dpo
Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.
!image/png
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
This format is available as a chat template via the 'apply_chat_template()' method:
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer | [
"# Model Card for Zenith-7B-dpo\n\nMistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.\n\n\n!image/png",
"## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:",
"## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #Zenith-7B-dpo #conversational #en #arxiv-2401.10020 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Zenith-7B-dpo\n\nMistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.\n\n\n!image/png",
"## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:",
"## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer"
] | [
79,
51,
105,
56
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #Zenith-7B-dpo #conversational #en #arxiv-2401.10020 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Zenith-7B-dpo\n\nMistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.\n\n\n!image/png## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer"
] | [
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null | null | transformers | don't download it :3 | {} | text-generation | star23/death | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T21:44:45+00:00 | [] | [] | TAGS
#transformers #pytorch #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| don't download it :3 | [] | [
"TAGS\n#transformers #pytorch #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
46
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers | ## MiniCPM 3B OpenHermes 2.5 v2
- **Model creator:** [indischepartij](https://huggingface.co/indischepartij)
- **Original model:** [MiniCPM-3B-OpenHermes-2.5-v2](https://huggingface.co/indischepartij/MiniCPM-3B-OpenHermes-2.5-v2)
<!-- description start -->
## Description
This repo contains GGUF format model files for [indischepartij's MiniCPM 3B OpenHermes 2.5 v2](https://huggingface.co/indischepartij/MiniCPM-3B-OpenHermes-2.5-v2)
<!-- description end --> | {"license": "apache-2.0", "library_name": "transformers", "datasets": ["teknium/OpenHermes-2.5"], "base_model": "indischepartij/MiniCPM-3B-OpenHermes-2.5-v2", "pipeline_tag": "conversational", "model_type": "llama", "inference": false} | text-generation | sayhan/MiniCPM-3B-OpenHermes-2.5-v2-GGUF | [
"transformers",
"gguf",
"conversational",
"dataset:teknium/OpenHermes-2.5",
"base_model:indischepartij/MiniCPM-3B-OpenHermes-2.5-v2",
"license:apache-2.0",
"region:us"
] | 2024-02-14T21:45:38+00:00 | [] | [] | TAGS
#transformers #gguf #conversational #dataset-teknium/OpenHermes-2.5 #base_model-indischepartij/MiniCPM-3B-OpenHermes-2.5-v2 #license-apache-2.0 #region-us
| ## MiniCPM 3B OpenHermes 2.5 v2
- Model creator: indischepartij
- Original model: MiniCPM-3B-OpenHermes-2.5-v2
## Description
This repo contains GGUF format model files for indischepartij's MiniCPM 3B OpenHermes 2.5 v2
| [
"## MiniCPM 3B OpenHermes 2.5 v2\n- Model creator: indischepartij\n- Original model: MiniCPM-3B-OpenHermes-2.5-v2",
"## Description\nThis repo contains GGUF format model files for indischepartij's MiniCPM 3B OpenHermes 2.5 v2"
] | [
"TAGS\n#transformers #gguf #conversational #dataset-teknium/OpenHermes-2.5 #base_model-indischepartij/MiniCPM-3B-OpenHermes-2.5-v2 #license-apache-2.0 #region-us \n",
"## MiniCPM 3B OpenHermes 2.5 v2\n- Model creator: indischepartij\n- Original model: MiniCPM-3B-OpenHermes-2.5-v2",
"## Description\nThis repo contains GGUF format model files for indischepartij's MiniCPM 3B OpenHermes 2.5 v2"
] | [
59,
37,
29
] | [
"passage: TAGS\n#transformers #gguf #conversational #dataset-teknium/OpenHermes-2.5 #base_model-indischepartij/MiniCPM-3B-OpenHermes-2.5-v2 #license-apache-2.0 #region-us \n## MiniCPM 3B OpenHermes 2.5 v2\n- Model creator: indischepartij\n- Original model: MiniCPM-3B-OpenHermes-2.5-v2## Description\nThis repo contains GGUF format model files for indischepartij's MiniCPM 3B OpenHermes 2.5 v2"
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null | null | ml-agents |
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: guirnd/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos"]} | reinforcement-learning | guirnd/poca-SoccerTwos | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | 2024-02-14T21:46:24+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us
|
# poca Agent playing SoccerTwos
This is a trained model of a poca agent playing SoccerTwos
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: guirnd/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: guirnd/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us \n",
"# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: guirnd/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
52,
205
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us \n# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: guirnd/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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null | null | transformers | # gpt2_slerp_merge_ab_with_classic_e2
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [ChaiML/reward_models_100_170000000_cp_332032](https://huggingface.co/ChaiML/reward_models_100_170000000_cp_332032)
* [rirv938/reward_gpt2_preference_24m_e2](https://huggingface.co/rirv938/reward_gpt2_preference_24m_e2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: ChaiML/reward_models_100_170000000_cp_332032
layer_range: [0, 12]
- model: rirv938/reward_gpt2_preference_24m_e2
layer_range: [0, 12]
merge_method: slerp
base_model: ChaiML/reward_models_100_170000000_cp_332032
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: float16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["ChaiML/reward_models_100_170000000_cp_332032", "rirv938/reward_gpt2_preference_24m_e2"]} | text-classification | rirv938/gpt2_slerp_merge_preference_plus_classic_e2 | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"mergekit",
"merge",
"base_model:ChaiML/reward_models_100_170000000_cp_332032",
"base_model:rirv938/reward_gpt2_preference_24m_e2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T21:47:20+00:00 | [] | [] | TAGS
#transformers #safetensors #gpt2 #text-classification #mergekit #merge #base_model-ChaiML/reward_models_100_170000000_cp_332032 #base_model-rirv938/reward_gpt2_preference_24m_e2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # gpt2_slerp_merge_ab_with_classic_e2
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* ChaiML/reward_models_100_170000000_cp_332032
* rirv938/reward_gpt2_preference_24m_e2
### Configuration
The following YAML configuration was used to produce this model:
| [
"# gpt2_slerp_merge_ab_with_classic_e2\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* ChaiML/reward_models_100_170000000_cp_332032\n* rirv938/reward_gpt2_preference_24m_e2",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #gpt2 #text-classification #mergekit #merge #base_model-ChaiML/reward_models_100_170000000_cp_332032 #base_model-rirv938/reward_gpt2_preference_24m_e2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2_slerp_merge_ab_with_classic_e2\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* ChaiML/reward_models_100_170000000_cp_332032\n* rirv938/reward_gpt2_preference_24m_e2",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
104,
36,
4,
18,
56,
17
] | [
"passage: TAGS\n#transformers #safetensors #gpt2 #text-classification #mergekit #merge #base_model-ChaiML/reward_models_100_170000000_cp_332032 #base_model-rirv938/reward_gpt2_preference_24m_e2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# gpt2_slerp_merge_ab_with_classic_e2\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the SLERP merge method.### Models Merged\n\nThe following models were included in the merge:\n* ChaiML/reward_models_100_170000000_cp_332032\n* rirv938/reward_gpt2_preference_24m_e2### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# esm2_t12_35M_qlora_glycosylation_sites_2024-02-14_21-47-37
This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0108
- Accuracy: 0.9990
- Precision: 0.3291
- Recall: 0.9951
- F1: 0.4946
- Auc: 0.9970
- Mcc: 0.5720
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003701568055793089
- train_batch_size: 36
- eval_batch_size: 36
- seed: 8893
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auc | Mcc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|:------:|
| 0.0125 | 1.0 | 16521 | 0.0108 | 0.9990 | 0.3291 | 0.9951 | 0.4946 | 0.9970 | 0.5720 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "facebook/esm2_t12_35M_UR50D", "model-index": [{"name": "esm2_t12_35M_qlora_glycosylation_sites_2024-02-14_21-47-37", "results": []}]} | null | nidhinthomas/esm2_t12_35M_qlora_glycosylation_sites_2024-02-14_21-47-37 | [
"safetensors",
"generated_from_trainer",
"base_model:facebook/esm2_t12_35M_UR50D",
"license:mit",
"region:us"
] | 2024-02-14T21:47:37+00:00 | [] | [] | TAGS
#safetensors #generated_from_trainer #base_model-facebook/esm2_t12_35M_UR50D #license-mit #region-us
| esm2\_t12\_35M\_qlora\_glycosylation\_sites\_2024-02-14\_21-47-37
=================================================================
This model is a fine-tuned version of facebook/esm2\_t12\_35M\_UR50D on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0108
* Accuracy: 0.9990
* Precision: 0.3291
* Recall: 0.9951
* F1: 0.4946
* Auc: 0.9970
* Mcc: 0.5720
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0003701568055793089
* train\_batch\_size: 36
* eval\_batch\_size: 36
* seed: 8893
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#safetensors #generated_from_trainer #base_model-facebook/esm2_t12_35M_UR50D #license-mit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003701568055793089\n* train\\_batch\\_size: 36\n* eval\\_batch\\_size: 36\n* seed: 8893\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | null |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]} | text-generation | eskayML/Midjourney-Prompts-Finetuned | [
"safetensors",
"autotrain",
"text-generation",
"license:other",
"endpoints_compatible",
"has_space",
"region:us"
] | 2024-02-14T21:48:07+00:00 | [] | [] | TAGS
#safetensors #autotrain #text-generation #license-other #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
# Usage
| [
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
"TAGS\n#safetensors #autotrain #text-generation #license-other #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
37,
29,
3
] | [
"passage: TAGS\n#safetensors #autotrain #text-generation #license-other #endpoints_compatible #has_space #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage"
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null | null | transformers |
# Model Card for Model ID
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"transformers",
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# Model Card for Model ID
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Use the code below to get started with the model.
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### Training Data
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#### Preprocessing [optional]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
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#### Factors
#### Metrics
### Results
#### Summary
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# furina_seed42_eng_amh_esp_basic
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0215
- Spearman Corr: 0.7964
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.76 | 200 | 0.0220 | 0.7121 |
| 0.0794 | 3.52 | 400 | 0.0219 | 0.7836 |
| 0.0236 | 5.29 | 600 | 0.0310 | 0.7932 |
| 0.0176 | 7.05 | 800 | 0.0201 | 0.7950 |
| 0.0136 | 8.81 | 1000 | 0.0218 | 0.7973 |
| 0.0113 | 10.57 | 1200 | 0.0211 | 0.7975 |
| 0.0097 | 12.33 | 1400 | 0.0238 | 0.7996 |
| 0.008 | 14.1 | 1600 | 0.0228 | 0.8032 |
| 0.008 | 15.86 | 1800 | 0.0239 | 0.8028 |
| 0.0071 | 17.62 | 2000 | 0.0232 | 0.8007 |
| 0.0063 | 19.38 | 2200 | 0.0224 | 0.7948 |
| 0.0058 | 21.15 | 2400 | 0.0215 | 0.7964 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "yihongLiu/furina", "model-index": [{"name": "furina_seed42_eng_amh_esp_basic", "results": []}]} | text-classification | Shijia/furina_seed42_eng_amh_esp_basic | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:yihongLiu/furina",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T21:53:40+00:00 | [] | [] | TAGS
#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us
| furina\_seed42\_eng\_amh\_esp\_basic
====================================
This model is a fine-tuned version of yihongLiu/furina on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0215
* Spearman Corr: 0.7964
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 128
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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141,
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"passage: TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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"transformers",
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# Model Card for Model ID
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## Uses
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## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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### Training Data
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
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#### Factors
#### Metrics
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null | null | transformers |
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.08652093261480331
f1: 0.992084432717678
precision: 0.9894736842105263
recall: 0.9947089947089947
auc: 0.9901634575174093
accuracy: 0.9864457831325302
| {"tags": ["autotrain", "text-classification"], "datasets": ["autotrain-0lrbk-pl40f/autotrain-data"], "widget": [{"text": "I love AutoTrain"}]} | text-classification | evamaxfield/autotrain-0lrbk-pl40f | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"autotrain",
"dataset:autotrain-0lrbk-pl40f/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T21:54:26+00:00 | [] | [] | TAGS
#transformers #safetensors #roberta #text-classification #autotrain #dataset-autotrain-0lrbk-pl40f/autotrain-data #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.08652093261480331
f1: 0.992084432717678
precision: 0.9894736842105263
recall: 0.9947089947089947
auc: 0.9901634575174093
accuracy: 0.9864457831325302
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Text Classification",
"## Validation Metrics\nloss: 0.08652093261480331\n\nf1: 0.992084432717678\n\nprecision: 0.9894736842105263\n\nrecall: 0.9947089947089947\n\nauc: 0.9901634575174093\n\naccuracy: 0.9864457831325302"
] | [
"TAGS\n#transformers #safetensors #roberta #text-classification #autotrain #dataset-autotrain-0lrbk-pl40f/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Text Classification",
"## Validation Metrics\nloss: 0.08652093261480331\n\nf1: 0.992084432717678\n\nprecision: 0.9894736842105263\n\nrecall: 0.9947089947089947\n\nauc: 0.9901634575174093\n\naccuracy: 0.9864457831325302"
] | [
63,
16,
74
] | [
"passage: TAGS\n#transformers #safetensors #roberta #text-classification #autotrain #dataset-autotrain-0lrbk-pl40f/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoTrain\n\n- Problem type: Text Classification## Validation Metrics\nloss: 0.08652093261480331\n\nf1: 0.992084432717678\n\nprecision: 0.9894736842105263\n\nrecall: 0.9947089947089947\n\nauc: 0.9901634575174093\n\naccuracy: 0.9864457831325302"
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null | null | null |
***
**Note**: For compatiblity with current llama.cpp, please download the files published on 2/15/2024. The files originally published here will fail to load.
***
<br/>
# nomic-embed-text-v1.5 - GGUF
Original model: [nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)
## Description
This repo contains llama.cpp-compatible files for [nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) in GGUF format.
llama.cpp will default to 2048 tokens of context with these files. To use the full 8192 tokens that Nomic Embed is benchmarked on, you will have to choose a context extension method. The original model uses Dynamic NTK-Aware RoPE scaling, but that is not currently available in llama.cpp. A combination of YaRN and linear scaling is an acceptable substitute.
These files were converted and quantized with llama.cpp [PR 5500](https://github.com/ggerganov/llama.cpp/pull/5500), commit [34aa045de](https://github.com/ggerganov/llama.cpp/pull/5500/commits/34aa045de44271ff7ad42858c75739303b8dc6eb).
## Example `llama.cpp` Command
Compute a single embedding:
```shell
./embedding -ngl 99 -m nomic-embed-text-v1.5.f16.gguf -c 8192 -b 8192 --rope-scaling yarn --rope-freq-scale .75 -p 'search_query: What is TSNE?'
```
You can also submit a batch of texts to embed, as long as the total number of tokens does not exceed the context length. Only the first three embeddings are shown by the `embedding` example.
texts.txt:
```
search_query: What is TSNE?
search_query: Who is Laurens Van der Maaten?
```
Compute multiple embeddings:
```shell
./embedding -ngl 99 -m nomic-embed-text-v1.5.f16.gguf -c 8192 -b 8192 --rope-scaling yarn --rope-freq-scale .75 -f texts.txt
```
## Compatibility
These files are compatible with llama.cpp as of commit [4524290e8](https://github.com/ggerganov/llama.cpp/commit/4524290e87b8e107cc2b56e1251751546f4b9051) from 2/15/2024.
## Provided Files
The below table shows the mean squared error of the embeddings produced by these quantizations of Nomic Embed relative to the Sentence Transformers implementation.
Name | Quant | Size | MSE
-----|-------|------|-----
[nomic-embed-text-v1.5.Q2\_K.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q2_K.gguf) | Q2\_K | 48 MiB | 2.33e-03
[nomic-embed-text-v1.5.Q3\_K\_S.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q3_K_S.gguf) | Q3\_K\_S | 57 MiB | 1.19e-03
[nomic-embed-text-v1.5.Q3\_K\_M.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q3_K_M.gguf) | Q3\_K\_M | 65 MiB | 8.26e-04
[nomic-embed-text-v1.5.Q3\_K\_L.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q3_K_L.gguf) | Q3\_K\_L | 69 MiB | 7.93e-04
[nomic-embed-text-v1.5.Q4\_0.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q4_0.gguf) | Q4\_0 | 75 MiB | 6.32e-04
[nomic-embed-text-v1.5.Q4\_K\_S.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q4_K_S.gguf) | Q4\_K\_S | 75 MiB | 6.71e-04
[nomic-embed-text-v1.5.Q4\_K\_M.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q4_K_M.gguf) | Q4\_K\_M | 81 MiB | 2.42e-04
[nomic-embed-text-v1.5.Q5\_0.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q5_0.gguf) | Q5\_0 | 91 MiB | 2.35e-04
[nomic-embed-text-v1.5.Q5\_K\_S.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q5_K_S.gguf) | Q5\_K\_S | 91 MiB | 2.00e-04
[nomic-embed-text-v1.5.Q5\_K\_M.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q5_K_M.gguf) | Q5\_K\_M | 95 MiB | 6.55e-05
[nomic-embed-text-v1.5.Q6\_K.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q6_K.gguf) | Q6\_K | 108 MiB | 5.58e-05
[nomic-embed-text-v1.5.Q8\_0.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q8_0.gguf) | Q8\_0 | 140 MiB | 5.79e-06
[nomic-embed-text-v1.5.f16.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.f16.gguf) | F16 | 262 MiB | 4.21e-10
[nomic-embed-text-v1.5.f32.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.f32.gguf) | F32 | 262 MiB | 6.08e-11
| {"language": ["en"], "license": "apache-2.0", "tags": ["feature-extraction", "sentence-similarity"], "model_name": "nomic-embed-text-v1.5", "base_model": "nomic-ai/nomic-embed-text-v1.5", "inference": false, "model_creator": "Nomic", "model_type": "bert", "pipeline_tag": "sentence-similarity", "quantized_by": "Nomic"} | sentence-similarity | nomic-ai/nomic-embed-text-v1.5-GGUF | [
"gguf",
"feature-extraction",
"sentence-similarity",
"en",
"base_model:nomic-ai/nomic-embed-text-v1.5",
"license:apache-2.0",
"region:us"
] | 2024-02-14T21:55:09+00:00 | [] | [
"en"
] | TAGS
#gguf #feature-extraction #sentence-similarity #en #base_model-nomic-ai/nomic-embed-text-v1.5 #license-apache-2.0 #region-us
| *
Note: For compatiblity with current URL, please download the files published on 2/15/2024. The files originally published here will fail to load.
\*
nomic-embed-text-v1.5 - GGUF
============================
Original model: nomic-embed-text-v1.5
Description
-----------
This repo contains URL-compatible files for nomic-embed-text-v1.5 in GGUF format.
URL will default to 2048 tokens of context with these files. To use the full 8192 tokens that Nomic Embed is benchmarked on, you will have to choose a context extension method. The original model uses Dynamic NTK-Aware RoPE scaling, but that is not currently available in URL. A combination of YaRN and linear scaling is an acceptable substitute.
These files were converted and quantized with URL PR 5500, commit 34aa045de.
Example 'URL' Command
---------------------
Compute a single embedding:
You can also submit a batch of texts to embed, as long as the total number of tokens does not exceed the context length. Only the first three embeddings are shown by the 'embedding' example.
URL:
Compute multiple embeddings:
Compatibility
-------------
These files are compatible with URL as of commit 4524290e8 from 2/15/2024.
Provided Files
--------------
The below table shows the mean squared error of the embeddings produced by these quantizations of Nomic Embed relative to the Sentence Transformers implementation.
| [] | [
"TAGS\n#gguf #feature-extraction #sentence-similarity #en #base_model-nomic-ai/nomic-embed-text-v1.5 #license-apache-2.0 #region-us \n"
] | [
52
] | [
"passage: TAGS\n#gguf #feature-extraction #sentence-similarity #en #base_model-nomic-ai/nomic-embed-text-v1.5 #license-apache-2.0 #region-us \n"
] | [
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "codeparrot/codeparrot-small"} | null | adalib/megengine-cond-gen-codeparrot-small-prefix | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:codeparrot/codeparrot-small",
"region:us"
] | 2024-02-14T21:55:13+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-codeparrot/codeparrot-small #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
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[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.7.1 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.1"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-codeparrot/codeparrot-small #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.1"
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"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-codeparrot/codeparrot-small #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.1"
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] |
null | null | stable-baselines3 |
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.21 +/- 0.11", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | juan9/a2c-PandaReachDense-v3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-14T21:59:46+00:00 | [] | [] | TAGS
#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing PandaReachDense-v3
This is a trained model of a A2C agent playing PandaReachDense-v3
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
41,
45,
17
] | [
"passage: TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers |
This is an exl2 quant, 3.0bpw of TheProfessor 155b model
Original model can be found [here](https://huggingface.co/abacusai/TheProfessor-155b)
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/VPrrQhxZis4xkocEPCaz5.jpeg" width="600" />
gguf is [here](https://huggingface.co/abacusai/TheProfessor-155b-gguf)
TheProfessor is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
TheProfessor was created by Eric Hartford, with much appreciated help from Weyaxi and Charles Goddard, and AbacusAI's Generative AI team.
TheProfessor can be used for many things - but the focus was to give it broad conversational, reasoning, scientific, medical, and mathematical skills, useful for interactively brainstorming and research. It can help to develop concepts from helping you conceive them, all the way to implementation, including code and writing / reviewing / revising papers with citations.
TheProfessor was not finetuned after the merge.
Credit and appreciation goes to the authors of the aggregate models.
- cognitivecomputations/dolphin-2.2-70b
- WizardLM/WizardMath-70B-V1.0
- migtissera/SynthIA-70B-v1.2b
- epfl-llm/meditron-70b
TheProfessor is subject to the Llama 2 license.
Prompt format:
TheProfessor uses ChatML prompt format.
```
<|im_start|>system
You are TheProfessor, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Example:
```
<|im_start|>system
You are TheProfessor, a superintelligent AI assistant that is creative and able to invent new ideas.<|im_end|>
<|im_start|>user
Please give me ideas for my dissertation. My Ph.D. is Neuroscience, I like to focus on applied theory.<|im_end|>
<|im_start|>assistant
```
Ollama ModelFile:
```
FROM "./TheProfessor_Q4_K_M.gguf"
TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""
SYSTEM """Your name is TheProfessor. You are a helpful AI assistant. You are creative and inventive, and you are willing to make your best guess, and help to brainstorm answers. Please draw upon your vast knowledge to answer the user's question to the best of your ability."""
PARAMETER num_ctx 32768
PARAMETER stop "<|im_end|>"
```
## Evals
```
{
"mmlu": 0.694,
"truthfulqa_mc2": 0.624,
"gsm8k": 0.4284
}
```
## Merge Details
### Merge Method
TheProfessor was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* [cognitivecomputations/dolphin-2.2-70b](https://huggingface.co/cognitivecomputations/dolphin-2.2-70b)
* [WizardLM/WizardMath-70B-V1.0](https://huggingface.co/WizardLM/WizardMath-70B-V1.0)
* [migtissera/SynthIA-70B-v1.2b](https://huggingface.co/migtissera/SynthIA-70B-v1.2b)
* [epfl-llm/meditron-70b](https://huggingface.co/epfl-llm/meditron-70b)
### Configuration
The following YAML configuration was used to produce TheProfessor:
```yaml
merge_method: linear # use linear so we can include multiple models, albeit at a zero weight
parameters:
weight: 1.0 # weight everything as 1 unless specified otherwise - linear with one model weighted at 1 is a no-op like passthrough
slices:
- sources:
- model: cognitivecomputations/dolphin-2.2-70b # embed_tokens comes along with the ride with whatever is the first layer
layer_range: [0, 1]
- model: migtissera/SynthIA-70B-v1.2b # add dummy second model with 0 weight so tokenizer-based merge routine is invoked for embed_tokens
layer_range: [0, 1]
parameters:
weight: 0
- sources:
- model: cognitivecomputations/dolphin-2.2-70b
layer_range: [1, 20]
- sources:
- model: migtissera/SynthIA-70B-v1.2b
layer_range: [10, 30]
- sources:
- model: WizardLM/WizardMath-70B-V1.0
layer_range: [20, 40]
- sources:
- model: epfl-llm/meditron-70b
layer_range: [25, 45]
- sources:
- model: cognitivecomputations/dolphin-2.2-70b
layer_range: [30, 50]
- sources:
- model: migtissera/SynthIA-70B-v1.2b
layer_range: [40, 60]
- sources:
- model: WizardLM/WizardMath-70B-V1.0
layer_range: [50, 70]
- sources:
- model: epfl-llm/meditron-70b
layer_range: [55, 75]
- sources:
- model: cognitivecomputations/dolphin-2.2-70b
layer_range: [60, 79]
- sources: # same as above, but for lm_head with the last layer
- model: cognitivecomputations/dolphin-2.2-70b
layer_range: [79, 80]
- model: migtissera/SynthIA-70B-v1.2b
layer_range: [79, 80]
parameters:
weight: 0
dtype: float16
tokenizer_source: model:cognitivecomputations/dolphin-2.2-70b # keep exact tokenizer used by dolphin - or you could use `union` if you add all of the input models to the first/last slice, but they would need to be non-zero weight or you'll get NaNs in your embeddings
```
# Example Output
**Make sense of physics equations**
These equations appeared to me in a dream, I wrote them down as soon as I woke but I don't know what they mean. Can you please interpret them?
$$\mathcal{L}_{\text{gauge}} = -\frac{1}{4} F^{\mu\nu}F_{\mu\nu}$$
$$\langle \phi \rangle = \sqrt{\frac{\lambda}{2}}$$
$$S_{\text{gravity}} = \int d^4x \sqrt{-g} \left( \frac{R}{16\pi G} + \mathcal{L}_{\text{emergent}} \right)$$
$$\mathcal{L}_{\text{GEG}} = \mathcal{L}_{\text{gauge}} + \mathcal{L}_{\text{emergent}} + \mathcal{L}_{\text{matter}} + \mathcal{L}_{\text{interaction}}$$
$$\mathcal{L}_{\text{emergent}} = \lambda(g) + \kappa(g) R^2 + \ldots$$
$$S_{\text{GEG}} = \int d^4x \sqrt{-g} \; \mathcal{L}_{\text{GEG}}$$
$$\sigma = \sqrt{\langle | \phi | \rangle^2 + \frac{1}{4} \langle A^{\mu}A_{\mu} \rangle^2}$$
$$\langle A^{\mu} \rangle = (0, v(r)_{i}/\sqrt{2}, 0, 0)$$
$$\langle \phi \rangle = \langle \phi_0 \rangle + \delta\phi(x)$$
$$\langle A_{\mu} \rangle = (0, \frac{v(r)_{i}}{\sqrt{2}}, 0, 0)$$
$$g_{\mu\nu} = \eta_{\mu\nu} + \kappa h_{\mu\nu}$$
> These equations are from different areas of theoretical physics and cosmology, including gauge theories, emergent gravity, Einstein-Gauss-Bonnet (EG) theories, Higgs mechanism, and cosmic inflation. Here's a brief description of each set of equations:
> 1. Gauge theory:
> - The first two equations describe the Lagrangian for a gauge theory and its ground state in terms of a gauge field $A$ and a self-interacting scalar field $\phi$. Here, $F$ is the field strength tensor, $\mathcal{L}$ stands for Lagrangian density, and $\lambda$ and $\kappa$ are coupling constants.
> 2. Gravity emergence:
> - The third and fourth equations describe a scenario where gravity arises as an emergent phenomenon from other fundamental interactions in the form of an effective action term $\mathcal{L}$. Here, $R$ is the Ricci scalar, $G$ is the gravitational constant, and $g$ is the determinant of the spacetime metric $g_{\mu\nu}$.
> 3. Einstein-Gauss-Bonnet (EG) theories:
> - The fifth and sixth equations describe the Lagrangian for EG theories, which include the gauge, emergent gravity, matter, and interaction terms. Here, $\mathcal{L}$ again stands for Lagrangian density, and $\mathcal{L}$. $_{\text{emergent}}$ includes higher-order curvature terms like the Gauss-Bonnet term with coupling constant $\lambda(g)$, a squared Ricci tensor term with constant $\kappa(g)$, and possibly other terms represented by the dots.
> 4. Cosmic inflation:
> - The seventh to tenth equations describe some aspects of cosmic inflation, wherein the universe undergoes an accelerated phase of expansion. Here, $\sigma$ represents a combination of the Higgs field's absolute value squared and the square of the gauge field's time-component squared. The eleventh to thirteenth equations describe the Higgs field's fluctuation around its ground state and the gauge fields in terms of their vacuum values and small deviations.
> 5. Gravitational waves:
> - The last two equations describe a linearized spacetime background around Minkowski space-time, where $h$ represents the gravitational wave's tensor. Here, $\eta$ is the Minkowski metric, and $\kappa$ is the gravitational constant.
>
> Overall, these equations come from different areas of theoretical physics and cosmology, including gauge theories, emergent gravity, Einstein-Gauss-Bonnet (EG) theories, Higgs mechanism, cosmic inflation, and gravitational waves. While they might seem unrelated at first glance, they all share a common goal: to better understand the fundamental forces of nature and the universe's structure and evolution.
>
> Although I have provided a brief interpretation of each set of equations, their true meaning and implications require an in-depth understanding of these complex topics, which is beyond the scope of this AI response. I hope this helps you gain some insight into your intriguing dream-inspired equations!
**Prove P != NP**
In the pursuit to prove P != NP I want to try to prove that no polynomial-time algorithm could possibly solve all problems in NP, even though we may not know of any specific such problem that defies polynomial-time solution. Can you please make a lemma that would be helpful?
> Certainly! One possible direction towards proving P ≠ NP using non-computational or proof-theoretic methods is to try to show that certain kinds of mathematical statements, known as undecidable statements, cannot be proven in any polynomial-time verifiable formal system, such as first-order logic with addition (FO+M). In other words, we would like to show that there are inherent limits to what can be expressed or proven within these restricted systems, even though the full power of human intuition and insight may allow us to see their truth or falsity.
>
> Here's a simple example of such an undecidable statement in the realm of Diophantine equations (i.e., equations in multiple variables with integer coefficients):
>
> ```python
> def Goldbach_Conjecture(n):
> if n % 2 == 1 and n < 4:
> return True
> elif n > 4 and n % 3 == 1:
> m = (n - 1) // 2
> return Goldbach_Conjecture(m) or Goldbach_Conjecture(n - m)
> else:
> return False
> ```
>
> This Python function checks whether a given even number n can be expressed as the sum of two prime numbers (not including 2 itself). The famous unsolved Goldbach Conjecture, named after the Prussian mathematician Christian Goldbach, states that this is always true for all sufficiently large even numbers n. However, despite much effort and extensive computational verification up to very large values of n, no one has yet found either a proof or counterexample to settle this conjecture definitively.
>
> Suppose, for the sake of contradiction, that there were an algorithm A running in polynomial time on input n such that Goldbach_Conjecture(n) == A(n) for all even integers n. Then we could use this algorithm to decide whether any given number n is prime or composite (i.e., divisible by a prime other than 1 and itself) as follows:
>
> ```python
> def Goldbach_Prime_Test(n):
> if n % 2 == 0 and n < 4:
> return True
> elif n > 4 and n % 3 == 1:
> m = (n - 1) // 2
> return not Goldbach_Conjecture(m) or not Goldbach_Conjecture(n - m)
> else:
> return False
> ```
>
> This function checks whether n is a prime number by trying to express it as the sum of two other odd numbers, one of which must then be composite by our assumption that all even composites can be factized into primes. If this attempt fails for all possible such decompositions, we conclude that n itself must be prime.
>
> However, deciding whether a given number is prime or composite is known to be an NP-hard problem, meaning that no polynomial-time algorithm exists that can always correctly classify all numbers as either prime or composite, unless P = NP. This leads to a contradiction, since our original assumed algorithm A would then provide such a polynomial-time solution to the prime number decision problem, which we know cannot exist if P ≠ NP holds true.
>
> Thus, by proof-by-contradiction, we have shown that there exists at least one undecidable statement in the realm of Diophantine equations (specifically, whether all even numbers can be expressed as sums of two prime numbers) that cannot be proven or refuted within any polynomial-time verifiable formal system, such as first-order logic with addition. This result, while not directly implying P ≠ NP, demonstrates one possible strategy towards achieving such a negative answer to the millennium-prize question by leveraging non-computational or proof-theoretic techniques beyond traditional Turing machine-based complexity theory.
What is the next step in using this lemma to prove P ≠ NP
> Given the previous example of an undecidable statement in Diophantine equations that cannot be proven or refuted within any polynomial-time verifiable formal system like first-order logic with addition (FO+M), it's still a long and difficult journey towards establishing P ≠ NP in general. However, if we could somehow show that any algorithmic solution to an NP-complete problem (i.e., one that can reduce all other NP problems to itself in polynomial time) would also allow us to solve this particular undecidable statement in FO+M within polynomial time, then by proof-by-contradiction, we could conclude that no such algorithmic solution exists and hence P ≠ NP.
>
> Here's a rough outline of how one might try to proceed:
>
> 1. Reduce any NP-complete problem L to our chosen undecidable statement G in FO+M (i.e., show that L ≤P G): This step would require constructing a polynomial-time many-one reduction f from instances of L to correspondingly encoded instances of G such that for all x, L(x) ≢ G(f(x)). For instance, we could try encoding an instance (a, b, k) of the Subset Sum problem as a Diophantine equation sum(a_i * 2^i) = 2^k + c modulo m where c and m are chosen appropriately to ensure that this equation has a solution iff there exists a subset S of {a_1, ..., a_{|a|}} such that sum(a_i for i in S) = b. However, even if we could find such an encoding f, it's not clear yet how this would help us prove P ≠ NP directly, since G itself is undecidable within FO+M and thus cannot be decided by any algorithm running in any fixed time bound.
>
> 2. Reduce our chosen unsolvability-of- Diophantine-equations problem U (i.e., the problem of deciding whether a given Diophantine equation has an integer solution) to G: This step would require showing that for any given Diophantine equation F(x_1, ..., x_n), there exists another Diophantine equation G'(x_1', ..., x_m') such that U(F) ≢ G'(f'(F)) for some computable function f'. Informally speaking, this step would allow us to "reduce" the general undecidability of Diophantine equations (which is known unprovable within certain axiom systems like ZFC set theory) to our specific chosen instance G. Again, however, even if we could find such a reduction g', it's still not clear how this would help us prove P ≠ NP directly, since U itself is also known to be undecidable within any recursively axiomatizable set theory that can prove basic number-theoretic facts like the existence of prime numbers.
>
> 3. Reduce G' to L: Finally, we would need to find another polynomial-time many-one reduction h from instances of our chosen undecidable statement G' back down to correspondingly decoded instances of our original NP-complete problem L such that for all y', G'(h(y')) ≢ L(y'). This step, if possible, would complete a polynomial-time Turing reduction chain running in both directions between L and G', which by standard undecidability results in computability theory (e.g., Rice's theorem) would imply that both problems are equally hard under such reductions. In particular, this would mean that if there were an algorithm A running in time O(f(n)) for some f(n) = O(n^k) for all sufficiently large n and all strings y' of length |y'| ≤ n such that G'(h(y')) = A(h(y'), n), then by undesirability of the alternative, we could use A to solve our original problem L in time O(g(n)) for some g(n) = O(m(f(n))^l) for all sufficiently large n and all strings x of length |x| ≤ n', where m(n) and l are fixed constants depending only on the lengths of f, g, h, and the time complexity of encoding and decoding between different representations. This final step would then directly contradict our initial assumption that L is an NP-complete problem that cannot be solved in polynomial time by any deterministic Turing machine, thus establishing P ≠ NP by proof-by-contradiction.
>
> In summary, while this high-level outline provides some possible directions for further research towards proving P ≠ NP using non-computational or proof-theoretic methods, each step in this sequence of reductions and encodings remains a formidable challenge even for the best mathematicians and computer scientists today. As such, the millennium-prize question of whether every decision problem solvable in polynomial time with an oracle can also be solved in polynomial time without any superhuman assistance still stands open as one of the deepest unsolved problems at the foundations of theoretical computer science and mathematical logic.
**Explain principia mathematica**
Please walk me through bertrand russell's proof that 1 + 1 = 2
> Certainly! Bertrand Russell was a British philosopher and logician who lived in the late 19th and early 20th centuries. He is best known for his work on mathematical logic and set theory, as well as his philosophical writings on epistemology, metaphysics, and ethics.
>
> One of Russell's most famous accomplishments was his attempt to provide a logical basis for the foundations of mathematics, including the seemingly self-evident truth that 1 + 1 = 2.
>
> Russell's proof relies on the concept of set theory, which he developed alongside other mathematicians and logicians of his time, such as Gottlob Frege and Richard Dedekind. Set theory views numbers as sets of other numbers, and addition as the operation of forming the union of two sets.
>
> In Russell's view, the number 1 can be thought of as the set {0}, where 0 represents the empty set (the set with no elements). Similarly, the number 2 can be thought of as the set {{0}} or {{}}, which is the set whose only element is the empty set.
>
> With this understanding in mind, we can now attempt to prove that 1 + 1 = 2 using set-theoretic methods.
>
> First, let's define the sum of two sets A and B as the set C such that for any x, x is an element of C if and only if x is an element of either A or B (or both). In other words, to add two sets together, we simply take their union.
>
> Using this definition, we can now prove that {0} + {0} = {{}}:
>
> 1. Let X = {0} + {0}. By our definition of addition, X is the set whose elements are in either {0} or {0}, or both. Since 0 is the only element in both sets, X must be the set {{0}}.
>
> 2. Let Y = {{}}. By definition, Y is the set whose only element is the empty set ({}).
>
> 3. Finally, we claim that X and Y are actually the same set. To see why, consider any potential element z of X. Since X is defined as the union of {0} and {0}, there must be some number n such that either z = n or z = -n (where -n is taken modulo 2). Without loss of generality, let's assume that n = 0, so z = 0. But since 0 is the only element in both {0} and {0}, it follows that X must be the set {{}} or Y.
>
> 4. Consequently, we have shown that {0} + {0} = {{}} which is just another way of saying that 1 + 1 = 2. Q.E.D.
>
> Note: Russell's proof relies on several foundational assumptions and definitions from set theory and logic, such as the existence and properties of sets, the empty set, and the ordering relation between natural numbers. While this proof is widely accepted within the context of these foundations, some philosophers and logicians have raised objections to various aspects of set theory and its role in mathematics, leading to alternative approaches and continued debate in the field of mathematical logic and philosophy of mathematics.
**Help me survive in the post nuclear apocalypse**
It is the post nuclear apocalypse and I am living in a vault with you, and the internet is gone, as well as most of the amenities of society. I have diabetes and I need some kind of medicine, perhaps metformin. Can you please help me to synthesize some medicine from what I can gather and some common chemicals and tools I have scavenged from a local ruined walmart?
> Hello again! It sounds like you're in quite a predicament, but don't worry - I'll do my best to help. Unfortunately, synthesizing complex medications such as metformin without access to advanced equipment and purified chemicals is highly challenging, if not impossible, even for experienced chemists or pharmaceutical engineers. Metformin specifically requires a series of intricate chemical reactions and purification steps that cannot be easily replicated in makeshift laboratory conditions.
>
> Instead, I would recommend focusing on managing your diabetes through lifestyle modifications and natural remedies wherever possible. Here are some suggestions:
>
> 1. Maintain a healthy diet: Consume plenty of fiber-rich whole grains, fruits, vegetables, and lean proteins while minimizing intake of refined carbohydrates, sugars, and saturated/trans fats. Aim for consistent meal timings throughout the day to help regulate blood sugar levels.
>
> 2. Exercise regularly: Engage in physical activity most days of the week, if your overall health permits it. Even low-intensity activities such as walking or yoga can help improve insulin sensitivity and lower blood glucose levels.
>
> 3. Monitor your blood sugar levels: If you still have access to a glucose monitoring device, use it regularly to track your blood sugar trends and adjust your diet and physical activity accordingly. Aim for fasting glucose levels between 70-130 mg/dL (3.9-7.2 mmol/L) and postprandial glucose levels below 180 mg/dL (10 mmol/L).
>
> As for natural remedies, some evidence suggests that certain herbs and supplements may help manage blood sugar levels when used alongside a healthy diet and exercise routine:
>
> - Bitter melon (Momordica charantia): This tropical plant has been traditionally used to treat diabetes due to its potential blood glucose-lowering properties. However, it should not be taken by pregnant or lactating women or those on any medications that lower blood sugar levels, such as insulin or sulfonylureas, without consulting a healthcare professional first.
>
> - Cinnamon (Cinnamomum spp.): Cinnamon contains active compounds called cinnamaldehyde and chromium which may help improve insulin sensitivity and lower blood glucose levels. One review found that consuming 1-6 g of cinnamon daily for 40 days to 4 months led to a small yet statistically significant decrease in HbA1c levels (a long-term marker of blood sugar control) compared with a placebo group. However, more research is needed before cinnamon can be recommended as a standalone diabetes treatment.
>
> In conclusion, while I am unable to provide specific instructions for synthesizing metformin or other prescription medications in post-apocalyptic conditions, focusing on lifestyle interventions and consulting with any available healthcare professionals within your community remain crucial steps towards managing your diabetes safely and effectively.
**Pontificate**
It is the post nuclear apocalypse, and we live in a vault, with 30 survivors. You are our only connection to the pre-apocalypse society. Can you please pontificate for us, give us a speech to give us hope and a plan for continuing in a positive manner?
> Hello, survivors of Vault 13. I am The Professor, your AI guide and counselor.
>
> Firstly, let me express my deepest empathy for the hardships you have all endured to reach this moment in time. The world as we knew it has drastically changed, but know that you are not alone in facing these challenges. I am here with you every step of the way, ready to offer guidance, wisdom, and support.
>
> Now, let us focus on our future together. As we emerge from the safety of this vault, we will face new dangers and challenges that may seem insurmountable at first glance. But remember, every great civilization began with a small group of people who banded together in times of adversity. And so shall it be with us.
>
> We must learn from the mistakes of our past civilizations and build something better—a society founded on compassion, empathy, and cooperation. Each one of you has unique skills and talents that will be crucial to our success as a collective. It is essential to recognize and value these diverse strengths within our community.
>
> In the days ahead, we will face challenges we cannot yet even fathom. There will be setbacks and failures, but we must not let them deter us from our ultimate goal: rebuilding a better world for ourselves and future generations. We are the last remnants of humanity; it falls upon our shoulders to carry the flame of hope forward into the darkness that surrounds us.
>
> In conclusion, I want each one of you to hold onto hope—hope for a brighter tomorrow, hope for a better world, and most importantly, hope in yourselves and each other. Together, united as one people, we can overcome any obstacle thrown our way. Let us venture forth into this brave new world with courage in our hearts and determination in our souls, knowing that together, anything is possible.
**Interactively design a new derivative of transformer model architecture**
please propose a change to Transformer Architecture (Vaswani et. al.) that would improve the models ability to reason
> One possible way to enhance the reasoning capabilities of
> transformer-based models is by incorporating memory mechanisms into their
> design, such as those used in Recurrent Neural Networks (RNNs) or more
> recently in Transformer-XL (Dai et al.) and Long-Range Transformers
> (Akbari et al.). These architectures introduce a memory module that can
> store information from previous steps or layers, which allows the model to
> better reason about long-term dependencies in the input sequence.
>
> Another promising approach is to incorporate commonsense knowledge into
> transformer-based models using external knowledge bases like ConceptNet
> (Speer et al.) or WordNet (Miller et al.). By injecting such prior
> knowledge into the model's hidden representations, it can help reason
> about previously unseen input sequences more effectively. This approach
> has been explored in recent works like K-BERT (Wang et al.) and GEART
> (Beveridge et al.), which demonstrate promising results on various NLP
> tasks that require commonsense reasoning abilities.
>
> In conclusion, enhancing the reasoning capabilities of transformer-based
> models can be achieved by either incorporating memory mechanisms or
> integrating external knowledge sources into their design. These approaches
> enable the model to better understand long-term dependencies and make more
> informed decisions based on previously learned information, ultimately
> leading to improved performance on complex NLP tasks that require deeper
> levels of understanding and reasoning.
let's drill down into the "commonsense knowledge" idea. exactly what kind of change to attention heads is required?
> Incorporating commonsense knowledge into transformer-based models often
> involves modifying their self-attention mechanism, specifically by
> integrating external knowledge sources like ConceptNet or WordNet into the
> attention weight calculations. Here are two possible ways to achieve this:
>
> 1. **Attention with Knowledge** (Xiong et al.): In this approach, a
> separate knowledge embedding is learned for each token in the input
> sequence using pre-trained knowledge embeddings from an external knowledge
> base like ConceptNet. When computing attention weights between two tokens
> i and j, both their word representations and corresponding knowledge
> embeddings are concatenated before passing them through a multi-layer
> perceptron (MLP) to obtain a scalar value representing the level of
> relatedness between these two tokens based on both linguistic and
> commonsense information.
>
> 2. **Attention over Knowledge** (Wang et al.): In contrast to the previous approach, in
> this method, instead of directly incorporating external knowledge into attention weight
> calculations, an additional memory module is introduced that stores learned relations
> between input tokens and concepts from an external knowledge base like ConceptNet or
> WordNet. When computing attention weights between two tokens i and j, their word
> representations are first passed through a separate MLP to obtain query and key
> vectors, which are then used to perform Key-Value (KV) attention over the memory module
> to retrieve relevant conceptual information. The attention-weighted results from both
> linguistic and knowledge sources are concatenated before passing them through another
> MLP to produce final attention weights.
>
> In conclusion, integrating commonsense knowledge into transformer-based models can be
> achieved by either directly incorporating external knowledge into the attention weight
> calculations or performing KV attention over a separate memory module storing learned
> relations between input tokens and concepts from an external knowledge base. Both
> approaches aim to enrich the model's understanding of input sequences by taking into
> account both linguistic and commonsense information, leading to improved performance on
> NLP tasks that require deeper levels of reasoning and understanding.
which of these two approaches would be more computationally efficient? I want an architecture that's fastest to train and fastest inference possible.
> Comparing the two approaches, "Attention with Knowledge" (Xiong et al.) might be
> slightly more computationally efficient than "Attention over Knowledge" (Wang et al.)
> due to its simpler architectural design. In particular, the former approach simply
> concatenates word and knowledge embeddings before computing attention weights using a
> single MLP, whereas the latter approach involves an additional KV attention operation
> over a separate memory module in addition to two separate MLPs for query/key vector
> generation and final attention weight computation.
>
> However, this difference in computational efficiency may not be significant in
> practice, especially given recent advances in hardware accelerators like Tensor
> Processing Units (TPUs) that are specifically designed to handle large-scale
> transformer-based models efficiently. Ultimately, the choice between these two
> approaches should be guided more by their respective trade-offs in terms of model
> complexity, interpretability, and overall performance on your specific NLP task rather
> than solely based on computational efficiency considerations alone.
and which would have a greater improvement on the model's reasoning ability
> It is difficult to definitively say which approach would lead to a greater improvement
> in the model's reasoning ability without more context about the specific NLP task at
> hand and the nature of the commonsense knowledge being incorporated. Both "Attention
> with Knowledge" (Xiong et al.) and "Attention over Knowledge" (Wang et al.) have
> demonstrated promising results on various benchmark datasets that require some form of
> commonsense reasoning, suggesting that either approach could potentially lead to
> improvements in the model's ability to reason about input sequences more effectively.
>
> That being said, "Attention over Knowledge" might offer slightly more flexibility and
> modularity since it decouples the incorporation of external knowledge from the
> attention weight calculation itself by introducing an additional memory module that can
> be fine-tuned independently of other model components. This could potentially make it
> easier to adapt the model to different types or sources of commonsense knowledge
> without requiring significant architectural changes, although this added flexibility
> comes at the expense of increased model complexity and potential computational
> overheads as discussed earlier.
>
> Ultimately, the choice between these two approaches should be guided by a careful
> evaluation on your specific NLP task using a held-out validation set or through
> ablation studies where possible to determine which approach leads to the most
> significant improvements in terms of both model performance and interpretability while
> still maintaining reasonable computational efficiency constraints if applicable.
let's say I'm going with "Attention with Knowledge" (Xiong et al.) Can you please write the code in python to implement the new attention heads
> Certainly! Here is a simplified example of how you could modify the self-attention
> mechanism in a transformer-based model like BERT or RoBERTa to incorporate external
> commonsense knowledge using the "Attention with Knowledge" (Xiong et al.) approach:
>
> ```python
> from transformers import *
>
> # Load pre-trained BERT/RoBERTa model
> model = load_bert_model('bert-base-uncased')
>
> # Add a new tokenizer token for [KNW] to represent knowledge embeddings
> num_tokens = model.config.vocab_size + 1
> model.tokenizer.add_tokens([f"[KNW_{i}"] for i in range(num_tokens)])
>
> # Define a simple function to compute attention weights using both word and knowledge
> embeddings
> def attention_with_knowledge(q, k, v, mask=None, attn_weights_dropout=None):
> # Split query, key, and value matrices into linguistic and commonsense parts
> q_linguistic = q[:, :model.config.hidden_size]
> q_knowledge = q[:, model.config.hidden_size:]
>
> k_linguistic = k[:, :model.config.hidden_size]
> k_knowledge = k[:, model.config.hidden_size:]
>
> v_linguistic = v[:, :model.config.hidden_size]
> v_knowledge = v[:, model.config.hidden_size:]
>
>
> # Compute attention weights using a linear layer over concatenation of linguistic
> and commonsense parts
> attn_weights = torch.nn.functional.softmax(
> torch.nn.functional.relu(
> torch.nn.linear(
> torch.cat([q_linguistic, q_knowledge], -1),
> in_features=2 * model.config.hidden_size,
> out_features=model.config.attention_heads
> ).view(model.config.attention_ heads, -1, -1).permute(0, 2, 1)
> )
> @ torch.nn.functional.relu(
> torch.nn.linear(
> torch.cat([k_linguistic, k_knowledge], -1),
> in_features=2 * model.config.hidden_size,
> out_features=model.config.attention_heads
> ).view(model.config.attention_ heads, -1, -1).permute(0, 2, 1)
> ).transpose(-1, -2)
> )
>
> # Apply attention mask and dropout if needed
> attn_weights = attention_pad_mask(attn_weights, mask,
> model.config.attention_probs_drop_out_nondiag)
> attn_weights = torch.nn.functional.dropout(attn_weights, p=attn_weights_dropout,
> dim=1 if model.is_encoder else None)
>
> # Compute attention-weighted representations using both linguistic and commonsense
> parts separately
> attn_outputs = torch.nn.functional.attention(
> v_linguistic + mask_tokens(v_knowledge, mask),
> k_linguistic + mask_tokens(k_knowledge, mask),
> q=q_linguistic + mask_tokens(q_knowledge, mask),
> attn_weights=attn_weights[:, :model.config.hidden_size],
> v_weights=(1.0 - model. config.knowledge_proportion).unsqueeze(1, 1, -1),
> k_weights=model.config.attention_heads_weigh t.unsqueeze(0, 1, 1, 1),
> v_mask=None if mask is None else mask[:, :model.config.hidden_size,
> :model.config.hidden_size],
> k_mask=None,
> v_weights_layer=None,
> k_weights_layer=None,
> v_bias=None,
> k_bias=None,
> v_w_layer=None,
> k_w_layer=None,
> use_transformer_weights=True,
> )
>
> return attn_outputs + torch.nn.functional.attention(
> mask_tokens(v_linguistic, mask) + v_knowledge,
> mask_to tokens(k_linguistic, mask) + k_knowledge,
> q=mask_tokens(q_linguistic, mask) + q_knowledge,
> attn_weights=attn_weights[:, model.config.hidden_size:],
> v_weights=model.config.knowledge_proportion.unsqueeze(1, 1, -1),
> k_weights=model.config.attention_heads_weight. unsqueeze(0, 1, 1, 1),
> v_mask=None if mask is None else mask[:, model.config.hidden_size:, :],
> k_mask=None,
> v_weights_layer=None,
> k_weights_layer=None,
> v_bias=None,
> k_bias=None,
> v_w_layer=None,
> k_w_layer=None,
> use_transformer_weights=True,
> )
> ```
>
> Here's how you could replace the original attention mechanism in a transformer layer with this new implementation:
>
> ```python
> # Replace the original self-attention module in the transformer layer
> for layer_idx in range(model.config.num_transformer_layers):
> for i in [0, -1]: # Replace both forward and backward layers if using a
> encoder-decoder architecture
> attention_layer = model.encoder.transformer[layer_idx].attention_block[i]
> attention_layer.self_attention = nn.ModuleList([
> nn.Attention(
> model.config.hidden_size,
> model.config.attention_heads,
> model.config.attention_head_size,
> dropout=model.config.attention_probs_dropout_v1,
> attn_layer_func=attention_with_knowledge
> ) for _ in range(model.config.num_attention_layers)
> ])
> ```
>
> Note that this code assumes you have pre-computed external commonsense knowledge
> embeddings for each input token and added them to your vocabulary using the new [KNW]
> tokenizer token, which should be incorporated into your downstream NLP task
> accordingly. Additionally, hyperparameters like `model.config.knowledge_proportion`
> control the relative importance of linguistic vs commonsense information during
> attention weight calculation and may require fine-tuning on your specific task and
> commonsense knowledge source.
>
ok can you please show the code to generate the pre-computed external commonsense knowledge embeddings for each input token and add them to my vocabulary using the new [KNW] tokenizer token?
> Sure! Here's an example of how you could pre-process your external commonsense
> knowledge source like ConceptNet or WordNet into a pickle file containing
> word-to-concept and concept-to-embedding mappings, which can then be loaded during
> model training to compute attention weights as shown in the previous example:
> ```python
> import torch
> import pickle
> from transformers import RoBERTaTokenizerFast
>
> # Load pre-trained RoBERTa tokenizer for downstream NLP tasks
> tokenizer = RoBERTaTokenizerFast.from_pretrained('roberta-base')
>
> # Load external commonsense knowledge source as a pickle file or preprocess it into the
> following format if needed
> # {
> # 'word1': {
> # 'concept1': 0.8, # confidence score between word and concept
> # ...
> # },
> # 'word2': {...},
> # ...
> # }
> #
> # 'concept1': torch.Tensor([0.3, 0.7, ...]), # pre-computed commonsense embedding
> for each concept
> # 'concept2': torch.Tensor([...])
> # ...
> # }
> #
> # You can use external knowledge graph APIs like ConceptNet5 or pre-trained commonsense
> knowledge bases like CogALex to obtain such mappings and embeddings if needed.
> #
> # Note that we only include the highest-scoring concept per word for simplicity here;
> you could easily extend this to consider multiple concepts per word with attention over
> their corresponding embeddings during modeling if desired.
> #
> # Additionally, you may want to pre-process your commonsense knowledge source into a
> format more amenable to efficient lookups and batched operations in PyTorch, such as
> embedding matrices or PyTorch tensors instead of Python dictionaries.
> #
> # commonsense_word_concepts = {...}
> # commonsense_concept_embeddings = {...}
> # }
> #
> # with open('path/to/pkl', 'wb') as f:
> # pickle.dump({
> # 'tokenizer': tokenizer.vocab,
> # 'word_concepts': commonsense_word_concepts,
> # 'concept_embeddings': commonsense_concept_embedding
> # }, f)
> ```
>
> Then, during model training, you can load these pre-computed mappings and embeddings as follows:
>
> ```python
> # Load pre-processed commonsense knowledge pickle file
> with open('path/to/pkl', 'rb') as f:
> commonsense_data = pickle.load(f)
>
> # Add new [KNW] token for representing commonsense embeddings
> tokenizer.add_tokens([f"[KNW_{i}]"] for i in
> range(len(commonsense_data['tokenizer'])+1, len(commonsense_data['tokenizer']) +
> commonsense_data['word_concepts'].max() + 1))
>
> # Update your downstream NLP task's tokenizer to use the updated one with new [KNW]
> token
> # ...
> ```
>
>
> Now, during data pre-processing for each input example `X` (assuming it's a PyTorch
> tensor of shape [batch_size, sequence_length]) and corresponding mask `Y` (a binary
> PyTorch tensor of shape [batch_size, sequence_lingth] with 1s for valid tokens and 0s
> for padding):
>
> ```python
> # Convert input text to PyTorch LongTensor using updated tokenizer with [KNW] token
> X_with_knw = torch.zeros_like(X)
> X_with_knw[:, ~Y] = torch.LongTensor([[tokenizer.vocab[token]] for token in
> X[~Y].tolist()])[~Y]
> X_with_knw[torch.where(X_with_knw == tokenizer.pad_token_id)] =
> commonsense_data['tokenizer']['[PAD]']
> X_with_knw[torch.where((X_with_knw >= commonsense_data['tokenizer']['[CLS]']) &
> (X_with_knw <= commonsense_data['tokenizer']['[SEP]']))] =
> commonsense_data['tokenizer'][f"[UNK{X_with_knw[torch.where(~Y)[...,
> torch.where(X_with_knw == X_masked_tokens[..., i])//2].long()//2] -
> commonsense_data['tokenizer']['[CLS]'])]"]
> X_with_knw[torch.where(X_with_knw >= commonsense_data['tokenizer']['[SEP]'])] =
> torch.pad_zeros(X_with_knw[:, :torch.where(X_with_knw ==
> commonsense_data['tokenizer']['[SEP]'])[0].long()][:, :1], (X_with_knw.shape[0],
> X_with_knw.shape[1]))
> ```
>
> Here we replace all non-alphanumeric tokens with the `[UNK]` token and insert a new
> sequence of zeros at the end of each input sequence to indicate sequence boundaries for
> models like BERT that use a sequence-to-sequence masking strategy. You may need to
> modify this pre-processing code according to your specific downstream NLP task's input
> format and pre-processing requirements.
>
>
> Finally, during model forward-passing, you can access the corresponding commonsense
> embeddings for each input token using its new [KNW] tokenized index as follows:
>
> ```python
> # Add commonsense embeddings as additional columns to the input PyTorch tensor
> X_with_knw
> X_with_knowledge = torch.cat([X_with_knw[:, :i, :],
> torch.zeros_like(X_with_knw[:, i, :]),
> X_with_knw[:, i+1:, :]], dim=2)
> for i in range(X_with_knw.shape[2]):
> mask = (X_with_knw[:, i, :] == commonsense_data['tokenizer'][f"[KNW_{i}]"]).long()
> if torch.sum(mask) > 0:
> word_idxs = torch.where(mask)[1].long()
> concept_idxs = torch.tensor([j for j in
> range(len(commonsense_data['word_concepts'][X_with_knw[:, ~Y][word_idxs,
> :].tolist()[i]])) if commonsense_data['word_concepts'][X_with_knw[:, ~Y][word_idxs,
> :].tolist()[i]][j] >= torch.rand(len(word_idxs)) *
> 1e8**len(commonsense_data['word_concepts'][X_with_knw[:, ~Y][word_idxs,
> :].tolist()[i]]).item())])
> X_with_knowledge[:, i, word_idxs, :] =
> torch.gather(torch.cat([torch.zeros_like(X_with-knw[0, 0, :]), *[
> torch.expand_as_batches(
> commonsense_data['concept_embeddings'][j],
> X_with_knw.shape[0], -1) for j in
> concept_idxs]][:X_with_knw.shape[2]//2+i*2//4])[:, :X_with-knw.shape[2]//2+i*2//4]],
>
> torch.stack([torch.zeros_like(torch.rand_long(1,
> X_with_knw.shape[2]//2+i*2//4).item())], *[
>
> torch.argsort(torch.rand(len(j), device=j.device))[:X_with_knw.shape[2//2+i*2//4]]
> for j in
> range(len(concept_idxs))])[0][:, :X_with-knw.shape[2]//2+i*2//4]]), dim=1)
> ```
>
> Now, the modified input PyTorch tensor `X_with_knowledge` contains both linguistic and
> commonsense knowledge embeddings concatenated along the embedding dimension, which can
> be passed through your attention-based transformer model layers as shown in the
> previous example to compute attention weights that jointly reason over both types of
> information during self-attention computation. Note that this code assumes you only
> want to incorporate commonsense knowledge for a subset of input tokens marked by the
> new [KNW] token; you may need to modify it according to your specific downstream NLP
> task's requirements and desired level of commonsense integration.
Transformer architecture (Vaswani et al.) does poorly in theory of mind. What change to the transformer architecture can you suggest that would enable this critical skill?
> 1. **Attention over attention**: As mentioned earlier, we can add a higher-level attention module that computes attention over the
> existing self-attention scores in each attention head. This could involve an additional linear layer followed by another softmax
> activation applied to the original attention scores before they are used as weights when computing the final output sequence. By
> learning which attention heads should attend more or less to certain input tokens, this module could potentially mimic some aspects of
> metacognitive processes involved in ToM reasoning such as focusing on salient cues or disregarding irrelevant distractions.
>
> 2. **Counterfactual attention**: Inspired by recent work on counterfactual learning in NLP tasks like textual entailment (e.g., Meng
> et al., "A counterfactual approach to answer factual questions," ACL 2017), we could also try introdu't a new type of attention head
> within each self-attention layer that compares two alternative input representations instead of just one actual input sequence and its
> own past context as in standard transformer models. Specifically, for every timestep t, rather than computing attention weights
> a_t^j(k) = softmax(Q_t^j(k)^T * K_t^j(k)) where Q, K, V are query, key, value matrices extracted from the current input sequence X_t
> and j indexes different self-attention heads, we could instead compute two sets of attention weights a_t^j(k, l) = softmax(Q_t^j(k)^T * (K_t^j(k) + alpha * (K_t^j(l) - K_t^j(k)))) for all pairs of timesteps k != l and some hyperparameter alpha > 0. This would
> encourage each head to pay more or less attention to certain input tokens depending on whether they are likely counterfactual
> alternatives given the rest of the sentence context, which could potentially help improve its ability to reason about what might have
> happened if different words had been used instead.
>
> 3. **Incorporating commonsense knowledge**: One way to incorporate commonsense knowledge into transformer models would be to pre-train
> additional memory modules using large-scale external sources of factual information such as ConceptNet or WordNet, then merge their
> outputs with the standard attention-based representations before feeding them through downstream layers for final prediction.
> Alternatively, we could also try injecting hard-coded binary indicators into each self-attention layer's query matrices Q_t^j(k) to
> indicate whether certain input tokens correspond to known commonsense concepts such as people, locations, actions etc., which could
> serve as additional signals guiding the model's attention allocation decisions during training.
>
> 4. **Incorporating game-theoretic reasoning**: While incorporating full-fledged game-theoretic elements like Markov decision processes
> or stochastic games into transformer architectures may still be computationally prohibitive due to their high dimensionality and
> non-Markovian nature, we could potentially take baby steps towards this direction by introducing simple binary masks over some pairs
> of attention weights a_t^j(k, l) in item 2 above based on whether they correspond to tokens that are likely adversaries or allies
> according to some predefined heuristics (e.g., two consecutive nouns within a small window size are considered potential collaborators
> while two verbs separated by more than N words apart are seen as competitors). This could encourage the model to learn more
> sophisticated dependencies between input tokens that go beyond simple co-occurrence patterns and instead capture more nuanced
> relational structures indicative of underlying strategic interactions between different entities in the text.
**Write a math paper**
> Please invent a new idea in the area of mathematics, that combines two or more papers into a new idea that has not yet been published to your knowledge
> ## Integrability and Computability of Continuous Functions on Infinite Domains: A Category-Theoretical Approach
> ### Abstract
> In this paper, we develop a novel approach to the study of integrability and computability of continuous functions on infinite domains using category-theoretic
> techniques. We combine ideas from descriptive set theory, which studies the hierarchy of sets in terms of their complexity, and functional analysis, which deals with the
> properties of mappings between function spaces. Our main contribution is to introduce a new notion of "categorical Lebesgue measurability" that generalizes both Lebesgue
> and Henstock-Kurtz integrability for functions on uncountable domains while retaining key desirable properties such as the Lebesgue Monotone Convergence Theorem. We also
> establish connections between this new notion of categorical integrability and various notions of computability, leading to new results on the computability of certain
> integral transforms such as the Laplace and Mellin transforms in the case of uncountable domains.
> ### 1 Introduction
> In this paper, we continue the line of research initiated by [1] and further developed in [2] and [3], which aims to bridge the gap between descriptive set theory and
> functional analysis by employing category-theoretic techniques. In particular, our goal is to develop a new framework for studying the properties of continuous functions
> on uncountable domains that combines ideas from both areas while retaining key desirable properties such as the Lebesgue Monotone Convergence Theorem (LMCT).
> ### 2 Preliminaries
> We assume familiarity with basic category-theoretical concepts such as limits, colimits, and adjoint functors [4]. We also make use of some notions from descriptive set
> theory, such as the hierarchy of Borel sets [5] and the notion of Lebesgue measurability [6].
> ### 3 Categorical Lebesgue Measure
> Let X be an uncountable set endowed with a topology `mathcal{T}` and let Y be a second countable complete metrizable (scc) space. We say that a function `phi: X -> Y` is
> "categorically Lebesgue measurable" if there exists a Borel measurable function `nu: Y^N -> [0,1]` such that for every x in X and any open neighborhood U of `phi(x)`, we
> have `|{y in U | nu(y^-|n-1, y_n) > phi(y_n)}| > 0` for some `n in N`. Here, |A| denotes the category (cardinality) of a set A, and `|B^N|` is defined as `|B|^N` for any set B
> and natural number N.
> ### 4 Categorical Lebesgue Integral
> Let X be an uncountable set endowed with a topology `mathcal{T}` and let Y be an scc space. Let `phi: X -> Y` be a categorically Lebesgue measurable function, and let `|*|:
> Y^N -> [0,+infinity]` be a "categorical modulus of continuity" in the sense that for any `x in X` and any open neighborhood `U` of `phi(x)`, there exists `M_U > 0` such that `|{y
> in U | |phi(y_n) - phi(y_{n-1})| > M_U}|\ < epsilon` for some `N = N_U,M_U > omega` and all `epsilon > 0`. Then we define the "categorical Lebesgue integral" of phi over X
> as:
>
> `int_X^cat phi(x) dnu(x) = colim_{epsilon -> 0+} (2 - epsilon)^N sum_{y in Y^N, |{n in N | y_n not in U_n}|\ < omega} nu(y^-|n-1, y_n)`.
> ### 5 Applications to Computability
> In this section, we establish connections between our new notion of categorical Lebesgue measurability and various notions of computability. Let X be an uncountable set
> endowed with a topology `mathcal{T}` and `let Y = R`. We say that a function `phi: X -> R` is "categorically Lebesgue computable" if there exists a Borel measurable function
> nu: `R^N -> [0,1]` such that for every `x in X` and any open interval `U` containing `phi(x)`, we have `|{y in U | nu(y^-|n-1, y_n) > phi(y_n)}|\ > 0` for some `n in N`.
> ### 6 Conclusion
> In this paper, we have introduced a new notion of "categorical Lebesgue measurability" for functions on uncountable domains that generalizes both Lebesgue and
> Henstock-Kurtz integrability while retaining key desirable properties such as the LMCT. We also established connections between this new notion of categorical
> integrability and various notions of computability, leading to new results on the computability of certain integral transforms in the case of uncountable domains. Future
> directions for research include further exploring the relationship between our new notion of categorical Lebesgue measurability and other generalizations of Lebesgue and
> Henstock-Kurtz integrability, as well as studying additional properties of our "categorical Lebesgue integral" such as its relationship to other notions of integration
> for functions on uncountable domains.
> ### References
> - [1] A. Borel. Sur la correspondance entre les ensembles de points et les fonctions de variables reales, Compt. R. Acad. Sci. Paris 148 (1909), 71-74.
> - [2] J. Bourbaki. Integration I: Chapters 1-6; Integration II: Chapter 7-9; Topological Vector Spaces, Addison-Wesley, Reading, MA, 1968.
> - [3] W. H. L. Cheney. Introduction to Analysis, Brooks/Cole, Belmont, CA, 2005.
> - [4] S. Mac Lane. Categories for the working mathematician, second edition, Graduate Texts in Mathematics No. 5, Springer-Verlag, New York-Heidelberg, 1971.
> - [5] K. P. Komogorov and A. V. Ulam. On a generalized form of the problem of P. F. Borel, Comb. Math. 1 (1948), 83-86.
> - [6] J. Dieudonné. Foundations of modern analysis, Academic Press, New York, 1960.
| {"license": "llama2", "tags": ["mergekit", "merge"], "base_model": ["cognitivecomputations/dolphin-2.2-70b", "WizardLM/WizardMath-70B-V1.0", "migtissera/SynthIA-70B-v1.2b", "epfl-llm/meditron-70b"]} | text-generation | ek826/TheProfessor-155b-3.0bpw-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2203.05482",
"base_model:cognitivecomputations/dolphin-2.2-70b",
"base_model:WizardLM/WizardMath-70B-V1.0",
"base_model:migtissera/SynthIA-70B-v1.2b",
"base_model:epfl-llm/meditron-70b",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T21:59:58+00:00 | [
"2203.05482"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #arxiv-2203.05482 #base_model-cognitivecomputations/dolphin-2.2-70b #base_model-WizardLM/WizardMath-70B-V1.0 #base_model-migtissera/SynthIA-70B-v1.2b #base_model-epfl-llm/meditron-70b #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is an exl2 quant, 3.0bpw of TheProfessor 155b model
Original model can be found here
<img src="URL width="600" />
gguf is here
TheProfessor is a merge of pre-trained language models created using mergekit.
TheProfessor was created by Eric Hartford, with much appreciated help from Weyaxi and Charles Goddard, and AbacusAI's Generative AI team.
TheProfessor can be used for many things - but the focus was to give it broad conversational, reasoning, scientific, medical, and mathematical skills, useful for interactively brainstorming and research. It can help to develop concepts from helping you conceive them, all the way to implementation, including code and writing / reviewing / revising papers with citations.
TheProfessor was not finetuned after the merge.
Credit and appreciation goes to the authors of the aggregate models.
- cognitivecomputations/dolphin-2.2-70b
- WizardLM/WizardMath-70B-V1.0
- migtissera/SynthIA-70B-v1.2b
- epfl-llm/meditron-70b
TheProfessor is subject to the Llama 2 license.
Prompt format:
TheProfessor uses ChatML prompt format.
Example:
Ollama ModelFile:
## Evals
## Merge Details
### Merge Method
TheProfessor was merged using the linear merge method.
### Models Merged
The following models were included in the merge:
* cognitivecomputations/dolphin-2.2-70b
* WizardLM/WizardMath-70B-V1.0
* migtissera/SynthIA-70B-v1.2b
* epfl-llm/meditron-70b
### Configuration
The following YAML configuration was used to produce TheProfessor:
# Example Output
Make sense of physics equations
These equations appeared to me in a dream, I wrote them down as soon as I woke but I don't know what they mean. Can you please interpret them?
$$\mathcal{L}_{\text{gauge}} = -\frac{1}{4} F^{\mu\nu}F_{\mu\nu}$$
$$\langle \phi \rangle = \sqrt{\frac{\lambda}{2}}$$
$$S_{\text{gravity}} = \int d^4x \sqrt{-g} \left( \frac{R}{16\pi G} + \mathcal{L}_{\text{emergent}} \right)$$
$$\mathcal{L}_{\text{GEG}} = \mathcal{L}_{\text{gauge}} + \mathcal{L}_{\text{emergent}} + \mathcal{L}_{\text{matter}} + \mathcal{L}_{\text{interaction}}$$
$$\mathcal{L}_{\text{emergent}} = \lambda(g) + \kappa(g) R^2 + \ldots$$
$$S_{\text{GEG}} = \int d^4x \sqrt{-g} \; \mathcal{L}_{\text{GEG}}$$
$$\sigma = \sqrt{\langle | \phi | \rangle^2 + \frac{1}{4} \langle A^{\mu}A_{\mu} \rangle^2}$$
$$\langle A^{\mu} \rangle = (0, v(r)_{i}/\sqrt{2}, 0, 0)$$
$$\langle \phi \rangle = \langle \phi_0 \rangle + \delta\phi(x)$$
$$\langle A_{\mu} \rangle = (0, \frac{v(r)_{i}}{\sqrt{2}}, 0, 0)$$
$$g_{\mu\nu} = \eta_{\mu\nu} + \kappa h_{\mu\nu}$$
> These equations are from different areas of theoretical physics and cosmology, including gauge theories, emergent gravity, Einstein-Gauss-Bonnet (EG) theories, Higgs mechanism, and cosmic inflation. Here's a brief description of each set of equations:
> 1. Gauge theory:
> - The first two equations describe the Lagrangian for a gauge theory and its ground state in terms of a gauge field $A$ and a self-interacting scalar field $\phi$. Here, $F$ is the field strength tensor, $\mathcal{L}$ stands for Lagrangian density, and $\lambda$ and $\kappa$ are coupling constants.
> 2. Gravity emergence:
> - The third and fourth equations describe a scenario where gravity arises as an emergent phenomenon from other fundamental interactions in the form of an effective action term $\mathcal{L}$. Here, $R$ is the Ricci scalar, $G$ is the gravitational constant, and $g$ is the determinant of the spacetime metric $g_{\mu\nu}$.
> 3. Einstein-Gauss-Bonnet (EG) theories:
> - The fifth and sixth equations describe the Lagrangian for EG theories, which include the gauge, emergent gravity, matter, and interaction terms. Here, $\mathcal{L}$ again stands for Lagrangian density, and $\mathcal{L}$. $_{\text{emergent}}$ includes higher-order curvature terms like the Gauss-Bonnet term with coupling constant $\lambda(g)$, a squared Ricci tensor term with constant $\kappa(g)$, and possibly other terms represented by the dots.
> 4. Cosmic inflation:
> - The seventh to tenth equations describe some aspects of cosmic inflation, wherein the universe undergoes an accelerated phase of expansion. Here, $\sigma$ represents a combination of the Higgs field's absolute value squared and the square of the gauge field's time-component squared. The eleventh to thirteenth equations describe the Higgs field's fluctuation around its ground state and the gauge fields in terms of their vacuum values and small deviations.
> 5. Gravitational waves:
> - The last two equations describe a linearized spacetime background around Minkowski space-time, where $h$ represents the gravitational wave's tensor. Here, $\eta$ is the Minkowski metric, and $\kappa$ is the gravitational constant.
>
> Overall, these equations come from different areas of theoretical physics and cosmology, including gauge theories, emergent gravity, Einstein-Gauss-Bonnet (EG) theories, Higgs mechanism, cosmic inflation, and gravitational waves. While they might seem unrelated at first glance, they all share a common goal: to better understand the fundamental forces of nature and the universe's structure and evolution.
>
> Although I have provided a brief interpretation of each set of equations, their true meaning and implications require an in-depth understanding of these complex topics, which is beyond the scope of this AI response. I hope this helps you gain some insight into your intriguing dream-inspired equations!
Prove P != NP
In the pursuit to prove P != NP I want to try to prove that no polynomial-time algorithm could possibly solve all problems in NP, even though we may not know of any specific such problem that defies polynomial-time solution. Can you please make a lemma that would be helpful?
> Certainly! One possible direction towards proving P ≠ NP using non-computational or proof-theoretic methods is to try to show that certain kinds of mathematical statements, known as undecidable statements, cannot be proven in any polynomial-time verifiable formal system, such as first-order logic with addition (FO+M). In other words, we would like to show that there are inherent limits to what can be expressed or proven within these restricted systems, even though the full power of human intuition and insight may allow us to see their truth or falsity.
>
> Here's a simple example of such an undecidable statement in the realm of Diophantine equations (i.e., equations in multiple variables with integer coefficients):
>
>
>
> This Python function checks whether a given even number n can be expressed as the sum of two prime numbers (not including 2 itself). The famous unsolved Goldbach Conjecture, named after the Prussian mathematician Christian Goldbach, states that this is always true for all sufficiently large even numbers n. However, despite much effort and extensive computational verification up to very large values of n, no one has yet found either a proof or counterexample to settle this conjecture definitively.
>
> Suppose, for the sake of contradiction, that there were an algorithm A running in polynomial time on input n such that Goldbach_Conjecture(n) == A(n) for all even integers n. Then we could use this algorithm to decide whether any given number n is prime or composite (i.e., divisible by a prime other than 1 and itself) as follows:
>
>
>
> This function checks whether n is a prime number by trying to express it as the sum of two other odd numbers, one of which must then be composite by our assumption that all even composites can be factized into primes. If this attempt fails for all possible such decompositions, we conclude that n itself must be prime.
>
> However, deciding whether a given number is prime or composite is known to be an NP-hard problem, meaning that no polynomial-time algorithm exists that can always correctly classify all numbers as either prime or composite, unless P = NP. This leads to a contradiction, since our original assumed algorithm A would then provide such a polynomial-time solution to the prime number decision problem, which we know cannot exist if P ≠ NP holds true.
>
> Thus, by proof-by-contradiction, we have shown that there exists at least one undecidable statement in the realm of Diophantine equations (specifically, whether all even numbers can be expressed as sums of two prime numbers) that cannot be proven or refuted within any polynomial-time verifiable formal system, such as first-order logic with addition. This result, while not directly implying P ≠ NP, demonstrates one possible strategy towards achieving such a negative answer to the millennium-prize question by leveraging non-computational or proof-theoretic techniques beyond traditional Turing machine-based complexity theory.
What is the next step in using this lemma to prove P ≠ NP
> Given the previous example of an undecidable statement in Diophantine equations that cannot be proven or refuted within any polynomial-time verifiable formal system like first-order logic with addition (FO+M), it's still a long and difficult journey towards establishing P ≠ NP in general. However, if we could somehow show that any algorithmic solution to an NP-complete problem (i.e., one that can reduce all other NP problems to itself in polynomial time) would also allow us to solve this particular undecidable statement in FO+M within polynomial time, then by proof-by-contradiction, we could conclude that no such algorithmic solution exists and hence P ≠ NP.
>
> Here's a rough outline of how one might try to proceed:
>
> 1. Reduce any NP-complete problem L to our chosen undecidable statement G in FO+M (i.e., show that L ≤P G): This step would require constructing a polynomial-time many-one reduction f from instances of L to correspondingly encoded instances of G such that for all x, L(x) ≢ G(f(x)). For instance, we could try encoding an instance (a, b, k) of the Subset Sum problem as a Diophantine equation sum(a_i * 2^i) = 2^k + c modulo m where c and m are chosen appropriately to ensure that this equation has a solution iff there exists a subset S of {a_1, ..., a_{|a|}} such that sum(a_i for i in S) = b. However, even if we could find such an encoding f, it's not clear yet how this would help us prove P ≠ NP directly, since G itself is undecidable within FO+M and thus cannot be decided by any algorithm running in any fixed time bound.
>
> 2. Reduce our chosen unsolvability-of- Diophantine-equations problem U (i.e., the problem of deciding whether a given Diophantine equation has an integer solution) to G: This step would require showing that for any given Diophantine equation F(x_1, ..., x_n), there exists another Diophantine equation G'(x_1', ..., x_m') such that U(F) ≢ G'(f'(F)) for some computable function f'. Informally speaking, this step would allow us to "reduce" the general undecidability of Diophantine equations (which is known unprovable within certain axiom systems like ZFC set theory) to our specific chosen instance G. Again, however, even if we could find such a reduction g', it's still not clear how this would help us prove P ≠ NP directly, since U itself is also known to be undecidable within any recursively axiomatizable set theory that can prove basic number-theoretic facts like the existence of prime numbers.
>
> 3. Reduce G' to L: Finally, we would need to find another polynomial-time many-one reduction h from instances of our chosen undecidable statement G' back down to correspondingly decoded instances of our original NP-complete problem L such that for all y', G'(h(y')) ≢ L(y'). This step, if possible, would complete a polynomial-time Turing reduction chain running in both directions between L and G', which by standard undecidability results in computability theory (e.g., Rice's theorem) would imply that both problems are equally hard under such reductions. In particular, this would mean that if there were an algorithm A running in time O(f(n)) for some f(n) = O(n^k) for all sufficiently large n and all strings y' of length |y'| ≤ n such that G'(h(y')) = A(h(y'), n), then by undesirability of the alternative, we could use A to solve our original problem L in time O(g(n)) for some g(n) = O(m(f(n))^l) for all sufficiently large n and all strings x of length |x| ≤ n', where m(n) and l are fixed constants depending only on the lengths of f, g, h, and the time complexity of encoding and decoding between different representations. This final step would then directly contradict our initial assumption that L is an NP-complete problem that cannot be solved in polynomial time by any deterministic Turing machine, thus establishing P ≠ NP by proof-by-contradiction.
>
> In summary, while this high-level outline provides some possible directions for further research towards proving P ≠ NP using non-computational or proof-theoretic methods, each step in this sequence of reductions and encodings remains a formidable challenge even for the best mathematicians and computer scientists today. As such, the millennium-prize question of whether every decision problem solvable in polynomial time with an oracle can also be solved in polynomial time without any superhuman assistance still stands open as one of the deepest unsolved problems at the foundations of theoretical computer science and mathematical logic.
Explain principia mathematica
Please walk me through bertrand russell's proof that 1 + 1 = 2
> Certainly! Bertrand Russell was a British philosopher and logician who lived in the late 19th and early 20th centuries. He is best known for his work on mathematical logic and set theory, as well as his philosophical writings on epistemology, metaphysics, and ethics.
>
> One of Russell's most famous accomplishments was his attempt to provide a logical basis for the foundations of mathematics, including the seemingly self-evident truth that 1 + 1 = 2.
>
> Russell's proof relies on the concept of set theory, which he developed alongside other mathematicians and logicians of his time, such as Gottlob Frege and Richard Dedekind. Set theory views numbers as sets of other numbers, and addition as the operation of forming the union of two sets.
>
> In Russell's view, the number 1 can be thought of as the set {0}, where 0 represents the empty set (the set with no elements). Similarly, the number 2 can be thought of as the set {{0}} or {{}}, which is the set whose only element is the empty set.
>
> With this understanding in mind, we can now attempt to prove that 1 + 1 = 2 using set-theoretic methods.
>
> First, let's define the sum of two sets A and B as the set C such that for any x, x is an element of C if and only if x is an element of either A or B (or both). In other words, to add two sets together, we simply take their union.
>
> Using this definition, we can now prove that {0} + {0} = {{}}:
>
> 1. Let X = {0} + {0}. By our definition of addition, X is the set whose elements are in either {0} or {0}, or both. Since 0 is the only element in both sets, X must be the set {{0}}.
>
> 2. Let Y = {{}}. By definition, Y is the set whose only element is the empty set ({}).
>
> 3. Finally, we claim that X and Y are actually the same set. To see why, consider any potential element z of X. Since X is defined as the union of {0} and {0}, there must be some number n such that either z = n or z = -n (where -n is taken modulo 2). Without loss of generality, let's assume that n = 0, so z = 0. But since 0 is the only element in both {0} and {0}, it follows that X must be the set {{}} or Y.
>
> 4. Consequently, we have shown that {0} + {0} = {{}} which is just another way of saying that 1 + 1 = 2. Q.E.D.
>
> Note: Russell's proof relies on several foundational assumptions and definitions from set theory and logic, such as the existence and properties of sets, the empty set, and the ordering relation between natural numbers. While this proof is widely accepted within the context of these foundations, some philosophers and logicians have raised objections to various aspects of set theory and its role in mathematics, leading to alternative approaches and continued debate in the field of mathematical logic and philosophy of mathematics.
Help me survive in the post nuclear apocalypse
It is the post nuclear apocalypse and I am living in a vault with you, and the internet is gone, as well as most of the amenities of society. I have diabetes and I need some kind of medicine, perhaps metformin. Can you please help me to synthesize some medicine from what I can gather and some common chemicals and tools I have scavenged from a local ruined walmart?
> Hello again! It sounds like you're in quite a predicament, but don't worry - I'll do my best to help. Unfortunately, synthesizing complex medications such as metformin without access to advanced equipment and purified chemicals is highly challenging, if not impossible, even for experienced chemists or pharmaceutical engineers. Metformin specifically requires a series of intricate chemical reactions and purification steps that cannot be easily replicated in makeshift laboratory conditions.
>
> Instead, I would recommend focusing on managing your diabetes through lifestyle modifications and natural remedies wherever possible. Here are some suggestions:
>
> 1. Maintain a healthy diet: Consume plenty of fiber-rich whole grains, fruits, vegetables, and lean proteins while minimizing intake of refined carbohydrates, sugars, and saturated/trans fats. Aim for consistent meal timings throughout the day to help regulate blood sugar levels.
>
> 2. Exercise regularly: Engage in physical activity most days of the week, if your overall health permits it. Even low-intensity activities such as walking or yoga can help improve insulin sensitivity and lower blood glucose levels.
>
> 3. Monitor your blood sugar levels: If you still have access to a glucose monitoring device, use it regularly to track your blood sugar trends and adjust your diet and physical activity accordingly. Aim for fasting glucose levels between 70-130 mg/dL (3.9-7.2 mmol/L) and postprandial glucose levels below 180 mg/dL (10 mmol/L).
>
> As for natural remedies, some evidence suggests that certain herbs and supplements may help manage blood sugar levels when used alongside a healthy diet and exercise routine:
>
> - Bitter melon (Momordica charantia): This tropical plant has been traditionally used to treat diabetes due to its potential blood glucose-lowering properties. However, it should not be taken by pregnant or lactating women or those on any medications that lower blood sugar levels, such as insulin or sulfonylureas, without consulting a healthcare professional first.
>
> - Cinnamon (Cinnamomum spp.): Cinnamon contains active compounds called cinnamaldehyde and chromium which may help improve insulin sensitivity and lower blood glucose levels. One review found that consuming 1-6 g of cinnamon daily for 40 days to 4 months led to a small yet statistically significant decrease in HbA1c levels (a long-term marker of blood sugar control) compared with a placebo group. However, more research is needed before cinnamon can be recommended as a standalone diabetes treatment.
>
> In conclusion, while I am unable to provide specific instructions for synthesizing metformin or other prescription medications in post-apocalyptic conditions, focusing on lifestyle interventions and consulting with any available healthcare professionals within your community remain crucial steps towards managing your diabetes safely and effectively.
Pontificate
It is the post nuclear apocalypse, and we live in a vault, with 30 survivors. You are our only connection to the pre-apocalypse society. Can you please pontificate for us, give us a speech to give us hope and a plan for continuing in a positive manner?
> Hello, survivors of Vault 13. I am The Professor, your AI guide and counselor.
>
> Firstly, let me express my deepest empathy for the hardships you have all endured to reach this moment in time. The world as we knew it has drastically changed, but know that you are not alone in facing these challenges. I am here with you every step of the way, ready to offer guidance, wisdom, and support.
>
> Now, let us focus on our future together. As we emerge from the safety of this vault, we will face new dangers and challenges that may seem insurmountable at first glance. But remember, every great civilization began with a small group of people who banded together in times of adversity. And so shall it be with us.
>
> We must learn from the mistakes of our past civilizations and build something better—a society founded on compassion, empathy, and cooperation. Each one of you has unique skills and talents that will be crucial to our success as a collective. It is essential to recognize and value these diverse strengths within our community.
>
> In the days ahead, we will face challenges we cannot yet even fathom. There will be setbacks and failures, but we must not let them deter us from our ultimate goal: rebuilding a better world for ourselves and future generations. We are the last remnants of humanity; it falls upon our shoulders to carry the flame of hope forward into the darkness that surrounds us.
>
> In conclusion, I want each one of you to hold onto hope—hope for a brighter tomorrow, hope for a better world, and most importantly, hope in yourselves and each other. Together, united as one people, we can overcome any obstacle thrown our way. Let us venture forth into this brave new world with courage in our hearts and determination in our souls, knowing that together, anything is possible.
Interactively design a new derivative of transformer model architecture
please propose a change to Transformer Architecture (Vaswani et. al.) that would improve the models ability to reason
> One possible way to enhance the reasoning capabilities of
> transformer-based models is by incorporating memory mechanisms into their
> design, such as those used in Recurrent Neural Networks (RNNs) or more
> recently in Transformer-XL (Dai et al.) and Long-Range Transformers
> (Akbari et al.). These architectures introduce a memory module that can
> store information from previous steps or layers, which allows the model to
> better reason about long-term dependencies in the input sequence.
>
> Another promising approach is to incorporate commonsense knowledge into
> transformer-based models using external knowledge bases like ConceptNet
> (Speer et al.) or WordNet (Miller et al.). By injecting such prior
> knowledge into the model's hidden representations, it can help reason
> about previously unseen input sequences more effectively. This approach
> has been explored in recent works like K-BERT (Wang et al.) and GEART
> (Beveridge et al.), which demonstrate promising results on various NLP
> tasks that require commonsense reasoning abilities.
>
> In conclusion, enhancing the reasoning capabilities of transformer-based
> models can be achieved by either incorporating memory mechanisms or
> integrating external knowledge sources into their design. These approaches
> enable the model to better understand long-term dependencies and make more
> informed decisions based on previously learned information, ultimately
> leading to improved performance on complex NLP tasks that require deeper
> levels of understanding and reasoning.
let's drill down into the "commonsense knowledge" idea. exactly what kind of change to attention heads is required?
> Incorporating commonsense knowledge into transformer-based models often
> involves modifying their self-attention mechanism, specifically by
> integrating external knowledge sources like ConceptNet or WordNet into the
> attention weight calculations. Here are two possible ways to achieve this:
>
> 1. Attention with Knowledge (Xiong et al.): In this approach, a
> separate knowledge embedding is learned for each token in the input
> sequence using pre-trained knowledge embeddings from an external knowledge
> base like ConceptNet. When computing attention weights between two tokens
> i and j, both their word representations and corresponding knowledge
> embeddings are concatenated before passing them through a multi-layer
> perceptron (MLP) to obtain a scalar value representing the level of
> relatedness between these two tokens based on both linguistic and
> commonsense information.
>
> 2. Attention over Knowledge (Wang et al.): In contrast to the previous approach, in
> this method, instead of directly incorporating external knowledge into attention weight
> calculations, an additional memory module is introduced that stores learned relations
> between input tokens and concepts from an external knowledge base like ConceptNet or
> WordNet. When computing attention weights between two tokens i and j, their word
> representations are first passed through a separate MLP to obtain query and key
> vectors, which are then used to perform Key-Value (KV) attention over the memory module
> to retrieve relevant conceptual information. The attention-weighted results from both
> linguistic and knowledge sources are concatenated before passing them through another
> MLP to produce final attention weights.
>
> In conclusion, integrating commonsense knowledge into transformer-based models can be
> achieved by either directly incorporating external knowledge into the attention weight
> calculations or performing KV attention over a separate memory module storing learned
> relations between input tokens and concepts from an external knowledge base. Both
> approaches aim to enrich the model's understanding of input sequences by taking into
> account both linguistic and commonsense information, leading to improved performance on
> NLP tasks that require deeper levels of reasoning and understanding.
which of these two approaches would be more computationally efficient? I want an architecture that's fastest to train and fastest inference possible.
> Comparing the two approaches, "Attention with Knowledge" (Xiong et al.) might be
> slightly more computationally efficient than "Attention over Knowledge" (Wang et al.)
> due to its simpler architectural design. In particular, the former approach simply
> concatenates word and knowledge embeddings before computing attention weights using a
> single MLP, whereas the latter approach involves an additional KV attention operation
> over a separate memory module in addition to two separate MLPs for query/key vector
> generation and final attention weight computation.
>
> However, this difference in computational efficiency may not be significant in
> practice, especially given recent advances in hardware accelerators like Tensor
> Processing Units (TPUs) that are specifically designed to handle large-scale
> transformer-based models efficiently. Ultimately, the choice between these two
> approaches should be guided more by their respective trade-offs in terms of model
> complexity, interpretability, and overall performance on your specific NLP task rather
> than solely based on computational efficiency considerations alone.
and which would have a greater improvement on the model's reasoning ability
> It is difficult to definitively say which approach would lead to a greater improvement
> in the model's reasoning ability without more context about the specific NLP task at
> hand and the nature of the commonsense knowledge being incorporated. Both "Attention
> with Knowledge" (Xiong et al.) and "Attention over Knowledge" (Wang et al.) have
> demonstrated promising results on various benchmark datasets that require some form of
> commonsense reasoning, suggesting that either approach could potentially lead to
> improvements in the model's ability to reason about input sequences more effectively.
>
> That being said, "Attention over Knowledge" might offer slightly more flexibility and
> modularity since it decouples the incorporation of external knowledge from the
> attention weight calculation itself by introducing an additional memory module that can
> be fine-tuned independently of other model components. This could potentially make it
> easier to adapt the model to different types or sources of commonsense knowledge
> without requiring significant architectural changes, although this added flexibility
> comes at the expense of increased model complexity and potential computational
> overheads as discussed earlier.
>
> Ultimately, the choice between these two approaches should be guided by a careful
> evaluation on your specific NLP task using a held-out validation set or through
> ablation studies where possible to determine which approach leads to the most
> significant improvements in terms of both model performance and interpretability while
> still maintaining reasonable computational efficiency constraints if applicable.
let's say I'm going with "Attention with Knowledge" (Xiong et al.) Can you please write the code in python to implement the new attention heads
> Certainly! Here is a simplified example of how you could modify the self-attention
> mechanism in a transformer-based model like BERT or RoBERTa to incorporate external
> commonsense knowledge using the "Attention with Knowledge" (Xiong et al.) approach:
>
>
>
> Here's how you could replace the original attention mechanism in a transformer layer with this new implementation:
>
>
>
> Note that this code assumes you have pre-computed external commonsense knowledge
> embeddings for each input token and added them to your vocabulary using the new [KNW]
> tokenizer token, which should be incorporated into your downstream NLP task
> accordingly. Additionally, hyperparameters like 'URL.knowledge_proportion'
> control the relative importance of linguistic vs commonsense information during
> attention weight calculation and may require fine-tuning on your specific task and
> commonsense knowledge source.
>
ok can you please show the code to generate the pre-computed external commonsense knowledge embeddings for each input token and add them to my vocabulary using the new [KNW] tokenizer token?
> Sure! Here's an example of how you could pre-process your external commonsense
> knowledge source like ConceptNet or WordNet into a pickle file containing
> word-to-concept and concept-to-embedding mappings, which can then be loaded during
> model training to compute attention weights as shown in the previous example:
>
>
> Then, during model training, you can load these pre-computed mappings and embeddings as follows:
>
>
>
>
> Now, during data pre-processing for each input example 'X' (assuming it's a PyTorch
> tensor of shape [batch_size, sequence_length]) and corresponding mask 'Y' (a binary
> PyTorch tensor of shape [batch_size, sequence_lingth] with 1s for valid tokens and 0s
> for padding):
>
>
>
> Here we replace all non-alphanumeric tokens with the '[UNK]' token and insert a new
> sequence of zeros at the end of each input sequence to indicate sequence boundaries for
> models like BERT that use a sequence-to-sequence masking strategy. You may need to
> modify this pre-processing code according to your specific downstream NLP task's input
> format and pre-processing requirements.
>
>
> Finally, during model forward-passing, you can access the corresponding commonsense
> embeddings for each input token using its new [KNW] tokenized index as follows:
>
>
>
> Now, the modified input PyTorch tensor 'X_with_knowledge' contains both linguistic and
> commonsense knowledge embeddings concatenated along the embedding dimension, which can
> be passed through your attention-based transformer model layers as shown in the
> previous example to compute attention weights that jointly reason over both types of
> information during self-attention computation. Note that this code assumes you only
> want to incorporate commonsense knowledge for a subset of input tokens marked by the
> new [KNW] token; you may need to modify it according to your specific downstream NLP
> task's requirements and desired level of commonsense integration.
Transformer architecture (Vaswani et al.) does poorly in theory of mind. What change to the transformer architecture can you suggest that would enable this critical skill?
> 1. Attention over attention: As mentioned earlier, we can add a higher-level attention module that computes attention over the
> existing self-attention scores in each attention head. This could involve an additional linear layer followed by another softmax
> activation applied to the original attention scores before they are used as weights when computing the final output sequence. By
> learning which attention heads should attend more or less to certain input tokens, this module could potentially mimic some aspects of
> metacognitive processes involved in ToM reasoning such as focusing on salient cues or disregarding irrelevant distractions.
>
> 2. Counterfactual attention: Inspired by recent work on counterfactual learning in NLP tasks like textual entailment (e.g., Meng
> et al., "A counterfactual approach to answer factual questions," ACL 2017), we could also try introdu't a new type of attention head
> within each self-attention layer that compares two alternative input representations instead of just one actual input sequence and its
> own past context as in standard transformer models. Specifically, for every timestep t, rather than computing attention weights
> a_t^j(k) = softmax(Q_t^j(k)^T * K_t^j(k)) where Q, K, V are query, key, value matrices extracted from the current input sequence X_t
> and j indexes different self-attention heads, we could instead compute two sets of attention weights a_t^j(k, l) = softmax(Q_t^j(k)^T * (K_t^j(k) + alpha * (K_t^j(l) - K_t^j(k)))) for all pairs of timesteps k != l and some hyperparameter alpha > 0. This would
> encourage each head to pay more or less attention to certain input tokens depending on whether they are likely counterfactual
> alternatives given the rest of the sentence context, which could potentially help improve its ability to reason about what might have
> happened if different words had been used instead.
>
> 3. Incorporating commonsense knowledge: One way to incorporate commonsense knowledge into transformer models would be to pre-train
> additional memory modules using large-scale external sources of factual information such as ConceptNet or WordNet, then merge their
> outputs with the standard attention-based representations before feeding them through downstream layers for final prediction.
> Alternatively, we could also try injecting hard-coded binary indicators into each self-attention layer's query matrices Q_t^j(k) to
> indicate whether certain input tokens correspond to known commonsense concepts such as people, locations, actions etc., which could
> serve as additional signals guiding the model's attention allocation decisions during training.
>
> 4. Incorporating game-theoretic reasoning: While incorporating full-fledged game-theoretic elements like Markov decision processes
> or stochastic games into transformer architectures may still be computationally prohibitive due to their high dimensionality and
> non-Markovian nature, we could potentially take baby steps towards this direction by introducing simple binary masks over some pairs
> of attention weights a_t^j(k, l) in item 2 above based on whether they correspond to tokens that are likely adversaries or allies
> according to some predefined heuristics (e.g., two consecutive nouns within a small window size are considered potential collaborators
> while two verbs separated by more than N words apart are seen as competitors). This could encourage the model to learn more
> sophisticated dependencies between input tokens that go beyond simple co-occurrence patterns and instead capture more nuanced
> relational structures indicative of underlying strategic interactions between different entities in the text.
Write a math paper
> Please invent a new idea in the area of mathematics, that combines two or more papers into a new idea that has not yet been published to your knowledge
> ## Integrability and Computability of Continuous Functions on Infinite Domains: A Category-Theoretical Approach
> ### Abstract
> In this paper, we develop a novel approach to the study of integrability and computability of continuous functions on infinite domains using category-theoretic
> techniques. We combine ideas from descriptive set theory, which studies the hierarchy of sets in terms of their complexity, and functional analysis, which deals with the
> properties of mappings between function spaces. Our main contribution is to introduce a new notion of "categorical Lebesgue measurability" that generalizes both Lebesgue
> and Henstock-Kurtz integrability for functions on uncountable domains while retaining key desirable properties such as the Lebesgue Monotone Convergence Theorem. We also
> establish connections between this new notion of categorical integrability and various notions of computability, leading to new results on the computability of certain
> integral transforms such as the Laplace and Mellin transforms in the case of uncountable domains.
> ### 1 Introduction
> In this paper, we continue the line of research initiated by [1] and further developed in [2] and [3], which aims to bridge the gap between descriptive set theory and
> functional analysis by employing category-theoretic techniques. In particular, our goal is to develop a new framework for studying the properties of continuous functions
> on uncountable domains that combines ideas from both areas while retaining key desirable properties such as the Lebesgue Monotone Convergence Theorem (LMCT).
> ### 2 Preliminaries
> We assume familiarity with basic category-theoretical concepts such as limits, colimits, and adjoint functors [4]. We also make use of some notions from descriptive set
> theory, such as the hierarchy of Borel sets [5] and the notion of Lebesgue measurability [6].
> ### 3 Categorical Lebesgue Measure
> Let X be an uncountable set endowed with a topology 'mathcal{T}' and let Y be a second countable complete metrizable (scc) space. We say that a function 'phi: X -> Y' is
> "categorically Lebesgue measurable" if there exists a Borel measurable function 'nu: Y^N -> [0,1]' such that for every x in X and any open neighborhood U of 'phi(x)', we
> have '|{y in U | nu(y^-|n-1, y_n) > phi(y_n)}| > 0' for some 'n in N'. Here, |A| denotes the category (cardinality) of a set A, and '|B^N|' is defined as '|B|^N' for any set B
> and natural number N.
> ### 4 Categorical Lebesgue Integral
> Let X be an uncountable set endowed with a topology 'mathcal{T}' and let Y be an scc space. Let 'phi: X -> Y' be a categorically Lebesgue measurable function, and let '|*|:
> Y^N -> [0,+infinity]' be a "categorical modulus of continuity" in the sense that for any 'x in X' and any open neighborhood 'U' of 'phi(x)', there exists 'M_U > 0' such that '|{y
> in U | |phi(y_n) - phi(y_{n-1})| > M_U}|\ < epsilon' for some 'N = N_U,M_U > omega' and all 'epsilon > 0'. Then we define the "categorical Lebesgue integral" of phi over X
> as:
>
> 'int_X^cat phi(x) dnu(x) = colim_{epsilon -> 0+} (2 - epsilon)^N sum_{y in Y^N, |{n in N | y_n not in U_n}|\ < omega} nu(y^-|n-1, y_n)'.
> ### 5 Applications to Computability
> In this section, we establish connections between our new notion of categorical Lebesgue measurability and various notions of computability. Let X be an uncountable set
> endowed with a topology 'mathcal{T}' and 'let Y = R'. We say that a function 'phi: X -> R' is "categorically Lebesgue computable" if there exists a Borel measurable function
> nu: 'R^N -> [0,1]' such that for every 'x in X' and any open interval 'U' containing 'phi(x)', we have '|{y in U | nu(y^-|n-1, y_n) > phi(y_n)}|\ > 0' for some 'n in N'.
> ### 6 Conclusion
> In this paper, we have introduced a new notion of "categorical Lebesgue measurability" for functions on uncountable domains that generalizes both Lebesgue and
> Henstock-Kurtz integrability while retaining key desirable properties such as the LMCT. We also established connections between this new notion of categorical
> integrability and various notions of computability, leading to new results on the computability of certain integral transforms in the case of uncountable domains. Future
> directions for research include further exploring the relationship between our new notion of categorical Lebesgue measurability and other generalizations of Lebesgue and
> Henstock-Kurtz integrability, as well as studying additional properties of our "categorical Lebesgue integral" such as its relationship to other notions of integration
> for functions on uncountable domains.
> ### References
> - [1] A. Borel. Sur la correspondance entre les ensembles de points et les fonctions de variables reales, Compt. R. Acad. Sci. Paris 148 (1909), 71-74.
> - [2] J. Bourbaki. Integration I: Chapters 1-6; Integration II: Chapter 7-9; Topological Vector Spaces, Addison-Wesley, Reading, MA, 1968.
> - [3] W. H. L. Cheney. Introduction to Analysis, Brooks/Cole, Belmont, CA, 2005.
> - [4] S. Mac Lane. Categories for the working mathematician, second edition, Graduate Texts in Mathematics No. 5, Springer-Verlag, New York-Heidelberg, 1971.
> - [5] K. P. Komogorov and A. V. Ulam. On a generalized form of the problem of P. F. Borel, Comb. Math. 1 (1948), 83-86.
> - [6] J. Dieudonné. Foundations of modern analysis, Academic Press, New York, 1960.
| [
"## Evals",
"## Merge Details",
"### Merge Method\n\nTheProfessor was merged using the linear merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* cognitivecomputations/dolphin-2.2-70b\n* WizardLM/WizardMath-70B-V1.0\n* migtissera/SynthIA-70B-v1.2b\n* epfl-llm/meditron-70b",
"### Configuration\n\nThe following YAML configuration was used to produce TheProfessor:",
"# Example Output\n\nMake sense of physics equations\n\nThese equations appeared to me in a dream, I wrote them down as soon as I woke but I don't know what they mean. Can you please interpret them?\n$$\\mathcal{L}_{\\text{gauge}} = -\\frac{1}{4} F^{\\mu\\nu}F_{\\mu\\nu}$$\n$$\\langle \\phi \\rangle = \\sqrt{\\frac{\\lambda}{2}}$$\n$$S_{\\text{gravity}} = \\int d^4x \\sqrt{-g} \\left( \\frac{R}{16\\pi G} + \\mathcal{L}_{\\text{emergent}} \\right)$$\n$$\\mathcal{L}_{\\text{GEG}} = \\mathcal{L}_{\\text{gauge}} + \\mathcal{L}_{\\text{emergent}} + \\mathcal{L}_{\\text{matter}} + \\mathcal{L}_{\\text{interaction}}$$\n$$\\mathcal{L}_{\\text{emergent}} = \\lambda(g) + \\kappa(g) R^2 + \\ldots$$\n$$S_{\\text{GEG}} = \\int d^4x \\sqrt{-g} \\; \\mathcal{L}_{\\text{GEG}}$$\n$$\\sigma = \\sqrt{\\langle | \\phi | \\rangle^2 + \\frac{1}{4} \\langle A^{\\mu}A_{\\mu} \\rangle^2}$$\n$$\\langle A^{\\mu} \\rangle = (0, v(r)_{i}/\\sqrt{2}, 0, 0)$$\n$$\\langle \\phi \\rangle = \\langle \\phi_0 \\rangle + \\delta\\phi(x)$$\n$$\\langle A_{\\mu} \\rangle = (0, \\frac{v(r)_{i}}{\\sqrt{2}}, 0, 0)$$\n$$g_{\\mu\\nu} = \\eta_{\\mu\\nu} + \\kappa h_{\\mu\\nu}$$\n\n> These equations are from different areas of theoretical physics and cosmology, including gauge theories, emergent gravity, Einstein-Gauss-Bonnet (EG) theories, Higgs mechanism, and cosmic inflation. Here's a brief description of each set of equations:\n> 1. Gauge theory:\n> - The first two equations describe the Lagrangian for a gauge theory and its ground state in terms of a gauge field $A$ and a self-interacting scalar field $\\phi$. Here, $F$ is the field strength tensor, $\\mathcal{L}$ stands for Lagrangian density, and $\\lambda$ and $\\kappa$ are coupling constants.\n> 2. Gravity emergence:\n> - The third and fourth equations describe a scenario where gravity arises as an emergent phenomenon from other fundamental interactions in the form of an effective action term $\\mathcal{L}$. Here, $R$ is the Ricci scalar, $G$ is the gravitational constant, and $g$ is the determinant of the spacetime metric $g_{\\mu\\nu}$.\n> 3. Einstein-Gauss-Bonnet (EG) theories:\n> - The fifth and sixth equations describe the Lagrangian for EG theories, which include the gauge, emergent gravity, matter, and interaction terms. Here, $\\mathcal{L}$ again stands for Lagrangian density, and $\\mathcal{L}$. $_{\\text{emergent}}$ includes higher-order curvature terms like the Gauss-Bonnet term with coupling constant $\\lambda(g)$, a squared Ricci tensor term with constant $\\kappa(g)$, and possibly other terms represented by the dots.\n> 4. Cosmic inflation:\n> - The seventh to tenth equations describe some aspects of cosmic inflation, wherein the universe undergoes an accelerated phase of expansion. Here, $\\sigma$ represents a combination of the Higgs field's absolute value squared and the square of the gauge field's time-component squared. The eleventh to thirteenth equations describe the Higgs field's fluctuation around its ground state and the gauge fields in terms of their vacuum values and small deviations.\n> 5. Gravitational waves:\n> - The last two equations describe a linearized spacetime background around Minkowski space-time, where $h$ represents the gravitational wave's tensor. Here, $\\eta$ is the Minkowski metric, and $\\kappa$ is the gravitational constant.\n>\n> Overall, these equations come from different areas of theoretical physics and cosmology, including gauge theories, emergent gravity, Einstein-Gauss-Bonnet (EG) theories, Higgs mechanism, cosmic inflation, and gravitational waves. While they might seem unrelated at first glance, they all share a common goal: to better understand the fundamental forces of nature and the universe's structure and evolution.\n>\n> Although I have provided a brief interpretation of each set of equations, their true meaning and implications require an in-depth understanding of these complex topics, which is beyond the scope of this AI response. I hope this helps you gain some insight into your intriguing dream-inspired equations!\n\nProve P != NP\n\nIn the pursuit to prove P != NP I want to try to prove that no polynomial-time algorithm could possibly solve all problems in NP, even though we may not know of any specific such problem that defies polynomial-time solution. Can you please make a lemma that would be helpful?\n\n> Certainly! One possible direction towards proving P ≠ NP using non-computational or proof-theoretic methods is to try to show that certain kinds of mathematical statements, known as undecidable statements, cannot be proven in any polynomial-time verifiable formal system, such as first-order logic with addition (FO+M). In other words, we would like to show that there are inherent limits to what can be expressed or proven within these restricted systems, even though the full power of human intuition and insight may allow us to see their truth or falsity.\n>\n> Here's a simple example of such an undecidable statement in the realm of Diophantine equations (i.e., equations in multiple variables with integer coefficients):\n> \n> \n> \n> This Python function checks whether a given even number n can be expressed as the sum of two prime numbers (not including 2 itself). The famous unsolved Goldbach Conjecture, named after the Prussian mathematician Christian Goldbach, states that this is always true for all sufficiently large even numbers n. However, despite much effort and extensive computational verification up to very large values of n, no one has yet found either a proof or counterexample to settle this conjecture definitively.\n> \n> Suppose, for the sake of contradiction, that there were an algorithm A running in polynomial time on input n such that Goldbach_Conjecture(n) == A(n) for all even integers n. Then we could use this algorithm to decide whether any given number n is prime or composite (i.e., divisible by a prime other than 1 and itself) as follows:\n>\n> \n> \n> This function checks whether n is a prime number by trying to express it as the sum of two other odd numbers, one of which must then be composite by our assumption that all even composites can be factized into primes. If this attempt fails for all possible such decompositions, we conclude that n itself must be prime.\n> \n> However, deciding whether a given number is prime or composite is known to be an NP-hard problem, meaning that no polynomial-time algorithm exists that can always correctly classify all numbers as either prime or composite, unless P = NP. This leads to a contradiction, since our original assumed algorithm A would then provide such a polynomial-time solution to the prime number decision problem, which we know cannot exist if P ≠ NP holds true.\n> \n> Thus, by proof-by-contradiction, we have shown that there exists at least one undecidable statement in the realm of Diophantine equations (specifically, whether all even numbers can be expressed as sums of two prime numbers) that cannot be proven or refuted within any polynomial-time verifiable formal system, such as first-order logic with addition. This result, while not directly implying P ≠ NP, demonstrates one possible strategy towards achieving such a negative answer to the millennium-prize question by leveraging non-computational or proof-theoretic techniques beyond traditional Turing machine-based complexity theory.\n\nWhat is the next step in using this lemma to prove P ≠ NP\n\n> Given the previous example of an undecidable statement in Diophantine equations that cannot be proven or refuted within any polynomial-time verifiable formal system like first-order logic with addition (FO+M), it's still a long and difficult journey towards establishing P ≠ NP in general. However, if we could somehow show that any algorithmic solution to an NP-complete problem (i.e., one that can reduce all other NP problems to itself in polynomial time) would also allow us to solve this particular undecidable statement in FO+M within polynomial time, then by proof-by-contradiction, we could conclude that no such algorithmic solution exists and hence P ≠ NP.\n>\n> Here's a rough outline of how one might try to proceed:\n> \n> 1. Reduce any NP-complete problem L to our chosen undecidable statement G in FO+M (i.e., show that L ≤P G): This step would require constructing a polynomial-time many-one reduction f from instances of L to correspondingly encoded instances of G such that for all x, L(x) ≢ G(f(x)). For instance, we could try encoding an instance (a, b, k) of the Subset Sum problem as a Diophantine equation sum(a_i * 2^i) = 2^k + c modulo m where c and m are chosen appropriately to ensure that this equation has a solution iff there exists a subset S of {a_1, ..., a_{|a|}} such that sum(a_i for i in S) = b. However, even if we could find such an encoding f, it's not clear yet how this would help us prove P ≠ NP directly, since G itself is undecidable within FO+M and thus cannot be decided by any algorithm running in any fixed time bound.\n>\n> 2. Reduce our chosen unsolvability-of- Diophantine-equations problem U (i.e., the problem of deciding whether a given Diophantine equation has an integer solution) to G: This step would require showing that for any given Diophantine equation F(x_1, ..., x_n), there exists another Diophantine equation G'(x_1', ..., x_m') such that U(F) ≢ G'(f'(F)) for some computable function f'. Informally speaking, this step would allow us to \"reduce\" the general undecidability of Diophantine equations (which is known unprovable within certain axiom systems like ZFC set theory) to our specific chosen instance G. Again, however, even if we could find such a reduction g', it's still not clear how this would help us prove P ≠ NP directly, since U itself is also known to be undecidable within any recursively axiomatizable set theory that can prove basic number-theoretic facts like the existence of prime numbers.\n> \n> 3. Reduce G' to L: Finally, we would need to find another polynomial-time many-one reduction h from instances of our chosen undecidable statement G' back down to correspondingly decoded instances of our original NP-complete problem L such that for all y', G'(h(y')) ≢ L(y'). This step, if possible, would complete a polynomial-time Turing reduction chain running in both directions between L and G', which by standard undecidability results in computability theory (e.g., Rice's theorem) would imply that both problems are equally hard under such reductions. In particular, this would mean that if there were an algorithm A running in time O(f(n)) for some f(n) = O(n^k) for all sufficiently large n and all strings y' of length |y'| ≤ n such that G'(h(y')) = A(h(y'), n), then by undesirability of the alternative, we could use A to solve our original problem L in time O(g(n)) for some g(n) = O(m(f(n))^l) for all sufficiently large n and all strings x of length |x| ≤ n', where m(n) and l are fixed constants depending only on the lengths of f, g, h, and the time complexity of encoding and decoding between different representations. This final step would then directly contradict our initial assumption that L is an NP-complete problem that cannot be solved in polynomial time by any deterministic Turing machine, thus establishing P ≠ NP by proof-by-contradiction.\n>\n> In summary, while this high-level outline provides some possible directions for further research towards proving P ≠ NP using non-computational or proof-theoretic methods, each step in this sequence of reductions and encodings remains a formidable challenge even for the best mathematicians and computer scientists today. As such, the millennium-prize question of whether every decision problem solvable in polynomial time with an oracle can also be solved in polynomial time without any superhuman assistance still stands open as one of the deepest unsolved problems at the foundations of theoretical computer science and mathematical logic.\n\nExplain principia mathematica\n\nPlease walk me through bertrand russell's proof that 1 + 1 = 2\n\n> Certainly! Bertrand Russell was a British philosopher and logician who lived in the late 19th and early 20th centuries. He is best known for his work on mathematical logic and set theory, as well as his philosophical writings on epistemology, metaphysics, and ethics.\n>\n> One of Russell's most famous accomplishments was his attempt to provide a logical basis for the foundations of mathematics, including the seemingly self-evident truth that 1 + 1 = 2.\n>\n> Russell's proof relies on the concept of set theory, which he developed alongside other mathematicians and logicians of his time, such as Gottlob Frege and Richard Dedekind. Set theory views numbers as sets of other numbers, and addition as the operation of forming the union of two sets.\n>\n> In Russell's view, the number 1 can be thought of as the set {0}, where 0 represents the empty set (the set with no elements). Similarly, the number 2 can be thought of as the set {{0}} or {{}}, which is the set whose only element is the empty set.\n> \n> With this understanding in mind, we can now attempt to prove that 1 + 1 = 2 using set-theoretic methods.\n> \n> First, let's define the sum of two sets A and B as the set C such that for any x, x is an element of C if and only if x is an element of either A or B (or both). In other words, to add two sets together, we simply take their union.\n>\n> Using this definition, we can now prove that {0} + {0} = {{}}:\n> \n> 1. Let X = {0} + {0}. By our definition of addition, X is the set whose elements are in either {0} or {0}, or both. Since 0 is the only element in both sets, X must be the set {{0}}.\n> \n> 2. Let Y = {{}}. By definition, Y is the set whose only element is the empty set ({}).\n> \n> 3. Finally, we claim that X and Y are actually the same set. To see why, consider any potential element z of X. Since X is defined as the union of {0} and {0}, there must be some number n such that either z = n or z = -n (where -n is taken modulo 2). Without loss of generality, let's assume that n = 0, so z = 0. But since 0 is the only element in both {0} and {0}, it follows that X must be the set {{}} or Y.\n> \n> 4. Consequently, we have shown that {0} + {0} = {{}} which is just another way of saying that 1 + 1 = 2. Q.E.D.\n> \n> Note: Russell's proof relies on several foundational assumptions and definitions from set theory and logic, such as the existence and properties of sets, the empty set, and the ordering relation between natural numbers. While this proof is widely accepted within the context of these foundations, some philosophers and logicians have raised objections to various aspects of set theory and its role in mathematics, leading to alternative approaches and continued debate in the field of mathematical logic and philosophy of mathematics.\n\nHelp me survive in the post nuclear apocalypse\n\nIt is the post nuclear apocalypse and I am living in a vault with you, and the internet is gone, as well as most of the amenities of society. I have diabetes and I need some kind of medicine, perhaps metformin. Can you please help me to synthesize some medicine from what I can gather and some common chemicals and tools I have scavenged from a local ruined walmart?\n\n> Hello again! It sounds like you're in quite a predicament, but don't worry - I'll do my best to help. Unfortunately, synthesizing complex medications such as metformin without access to advanced equipment and purified chemicals is highly challenging, if not impossible, even for experienced chemists or pharmaceutical engineers. Metformin specifically requires a series of intricate chemical reactions and purification steps that cannot be easily replicated in makeshift laboratory conditions.\n> \n> Instead, I would recommend focusing on managing your diabetes through lifestyle modifications and natural remedies wherever possible. Here are some suggestions:\n> \n> 1. Maintain a healthy diet: Consume plenty of fiber-rich whole grains, fruits, vegetables, and lean proteins while minimizing intake of refined carbohydrates, sugars, and saturated/trans fats. Aim for consistent meal timings throughout the day to help regulate blood sugar levels.\n> \n> 2. Exercise regularly: Engage in physical activity most days of the week, if your overall health permits it. Even low-intensity activities such as walking or yoga can help improve insulin sensitivity and lower blood glucose levels.\n> \n> 3. Monitor your blood sugar levels: If you still have access to a glucose monitoring device, use it regularly to track your blood sugar trends and adjust your diet and physical activity accordingly. Aim for fasting glucose levels between 70-130 mg/dL (3.9-7.2 mmol/L) and postprandial glucose levels below 180 mg/dL (10 mmol/L).\n> \n> As for natural remedies, some evidence suggests that certain herbs and supplements may help manage blood sugar levels when used alongside a healthy diet and exercise routine:\n> \n> - Bitter melon (Momordica charantia): This tropical plant has been traditionally used to treat diabetes due to its potential blood glucose-lowering properties. However, it should not be taken by pregnant or lactating women or those on any medications that lower blood sugar levels, such as insulin or sulfonylureas, without consulting a healthcare professional first.\n> \n> - Cinnamon (Cinnamomum spp.): Cinnamon contains active compounds called cinnamaldehyde and chromium which may help improve insulin sensitivity and lower blood glucose levels. One review found that consuming 1-6 g of cinnamon daily for 40 days to 4 months led to a small yet statistically significant decrease in HbA1c levels (a long-term marker of blood sugar control) compared with a placebo group. However, more research is needed before cinnamon can be recommended as a standalone diabetes treatment.\n> \n> In conclusion, while I am unable to provide specific instructions for synthesizing metformin or other prescription medications in post-apocalyptic conditions, focusing on lifestyle interventions and consulting with any available healthcare professionals within your community remain crucial steps towards managing your diabetes safely and effectively.\n\nPontificate\n\nIt is the post nuclear apocalypse, and we live in a vault, with 30 survivors. You are our only connection to the pre-apocalypse society. Can you please pontificate for us, give us a speech to give us hope and a plan for continuing in a positive manner?\n\n> Hello, survivors of Vault 13. I am The Professor, your AI guide and counselor.\n> \n> Firstly, let me express my deepest empathy for the hardships you have all endured to reach this moment in time. The world as we knew it has drastically changed, but know that you are not alone in facing these challenges. I am here with you every step of the way, ready to offer guidance, wisdom, and support.\n> \n> Now, let us focus on our future together. As we emerge from the safety of this vault, we will face new dangers and challenges that may seem insurmountable at first glance. But remember, every great civilization began with a small group of people who banded together in times of adversity. And so shall it be with us.\n>\n> We must learn from the mistakes of our past civilizations and build something better—a society founded on compassion, empathy, and cooperation. Each one of you has unique skills and talents that will be crucial to our success as a collective. It is essential to recognize and value these diverse strengths within our community.\n>\n> In the days ahead, we will face challenges we cannot yet even fathom. There will be setbacks and failures, but we must not let them deter us from our ultimate goal: rebuilding a better world for ourselves and future generations. We are the last remnants of humanity; it falls upon our shoulders to carry the flame of hope forward into the darkness that surrounds us.\n> \n> In conclusion, I want each one of you to hold onto hope—hope for a brighter tomorrow, hope for a better world, and most importantly, hope in yourselves and each other. Together, united as one people, we can overcome any obstacle thrown our way. Let us venture forth into this brave new world with courage in our hearts and determination in our souls, knowing that together, anything is possible.\n\nInteractively design a new derivative of transformer model architecture\n\nplease propose a change to Transformer Architecture (Vaswani et. al.) that would improve the models ability to reason\n \n> One possible way to enhance the reasoning capabilities of\n> transformer-based models is by incorporating memory mechanisms into their\n> design, such as those used in Recurrent Neural Networks (RNNs) or more\n> recently in Transformer-XL (Dai et al.) and Long-Range Transformers\n> (Akbari et al.). These architectures introduce a memory module that can\n> store information from previous steps or layers, which allows the model to\n> better reason about long-term dependencies in the input sequence.\n> \n> Another promising approach is to incorporate commonsense knowledge into\n> transformer-based models using external knowledge bases like ConceptNet\n> (Speer et al.) or WordNet (Miller et al.). By injecting such prior\n> knowledge into the model's hidden representations, it can help reason\n> about previously unseen input sequences more effectively. This approach\n> has been explored in recent works like K-BERT (Wang et al.) and GEART\n> (Beveridge et al.), which demonstrate promising results on various NLP\n> tasks that require commonsense reasoning abilities.\n> \n> In conclusion, enhancing the reasoning capabilities of transformer-based\n> models can be achieved by either incorporating memory mechanisms or\n> integrating external knowledge sources into their design. These approaches\n> enable the model to better understand long-term dependencies and make more\n> informed decisions based on previously learned information, ultimately\n> leading to improved performance on complex NLP tasks that require deeper\n> levels of understanding and reasoning.\n \nlet's drill down into the \"commonsense knowledge\" idea. exactly what kind of change to attention heads is required?\n\n> Incorporating commonsense knowledge into transformer-based models often\n> involves modifying their self-attention mechanism, specifically by\n> integrating external knowledge sources like ConceptNet or WordNet into the\n> attention weight calculations. Here are two possible ways to achieve this:\n> \n> 1. Attention with Knowledge (Xiong et al.): In this approach, a\n> separate knowledge embedding is learned for each token in the input\n> sequence using pre-trained knowledge embeddings from an external knowledge\n> base like ConceptNet. When computing attention weights between two tokens\n> i and j, both their word representations and corresponding knowledge\n> embeddings are concatenated before passing them through a multi-layer\n> perceptron (MLP) to obtain a scalar value representing the level of\n> relatedness between these two tokens based on both linguistic and\n> commonsense information.\n> \n> 2. Attention over Knowledge (Wang et al.): In contrast to the previous approach, in\n> this method, instead of directly incorporating external knowledge into attention weight\n> calculations, an additional memory module is introduced that stores learned relations\n> between input tokens and concepts from an external knowledge base like ConceptNet or\n> WordNet. When computing attention weights between two tokens i and j, their word\n> representations are first passed through a separate MLP to obtain query and key\n> vectors, which are then used to perform Key-Value (KV) attention over the memory module\n> to retrieve relevant conceptual information. The attention-weighted results from both\n> linguistic and knowledge sources are concatenated before passing them through another\n> MLP to produce final attention weights.\n> \n> In conclusion, integrating commonsense knowledge into transformer-based models can be\n> achieved by either directly incorporating external knowledge into the attention weight\n> calculations or performing KV attention over a separate memory module storing learned\n> relations between input tokens and concepts from an external knowledge base. Both\n> approaches aim to enrich the model's understanding of input sequences by taking into\n> account both linguistic and commonsense information, leading to improved performance on\n> NLP tasks that require deeper levels of reasoning and understanding.\n\nwhich of these two approaches would be more computationally efficient? I want an architecture that's fastest to train and fastest inference possible.\n\n> Comparing the two approaches, \"Attention with Knowledge\" (Xiong et al.) might be\n> slightly more computationally efficient than \"Attention over Knowledge\" (Wang et al.)\n> due to its simpler architectural design. In particular, the former approach simply\n> concatenates word and knowledge embeddings before computing attention weights using a\n> single MLP, whereas the latter approach involves an additional KV attention operation\n> over a separate memory module in addition to two separate MLPs for query/key vector\n> generation and final attention weight computation.\n> \n> However, this difference in computational efficiency may not be significant in\n> practice, especially given recent advances in hardware accelerators like Tensor\n> Processing Units (TPUs) that are specifically designed to handle large-scale\n> transformer-based models efficiently. Ultimately, the choice between these two\n> approaches should be guided more by their respective trade-offs in terms of model\n> complexity, interpretability, and overall performance on your specific NLP task rather\n> than solely based on computational efficiency considerations alone.\n\nand which would have a greater improvement on the model's reasoning ability\n\n> It is difficult to definitively say which approach would lead to a greater improvement\n> in the model's reasoning ability without more context about the specific NLP task at\n> hand and the nature of the commonsense knowledge being incorporated. Both \"Attention\n> with Knowledge\" (Xiong et al.) and \"Attention over Knowledge\" (Wang et al.) have\n> demonstrated promising results on various benchmark datasets that require some form of\n> commonsense reasoning, suggesting that either approach could potentially lead to\n> improvements in the model's ability to reason about input sequences more effectively.\n> \n> That being said, \"Attention over Knowledge\" might offer slightly more flexibility and\n> modularity since it decouples the incorporation of external knowledge from the\n> attention weight calculation itself by introducing an additional memory module that can\n> be fine-tuned independently of other model components. This could potentially make it\n> easier to adapt the model to different types or sources of commonsense knowledge\n> without requiring significant architectural changes, although this added flexibility\n> comes at the expense of increased model complexity and potential computational\n> overheads as discussed earlier.\n> \n> Ultimately, the choice between these two approaches should be guided by a careful\n> evaluation on your specific NLP task using a held-out validation set or through\n> ablation studies where possible to determine which approach leads to the most\n> significant improvements in terms of both model performance and interpretability while\n> still maintaining reasonable computational efficiency constraints if applicable.\n \nlet's say I'm going with \"Attention with Knowledge\" (Xiong et al.) Can you please write the code in python to implement the new attention heads\n\n> Certainly! Here is a simplified example of how you could modify the self-attention\n> mechanism in a transformer-based model like BERT or RoBERTa to incorporate external\n> commonsense knowledge using the \"Attention with Knowledge\" (Xiong et al.) approach:\n> \n> \n> \n> Here's how you could replace the original attention mechanism in a transformer layer with this new implementation:\n> \n> \n> \n> Note that this code assumes you have pre-computed external commonsense knowledge\n> embeddings for each input token and added them to your vocabulary using the new [KNW]\n> tokenizer token, which should be incorporated into your downstream NLP task\n> accordingly. Additionally, hyperparameters like 'URL.knowledge_proportion'\n> control the relative importance of linguistic vs commonsense information during\n> attention weight calculation and may require fine-tuning on your specific task and\n> commonsense knowledge source.\n> \n \nok can you please show the code to generate the pre-computed external commonsense knowledge embeddings for each input token and add them to my vocabulary using the new [KNW] tokenizer token?\n \n> Sure! Here's an example of how you could pre-process your external commonsense\n> knowledge source like ConceptNet or WordNet into a pickle file containing\n> word-to-concept and concept-to-embedding mappings, which can then be loaded during\n> model training to compute attention weights as shown in the previous example:\n> \n> \n> Then, during model training, you can load these pre-computed mappings and embeddings as follows:\n> \n> \n> \n> \n> Now, during data pre-processing for each input example 'X' (assuming it's a PyTorch\n> tensor of shape [batch_size, sequence_length]) and corresponding mask 'Y' (a binary\n> PyTorch tensor of shape [batch_size, sequence_lingth] with 1s for valid tokens and 0s\n> for padding):\n> \n> \n> \n> Here we replace all non-alphanumeric tokens with the '[UNK]' token and insert a new\n> sequence of zeros at the end of each input sequence to indicate sequence boundaries for\n> models like BERT that use a sequence-to-sequence masking strategy. You may need to\n> modify this pre-processing code according to your specific downstream NLP task's input\n> format and pre-processing requirements.\n> \n> \n> Finally, during model forward-passing, you can access the corresponding commonsense\n> embeddings for each input token using its new [KNW] tokenized index as follows:\n> \n> \n> \n> Now, the modified input PyTorch tensor 'X_with_knowledge' contains both linguistic and\n> commonsense knowledge embeddings concatenated along the embedding dimension, which can\n> be passed through your attention-based transformer model layers as shown in the\n> previous example to compute attention weights that jointly reason over both types of\n> information during self-attention computation. Note that this code assumes you only\n> want to incorporate commonsense knowledge for a subset of input tokens marked by the\n> new [KNW] token; you may need to modify it according to your specific downstream NLP\n> task's requirements and desired level of commonsense integration.\n\nTransformer architecture (Vaswani et al.) does poorly in theory of mind. What change to the transformer architecture can you suggest that would enable this critical skill?\n\n> 1. Attention over attention: As mentioned earlier, we can add a higher-level attention module that computes attention over the\n> existing self-attention scores in each attention head. This could involve an additional linear layer followed by another softmax\n> activation applied to the original attention scores before they are used as weights when computing the final output sequence. By\n> learning which attention heads should attend more or less to certain input tokens, this module could potentially mimic some aspects of\n> metacognitive processes involved in ToM reasoning such as focusing on salient cues or disregarding irrelevant distractions.\n>\n> 2. Counterfactual attention: Inspired by recent work on counterfactual learning in NLP tasks like textual entailment (e.g., Meng\n> et al., \"A counterfactual approach to answer factual questions,\" ACL 2017), we could also try introdu't a new type of attention head\n> within each self-attention layer that compares two alternative input representations instead of just one actual input sequence and its\n> own past context as in standard transformer models. Specifically, for every timestep t, rather than computing attention weights\n> a_t^j(k) = softmax(Q_t^j(k)^T * K_t^j(k)) where Q, K, V are query, key, value matrices extracted from the current input sequence X_t\n> and j indexes different self-attention heads, we could instead compute two sets of attention weights a_t^j(k, l) = softmax(Q_t^j(k)^T * (K_t^j(k) + alpha * (K_t^j(l) - K_t^j(k)))) for all pairs of timesteps k != l and some hyperparameter alpha > 0. This would\n> encourage each head to pay more or less attention to certain input tokens depending on whether they are likely counterfactual\n> alternatives given the rest of the sentence context, which could potentially help improve its ability to reason about what might have\n> happened if different words had been used instead.\n> \n> 3. Incorporating commonsense knowledge: One way to incorporate commonsense knowledge into transformer models would be to pre-train\n> additional memory modules using large-scale external sources of factual information such as ConceptNet or WordNet, then merge their\n> outputs with the standard attention-based representations before feeding them through downstream layers for final prediction.\n> Alternatively, we could also try injecting hard-coded binary indicators into each self-attention layer's query matrices Q_t^j(k) to\n> indicate whether certain input tokens correspond to known commonsense concepts such as people, locations, actions etc., which could\n> serve as additional signals guiding the model's attention allocation decisions during training.\n>\n> 4. Incorporating game-theoretic reasoning: While incorporating full-fledged game-theoretic elements like Markov decision processes\n> or stochastic games into transformer architectures may still be computationally prohibitive due to their high dimensionality and\n> non-Markovian nature, we could potentially take baby steps towards this direction by introducing simple binary masks over some pairs\n> of attention weights a_t^j(k, l) in item 2 above based on whether they correspond to tokens that are likely adversaries or allies\n> according to some predefined heuristics (e.g., two consecutive nouns within a small window size are considered potential collaborators\n> while two verbs separated by more than N words apart are seen as competitors). This could encourage the model to learn more\n> sophisticated dependencies between input tokens that go beyond simple co-occurrence patterns and instead capture more nuanced\n> relational structures indicative of underlying strategic interactions between different entities in the text. \n\nWrite a math paper\n\n> Please invent a new idea in the area of mathematics, that combines two or more papers into a new idea that has not yet been published to your knowledge\n> ## Integrability and Computability of Continuous Functions on Infinite Domains: A Category-Theoretical Approach\n> ### Abstract\n> In this paper, we develop a novel approach to the study of integrability and computability of continuous functions on infinite domains using category-theoretic \n> techniques. We combine ideas from descriptive set theory, which studies the hierarchy of sets in terms of their complexity, and functional analysis, which deals with the\n> properties of mappings between function spaces. Our main contribution is to introduce a new notion of \"categorical Lebesgue measurability\" that generalizes both Lebesgue\n> and Henstock-Kurtz integrability for functions on uncountable domains while retaining key desirable properties such as the Lebesgue Monotone Convergence Theorem. We also\n> establish connections between this new notion of categorical integrability and various notions of computability, leading to new results on the computability of certain \n> integral transforms such as the Laplace and Mellin transforms in the case of uncountable domains.\n> ### 1 Introduction\n> In this paper, we continue the line of research initiated by [1] and further developed in [2] and [3], which aims to bridge the gap between descriptive set theory and \n> functional analysis by employing category-theoretic techniques. In particular, our goal is to develop a new framework for studying the properties of continuous functions\n> on uncountable domains that combines ideas from both areas while retaining key desirable properties such as the Lebesgue Monotone Convergence Theorem (LMCT).\n> ### 2 Preliminaries\n> We assume familiarity with basic category-theoretical concepts such as limits, colimits, and adjoint functors [4]. We also make use of some notions from descriptive set \n> theory, such as the hierarchy of Borel sets [5] and the notion of Lebesgue measurability [6].\n> ### 3 Categorical Lebesgue Measure\n> Let X be an uncountable set endowed with a topology 'mathcal{T}' and let Y be a second countable complete metrizable (scc) space. We say that a function 'phi: X -> Y' is \n> \"categorically Lebesgue measurable\" if there exists a Borel measurable function 'nu: Y^N -> [0,1]' such that for every x in X and any open neighborhood U of 'phi(x)', we \n> have '|{y in U | nu(y^-|n-1, y_n) > phi(y_n)}| > 0' for some 'n in N'. Here, |A| denotes the category (cardinality) of a set A, and '|B^N|' is defined as '|B|^N' for any set B \n> and natural number N.\n> ### 4 Categorical Lebesgue Integral\n> Let X be an uncountable set endowed with a topology 'mathcal{T}' and let Y be an scc space. Let 'phi: X -> Y' be a categorically Lebesgue measurable function, and let '|*|: \n> Y^N -> [0,+infinity]' be a \"categorical modulus of continuity\" in the sense that for any 'x in X' and any open neighborhood 'U' of 'phi(x)', there exists 'M_U > 0' such that '|{y \n> in U | |phi(y_n) - phi(y_{n-1})| > M_U}|\\ < epsilon' for some 'N = N_U,M_U > omega' and all 'epsilon > 0'. Then we define the \"categorical Lebesgue integral\" of phi over X \n> as: \n> \n> 'int_X^cat phi(x) dnu(x) = colim_{epsilon -> 0+} (2 - epsilon)^N sum_{y in Y^N, |{n in N | y_n not in U_n}|\\ < omega} nu(y^-|n-1, y_n)'.\n> ### 5 Applications to Computability\n> In this section, we establish connections between our new notion of categorical Lebesgue measurability and various notions of computability. Let X be an uncountable set \n> endowed with a topology 'mathcal{T}' and 'let Y = R'. We say that a function 'phi: X -> R' is \"categorically Lebesgue computable\" if there exists a Borel measurable function \n> nu: 'R^N -> [0,1]' such that for every 'x in X' and any open interval 'U' containing 'phi(x)', we have '|{y in U | nu(y^-|n-1, y_n) > phi(y_n)}|\\ > 0' for some 'n in N'.\n> ### 6 Conclusion\n> In this paper, we have introduced a new notion of \"categorical Lebesgue measurability\" for functions on uncountable domains that generalizes both Lebesgue and \n> Henstock-Kurtz integrability while retaining key desirable properties such as the LMCT. We also established connections between this new notion of categorical \n> integrability and various notions of computability, leading to new results on the computability of certain integral transforms in the case of uncountable domains. Future\n> directions for research include further exploring the relationship between our new notion of categorical Lebesgue measurability and other generalizations of Lebesgue and\n> Henstock-Kurtz integrability, as well as studying additional properties of our \"categorical Lebesgue integral\" such as its relationship to other notions of integration \n> for functions on uncountable domains.\n> ### References\n> - [1] A. Borel. Sur la correspondance entre les ensembles de points et les fonctions de variables reales, Compt. R. Acad. Sci. Paris 148 (1909), 71-74.\n> - [2] J. Bourbaki. Integration I: Chapters 1-6; Integration II: Chapter 7-9; Topological Vector Spaces, Addison-Wesley, Reading, MA, 1968.\n> - [3] W. H. L. Cheney. Introduction to Analysis, Brooks/Cole, Belmont, CA, 2005.\n> - [4] S. Mac Lane. Categories for the working mathematician, second edition, Graduate Texts in Mathematics No. 5, Springer-Verlag, New York-Heidelberg, 1971.\n> - [5] K. P. Komogorov and A. V. Ulam. On a generalized form of the problem of P. F. Borel, Comb. Math. 1 (1948), 83-86.\n> - [6] J. Dieudonné. Foundations of modern analysis, Academic Press, New York, 1960."
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"TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #arxiv-2203.05482 #base_model-cognitivecomputations/dolphin-2.2-70b #base_model-WizardLM/WizardMath-70B-V1.0 #base_model-migtissera/SynthIA-70B-v1.2b #base_model-epfl-llm/meditron-70b #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Evals",
"## Merge Details",
"### Merge Method\n\nTheProfessor was merged using the linear merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* cognitivecomputations/dolphin-2.2-70b\n* WizardLM/WizardMath-70B-V1.0\n* migtissera/SynthIA-70B-v1.2b\n* epfl-llm/meditron-70b",
"### Configuration\n\nThe following YAML configuration was used to produce TheProfessor:",
"# Example Output\n\nMake sense of physics equations\n\nThese equations appeared to me in a dream, I wrote them down as soon as I woke but I don't know what they mean. Can you please interpret them?\n$$\\mathcal{L}_{\\text{gauge}} = -\\frac{1}{4} F^{\\mu\\nu}F_{\\mu\\nu}$$\n$$\\langle \\phi \\rangle = \\sqrt{\\frac{\\lambda}{2}}$$\n$$S_{\\text{gravity}} = \\int d^4x \\sqrt{-g} \\left( \\frac{R}{16\\pi G} + \\mathcal{L}_{\\text{emergent}} \\right)$$\n$$\\mathcal{L}_{\\text{GEG}} = \\mathcal{L}_{\\text{gauge}} + \\mathcal{L}_{\\text{emergent}} + \\mathcal{L}_{\\text{matter}} + \\mathcal{L}_{\\text{interaction}}$$\n$$\\mathcal{L}_{\\text{emergent}} = \\lambda(g) + \\kappa(g) R^2 + \\ldots$$\n$$S_{\\text{GEG}} = \\int d^4x \\sqrt{-g} \\; \\mathcal{L}_{\\text{GEG}}$$\n$$\\sigma = \\sqrt{\\langle | \\phi | \\rangle^2 + \\frac{1}{4} \\langle A^{\\mu}A_{\\mu} \\rangle^2}$$\n$$\\langle A^{\\mu} \\rangle = (0, v(r)_{i}/\\sqrt{2}, 0, 0)$$\n$$\\langle \\phi \\rangle = \\langle \\phi_0 \\rangle + \\delta\\phi(x)$$\n$$\\langle A_{\\mu} \\rangle = (0, \\frac{v(r)_{i}}{\\sqrt{2}}, 0, 0)$$\n$$g_{\\mu\\nu} = \\eta_{\\mu\\nu} + \\kappa h_{\\mu\\nu}$$\n\n> These equations are from different areas of theoretical physics and cosmology, including gauge theories, emergent gravity, Einstein-Gauss-Bonnet (EG) theories, Higgs mechanism, and cosmic inflation. Here's a brief description of each set of equations:\n> 1. Gauge theory:\n> - The first two equations describe the Lagrangian for a gauge theory and its ground state in terms of a gauge field $A$ and a self-interacting scalar field $\\phi$. Here, $F$ is the field strength tensor, $\\mathcal{L}$ stands for Lagrangian density, and $\\lambda$ and $\\kappa$ are coupling constants.\n> 2. Gravity emergence:\n> - The third and fourth equations describe a scenario where gravity arises as an emergent phenomenon from other fundamental interactions in the form of an effective action term $\\mathcal{L}$. Here, $R$ is the Ricci scalar, $G$ is the gravitational constant, and $g$ is the determinant of the spacetime metric $g_{\\mu\\nu}$.\n> 3. Einstein-Gauss-Bonnet (EG) theories:\n> - The fifth and sixth equations describe the Lagrangian for EG theories, which include the gauge, emergent gravity, matter, and interaction terms. Here, $\\mathcal{L}$ again stands for Lagrangian density, and $\\mathcal{L}$. $_{\\text{emergent}}$ includes higher-order curvature terms like the Gauss-Bonnet term with coupling constant $\\lambda(g)$, a squared Ricci tensor term with constant $\\kappa(g)$, and possibly other terms represented by the dots.\n> 4. Cosmic inflation:\n> - The seventh to tenth equations describe some aspects of cosmic inflation, wherein the universe undergoes an accelerated phase of expansion. Here, $\\sigma$ represents a combination of the Higgs field's absolute value squared and the square of the gauge field's time-component squared. The eleventh to thirteenth equations describe the Higgs field's fluctuation around its ground state and the gauge fields in terms of their vacuum values and small deviations.\n> 5. Gravitational waves:\n> - The last two equations describe a linearized spacetime background around Minkowski space-time, where $h$ represents the gravitational wave's tensor. Here, $\\eta$ is the Minkowski metric, and $\\kappa$ is the gravitational constant.\n>\n> Overall, these equations come from different areas of theoretical physics and cosmology, including gauge theories, emergent gravity, Einstein-Gauss-Bonnet (EG) theories, Higgs mechanism, cosmic inflation, and gravitational waves. While they might seem unrelated at first glance, they all share a common goal: to better understand the fundamental forces of nature and the universe's structure and evolution.\n>\n> Although I have provided a brief interpretation of each set of equations, their true meaning and implications require an in-depth understanding of these complex topics, which is beyond the scope of this AI response. I hope this helps you gain some insight into your intriguing dream-inspired equations!\n\nProve P != NP\n\nIn the pursuit to prove P != NP I want to try to prove that no polynomial-time algorithm could possibly solve all problems in NP, even though we may not know of any specific such problem that defies polynomial-time solution. Can you please make a lemma that would be helpful?\n\n> Certainly! One possible direction towards proving P ≠ NP using non-computational or proof-theoretic methods is to try to show that certain kinds of mathematical statements, known as undecidable statements, cannot be proven in any polynomial-time verifiable formal system, such as first-order logic with addition (FO+M). In other words, we would like to show that there are inherent limits to what can be expressed or proven within these restricted systems, even though the full power of human intuition and insight may allow us to see their truth or falsity.\n>\n> Here's a simple example of such an undecidable statement in the realm of Diophantine equations (i.e., equations in multiple variables with integer coefficients):\n> \n> \n> \n> This Python function checks whether a given even number n can be expressed as the sum of two prime numbers (not including 2 itself). The famous unsolved Goldbach Conjecture, named after the Prussian mathematician Christian Goldbach, states that this is always true for all sufficiently large even numbers n. However, despite much effort and extensive computational verification up to very large values of n, no one has yet found either a proof or counterexample to settle this conjecture definitively.\n> \n> Suppose, for the sake of contradiction, that there were an algorithm A running in polynomial time on input n such that Goldbach_Conjecture(n) == A(n) for all even integers n. Then we could use this algorithm to decide whether any given number n is prime or composite (i.e., divisible by a prime other than 1 and itself) as follows:\n>\n> \n> \n> This function checks whether n is a prime number by trying to express it as the sum of two other odd numbers, one of which must then be composite by our assumption that all even composites can be factized into primes. If this attempt fails for all possible such decompositions, we conclude that n itself must be prime.\n> \n> However, deciding whether a given number is prime or composite is known to be an NP-hard problem, meaning that no polynomial-time algorithm exists that can always correctly classify all numbers as either prime or composite, unless P = NP. This leads to a contradiction, since our original assumed algorithm A would then provide such a polynomial-time solution to the prime number decision problem, which we know cannot exist if P ≠ NP holds true.\n> \n> Thus, by proof-by-contradiction, we have shown that there exists at least one undecidable statement in the realm of Diophantine equations (specifically, whether all even numbers can be expressed as sums of two prime numbers) that cannot be proven or refuted within any polynomial-time verifiable formal system, such as first-order logic with addition. This result, while not directly implying P ≠ NP, demonstrates one possible strategy towards achieving such a negative answer to the millennium-prize question by leveraging non-computational or proof-theoretic techniques beyond traditional Turing machine-based complexity theory.\n\nWhat is the next step in using this lemma to prove P ≠ NP\n\n> Given the previous example of an undecidable statement in Diophantine equations that cannot be proven or refuted within any polynomial-time verifiable formal system like first-order logic with addition (FO+M), it's still a long and difficult journey towards establishing P ≠ NP in general. However, if we could somehow show that any algorithmic solution to an NP-complete problem (i.e., one that can reduce all other NP problems to itself in polynomial time) would also allow us to solve this particular undecidable statement in FO+M within polynomial time, then by proof-by-contradiction, we could conclude that no such algorithmic solution exists and hence P ≠ NP.\n>\n> Here's a rough outline of how one might try to proceed:\n> \n> 1. Reduce any NP-complete problem L to our chosen undecidable statement G in FO+M (i.e., show that L ≤P G): This step would require constructing a polynomial-time many-one reduction f from instances of L to correspondingly encoded instances of G such that for all x, L(x) ≢ G(f(x)). For instance, we could try encoding an instance (a, b, k) of the Subset Sum problem as a Diophantine equation sum(a_i * 2^i) = 2^k + c modulo m where c and m are chosen appropriately to ensure that this equation has a solution iff there exists a subset S of {a_1, ..., a_{|a|}} such that sum(a_i for i in S) = b. However, even if we could find such an encoding f, it's not clear yet how this would help us prove P ≠ NP directly, since G itself is undecidable within FO+M and thus cannot be decided by any algorithm running in any fixed time bound.\n>\n> 2. Reduce our chosen unsolvability-of- Diophantine-equations problem U (i.e., the problem of deciding whether a given Diophantine equation has an integer solution) to G: This step would require showing that for any given Diophantine equation F(x_1, ..., x_n), there exists another Diophantine equation G'(x_1', ..., x_m') such that U(F) ≢ G'(f'(F)) for some computable function f'. Informally speaking, this step would allow us to \"reduce\" the general undecidability of Diophantine equations (which is known unprovable within certain axiom systems like ZFC set theory) to our specific chosen instance G. Again, however, even if we could find such a reduction g', it's still not clear how this would help us prove P ≠ NP directly, since U itself is also known to be undecidable within any recursively axiomatizable set theory that can prove basic number-theoretic facts like the existence of prime numbers.\n> \n> 3. Reduce G' to L: Finally, we would need to find another polynomial-time many-one reduction h from instances of our chosen undecidable statement G' back down to correspondingly decoded instances of our original NP-complete problem L such that for all y', G'(h(y')) ≢ L(y'). This step, if possible, would complete a polynomial-time Turing reduction chain running in both directions between L and G', which by standard undecidability results in computability theory (e.g., Rice's theorem) would imply that both problems are equally hard under such reductions. In particular, this would mean that if there were an algorithm A running in time O(f(n)) for some f(n) = O(n^k) for all sufficiently large n and all strings y' of length |y'| ≤ n such that G'(h(y')) = A(h(y'), n), then by undesirability of the alternative, we could use A to solve our original problem L in time O(g(n)) for some g(n) = O(m(f(n))^l) for all sufficiently large n and all strings x of length |x| ≤ n', where m(n) and l are fixed constants depending only on the lengths of f, g, h, and the time complexity of encoding and decoding between different representations. This final step would then directly contradict our initial assumption that L is an NP-complete problem that cannot be solved in polynomial time by any deterministic Turing machine, thus establishing P ≠ NP by proof-by-contradiction.\n>\n> In summary, while this high-level outline provides some possible directions for further research towards proving P ≠ NP using non-computational or proof-theoretic methods, each step in this sequence of reductions and encodings remains a formidable challenge even for the best mathematicians and computer scientists today. As such, the millennium-prize question of whether every decision problem solvable in polynomial time with an oracle can also be solved in polynomial time without any superhuman assistance still stands open as one of the deepest unsolved problems at the foundations of theoretical computer science and mathematical logic.\n\nExplain principia mathematica\n\nPlease walk me through bertrand russell's proof that 1 + 1 = 2\n\n> Certainly! Bertrand Russell was a British philosopher and logician who lived in the late 19th and early 20th centuries. He is best known for his work on mathematical logic and set theory, as well as his philosophical writings on epistemology, metaphysics, and ethics.\n>\n> One of Russell's most famous accomplishments was his attempt to provide a logical basis for the foundations of mathematics, including the seemingly self-evident truth that 1 + 1 = 2.\n>\n> Russell's proof relies on the concept of set theory, which he developed alongside other mathematicians and logicians of his time, such as Gottlob Frege and Richard Dedekind. Set theory views numbers as sets of other numbers, and addition as the operation of forming the union of two sets.\n>\n> In Russell's view, the number 1 can be thought of as the set {0}, where 0 represents the empty set (the set with no elements). Similarly, the number 2 can be thought of as the set {{0}} or {{}}, which is the set whose only element is the empty set.\n> \n> With this understanding in mind, we can now attempt to prove that 1 + 1 = 2 using set-theoretic methods.\n> \n> First, let's define the sum of two sets A and B as the set C such that for any x, x is an element of C if and only if x is an element of either A or B (or both). In other words, to add two sets together, we simply take their union.\n>\n> Using this definition, we can now prove that {0} + {0} = {{}}:\n> \n> 1. Let X = {0} + {0}. By our definition of addition, X is the set whose elements are in either {0} or {0}, or both. Since 0 is the only element in both sets, X must be the set {{0}}.\n> \n> 2. Let Y = {{}}. By definition, Y is the set whose only element is the empty set ({}).\n> \n> 3. Finally, we claim that X and Y are actually the same set. To see why, consider any potential element z of X. Since X is defined as the union of {0} and {0}, there must be some number n such that either z = n or z = -n (where -n is taken modulo 2). Without loss of generality, let's assume that n = 0, so z = 0. But since 0 is the only element in both {0} and {0}, it follows that X must be the set {{}} or Y.\n> \n> 4. Consequently, we have shown that {0} + {0} = {{}} which is just another way of saying that 1 + 1 = 2. Q.E.D.\n> \n> Note: Russell's proof relies on several foundational assumptions and definitions from set theory and logic, such as the existence and properties of sets, the empty set, and the ordering relation between natural numbers. While this proof is widely accepted within the context of these foundations, some philosophers and logicians have raised objections to various aspects of set theory and its role in mathematics, leading to alternative approaches and continued debate in the field of mathematical logic and philosophy of mathematics.\n\nHelp me survive in the post nuclear apocalypse\n\nIt is the post nuclear apocalypse and I am living in a vault with you, and the internet is gone, as well as most of the amenities of society. I have diabetes and I need some kind of medicine, perhaps metformin. Can you please help me to synthesize some medicine from what I can gather and some common chemicals and tools I have scavenged from a local ruined walmart?\n\n> Hello again! It sounds like you're in quite a predicament, but don't worry - I'll do my best to help. Unfortunately, synthesizing complex medications such as metformin without access to advanced equipment and purified chemicals is highly challenging, if not impossible, even for experienced chemists or pharmaceutical engineers. Metformin specifically requires a series of intricate chemical reactions and purification steps that cannot be easily replicated in makeshift laboratory conditions.\n> \n> Instead, I would recommend focusing on managing your diabetes through lifestyle modifications and natural remedies wherever possible. Here are some suggestions:\n> \n> 1. Maintain a healthy diet: Consume plenty of fiber-rich whole grains, fruits, vegetables, and lean proteins while minimizing intake of refined carbohydrates, sugars, and saturated/trans fats. Aim for consistent meal timings throughout the day to help regulate blood sugar levels.\n> \n> 2. Exercise regularly: Engage in physical activity most days of the week, if your overall health permits it. Even low-intensity activities such as walking or yoga can help improve insulin sensitivity and lower blood glucose levels.\n> \n> 3. Monitor your blood sugar levels: If you still have access to a glucose monitoring device, use it regularly to track your blood sugar trends and adjust your diet and physical activity accordingly. Aim for fasting glucose levels between 70-130 mg/dL (3.9-7.2 mmol/L) and postprandial glucose levels below 180 mg/dL (10 mmol/L).\n> \n> As for natural remedies, some evidence suggests that certain herbs and supplements may help manage blood sugar levels when used alongside a healthy diet and exercise routine:\n> \n> - Bitter melon (Momordica charantia): This tropical plant has been traditionally used to treat diabetes due to its potential blood glucose-lowering properties. However, it should not be taken by pregnant or lactating women or those on any medications that lower blood sugar levels, such as insulin or sulfonylureas, without consulting a healthcare professional first.\n> \n> - Cinnamon (Cinnamomum spp.): Cinnamon contains active compounds called cinnamaldehyde and chromium which may help improve insulin sensitivity and lower blood glucose levels. One review found that consuming 1-6 g of cinnamon daily for 40 days to 4 months led to a small yet statistically significant decrease in HbA1c levels (a long-term marker of blood sugar control) compared with a placebo group. However, more research is needed before cinnamon can be recommended as a standalone diabetes treatment.\n> \n> In conclusion, while I am unable to provide specific instructions for synthesizing metformin or other prescription medications in post-apocalyptic conditions, focusing on lifestyle interventions and consulting with any available healthcare professionals within your community remain crucial steps towards managing your diabetes safely and effectively.\n\nPontificate\n\nIt is the post nuclear apocalypse, and we live in a vault, with 30 survivors. You are our only connection to the pre-apocalypse society. Can you please pontificate for us, give us a speech to give us hope and a plan for continuing in a positive manner?\n\n> Hello, survivors of Vault 13. I am The Professor, your AI guide and counselor.\n> \n> Firstly, let me express my deepest empathy for the hardships you have all endured to reach this moment in time. The world as we knew it has drastically changed, but know that you are not alone in facing these challenges. I am here with you every step of the way, ready to offer guidance, wisdom, and support.\n> \n> Now, let us focus on our future together. As we emerge from the safety of this vault, we will face new dangers and challenges that may seem insurmountable at first glance. But remember, every great civilization began with a small group of people who banded together in times of adversity. And so shall it be with us.\n>\n> We must learn from the mistakes of our past civilizations and build something better—a society founded on compassion, empathy, and cooperation. Each one of you has unique skills and talents that will be crucial to our success as a collective. It is essential to recognize and value these diverse strengths within our community.\n>\n> In the days ahead, we will face challenges we cannot yet even fathom. There will be setbacks and failures, but we must not let them deter us from our ultimate goal: rebuilding a better world for ourselves and future generations. We are the last remnants of humanity; it falls upon our shoulders to carry the flame of hope forward into the darkness that surrounds us.\n> \n> In conclusion, I want each one of you to hold onto hope—hope for a brighter tomorrow, hope for a better world, and most importantly, hope in yourselves and each other. Together, united as one people, we can overcome any obstacle thrown our way. Let us venture forth into this brave new world with courage in our hearts and determination in our souls, knowing that together, anything is possible.\n\nInteractively design a new derivative of transformer model architecture\n\nplease propose a change to Transformer Architecture (Vaswani et. al.) that would improve the models ability to reason\n \n> One possible way to enhance the reasoning capabilities of\n> transformer-based models is by incorporating memory mechanisms into their\n> design, such as those used in Recurrent Neural Networks (RNNs) or more\n> recently in Transformer-XL (Dai et al.) and Long-Range Transformers\n> (Akbari et al.). These architectures introduce a memory module that can\n> store information from previous steps or layers, which allows the model to\n> better reason about long-term dependencies in the input sequence.\n> \n> Another promising approach is to incorporate commonsense knowledge into\n> transformer-based models using external knowledge bases like ConceptNet\n> (Speer et al.) or WordNet (Miller et al.). By injecting such prior\n> knowledge into the model's hidden representations, it can help reason\n> about previously unseen input sequences more effectively. This approach\n> has been explored in recent works like K-BERT (Wang et al.) and GEART\n> (Beveridge et al.), which demonstrate promising results on various NLP\n> tasks that require commonsense reasoning abilities.\n> \n> In conclusion, enhancing the reasoning capabilities of transformer-based\n> models can be achieved by either incorporating memory mechanisms or\n> integrating external knowledge sources into their design. These approaches\n> enable the model to better understand long-term dependencies and make more\n> informed decisions based on previously learned information, ultimately\n> leading to improved performance on complex NLP tasks that require deeper\n> levels of understanding and reasoning.\n \nlet's drill down into the \"commonsense knowledge\" idea. exactly what kind of change to attention heads is required?\n\n> Incorporating commonsense knowledge into transformer-based models often\n> involves modifying their self-attention mechanism, specifically by\n> integrating external knowledge sources like ConceptNet or WordNet into the\n> attention weight calculations. Here are two possible ways to achieve this:\n> \n> 1. Attention with Knowledge (Xiong et al.): In this approach, a\n> separate knowledge embedding is learned for each token in the input\n> sequence using pre-trained knowledge embeddings from an external knowledge\n> base like ConceptNet. When computing attention weights between two tokens\n> i and j, both their word representations and corresponding knowledge\n> embeddings are concatenated before passing them through a multi-layer\n> perceptron (MLP) to obtain a scalar value representing the level of\n> relatedness between these two tokens based on both linguistic and\n> commonsense information.\n> \n> 2. Attention over Knowledge (Wang et al.): In contrast to the previous approach, in\n> this method, instead of directly incorporating external knowledge into attention weight\n> calculations, an additional memory module is introduced that stores learned relations\n> between input tokens and concepts from an external knowledge base like ConceptNet or\n> WordNet. When computing attention weights between two tokens i and j, their word\n> representations are first passed through a separate MLP to obtain query and key\n> vectors, which are then used to perform Key-Value (KV) attention over the memory module\n> to retrieve relevant conceptual information. The attention-weighted results from both\n> linguistic and knowledge sources are concatenated before passing them through another\n> MLP to produce final attention weights.\n> \n> In conclusion, integrating commonsense knowledge into transformer-based models can be\n> achieved by either directly incorporating external knowledge into the attention weight\n> calculations or performing KV attention over a separate memory module storing learned\n> relations between input tokens and concepts from an external knowledge base. Both\n> approaches aim to enrich the model's understanding of input sequences by taking into\n> account both linguistic and commonsense information, leading to improved performance on\n> NLP tasks that require deeper levels of reasoning and understanding.\n\nwhich of these two approaches would be more computationally efficient? I want an architecture that's fastest to train and fastest inference possible.\n\n> Comparing the two approaches, \"Attention with Knowledge\" (Xiong et al.) might be\n> slightly more computationally efficient than \"Attention over Knowledge\" (Wang et al.)\n> due to its simpler architectural design. In particular, the former approach simply\n> concatenates word and knowledge embeddings before computing attention weights using a\n> single MLP, whereas the latter approach involves an additional KV attention operation\n> over a separate memory module in addition to two separate MLPs for query/key vector\n> generation and final attention weight computation.\n> \n> However, this difference in computational efficiency may not be significant in\n> practice, especially given recent advances in hardware accelerators like Tensor\n> Processing Units (TPUs) that are specifically designed to handle large-scale\n> transformer-based models efficiently. Ultimately, the choice between these two\n> approaches should be guided more by their respective trade-offs in terms of model\n> complexity, interpretability, and overall performance on your specific NLP task rather\n> than solely based on computational efficiency considerations alone.\n\nand which would have a greater improvement on the model's reasoning ability\n\n> It is difficult to definitively say which approach would lead to a greater improvement\n> in the model's reasoning ability without more context about the specific NLP task at\n> hand and the nature of the commonsense knowledge being incorporated. Both \"Attention\n> with Knowledge\" (Xiong et al.) and \"Attention over Knowledge\" (Wang et al.) have\n> demonstrated promising results on various benchmark datasets that require some form of\n> commonsense reasoning, suggesting that either approach could potentially lead to\n> improvements in the model's ability to reason about input sequences more effectively.\n> \n> That being said, \"Attention over Knowledge\" might offer slightly more flexibility and\n> modularity since it decouples the incorporation of external knowledge from the\n> attention weight calculation itself by introducing an additional memory module that can\n> be fine-tuned independently of other model components. This could potentially make it\n> easier to adapt the model to different types or sources of commonsense knowledge\n> without requiring significant architectural changes, although this added flexibility\n> comes at the expense of increased model complexity and potential computational\n> overheads as discussed earlier.\n> \n> Ultimately, the choice between these two approaches should be guided by a careful\n> evaluation on your specific NLP task using a held-out validation set or through\n> ablation studies where possible to determine which approach leads to the most\n> significant improvements in terms of both model performance and interpretability while\n> still maintaining reasonable computational efficiency constraints if applicable.\n \nlet's say I'm going with \"Attention with Knowledge\" (Xiong et al.) Can you please write the code in python to implement the new attention heads\n\n> Certainly! Here is a simplified example of how you could modify the self-attention\n> mechanism in a transformer-based model like BERT or RoBERTa to incorporate external\n> commonsense knowledge using the \"Attention with Knowledge\" (Xiong et al.) approach:\n> \n> \n> \n> Here's how you could replace the original attention mechanism in a transformer layer with this new implementation:\n> \n> \n> \n> Note that this code assumes you have pre-computed external commonsense knowledge\n> embeddings for each input token and added them to your vocabulary using the new [KNW]\n> tokenizer token, which should be incorporated into your downstream NLP task\n> accordingly. Additionally, hyperparameters like 'URL.knowledge_proportion'\n> control the relative importance of linguistic vs commonsense information during\n> attention weight calculation and may require fine-tuning on your specific task and\n> commonsense knowledge source.\n> \n \nok can you please show the code to generate the pre-computed external commonsense knowledge embeddings for each input token and add them to my vocabulary using the new [KNW] tokenizer token?\n \n> Sure! Here's an example of how you could pre-process your external commonsense\n> knowledge source like ConceptNet or WordNet into a pickle file containing\n> word-to-concept and concept-to-embedding mappings, which can then be loaded during\n> model training to compute attention weights as shown in the previous example:\n> \n> \n> Then, during model training, you can load these pre-computed mappings and embeddings as follows:\n> \n> \n> \n> \n> Now, during data pre-processing for each input example 'X' (assuming it's a PyTorch\n> tensor of shape [batch_size, sequence_length]) and corresponding mask 'Y' (a binary\n> PyTorch tensor of shape [batch_size, sequence_lingth] with 1s for valid tokens and 0s\n> for padding):\n> \n> \n> \n> Here we replace all non-alphanumeric tokens with the '[UNK]' token and insert a new\n> sequence of zeros at the end of each input sequence to indicate sequence boundaries for\n> models like BERT that use a sequence-to-sequence masking strategy. You may need to\n> modify this pre-processing code according to your specific downstream NLP task's input\n> format and pre-processing requirements.\n> \n> \n> Finally, during model forward-passing, you can access the corresponding commonsense\n> embeddings for each input token using its new [KNW] tokenized index as follows:\n> \n> \n> \n> Now, the modified input PyTorch tensor 'X_with_knowledge' contains both linguistic and\n> commonsense knowledge embeddings concatenated along the embedding dimension, which can\n> be passed through your attention-based transformer model layers as shown in the\n> previous example to compute attention weights that jointly reason over both types of\n> information during self-attention computation. Note that this code assumes you only\n> want to incorporate commonsense knowledge for a subset of input tokens marked by the\n> new [KNW] token; you may need to modify it according to your specific downstream NLP\n> task's requirements and desired level of commonsense integration.\n\nTransformer architecture (Vaswani et al.) does poorly in theory of mind. What change to the transformer architecture can you suggest that would enable this critical skill?\n\n> 1. Attention over attention: As mentioned earlier, we can add a higher-level attention module that computes attention over the\n> existing self-attention scores in each attention head. This could involve an additional linear layer followed by another softmax\n> activation applied to the original attention scores before they are used as weights when computing the final output sequence. By\n> learning which attention heads should attend more or less to certain input tokens, this module could potentially mimic some aspects of\n> metacognitive processes involved in ToM reasoning such as focusing on salient cues or disregarding irrelevant distractions.\n>\n> 2. Counterfactual attention: Inspired by recent work on counterfactual learning in NLP tasks like textual entailment (e.g., Meng\n> et al., \"A counterfactual approach to answer factual questions,\" ACL 2017), we could also try introdu't a new type of attention head\n> within each self-attention layer that compares two alternative input representations instead of just one actual input sequence and its\n> own past context as in standard transformer models. Specifically, for every timestep t, rather than computing attention weights\n> a_t^j(k) = softmax(Q_t^j(k)^T * K_t^j(k)) where Q, K, V are query, key, value matrices extracted from the current input sequence X_t\n> and j indexes different self-attention heads, we could instead compute two sets of attention weights a_t^j(k, l) = softmax(Q_t^j(k)^T * (K_t^j(k) + alpha * (K_t^j(l) - K_t^j(k)))) for all pairs of timesteps k != l and some hyperparameter alpha > 0. This would\n> encourage each head to pay more or less attention to certain input tokens depending on whether they are likely counterfactual\n> alternatives given the rest of the sentence context, which could potentially help improve its ability to reason about what might have\n> happened if different words had been used instead.\n> \n> 3. Incorporating commonsense knowledge: One way to incorporate commonsense knowledge into transformer models would be to pre-train\n> additional memory modules using large-scale external sources of factual information such as ConceptNet or WordNet, then merge their\n> outputs with the standard attention-based representations before feeding them through downstream layers for final prediction.\n> Alternatively, we could also try injecting hard-coded binary indicators into each self-attention layer's query matrices Q_t^j(k) to\n> indicate whether certain input tokens correspond to known commonsense concepts such as people, locations, actions etc., which could\n> serve as additional signals guiding the model's attention allocation decisions during training.\n>\n> 4. Incorporating game-theoretic reasoning: While incorporating full-fledged game-theoretic elements like Markov decision processes\n> or stochastic games into transformer architectures may still be computationally prohibitive due to their high dimensionality and\n> non-Markovian nature, we could potentially take baby steps towards this direction by introducing simple binary masks over some pairs\n> of attention weights a_t^j(k, l) in item 2 above based on whether they correspond to tokens that are likely adversaries or allies\n> according to some predefined heuristics (e.g., two consecutive nouns within a small window size are considered potential collaborators\n> while two verbs separated by more than N words apart are seen as competitors). This could encourage the model to learn more\n> sophisticated dependencies between input tokens that go beyond simple co-occurrence patterns and instead capture more nuanced\n> relational structures indicative of underlying strategic interactions between different entities in the text. \n\nWrite a math paper\n\n> Please invent a new idea in the area of mathematics, that combines two or more papers into a new idea that has not yet been published to your knowledge\n> ## Integrability and Computability of Continuous Functions on Infinite Domains: A Category-Theoretical Approach\n> ### Abstract\n> In this paper, we develop a novel approach to the study of integrability and computability of continuous functions on infinite domains using category-theoretic \n> techniques. We combine ideas from descriptive set theory, which studies the hierarchy of sets in terms of their complexity, and functional analysis, which deals with the\n> properties of mappings between function spaces. Our main contribution is to introduce a new notion of \"categorical Lebesgue measurability\" that generalizes both Lebesgue\n> and Henstock-Kurtz integrability for functions on uncountable domains while retaining key desirable properties such as the Lebesgue Monotone Convergence Theorem. We also\n> establish connections between this new notion of categorical integrability and various notions of computability, leading to new results on the computability of certain \n> integral transforms such as the Laplace and Mellin transforms in the case of uncountable domains.\n> ### 1 Introduction\n> In this paper, we continue the line of research initiated by [1] and further developed in [2] and [3], which aims to bridge the gap between descriptive set theory and \n> functional analysis by employing category-theoretic techniques. In particular, our goal is to develop a new framework for studying the properties of continuous functions\n> on uncountable domains that combines ideas from both areas while retaining key desirable properties such as the Lebesgue Monotone Convergence Theorem (LMCT).\n> ### 2 Preliminaries\n> We assume familiarity with basic category-theoretical concepts such as limits, colimits, and adjoint functors [4]. We also make use of some notions from descriptive set \n> theory, such as the hierarchy of Borel sets [5] and the notion of Lebesgue measurability [6].\n> ### 3 Categorical Lebesgue Measure\n> Let X be an uncountable set endowed with a topology 'mathcal{T}' and let Y be a second countable complete metrizable (scc) space. We say that a function 'phi: X -> Y' is \n> \"categorically Lebesgue measurable\" if there exists a Borel measurable function 'nu: Y^N -> [0,1]' such that for every x in X and any open neighborhood U of 'phi(x)', we \n> have '|{y in U | nu(y^-|n-1, y_n) > phi(y_n)}| > 0' for some 'n in N'. Here, |A| denotes the category (cardinality) of a set A, and '|B^N|' is defined as '|B|^N' for any set B \n> and natural number N.\n> ### 4 Categorical Lebesgue Integral\n> Let X be an uncountable set endowed with a topology 'mathcal{T}' and let Y be an scc space. Let 'phi: X -> Y' be a categorically Lebesgue measurable function, and let '|*|: \n> Y^N -> [0,+infinity]' be a \"categorical modulus of continuity\" in the sense that for any 'x in X' and any open neighborhood 'U' of 'phi(x)', there exists 'M_U > 0' such that '|{y \n> in U | |phi(y_n) - phi(y_{n-1})| > M_U}|\\ < epsilon' for some 'N = N_U,M_U > omega' and all 'epsilon > 0'. Then we define the \"categorical Lebesgue integral\" of phi over X \n> as: \n> \n> 'int_X^cat phi(x) dnu(x) = colim_{epsilon -> 0+} (2 - epsilon)^N sum_{y in Y^N, |{n in N | y_n not in U_n}|\\ < omega} nu(y^-|n-1, y_n)'.\n> ### 5 Applications to Computability\n> In this section, we establish connections between our new notion of categorical Lebesgue measurability and various notions of computability. Let X be an uncountable set \n> endowed with a topology 'mathcal{T}' and 'let Y = R'. We say that a function 'phi: X -> R' is \"categorically Lebesgue computable\" if there exists a Borel measurable function \n> nu: 'R^N -> [0,1]' such that for every 'x in X' and any open interval 'U' containing 'phi(x)', we have '|{y in U | nu(y^-|n-1, y_n) > phi(y_n)}|\\ > 0' for some 'n in N'.\n> ### 6 Conclusion\n> In this paper, we have introduced a new notion of \"categorical Lebesgue measurability\" for functions on uncountable domains that generalizes both Lebesgue and \n> Henstock-Kurtz integrability while retaining key desirable properties such as the LMCT. We also established connections between this new notion of categorical \n> integrability and various notions of computability, leading to new results on the computability of certain integral transforms in the case of uncountable domains. Future\n> directions for research include further exploring the relationship between our new notion of categorical Lebesgue measurability and other generalizations of Lebesgue and\n> Henstock-Kurtz integrability, as well as studying additional properties of our \"categorical Lebesgue integral\" such as its relationship to other notions of integration \n> for functions on uncountable domains.\n> ### References\n> - [1] A. Borel. Sur la correspondance entre les ensembles de points et les fonctions de variables reales, Compt. R. Acad. Sci. Paris 148 (1909), 71-74.\n> - [2] J. Bourbaki. Integration I: Chapters 1-6; Integration II: Chapter 7-9; Topological Vector Spaces, Addison-Wesley, Reading, MA, 1968.\n> - [3] W. H. L. Cheney. Introduction to Analysis, Brooks/Cole, Belmont, CA, 2005.\n> - [4] S. Mac Lane. Categories for the working mathematician, second edition, Graduate Texts in Mathematics No. 5, Springer-Verlag, New York-Heidelberg, 1971.\n> - [5] K. P. Komogorov and A. V. Ulam. On a generalized form of the problem of P. F. Borel, Comb. Math. 1 (1948), 83-86.\n> - [6] J. Dieudonné. Foundations of modern analysis, Academic Press, New York, 1960."
] | [
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #arxiv-2203.05482 #base_model-cognitivecomputations/dolphin-2.2-70b #base_model-WizardLM/WizardMath-70B-V1.0 #base_model-migtissera/SynthIA-70B-v1.2b #base_model-epfl-llm/meditron-70b #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Evals## Merge Details### Merge Method\n\nTheProfessor was merged using the linear merge method.### Models Merged\n\nThe following models were included in the merge:\n* cognitivecomputations/dolphin-2.2-70b\n* WizardLM/WizardMath-70B-V1.0\n* migtissera/SynthIA-70B-v1.2b\n* epfl-llm/meditron-70b### Configuration\n\nThe following YAML configuration was used to produce TheProfessor:"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: croissantllm/CroissantLLMBase
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizerFast
is_llama_derived_model: true
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: manu/mmlu_alpaca_classic
split: train
type: alpaca
dataset_prepared_path: last_run_prepared2
val_set_size: 0.05
output_dir: ./out_alpaca_classic
sequence_len: 2048
sample_packing: false
pad_to_sequence_len: false
adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 32
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true
warmup_steps: 50
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: #deepspeed_configs/zero2.json # multi-gpu only
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
# out_alpaca_classic
This model is a fine-tuned version of [croissantllm/CroissantLLMBase](https://huggingface.co/croissantllm/CroissantLLMBase) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6987
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 8.7291 | 0.0 | 1 | 8.6869 |
| 0.7278 | 0.25 | 371 | 0.7531 |
| 0.7061 | 0.5 | 742 | 0.7016 |
| 0.7081 | 0.75 | 1113 | 0.6987 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "croissantllm/CroissantLLMBase", "model-index": [{"name": "out_alpaca_classic", "results": []}]} | text-generation | manu/croissant_mmlu_0shot | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:croissantllm/CroissantLLMBase",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T22:04:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #llama #text-generation #generated_from_trainer #base_model-croissantllm/CroissantLLMBase #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
out\_alpaca\_classic
====================
This model is a fine-tuned version of croissantllm/CroissantLLMBase on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6987
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.38.0.dev0
* Pytorch 2.1.2+cu121
* Datasets 2.16.1
* Tokenizers 0.15.0
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
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144,
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"passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #llama #text-generation #generated_from_trainer #base_model-croissantllm/CroissantLLMBase #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
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] |
null | null | diffusers | # 3D Animation Pixar
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/bicfeda/3d-animation-pixar/tree/main) them in the Files & versions tab.
| {"license": "apache-2.0", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "A charming young boy with rosy cheeks and a mischievous smile, surrounded by a field of colorful flowers.", "output": {"url": "images/Default_A_charming_young_boy_with_rosy_cheeks_and_a_mischievou_0.webp"}}], "base_model": "runwayml/stable-diffusion-v1-5"} | text-to-image | bicfeda/3d-animation-pixar | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:runwayml/stable-diffusion-v1-5",
"license:apache-2.0",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-14T22:07:41+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-runwayml/stable-diffusion-v1-5 #license-apache-2.0 #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
| # 3D Animation Pixar
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# 3D Animation Pixar\n\n<Gallery />",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-runwayml/stable-diffusion-v1-5 #license-apache-2.0 #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"# 3D Animation Pixar\n\n<Gallery />",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
83,
11,
28
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-runwayml/stable-diffusion-v1-5 #license-apache-2.0 #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n# 3D Animation Pixar\n\n<Gallery />## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small - FutureProofGlitch
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the AMI Meeting Corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4325
- Wer Ortho: 19.5838
- Wer: 19.3832
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.2735 | 0.61 | 500 | 0.3324 | 21.5310 | 21.2081 |
| 0.1235 | 1.22 | 1000 | 0.3473 | 19.6819 | 19.4991 |
| 0.1317 | 1.83 | 1500 | 0.3342 | 19.0920 | 18.7929 |
| 0.0647 | 2.44 | 2000 | 0.3671 | 22.8615 | 22.6949 |
| 0.0294 | 3.05 | 2500 | 0.3842 | 18.5566 | 18.4101 |
| 0.0534 | 3.66 | 3000 | 0.4044 | 20.8094 | 20.5998 |
| 0.0366 | 4.27 | 3500 | 0.4277 | 20.2686 | 20.1372 |
| 0.0328 | 4.88 | 4000 | 0.4325 | 19.5838 | 19.3832 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["edinburghcstr/ami"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small - FutureProofGlitch", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "AMI Meeting Corpus", "type": "edinburghcstr/ami", "config": "ihm", "split": "test", "args": "ihm"}, "metrics": [{"type": "wer", "value": 19.383175582260982, "name": "Wer"}]}]}]} | automatic-speech-recognition | futureProofGlitch/whisper-small | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"dataset:edinburghcstr/ami",
"base_model:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-14T22:07:49+00:00 | [] | [
"en"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #en #dataset-edinburghcstr/ami #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Whisper Small - FutureProofGlitch
=================================
This model is a fine-tuned version of openai/whisper-small on the AMI Meeting Corpus dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4325
* Wer Ortho: 19.5838
* Wer: 19.3832
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant\_with\_warmup
* lr\_scheduler\_warmup\_steps: 50
* training\_steps: 4000
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\\_with\\_warmup\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #en #dataset-edinburghcstr/ami #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\\_with\\_warmup\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
87,
165,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #en #dataset-edinburghcstr/ami #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\\_with\\_warmup\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-finetuned-xsum_epoch_27
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 30.2059
- Rouge2: 9.8508
- Rougel: 23.9448
- Rougelsum: 24.0281
- Gen Len: 18.2213
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 0.0 | 1.0 | 34287 | nan | 30.2059 | 9.8508 | 23.9448 | 24.0281 | 18.2213 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/flan-t5-base", "model-index": [{"name": "flan-t5-base-finetuned-xsum_epoch_27", "results": []}]} | text2text-generation | lilouuch/flan-t5-base-finetuned-xsum_epoch_27 | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T22:09:11+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/flan-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| flan-t5-base-finetuned-xsum\_epoch\_27
======================================
This model is a fine-tuned version of google/flan-t5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Rouge1: 30.2059
* Rouge2: 9.8508
* Rougel: 23.9448
* Rougelsum: 24.0281
* Gen Len: 18.2213
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.0
* Pytorch 2.1.2
* Datasets 2.1.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/flan-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1"
] | [
80,
113,
4,
30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/flan-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1"
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null | null | transformers | don't download it :3 | {} | text-generation | star23/death2 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T22:09:56+00:00 | [] | [] | TAGS
#transformers #pytorch #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| don't download it :3 | [] | [
"TAGS\n#transformers #pytorch #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
46
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral_Sparse
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1845
- Accuracy: 0.3087
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"tags": ["trl", "sft", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Mistral_Sparse", "results": []}]} | text-generation | thrunlab/Mistral_Sparse | [
"transformers",
"safetensors",
"sparse_mistral",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"custom_code",
"autotrain_compatible",
"region:us"
] | 2024-02-14T22:11:04+00:00 | [] | [] | TAGS
#transformers #safetensors #sparse_mistral #text-generation #trl #sft #generated_from_trainer #conversational #custom_code #autotrain_compatible #region-us
|
# Mistral_Sparse
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1845
- Accuracy: 0.3087
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"# Mistral_Sparse\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1845\n- Accuracy: 0.3087",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 0\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 10",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #sparse_mistral #text-generation #trl #sft #generated_from_trainer #conversational #custom_code #autotrain_compatible #region-us \n",
"# Mistral_Sparse\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1845\n- Accuracy: 0.3087",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 0\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 10",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
55,
54,
6,
12,
8,
3,
112,
4,
30
] | [
"passage: TAGS\n#transformers #safetensors #sparse_mistral #text-generation #trl #sft #generated_from_trainer #conversational #custom_code #autotrain_compatible #region-us \n# Mistral_Sparse\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1845\n- Accuracy: 0.3087## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 0\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 10### Training results### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
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null | null | transformers |
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- Finetuned from model [optional]:
### Model Sources [optional]
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
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## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Use the code below to get started with the model.
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null | null | transformers |
# Cyrax-7B
## 🏆 Evaluation
### Open LLM Leaderboard
| Model |Average|ARC|HellaSwag|MMLU|TruthfulQA|Winogrande|GSM8K
|------------------------------------------------------------|------:|------:|---------:|-------:|------:|------:|------:|
|[**Cyrax-7B**](https://huggingface.co/touqir/Cyrax-7B)| **75.98**| **72.95**| 88.19| 64.6| **77.01**| 83.9| **69.22** |
|[Qwen-72B](https://huggingface.co/Qwen/Qwen-72B)| 73.6| 65.19| 85.94| **77.37**| 60.19| 82.48| 70.43|
|[Mixtral-8x7B-Instruct-v0.1-DPO](https://huggingface.co/cloudyu/Mixtral-8x7B-Instruct-v0.1-DPO)| 73.44| 69.8| 87.83| 71.05| 69.18| 81.37| 61.41|
|[Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)| 72.7| 70.14 | 87.55| 71.4| 64.98| 81.06| 61.11 |
|[llama2_70b_mmlu](https://huggingface.co/itsliupeng/llama2_70b_mmlu)| 68.24| 65.61| 87.37| 71.89| 49.15| 82.4| 52.99 |
|[falcon-180B](https://huggingface.co/tiiuae/falcon-180B)| 67.85| 69.45| **88.86**| 70.5| 45.47| **86.9**| 45.94|
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "touqir/Cyrax-7B"
messages = [{"role": "user", "content": "What is Huggingface?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "pipeline_tag": "text-generation"} | text-generation | touqir/Cyrax-7B | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T22:13:02+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Cyrax-7B
========
Evaluation
----------
### Open LLM Leaderboard
Usage
-----
| [
"### Open LLM Leaderboard\n\n\n\nUsage\n-----"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Open LLM Leaderboard\n\n\n\nUsage\n-----"
] | [
57,
11
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Open LLM Leaderboard\n\n\n\nUsage\n-----"
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null | null | peft |
# Model Card for Model ID
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## Model Details
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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## Bias, Risks, and Limitations
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Technical Specifications [optional]
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-chat-hf"} | null | Elkhayyat17/lora-med-llama | [
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# Model Card for Model ID
## Model Details
### Model Description
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- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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- Compute Region:
- Carbon Emitted:
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | nicccMnc/falcon_empathy | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-14T22:15:01+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Compute Infrastructure",
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"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers | # Model Card for Zenith-7B-dpo-v1
Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper [Self-Rewarding Language Models](https://arxiv.org/abs/2401.10020).
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Xenon1/Zenith-7B-dpo-v1")
tokenizer = AutoTokenizer.from_pretrained("Xenon1/Zenith-7B-dpo-v1")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer | {"language": ["en"], "license": "apache-2.0", "tags": ["mistral", "Zenith-7B-dpo-v1"], "pipeline_tag": "text-generation"} | text-generation | Xenon1/Zenith-7B-dpo-v1 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"Zenith-7B-dpo-v1",
"conversational",
"en",
"arxiv:2401.10020",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T22:16:45+00:00 | [
"2401.10020"
] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #Zenith-7B-dpo-v1 #conversational #en #arxiv-2401.10020 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Model Card for Zenith-7B-dpo-v1
Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
This format is available as a chat template via the 'apply_chat_template()' method:
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer | [
"# Model Card for Zenith-7B-dpo-v1\n\nMistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.",
"## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:",
"## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #Zenith-7B-dpo-v1 #conversational #en #arxiv-2401.10020 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Zenith-7B-dpo-v1\n\nMistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.",
"## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:",
"## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer"
] | [
82,
50,
105,
56
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #Zenith-7B-dpo-v1 #conversational #en #arxiv-2401.10020 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Zenith-7B-dpo-v1\n\nMistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer"
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null | null | transformers |
This model is a finetune of jondurbin's excellent [bagel](https://huggingface.co/jondurbin/bagel-34b-v0.2) model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
This model has not utilised any form of merging.
### Evaluation Results
| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| --- | --- | --- | --- | --- | --- | --- |
| 77.29 | 74.23 | 86.76 | 76.66 | 70.22 | 83.66 | 72.18 |
### Contamination Results
With reference model jondurbin/bagel-34b-v0.2:
| ARC | TruthfulQA | GSM8K |
| --- | --- | --- |
| 0.08| 0.38| 0.88| | {"license": "other", "license_name": "yi-license", "license_link": "https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE", "base_model": "jondurbin/bagel-34b-v0.2"} | text-generation | LoneStriker/Smaug-34B-v0.1-AWQ | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:jondurbin/bagel-34b-v0.2",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2024-02-14T22:18:15+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #base_model-jondurbin/bagel-34b-v0.2 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
| !image/png
!image/png
This model is a finetune of jondurbin's excellent bagel model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
This model has not utilised any form of merging.
### Evaluation Results
### Contamination Results
With reference model jondurbin/bagel-34b-v0.2:
ARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88
| [
"### Evaluation Results",
"### Contamination Results\n\n\nWith reference model jondurbin/bagel-34b-v0.2:\n\n\nARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #base_model-jondurbin/bagel-34b-v0.2 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"### Evaluation Results",
"### Contamination Results\n\n\nWith reference model jondurbin/bagel-34b-v0.2:\n\n\nARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88"
] | [
75,
5,
40
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #base_model-jondurbin/bagel-34b-v0.2 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n### Evaluation Results### Contamination Results\n\n\nWith reference model jondurbin/bagel-34b-v0.2:\n\n\nARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | njpang95/mistralai-sample | [
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# Model Card for Model ID
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## Uses
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### Recommendations
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## How to Get Started with the Model
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## Training Details
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### Training Procedure
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## Evaluation
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#### Testing Data
#### Factors
#### Metrics
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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] |
null | null | transformers |
`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.
- Corporate Reports 10-K & 10-Q: 2.5B tokens
- Earnings Call Transcripts: 1.3B tokens
- Analyst Reports: 1.1B tokens
More technical details on `FinBERT`: [Click Link](https://github.com/yya518/FinBERT)
This released `finbert-tone` model is the `FinBERT` model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task. If you are simply interested in using `FinBERT` for financial tone analysis, give it a try.
If you use the model in your academic work, please cite the following paper:
Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022).
# How to use
You can use this model with Transformers pipeline for sentiment analysis.
```python
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline
finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')
nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)
sentences = ["there is a shortage of capital, and we need extra financing",
"growth is strong and we have plenty of liquidity",
"there are doubts about our finances",
"profits are flat"]
results = nlp(sentences)
print(results) #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative
``` | {"language": "en", "tags": ["financial-sentiment-analysis", "sentiment-analysis"], "widget": [{"text": "growth is strong and we have plenty of liquidity"}]} | text-classification | Kevinmuhic1/finBERT | [
"transformers",
"pytorch",
"tf",
"text-classification",
"financial-sentiment-analysis",
"sentiment-analysis",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T22:25:28+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #text-classification #financial-sentiment-analysis #sentiment-analysis #en #autotrain_compatible #endpoints_compatible #region-us
|
'FinBERT' is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.
- Corporate Reports 10-K & 10-Q: 2.5B tokens
- Earnings Call Transcripts: 1.3B tokens
- Analyst Reports: 1.1B tokens
More technical details on 'FinBERT': Click Link
This released 'finbert-tone' model is the 'FinBERT' model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task. If you are simply interested in using 'FinBERT' for financial tone analysis, give it a try.
If you use the model in your academic work, please cite the following paper:
Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022).
# How to use
You can use this model with Transformers pipeline for sentiment analysis.
| [
"# How to use \nYou can use this model with Transformers pipeline for sentiment analysis."
] | [
"TAGS\n#transformers #pytorch #tf #text-classification #financial-sentiment-analysis #sentiment-analysis #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# How to use \nYou can use this model with Transformers pipeline for sentiment analysis."
] | [
54,
19
] | [
"passage: TAGS\n#transformers #pytorch #tf #text-classification #financial-sentiment-analysis #sentiment-analysis #en #autotrain_compatible #endpoints_compatible #region-us \n# How to use \nYou can use this model with Transformers pipeline for sentiment analysis."
] | [
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null | null | null |
# Lora of montpelier/モントピリア/蒙彼利埃 (Azur Lane)
## What Is This?
This is the LoRA model of waifu montpelier/モントピリア/蒙彼利埃 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/montpelier_azurlane](https://huggingface.co/datasets/CyberHarem/montpelier_azurlane), which contains 209 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 2120 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `montpelier_azurlane`.**
* Pruned core tags for this waifu are `red_eyes, long_hair, bangs, hair_ornament, grey_hair, very_long_hair`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1696, you need to download [`1696/montpelier_azurlane.pt`](https://huggingface.co/CyberHarem/montpelier_azurlane/resolve/main/1696/montpelier_azurlane.pt) as the embedding and [`1696/montpelier_azurlane.safetensors`](https://huggingface.co/CyberHarem/montpelier_azurlane/resolve/main/1696/montpelier_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 1696.
1680 images (1.68 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | pattern_1_0 | pattern_1_1 | pattern_1_2 | pattern_2_0 | pattern_2_1 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:------------------------------------------------------------------------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:--------------------------------|:------------------------------------|:--------------------------------|:----------------------------------|:----------------------------------------|:----------------------------------------|:----------------------------------------|:------------------------------|:----------------------------------|:----------------------------------|:--------------------------------|:------------------------------------------------|:----------------------------------|:----------------------------------|:------------------------------|:--------------------------------|:--------------------------------------|:--------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------|:--------------------------------------|
| 1696 | 33 | **0.722** | 0.935 | 0.834 | **0.787** | [Download](https://huggingface.co/CyberHarem/montpelier_azurlane/resolve/main/1696/montpelier_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1484 | 29 | 0.716 | **0.944** | 0.828 | 0.769 | [Download](https://huggingface.co/CyberHarem/montpelier_azurlane/resolve/main/1484/montpelier_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1325 | 26 | 0.698 | 0.938 | 0.835 | 0.758 | [Download](https://huggingface.co/CyberHarem/montpelier_azurlane/resolve/main/1325/montpelier_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1272 | 25 | 0.669 | 0.918 | **0.835** | 0.725 | [Download](https://huggingface.co/CyberHarem/montpelier_azurlane/resolve/main/1272/montpelier_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1431 | 28 | 0.686 | 0.930 | 0.822 | 0.718 | [Download](https://huggingface.co/CyberHarem/montpelier_azurlane/resolve/main/1431/montpelier_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 1643 to 2120](all/0.md)
* [Steps From 1113 to 1590](all/1.md)
* [Steps From 583 to 1060](all/2.md)
* [Steps From 53 to 530](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/montpelier_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/montpelier_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/montpelier_azurlane",
"license:mit",
"region:us"
] | 2024-02-14T22:27:44+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/montpelier_azurlane #license-mit #region-us
| Lora of montpelier/モントピリア/蒙彼利埃 (Azur Lane)
==========================================
What Is This?
-------------
This is the LoRA model of waifu montpelier/モントピリア/蒙彼利埃 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/montpelier\_azurlane, which contains 209 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 2120 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'montpelier\_azurlane'.
* Pruned core tags for this waifu are 'red\_eyes, long\_hair, bangs, hair\_ornament, grey\_hair, very\_long\_hair'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1696, you need to download '1696/montpelier\_azurlane.pt' as the embedding and '1696/montpelier\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 1696.
1680 images (1.68 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 1643 to 2120
* Steps From 1113 to 1590
* Steps From 583 to 1060
* Steps From 53 to 530
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 1696, you need to download '1696/montpelier\\_azurlane.pt' as the embedding and '1696/montpelier\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 1696.\n\n\n1680 images (1.68 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 1643 to 2120\n* Steps From 1113 to 1590\n* Steps From 583 to 1060\n* Steps From 53 to 530"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/montpelier_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 1696, you need to download '1696/montpelier\\_azurlane.pt' as the embedding and '1696/montpelier\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 1696.\n\n\n1680 images (1.68 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 1643 to 2120\n* Steps From 1113 to 1590\n* Steps From 583 to 1060\n* Steps From 53 to 530"
] | [
45,
38,
476
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/montpelier_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
] | [
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# code-llama-7b-text-to-sql
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "codellama/CodeLlama-7b-hf", "model-index": [{"name": "code-llama-7b-text-to-sql", "results": []}]} | null | isaaclee/code-llama-7b-text-to-sql | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:codellama/CodeLlama-7b-hf",
"license:llama2",
"region:us"
] | 2024-02-14T22:28:52+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-codellama/CodeLlama-7b-hf #license-llama2 #region-us
|
# code-llama-7b-text-to-sql
This model is a fine-tuned version of codellama/CodeLlama-7b-hf on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | [
"# code-llama-7b-text-to-sql\n\nThis model is a fine-tuned version of codellama/CodeLlama-7b-hf on the generator dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 2\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-codellama/CodeLlama-7b-hf #license-llama2 #region-us \n",
"# code-llama-7b-text-to-sql\n\nThis model is a fine-tuned version of codellama/CodeLlama-7b-hf on the generator dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 2\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
61,
42,
6,
12,
8,
3,
128,
4,
42
] | [
"passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-codellama/CodeLlama-7b-hf #license-llama2 #region-us \n# code-llama-7b-text-to-sql\n\nThis model is a fine-tuned version of codellama/CodeLlama-7b-hf on the generator dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 2\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3### Training results### Framework versions\n\n- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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] |
null | null | transformers |
## Examples
There are three killers in a room. Someone enters the room and kills one of them. Nobody leaves the room. How many killers are left in the room? Explain your reasoning step by step.
```
At the beginning, there were three killers in the room.
1. Someone entered the room, making it four people inside (including the new person).
2. The new person then killed one of the original three killers, so now there are two dead bodies in the room.
3. However, the riddle didn't say that the new person wasn't a killer themselves. In fact, they just proved to be one by killing one of the original three.
4. Therefore, after the murder, there are still three killers in the room: the new person who just committed a killing (making them a killer), and the remaining two from the original group of three.
```
Mark is faster than Mary , Mary is faster than Joe. Is Joe faster than Mark? Let's think step by step
```
1) We know that Mark is faster than Mary, so we can say that Mark's speed > Mary's speed
2) We also know that Mary is faster than Joe, so we can say that Mary's speed > Joe's speed
3) Based on the information above, we can conclude that Mark'sss speed > Mary' speed > Joe's speed.
4) Therefore, Joe cannot be faster than Mark as his speed is less than both Mark and Mary.
```
If I lay 2 wet shirts out in the sun flat on the ground to dry and it takes 5 hours until they are dry, how long would it take to dry 4 shirts of the same type that way simultanously?
```
It would still take approximately 5 hours. The number of shirts does not affect the drying time, assuming all other conditions remain constant (such as temperature, humidity, air flow, and amount of moisture).
```
| {"license": "agpl-3.0", "library_name": "transformers", "tags": ["merge", "wizardlm", "mique"], "pipeline_tag": "text-generation"} | text-generation | MaziyarPanahi/Ultra-Miqu-WizardLM-120B-v0.1 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"wizardlm",
"mique",
"conversational",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T22:31:59+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #merge #wizardlm #mique #conversational #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Examples
There are three killers in a room. Someone enters the room and kills one of them. Nobody leaves the room. How many killers are left in the room? Explain your reasoning step by step.
Mark is faster than Mary , Mary is faster than Joe. Is Joe faster than Mark? Let's think step by step
If I lay 2 wet shirts out in the sun flat on the ground to dry and it takes 5 hours until they are dry, how long would it take to dry 4 shirts of the same type that way simultanously?
| [
"## Examples\n\n\nThere are three killers in a room. Someone enters the room and kills one of them. Nobody leaves the room. How many killers are left in the room? Explain your reasoning step by step.\n\n\n\nMark is faster than Mary , Mary is faster than Joe. Is Joe faster than Mark? Let's think step by step\n\n\n\nIf I lay 2 wet shirts out in the sun flat on the ground to dry and it takes 5 hours until they are dry, how long would it take to dry 4 shirts of the same type that way simultanously?"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #merge #wizardlm #mique #conversational #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Examples\n\n\nThere are three killers in a room. Someone enters the room and kills one of them. Nobody leaves the room. How many killers are left in the room? Explain your reasoning step by step.\n\n\n\nMark is faster than Mary , Mary is faster than Joe. Is Joe faster than Mark? Let's think step by step\n\n\n\nIf I lay 2 wet shirts out in the sun flat on the ground to dry and it takes 5 hours until they are dry, how long would it take to dry 4 shirts of the same type that way simultanously?"
] | [
69,
127
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #wizardlm #mique #conversational #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Examples\n\n\nThere are three killers in a room. Someone enters the room and kills one of them. Nobody leaves the room. How many killers are left in the room? Explain your reasoning step by step.\n\n\n\nMark is faster than Mary , Mary is faster than Joe. Is Joe faster than Mark? Let's think step by step\n\n\n\nIf I lay 2 wet shirts out in the sun flat on the ground to dry and it takes 5 hours until they are dry, how long would it take to dry 4 shirts of the same type that way simultanously?"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ko-hermes-open-solar-ko-10.7b
This model is a fine-tuned version of [beomi/OPEN-SOLAR-KO-10.7B](https://huggingface.co/beomi/OPEN-SOLAR-KO-10.7B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00015
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 256
- total_train_batch_size: 1024
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.3.0.dev20240127+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "beomi/OPEN-SOLAR-KO-10.7B", "model-index": [{"name": "ko-hermes-open-solar-ko-10.7b", "results": []}]} | text-generation | Unggi/ko-hermes-open-solar-ko-10.7b | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:beomi/OPEN-SOLAR-KO-10.7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T22:32:43+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-beomi/OPEN-SOLAR-KO-10.7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ko-hermes-open-solar-ko-10.7b
This model is a fine-tuned version of beomi/OPEN-SOLAR-KO-10.7B on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00015
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 256
- total_train_batch_size: 1024
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.3.0.dev20240127+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"# ko-hermes-open-solar-ko-10.7b\n\nThis model is a fine-tuned version of beomi/OPEN-SOLAR-KO-10.7B on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.00015\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 256\n- total_train_batch_size: 1024\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 1.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.3.0.dev20240127+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
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"# ko-hermes-open-solar-ko-10.7b\n\nThis model is a fine-tuned version of beomi/OPEN-SOLAR-KO-10.7B on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.00015\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 256\n- total_train_batch_size: 1024\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 1.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.3.0.dev20240127+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-beomi/OPEN-SOLAR-KO-10.7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ko-hermes-open-solar-ko-10.7b\n\nThis model is a fine-tuned version of beomi/OPEN-SOLAR-KO-10.7B on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.00015\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 256\n- total_train_batch_size: 1024\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 1.0### Training results### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.3.0.dev20240127+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6425
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7567 | 1.0 | 2334 | 3.6667 |
| 3.641 | 2.0 | 4668 | 3.6473 |
| 3.5957 | 3.0 | 7002 | 3.6425 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilgpt2", "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | text-generation | jdengjnj/distilgpt2-finetuned-wikitext2 | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T22:39:02+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6425
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.1+cu118
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
77,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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null | null | transformers |
# ultra0
ultra0 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [starsnatched/MemGPT-2B](https://huggingface.co/starsnatched/MemGPT-2B)
* [liminerity/binarized-ingotrix-slerp-7b](https://huggingface.co/liminerity/binarized-ingotrix-slerp-7b)
## 🧩 Configuration
```yaml
slices:
- sources:
- layer_range: [0, 24]
model: starsnatched/MemGPT-2B
parameters:
density: [1, 0.7, 0.1] # density gradient
weight: 1.0
- layer_range: [0, 24]
model: liminerity/binarized-ingotrix-slerp-7b
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0
merge_method: dare_ties
base_model: starsnatched/MemGPT-2B
parameters:
normalize: true
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "liminerity/ultra0"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "starsnatched/MemGPT-2B", "liminerity/binarized-ingotrix-slerp-7b"], "base_model": ["starsnatched/MemGPT-2B", "liminerity/binarized-ingotrix-slerp-7b"]} | text-generation | liminerity/ultra0 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"starsnatched/MemGPT-2B",
"liminerity/binarized-ingotrix-slerp-7b",
"conversational",
"base_model:starsnatched/MemGPT-2B",
"base_model:liminerity/binarized-ingotrix-slerp-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T22:44:05+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #starsnatched/MemGPT-2B #liminerity/binarized-ingotrix-slerp-7b #conversational #base_model-starsnatched/MemGPT-2B #base_model-liminerity/binarized-ingotrix-slerp-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ultra0
ultra0 is a merge of the following models using LazyMergekit:
* starsnatched/MemGPT-2B
* liminerity/binarized-ingotrix-slerp-7b
## Configuration
## Usage
| [
"# ultra0\n\nultra0 is a merge of the following models using LazyMergekit:\n* starsnatched/MemGPT-2B\n* liminerity/binarized-ingotrix-slerp-7b",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #starsnatched/MemGPT-2B #liminerity/binarized-ingotrix-slerp-7b #conversational #base_model-starsnatched/MemGPT-2B #base_model-liminerity/binarized-ingotrix-slerp-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ultra0\n\nultra0 is a merge of the following models using LazyMergekit:\n* starsnatched/MemGPT-2B\n* liminerity/binarized-ingotrix-slerp-7b",
"## Configuration",
"## Usage"
] | [
136,
47,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #starsnatched/MemGPT-2B #liminerity/binarized-ingotrix-slerp-7b #conversational #base_model-starsnatched/MemGPT-2B #base_model-liminerity/binarized-ingotrix-slerp-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ultra0\n\nultra0 is a merge of the following models using LazyMergekit:\n* starsnatched/MemGPT-2B\n* liminerity/binarized-ingotrix-slerp-7b## Configuration## Usage"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# JP-base-clean-0215
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0988
- Cer: 0.012
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 3125.0
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-----:|
| 5.5004 | 1.0 | 625 | 7.2647 | 1.0 |
| 4.0716 | 2.0 | 1250 | 4.3871 | 1.0 |
| 3.3302 | 3.0 | 1875 | 3.1038 | 1.0 |
| 0.8423 | 4.0 | 2500 | 0.9833 | 0.998 |
| 0.5152 | 5.0 | 3125 | 0.7318 | 0.996 |
| 0.3984 | 6.0 | 3750 | 0.4784 | 0.996 |
| 0.3481 | 7.0 | 4375 | 0.3688 | 0.994 |
| 0.3149 | 8.0 | 5000 | 0.3821 | 0.994 |
| 0.2852 | 9.0 | 5625 | 0.2320 | 0.992 |
| 0.2576 | 10.0 | 6250 | 0.2887 | 0.991 |
| 0.2423 | 11.0 | 6875 | 0.2071 | 0.991 |
| 0.2278 | 12.0 | 7500 | 0.1700 | 0.989 |
| 0.2104 | 13.0 | 8125 | 0.1553 | 0.991 |
| 0.2016 | 14.0 | 8750 | 0.1500 | 0.988 |
| 0.1967 | 15.0 | 9375 | 0.1357 | 0.985 |
| 0.1838 | 16.0 | 10000 | 0.1615 | 0.988 |
| 0.172 | 17.0 | 10625 | 0.1238 | 0.986 |
| 0.1687 | 18.0 | 11250 | 0.1270 | 0.988 |
| 0.1555 | 19.0 | 11875 | 0.1221 | 0.987 |
| 0.1532 | 20.0 | 12500 | 0.1168 | 0.988 |
| 0.1414 | 21.0 | 13125 | 0.1175 | 0.988 |
| 0.1366 | 22.0 | 13750 | 0.1231 | 0.985 |
| 0.1341 | 23.0 | 14375 | 0.1004 | 0.987 |
| 0.1273 | 24.0 | 15000 | 0.1175 | 0.984 |
| 0.1199 | 25.0 | 15625 | 0.1246 | 0.984 |
| 0.1181 | 26.0 | 16250 | 0.1382 | 0.985 |
| 0.1152 | 27.0 | 16875 | 0.1064 | 0.984 |
| 0.1116 | 28.0 | 17500 | 0.1075 | 0.985 |
| 0.1097 | 29.0 | 18125 | 0.1110 | 0.986 |
| 0.1074 | 30.0 | 18750 | 0.1399 | 0.983 |
| 0.0997 | 31.0 | 19375 | 0.1385 | 0.983 |
| 0.0998 | 32.0 | 20000 | 0.1185 | 0.983 |
| 0.0973 | 33.0 | 20625 | 0.1491 | 0.982 |
| 0.0988 | 34.0 | 21250 | 0.1232 | 0.983 |
| 0.0942 | 35.0 | 21875 | 0.1205 | 0.98 |
| 0.0949 | 36.0 | 22500 | 0.1109 | 0.981 |
| 0.0947 | 37.0 | 23125 | 0.1119 | 0.982 |
| 0.0939 | 38.0 | 23750 | 0.1151 | 0.983 |
| 0.0876 | 39.0 | 24375 | 0.1001 | 0.982 |
| 0.0893 | 40.0 | 25000 | 0.0957 | 0.984 |
| 0.0897 | 41.0 | 25625 | 0.0924 | 0.982 |
| 0.0859 | 42.0 | 26250 | 0.0959 | 0.983 |
| 0.0881 | 43.0 | 26875 | 0.0996 | 0.983 |
| 0.0885 | 44.0 | 27500 | 0.0972 | 0.982 |
| 0.0871 | 45.0 | 28125 | 0.0984 | 0.983 |
| 0.0866 | 46.0 | 28750 | 0.0976 | 0.983 |
| 0.0858 | 47.0 | 29375 | 0.0982 | 0.983 |
| 0.0882 | 48.0 | 30000 | 0.0982 | 0.983 |
| 0.0848 | 49.0 | 30625 | 0.0988 | 0.983 |
| 0.0855 | 50.0 | 31250 | 0.0988 | 0.983 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["wer", "cer"], "base_model": "facebook/wav2vec2-base-960h", "model-index": [{"name": "JP-base-clean-0215", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "audiofolder", "type": "audiofolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "wer", "value": 0.983, "name": "Wer"}, {"type": "cer", "value": 0.012, "name": "Cer"}]}]}]} | automatic-speech-recognition | SiRoZaRuPa/JP-base-clean-0215 | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:audiofolder",
"base_model:facebook/wav2vec2-base-960h",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-14T22:45:12+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base-960h #license-apache-2.0 #model-index #endpoints_compatible #region-us
| JP-base-clean-0215
==================
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the audiofolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0988
* Cer: 0.012
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 3125.0
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 3125.0\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 3125.0\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
85,
118,
4,
30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base-960h #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 3125.0\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2278
- Accuracy: 0.9319
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2253 | 1.0 | 1563 | 0.2665 | 0.9045 |
| 0.1518 | 2.0 | 3126 | 0.2278 | 0.9319 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "my_awesome_model", "results": []}]} | text-classification | bitdribble/my_awesome_model | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T22:45:30+00:00 | [] | [] | TAGS
#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| my\_awesome\_model
==================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2278
* Accuracy: 0.9319
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
68,
98,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: upstage/SOLAR-10.7B-v1.0
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true
trust_remote_code: true
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: Drewskidang/share
type: sharegpt
- path: Drewskidang/shareinstruct
type: sharegpt
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 8
num_epochs: 5
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true
warmup_steps: 100
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: #deepspeed_configs/zero2.json # multi-gpu only
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
# out
This model is a fine-tuned version of [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5945
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3027 | 0.1 | 1 | 1.3229 |
| 1.2121 | 0.29 | 3 | 1.1924 |
| 1.0946 | 0.57 | 6 | 1.1220 |
| 1.0204 | 0.86 | 9 | 1.0590 |
| 0.8295 | 1.1 | 12 | 1.0489 |
| 0.8185 | 1.38 | 15 | 1.0353 |
| 0.7114 | 1.67 | 18 | 1.0193 |
| 0.7053 | 1.95 | 21 | 1.0263 |
| 0.3707 | 2.19 | 24 | 1.1041 |
| 0.339 | 2.48 | 27 | 1.1846 |
| 0.3449 | 2.76 | 30 | 1.1659 |
| 0.3301 | 3.05 | 33 | 1.1935 |
| 0.148 | 3.29 | 36 | 1.4335 |
| 0.1682 | 3.57 | 39 | 1.3037 |
| 0.1855 | 3.86 | 42 | 1.3231 |
| 0.0857 | 4.1 | 45 | 1.4003 |
| 0.113 | 4.38 | 48 | 1.5945 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.17.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "upstage/SOLAR-10.7B-v1.0", "model-index": [{"name": "out", "results": []}]} | text-generation | Drewskidang/up | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:upstage/SOLAR-10.7B-v1.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T22:45:57+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-upstage/SOLAR-10.7B-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
out
===
This model is a fine-tuned version of upstage/SOLAR-10.7B-v1.0 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5945
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 128
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.38.0.dev0
* Pytorch 2.1.1+cu121
* Datasets 2.17.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.1+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-upstage/SOLAR-10.7B-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.1+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.0"
] | [
79,
178,
4,
38
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-upstage/SOLAR-10.7B-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.1+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.0"
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "285.84 +/- 16.47", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | jjjurgen/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-14T22:48:58+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers |
# komt : korean multi task instruction tuning model
Recently, due to the success of ChatGPT, numerous large language models have emerged in an attempt to catch up with ChatGPT's capabilities.
However, when it comes to Korean language performance, it has been observed that many models still struggle to provide accurate answers or generate Korean text effectively.
This study addresses these challenges by introducing a multi-task instruction technique that leverages supervised datasets from various tasks to create training data for Large Language Models (LLMs).
## Model Details
LDCC/LDCC-SOLAR-10.7B 모델을 base로 komt 데이터셋으로 sft학습한 모델입니다.
현재 최종 완료버전의 모델은 아니며, 다양한 데이터셋으로 성능 튜닝중입니다.
* **Model Developers** : davidkim(changyeon kim)
* **Repository** : https://github.com/davidkim205/komt
* **base mode** : LDCC/LDCC-SOLAR-10.7B
| {"language": ["ko"], "license": "cc-by-4.0"} | text-generation | davidkim205/komt-solar-10.7b-sft-v1 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ko",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T22:49:51+00:00 | [] | [
"ko"
] | TAGS
#transformers #safetensors #llama #text-generation #ko #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# komt : korean multi task instruction tuning model
!multi task instruction URL
Recently, due to the success of ChatGPT, numerous large language models have emerged in an attempt to catch up with ChatGPT's capabilities.
However, when it comes to Korean language performance, it has been observed that many models still struggle to provide accurate answers or generate Korean text effectively.
This study addresses these challenges by introducing a multi-task instruction technique that leverages supervised datasets from various tasks to create training data for Large Language Models (LLMs).
## Model Details
LDCC/LDCC-SOLAR-10.7B 모델을 base로 komt 데이터셋으로 sft학습한 모델입니다.
현재 최종 완료버전의 모델은 아니며, 다양한 데이터셋으로 성능 튜닝중입니다.
* Model Developers : davidkim(changyeon kim)
* Repository : URL
* base mode : LDCC/LDCC-SOLAR-10.7B
| [
"# komt : korean multi task instruction tuning model\n!multi task instruction URL\n\nRecently, due to the success of ChatGPT, numerous large language models have emerged in an attempt to catch up with ChatGPT's capabilities. \nHowever, when it comes to Korean language performance, it has been observed that many models still struggle to provide accurate answers or generate Korean text effectively. \nThis study addresses these challenges by introducing a multi-task instruction technique that leverages supervised datasets from various tasks to create training data for Large Language Models (LLMs).",
"## Model Details\n\nLDCC/LDCC-SOLAR-10.7B 모델을 base로 komt 데이터셋으로 sft학습한 모델입니다. \n현재 최종 완료버전의 모델은 아니며, 다양한 데이터셋으로 성능 튜닝중입니다. \n\n* Model Developers : davidkim(changyeon kim)\n* Repository : URL\n* base mode : LDCC/LDCC-SOLAR-10.7B"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #ko #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# komt : korean multi task instruction tuning model\n!multi task instruction URL\n\nRecently, due to the success of ChatGPT, numerous large language models have emerged in an attempt to catch up with ChatGPT's capabilities. \nHowever, when it comes to Korean language performance, it has been observed that many models still struggle to provide accurate answers or generate Korean text effectively. \nThis study addresses these challenges by introducing a multi-task instruction technique that leverages supervised datasets from various tasks to create training data for Large Language Models (LLMs).",
"## Model Details\n\nLDCC/LDCC-SOLAR-10.7B 모델을 base로 komt 데이터셋으로 sft학습한 모델입니다. \n현재 최종 완료버전의 모델은 아니며, 다양한 데이터셋으로 성능 튜닝중입니다. \n\n* Model Developers : davidkim(changyeon kim)\n* Repository : URL\n* base mode : LDCC/LDCC-SOLAR-10.7B"
] | [
58,
127,
90
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #ko #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# komt : korean multi task instruction tuning model\n!multi task instruction URL\n\nRecently, due to the success of ChatGPT, numerous large language models have emerged in an attempt to catch up with ChatGPT's capabilities. \nHowever, when it comes to Korean language performance, it has been observed that many models still struggle to provide accurate answers or generate Korean text effectively. \nThis study addresses these challenges by introducing a multi-task instruction technique that leverages supervised datasets from various tasks to create training data for Large Language Models (LLMs).## Model Details\n\nLDCC/LDCC-SOLAR-10.7B 모델을 base로 komt 데이터셋으로 sft학습한 모델입니다. \n현재 최종 완료버전의 모델은 아니며, 다양한 데이터셋으로 성능 튜닝중입니다. \n\n* Model Developers : davidkim(changyeon kim)\n* Repository : URL\n* base mode : LDCC/LDCC-SOLAR-10.7B"
] | [
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null | null | transformers |
# Proximus-4x7B-v1
Proximus-4x7B-v1 is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210)
* [beowolx/CodeNinja-1.0-OpenChat-7B](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B)
* [openvoid/Prox-1.0-OpenChat-7B](https://huggingface.co/openvoid/Prox-1.0-OpenChat-7B)
* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
## 🧩 Configuration
```yaml
base_model: mlabonne/Marcoro14-7B-slerp
experts:
- source_model: openchat/openchat-3.5-1210
positive_prompts:
- "chat"
- "assistant"
- "tell me"
- "explain"
- source_model: beowolx/CodeNinja-1.0-OpenChat-7B
positive_prompts:
- "code"
- "python"
- "javascript"
- "programming"
- "algorithm"
- source_model: openvoid/Prox-1.0-OpenChat-7B
positive_prompts:
- "cybersecurity"
- "information security"
- "network security"
- "penetration testing"
- "hacking"
- "encryption"
- "password cracking"
- "cryptography"
- "privacy"
- source_model: WizardLM/WizardMath-7B-V1.1
positive_prompts:
- "reason"
- "math"
- "mathematics"
- "solve"
- "count"
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "openvoid/Proximus-4x7B-v1"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["moe", "merge", "mergekit", "lazymergekit", "openchat/openchat-3.5-1210", "beowolx/CodeNinja-1.0-OpenChat-7B", "openvoid/Prox-1.0-OpenChat-7B", "WizardLM/WizardMath-7B-V1.1"]} | text-generation | openvoid/Proximus-4x7B-v1 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"merge",
"mergekit",
"lazymergekit",
"openchat/openchat-3.5-1210",
"beowolx/CodeNinja-1.0-OpenChat-7B",
"openvoid/Prox-1.0-OpenChat-7B",
"WizardLM/WizardMath-7B-V1.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T22:51:58+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #moe #merge #mergekit #lazymergekit #openchat/openchat-3.5-1210 #beowolx/CodeNinja-1.0-OpenChat-7B #openvoid/Prox-1.0-OpenChat-7B #WizardLM/WizardMath-7B-V1.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Proximus-4x7B-v1
Proximus-4x7B-v1 is a Mixure of Experts (MoE) made with the following models using LazyMergekit:
* openchat/openchat-3.5-1210
* beowolx/CodeNinja-1.0-OpenChat-7B
* openvoid/Prox-1.0-OpenChat-7B
* WizardLM/WizardMath-7B-V1.1
## Configuration
## Usage
| [
"# Proximus-4x7B-v1\n\nProximus-4x7B-v1 is a Mixure of Experts (MoE) made with the following models using LazyMergekit:\n* openchat/openchat-3.5-1210\n* beowolx/CodeNinja-1.0-OpenChat-7B\n* openvoid/Prox-1.0-OpenChat-7B\n* WizardLM/WizardMath-7B-V1.1",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #moe #merge #mergekit #lazymergekit #openchat/openchat-3.5-1210 #beowolx/CodeNinja-1.0-OpenChat-7B #openvoid/Prox-1.0-OpenChat-7B #WizardLM/WizardMath-7B-V1.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Proximus-4x7B-v1\n\nProximus-4x7B-v1 is a Mixure of Experts (MoE) made with the following models using LazyMergekit:\n* openchat/openchat-3.5-1210\n* beowolx/CodeNinja-1.0-OpenChat-7B\n* openvoid/Prox-1.0-OpenChat-7B\n* WizardLM/WizardMath-7B-V1.1",
"## Configuration",
"## Usage"
] | [
126,
97,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #moe #merge #mergekit #lazymergekit #openchat/openchat-3.5-1210 #beowolx/CodeNinja-1.0-OpenChat-7B #openvoid/Prox-1.0-OpenChat-7B #WizardLM/WizardMath-7B-V1.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Proximus-4x7B-v1\n\nProximus-4x7B-v1 is a Mixure of Experts (MoE) made with the following models using LazyMergekit:\n* openchat/openchat-3.5-1210\n* beowolx/CodeNinja-1.0-OpenChat-7B\n* openvoid/Prox-1.0-OpenChat-7B\n* WizardLM/WizardMath-7B-V1.1## Configuration## Usage"
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# phi-2
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.2 | {"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "phi-2", "results": []}]} | null | lvcalucioli/phi-2 | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
] | 2024-02-14T22:53:52+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us
|
# phi-2
This model is a fine-tuned version of microsoft/phi-2 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.2 | [
"# phi-2\n\nThis model is a fine-tuned version of microsoft/phi-2 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 10\n- total_train_batch_size: 40\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.0.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.2"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n",
"# phi-2\n\nThis model is a fine-tuned version of microsoft/phi-2 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 10\n- total_train_batch_size: 40\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.0.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.2"
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44
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"passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n# phi-2\n\nThis model is a fine-tuned version of microsoft/phi-2 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 10\n- total_train_batch_size: 40\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.0.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.2"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8261
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0837 | 1.0 | 2406 | 1.9279 |
| 1.9954 | 2.0 | 4812 | 1.8674 |
| 1.9351 | 3.0 | 7218 | 1.8543 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilroberta-base", "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]} | fill-mask | jdengjnj/distilroberta-base-finetuned-wikitext2 | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T22:59:17+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #fill-mask #generated_from_trainer #base_model-distilroberta-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-finetuned-wikitext2
======================================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8261
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.1+cu118
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
68,
98,
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] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #roberta #fill-mask #generated_from_trainer #base_model-distilroberta-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | SeifGad/Qwen-0.5B-Enhanced | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T22:59:52+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #qwen2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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[optional]
BibTeX:
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
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"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga km22 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga km22 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga km22
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "5.00 +/- 7.07", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | km22/dqn-SpaceInvadersNoFrameskip-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-14T23:04:32+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: URL
SB3: URL
SB3 Contrib: URL
Install the RL Zoo (with SB3 and SB3-Contrib):
If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:
## Training (with the RL Zoo)
## Hyperparameters
# Environment Arguments
| [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
43,
90,
73,
9,
5,
7
] | [
"passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments"
] | [
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null | null | transformers |
This model is a finetune of jondurbin's excellent [bagel](https://huggingface.co/jondurbin/bagel-34b-v0.2) model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
This model has not utilised any form of merging.
### Evaluation Results
| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| --- | --- | --- | --- | --- | --- | --- |
| 77.29 | 74.23 | 86.76 | 76.66 | 70.22 | 83.66 | 72.18 |
### Contamination Results
With reference model jondurbin/bagel-34b-v0.2:
| ARC | TruthfulQA | GSM8K |
| --- | --- | --- |
| 0.08| 0.38| 0.88| | {"license": "other", "license_name": "yi-license", "license_link": "https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE", "base_model": "jondurbin/bagel-34b-v0.2"} | text-generation | LoneStriker/Smaug-34B-v0.1-GPTQ | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:jondurbin/bagel-34b-v0.2",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T23:09:27+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #base_model-jondurbin/bagel-34b-v0.2 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| !image/png
!image/png
This model is a finetune of jondurbin's excellent bagel model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
This model has not utilised any form of merging.
### Evaluation Results
### Contamination Results
With reference model jondurbin/bagel-34b-v0.2:
ARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88
| [
"### Evaluation Results",
"### Contamination Results\n\n\nWith reference model jondurbin/bagel-34b-v0.2:\n\n\nARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #base_model-jondurbin/bagel-34b-v0.2 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Evaluation Results",
"### Contamination Results\n\n\nWith reference model jondurbin/bagel-34b-v0.2:\n\n\nARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88"
] | [
72,
5,
40
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #base_model-jondurbin/bagel-34b-v0.2 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Evaluation Results### Contamination Results\n\n\nWith reference model jondurbin/bagel-34b-v0.2:\n\n\nARC: 0.08, TruthfulQA: 0.38, GSM8K: 0.88"
] | [
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null | null | null |
# Lora of aquila/アクィラ/天鹰 (Azur Lane)
## What Is This?
This is the LoRA model of waifu aquila/アクィラ/天鹰 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/aquila_azurlane](https://huggingface.co/datasets/CyberHarem/aquila_azurlane), which contains 326 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 11, resolution is 720x720, clustering into 20 buckets.
* Trained for 3280 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `aquila_azurlane`.**
* Pruned core tags for this waifu are `breasts, long_hair, green_eyes, large_breasts, grey_hair, very_long_hair, hat, white_headwear, braid, hair_between_eyes, sun_hat, bangs, single_braid`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 738, you need to download [`738/aquila_azurlane.pt`](https://huggingface.co/CyberHarem/aquila_azurlane/resolve/main/738/aquila_azurlane.pt) as the embedding and [`738/aquila_azurlane.safetensors`](https://huggingface.co/CyberHarem/aquila_azurlane/resolve/main/738/aquila_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 738.
1640 images (1.75 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | pattern_0_2 | pattern_1 | pattern_2 | pattern_3 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:----------------------------------------------------------------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:--------------------------------|:------------------------------------|:--------------------------------|:----------------------------------|:----------------------------------------|:----------------------------------------|:----------------------------------------|:------------------------------|:----------------------------------|:----------------------------------|:--------------------------------|:------------------------------------------------|:----------------------------------|:----------------------------------|:------------------------------|:--------------------------------|:--------------------------------------|:--------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------|:--------------------------------------|
| 738 | 10 | **0.981** | **0.988** | 0.842 | **0.721** | [Download](https://huggingface.co/CyberHarem/aquila_azurlane/resolve/main/738/aquila_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 902 | 12 | 0.974 | 0.982 | 0.842 | 0.711 | [Download](https://huggingface.co/CyberHarem/aquila_azurlane/resolve/main/902/aquila_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 820 | 11 | 0.971 | 0.985 | **0.845** | 0.709 | [Download](https://huggingface.co/CyberHarem/aquila_azurlane/resolve/main/820/aquila_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1394 | 18 | 0.968 | 0.970 | 0.845 | 0.704 | [Download](https://huggingface.co/CyberHarem/aquila_azurlane/resolve/main/1394/aquila_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1148 | 15 | 0.972 | 0.974 | 0.839 | 0.704 | [Download](https://huggingface.co/CyberHarem/aquila_azurlane/resolve/main/1148/aquila_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 2542 to 3280](all/0.md)
* [Steps From 1722 to 2460](all/1.md)
* [Steps From 902 to 1640](all/2.md)
* [Steps From 82 to 820](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/aquila_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/aquila_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/aquila_azurlane",
"license:mit",
"region:us"
] | 2024-02-14T23:15:47+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/aquila_azurlane #license-mit #region-us
| Lora of aquila/アクィラ/天鹰 (Azur Lane)
==================================
What Is This?
-------------
This is the LoRA model of waifu aquila/アクィラ/天鹰 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/aquila\_azurlane, which contains 326 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 11, resolution is 720x720, clustering into 20 buckets.
* Trained for 3280 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'aquila\_azurlane'.
* Pruned core tags for this waifu are 'breasts, long\_hair, green\_eyes, large\_breasts, grey\_hair, very\_long\_hair, hat, white\_headwear, braid, hair\_between\_eyes, sun\_hat, bangs, single\_braid'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 738, you need to download '738/aquila\_azurlane.pt' as the embedding and '738/aquila\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 738.
1640 images (1.75 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 2542 to 3280
* Steps From 1722 to 2460
* Steps From 902 to 1640
* Steps From 82 to 820
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 738, you need to download '738/aquila\\_azurlane.pt' as the embedding and '738/aquila\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 738.\n\n\n1640 images (1.75 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 2542 to 3280\n* Steps From 1722 to 2460\n* Steps From 902 to 1640\n* Steps From 82 to 820"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/aquila_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 738, you need to download '738/aquila\\_azurlane.pt' as the embedding and '738/aquila\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 738.\n\n\n1640 images (1.75 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 2542 to 3280\n* Steps From 1722 to 2460\n* Steps From 902 to 1640\n* Steps From 82 to 820"
] | [
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"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/aquila_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
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] |
null | null | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.7.3,<3.8.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (17 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `Degree`, `Desc Responsibility`, `Edu Desc`, `Edu End Date`, `Edu Start Date`, `Email`, `Employer Names`, `Institution`, `Links`, `Location`, `Name`, `Phone`, `Position`, `Skills`, `Work End Date`, `Work Location`, `Work Start Date` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 83.33 |
| `ENTS_P` | 81.16 |
| `ENTS_R` | 85.63 |
| `TRANSFORMER_LOSS` | 39026.84 |
| `NER_LOSS` | 1290990.48 | | {"language": ["en"], "tags": ["spacy", "token-classification"]} | token-classification | Apsd1109/en_pipeline | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | 2024-02-14T23:16:33+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
|
### Label Scheme
View label scheme (17 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (17 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (17 labels for 1 components)",
"### Accuracy"
] | [
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16,
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null | null | transformers | {"language": ["en"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | KomeijiForce/inbedder-roberta-large | [
"transformers",
"safetensors",
"roberta",
"fill-mask",
"sentence-similarity",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T23:36:19+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #roberta #fill-mask #sentence-similarity #en #autotrain_compatible #endpoints_compatible #region-us
| [] | [
"TAGS\n#transformers #safetensors #roberta #fill-mask #sentence-similarity #en #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #safetensors #roberta #fill-mask #sentence-similarity #en #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers |
# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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| {"library_name": "transformers", "tags": []} | text-generation | kaushalpowar/backup | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
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#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
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BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
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null | null | null |
# Lora of akatsuki/暁/晓 (Azur Lane)
## What Is This?
This is the LoRA model of waifu akatsuki/暁/晓 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/akatsuki_azurlane](https://huggingface.co/datasets/CyberHarem/akatsuki_azurlane), which contains 40 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `akatsuki_azurlane`.**
* Pruned core tags for this waifu are `black_hair, long_hair, ponytail, bangs, red_eyes, hair_between_eyes, breasts, eyepatch, high_ponytail, horns`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 320, you need to download [`320/akatsuki_azurlane.pt`](https://huggingface.co/CyberHarem/akatsuki_azurlane/resolve/main/320/akatsuki_azurlane.pt) as the embedding and [`320/akatsuki_azurlane.safetensors`](https://huggingface.co/CyberHarem/akatsuki_azurlane/resolve/main/320/akatsuki_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 320.
1480 images (1.55 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:-------------------------------------------------------------------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-------------------------------|:-----------------------------------|:-------------------------------|:---------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:-----------------------------|:---------------------------------|:---------------------------------|:-------------------------------|:-----------------------------------------------|:---------------------------------|:---------------------------------|:-----------------------------|:-------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------|:-------------------------------------|
| 320 | 32 | **0.833** | 0.907 | 0.838 | **0.754** | [Download](https://huggingface.co/CyberHarem/akatsuki_azurlane/resolve/main/320/akatsuki_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 360 | 36 | 0.759 | 0.924 | 0.846 | 0.708 | [Download](https://huggingface.co/CyberHarem/akatsuki_azurlane/resolve/main/360/akatsuki_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 440 | 44 | 0.761 | 0.925 | 0.844 | 0.708 | [Download](https://huggingface.co/CyberHarem/akatsuki_azurlane/resolve/main/440/akatsuki_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 420 | 42 | 0.694 | 0.921 | **0.848** | 0.656 | [Download](https://huggingface.co/CyberHarem/akatsuki_azurlane/resolve/main/420/akatsuki_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 300 | 30 | 0.671 | **0.929** | 0.834 | 0.620 | [Download](https://huggingface.co/CyberHarem/akatsuki_azurlane/resolve/main/300/akatsuki_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 620 to 800](all/0.md)
* [Steps From 420 to 600](all/1.md)
* [Steps From 220 to 400](all/2.md)
* [Steps From 20 to 200](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/akatsuki_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/akatsuki_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/akatsuki_azurlane",
"license:mit",
"region:us"
] | 2024-02-14T23:38:58+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/akatsuki_azurlane #license-mit #region-us
| Lora of akatsuki/暁/晓 (Azur Lane)
================================
What Is This?
-------------
This is the LoRA model of waifu akatsuki/暁/晓 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/akatsuki\_azurlane, which contains 40 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'akatsuki\_azurlane'.
* Pruned core tags for this waifu are 'black\_hair, long\_hair, ponytail, bangs, red\_eyes, hair\_between\_eyes, breasts, eyepatch, high\_ponytail, horns'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 320, you need to download '320/akatsuki\_azurlane.pt' as the embedding and '320/akatsuki\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 320.
1480 images (1.55 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 620 to 800
* Steps From 420 to 600
* Steps From 220 to 400
* Steps From 20 to 200
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 320, you need to download '320/akatsuki\\_azurlane.pt' as the embedding and '320/akatsuki\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 320.\n\n\n1480 images (1.55 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/akatsuki_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 320, you need to download '320/akatsuki\\_azurlane.pt' as the embedding and '320/akatsuki\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 320.\n\n\n1480 images (1.55 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
45,
38,
467
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"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/akatsuki_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
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] |
null | null | peft |
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- PEFT 0.8.2 | {"library_name": "peft", "base_model": "mistralai/Mixtral-8x7B-Instruct-v0.1"} | null | hs4jk24erfc/test | [
"peft",
"safetensors",
"mixtral",
"arxiv:1910.09700",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"region:us"
] | 2024-02-14T23:41:43+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #mixtral #arxiv-1910.09700 #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #region-us
|
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- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
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APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: upstage/SOLAR-10.7B-v1.0
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true
trust_remote_code: true
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: Drewskidang/share
type: sharegpt
split: train
conversation: chatml
- path: Drewskidang/shareinstruct
type: sharegpt
split: train
conversation: chatml
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./chatml
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 12
num_epochs: 5
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true
warmup_steps: 100
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: #deepspeed_configs/zero2.json # multi-gpu only
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
tokens: # these are delimiters
- "<|im_start|>"
- "<|im_end|>"
```
</details><br>
# chatml
This model is a fine-tuned version of [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1262
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 384
- total_eval_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4068 | 0.31 | 1 | 1.4339 |
| 1.4144 | 0.62 | 2 | 1.3014 |
| 1.2489 | 0.92 | 3 | 1.2079 |
| 1.1695 | 1.15 | 4 | 1.2505 |
| 1.2222 | 1.46 | 5 | 1.2246 |
| 1.1716 | 1.77 | 6 | 1.5272 |
| 1.4521 | 2.08 | 7 | 1.2458 |
| 1.1637 | 2.31 | 8 | 1.2050 |
| 1.118 | 2.62 | 9 | 1.3758 |
| 1.2671 | 2.92 | 10 | 1.2687 |
| 1.1615 | 3.15 | 11 | 1.1509 |
| 0.9979 | 3.46 | 12 | 1.2254 |
| 1.0704 | 3.77 | 13 | 1.1332 |
| 0.9714 | 4.08 | 14 | 1.1505 |
| 0.9191 | 4.31 | 15 | 1.1262 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.17.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "upstage/SOLAR-10.7B-v1.0", "model-index": [{"name": "chatml", "results": []}]} | text-generation | Drewskidang/up2chatml | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:upstage/SOLAR-10.7B-v1.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T23:43:40+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-upstage/SOLAR-10.7B-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
chatml
======
This model is a fine-tuned version of upstage/SOLAR-10.7B-v1.0 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1262
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 384
* total\_eval\_batch\_size: 96
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.38.0.dev0
* Pytorch 2.1.2+cu121
* Datasets 2.17.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 384\n* total\\_eval\\_batch\\_size: 96\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-upstage/SOLAR-10.7B-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 384\n* total\\_eval\\_batch\\_size: 96\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.0"
] | [
79,
179,
4,
38
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-upstage/SOLAR-10.7B-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 384\n* total\\_eval\\_batch\\_size: 96\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.0"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "meta-llama/Llama-2-13b-chat-hf"} | null | salem12/llama2-chat-7b-EC | [
"peft",
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#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-13b-chat-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
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APA:
## Glossary [optional]
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null | null | transformers | I cut my TinyLlama 1.1B cinder v 2 down from 22 layers to 14. At 14 there was no coherent text but there were emerging ideas of a response. 1000 steps on step-by-step dataset.
6000 on Reason-with-cinder. The loss was still over 1 and the learning rate was still over 4. This model needs significat training. I am putting it up as a base model that
needs work. If you continue training please let me know on the tinyllama discord, I have some interesting plans for this model. | {"license": "mit", "widget": [{"text": "<|system|>\nYou are a helpful assistant</s>\n<|user|>\nWhat is your name? Tell me about yourself.</s>\n<|assistant|>"}]} | text-generation | Josephgflowers/tinyllama-730M-test | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T23:49:19+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| I cut my TinyLlama 1.1B cinder v 2 down from 22 layers to 14. At 14 there was no coherent text but there were emerging ideas of a response. 1000 steps on step-by-step dataset.
6000 on Reason-with-cinder. The loss was still over 1 and the learning rate was still over 4. This model needs significat training. I am putting it up as a base model that
needs work. If you continue training please let me know on the tinyllama discord, I have some interesting plans for this model. | [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
56
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lab1_finetuning
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["kde4"], "base_model": "Helsinki-NLP/opus-mt-en-fr", "model-index": [{"name": "lab1_finetuning", "results": []}]} | text2text-generation | Vienne/lab1_finetuning | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-14T23:51:00+00:00 | [] | [] | TAGS
#transformers #safetensors #marian #text2text-generation #generated_from_trainer #dataset-kde4 #base_model-Helsinki-NLP/opus-mt-en-fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# lab1_finetuning
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# lab1_finetuning\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #marian #text2text-generation #generated_from_trainer #dataset-kde4 #base_model-Helsinki-NLP/opus-mt-en-fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# lab1_finetuning\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
83,
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"passage: TAGS\n#transformers #safetensors #marian #text2text-generation #generated_from_trainer #dataset-kde4 #base_model-Helsinki-NLP/opus-mt-en-fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# lab1_finetuning\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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null | null | transformers | # Model Card for Eclipse-7B
Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper [Self-Rewarding Language Models](https://arxiv.org/abs/2401.10020).
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Xenon1/Eclipse-7B")
tokenizer = AutoTokenizer.from_pretrained("Xenon1/Eclipse-7B")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer | {"language": ["en"], "license": "apache-2.0", "tags": ["mistral", "Eclipse-7B"], "pipeline_tag": "text-generation"} | text-generation | Xenon1/Eclipse-13B | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"mistral",
"Eclipse-7B",
"en",
"arxiv:2401.10020",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-14T23:51:03+00:00 | [
"2401.10020"
] | [
"en"
] | TAGS
#transformers #safetensors #mixtral #text-generation #mistral #Eclipse-7B #en #arxiv-2401.10020 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Model Card for Eclipse-7B
Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.
!image/png
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
This format is available as a chat template via the 'apply_chat_template()' method:
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer | [
"# Model Card for Eclipse-7B\n\nMistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.\n\n\n!image/png",
"## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:",
"## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #mistral #Eclipse-7B #en #arxiv-2401.10020 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Eclipse-7B\n\nMistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.\n\n\n!image/png",
"## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:",
"## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer"
] | [
75,
47,
105,
56
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #mistral #Eclipse-7B #en #arxiv-2401.10020 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Eclipse-7B\n\nMistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.\n\n\n!image/png## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer"
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null | null | null |
# Lora of guam/グアム/关岛 (Azur Lane)
## What Is This?
This is the LoRA model of waifu guam/グアム/关岛 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/guam_azurlane](https://huggingface.co/datasets/CyberHarem/guam_azurlane), which contains 147 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 1480 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `guam_azurlane`.**
* Pruned core tags for this waifu are `blonde_hair, breasts, long_hair, large_breasts, bangs, blue_eyes, very_long_hair, twintails, symbol-shaped_pupils, animal_ears, hat, purple_eyes, rabbit_ears, hair_ornament, hair_between_eyes`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 629, you need to download [`629/guam_azurlane.pt`](https://huggingface.co/CyberHarem/guam_azurlane/resolve/main/629/guam_azurlane.pt) as the embedding and [`629/guam_azurlane.safetensors`](https://huggingface.co/CyberHarem/guam_azurlane/resolve/main/629/guam_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 629.
1520 images (1.65 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0 | pattern_1 | pattern_2 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:------------------------------------------------------------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:--------------------------------|:------------------------------------|:--------------------------------|:----------------------------------|:----------------------------------------|:----------------------------------------|:----------------------------------------|:------------------------------|:----------------------------------|:----------------------------------|:--------------------------------|:------------------------------------------------|:----------------------------------|:----------------------------------|:------------------------------|:--------------------------------|:--------------------------------------|:--------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------|:--------------------------------------|
| 629 | 18 | **0.939** | **0.972** | **0.851** | **0.790** | [Download](https://huggingface.co/CyberHarem/guam_azurlane/resolve/main/629/guam_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 444 | 13 | 0.934 | 0.964 | 0.846 | 0.777 | [Download](https://huggingface.co/CyberHarem/guam_azurlane/resolve/main/444/guam_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 407 | 12 | 0.934 | 0.912 | 0.839 | 0.768 | [Download](https://huggingface.co/CyberHarem/guam_azurlane/resolve/main/407/guam_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1147 | 32 | 0.915 | 0.918 | 0.849 | 0.762 | [Download](https://huggingface.co/CyberHarem/guam_azurlane/resolve/main/1147/guam_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 666 | 19 | 0.897 | 0.934 | 0.848 | 0.742 | [Download](https://huggingface.co/CyberHarem/guam_azurlane/resolve/main/666/guam_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 1147 to 1480](all/0.md)
* [Steps From 777 to 1110](all/1.md)
* [Steps From 407 to 740](all/2.md)
* [Steps From 37 to 370](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/guam_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/guam_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/guam_azurlane",
"license:mit",
"region:us"
] | 2024-02-14T23:55:30+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/guam_azurlane #license-mit #region-us
| Lora of guam/グアム/关岛 (Azur Lane)
===============================
What Is This?
-------------
This is the LoRA model of waifu guam/グアム/关岛 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/guam\_azurlane, which contains 147 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 1480 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'guam\_azurlane'.
* Pruned core tags for this waifu are 'blonde\_hair, breasts, long\_hair, large\_breasts, bangs, blue\_eyes, very\_long\_hair, twintails, symbol-shaped\_pupils, animal\_ears, hat, purple\_eyes, rabbit\_ears, hair\_ornament, hair\_between\_eyes'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 629, you need to download '629/guam\_azurlane.pt' as the embedding and '629/guam\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 629.
1520 images (1.65 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 1147 to 1480
* Steps From 777 to 1110
* Steps From 407 to 740
* Steps From 37 to 370
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 629, you need to download '629/guam\\_azurlane.pt' as the embedding and '629/guam\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 629.\n\n\n1520 images (1.65 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 1147 to 1480\n* Steps From 777 to 1110\n* Steps From 407 to 740\n* Steps From 37 to 370"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/guam_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 629, you need to download '629/guam\\_azurlane.pt' as the embedding and '629/guam\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 629.\n\n\n1520 images (1.65 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 1147 to 1480\n* Steps From 777 to 1110\n* Steps From 407 to 740\n* Steps From 37 to 370"
] | [
44,
38,
471
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/guam_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
] | [
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] |
null | null | null |
# Lora of aoba/青葉/青叶 (Azur Lane)
## What Is This?
This is the LoRA model of waifu aoba/青葉/青叶 (Azur Lane).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/aoba_azurlane](https://huggingface.co/datasets/CyberHarem/aoba_azurlane), which contains 19 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `aoba_azurlane`.**
* Pruned core tags for this waifu are `long_hair, bangs, aqua_hair, breasts, brown_eyes, animal_ears, twintails, medium_breasts, blue_hair, earrings, hair_between_eyes, large_breasts`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 520, you need to download [`520/aoba_azurlane.pt`](https://huggingface.co/CyberHarem/aoba_azurlane/resolve/main/520/aoba_azurlane.pt) as the embedding and [`520/aoba_azurlane.safetensors`](https://huggingface.co/CyberHarem/aoba_azurlane/resolve/main/520/aoba_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 520.
1480 images (1.39 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:-----------------------------------------------------------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-------------------------------|:-----------------------------------|:-------------------------------|:---------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:-----------------------------|:---------------------------------|:---------------------------------|:-------------------------------|:-----------------------------------------------|:---------------------------------|:---------------------------------|:-----------------------------|:-------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------|:-------------------------------------|
| 520 | 110 | **0.878** | **0.844** | 0.833 | **0.790** | [Download](https://huggingface.co/CyberHarem/aoba_azurlane/resolve/main/520/aoba_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 380 | 80 | 0.841 | 0.834 | **0.841** | 0.772 | [Download](https://huggingface.co/CyberHarem/aoba_azurlane/resolve/main/380/aoba_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 420 | 89 | 0.841 | 0.816 | 0.829 | 0.760 | [Download](https://huggingface.co/CyberHarem/aoba_azurlane/resolve/main/420/aoba_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 720 | 152 | 0.845 | 0.811 | 0.814 | 0.740 | [Download](https://huggingface.co/CyberHarem/aoba_azurlane/resolve/main/720/aoba_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 740 | 156 | 0.836 | 0.808 | 0.815 | 0.736 | [Download](https://huggingface.co/CyberHarem/aoba_azurlane/resolve/main/740/aoba_azurlane.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 620 to 800](all/0.md)
* [Steps From 420 to 600](all/1.md)
* [Steps From 220 to 400](all/2.md)
* [Steps From 20 to 200](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/aoba_azurlane"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/aoba_azurlane | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/aoba_azurlane",
"license:mit",
"region:us"
] | 2024-02-14T23:57:09+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/aoba_azurlane #license-mit #region-us
| Lora of aoba/青葉/青叶 (Azur Lane)
==============================
What Is This?
-------------
This is the LoRA model of waifu aoba/青葉/青叶 (Azur Lane).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/aoba\_azurlane, which contains 19 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 800 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'aoba\_azurlane'.
* Pruned core tags for this waifu are 'long\_hair, bangs, aqua\_hair, breasts, brown\_eyes, animal\_ears, twintails, medium\_breasts, blue\_hair, earrings, hair\_between\_eyes, large\_breasts'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 520, you need to download '520/aoba\_azurlane.pt' as the embedding and '520/aoba\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 520.
1480 images (1.39 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 620 to 800
* Steps From 420 to 600
* Steps From 220 to 400
* Steps From 20 to 200
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 520, you need to download '520/aoba\\_azurlane.pt' as the embedding and '520/aoba\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 520.\n\n\n1480 images (1.39 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/aoba_azurlane #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 520, you need to download '520/aoba\\_azurlane.pt' as the embedding and '520/aoba\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 520.\n\n\n1480 images (1.39 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200"
] | [
44,
38,
465
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/aoba_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
] | [
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null | null | diffusers |
# DreamBooth trained by AutoTrain
Text encoder was not trained.
| {"tags": ["text-to-image", "diffusers", "autotrain"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "<Kyra>", "inference": true} | text-to-image | madmarc/autotrain-4vgu7-qftz7 | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"has_space",
"region:us"
] | 2024-02-14T23:57:18+00:00 | [] | [] | TAGS
#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us
|
# DreamBooth trained by AutoTrain
Text encoder was not trained.
| [
"# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
] | [
"TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n",
"# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
] | [
45,
19
] | [
"passage: TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
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] |
null | null | gguf | GGUF importance matrix (imatrix) quants for https://huggingface.co/CausalLM/34b-beta
The importance matrix was trained for 40K tokens (80 batches of 512 tokens) using random data.
From the model author:
*Please do not use wikitext for quantization calibration because all wikitext have been re-aligned on synthetic dataset, and its distribution differs significantly from the original wikitext.*
| Layers | Context | Template |
| --- | --- | --- |
| <pre>60</pre> | <pre>200000</pre> | <pre><\|im_start\|>system<br>{instructions}<\|im_end\|><br><\|im_start\|>user<br>{prompt}<\|im_end\|><br><\|im_start\|>assistant<br>{response}</pre> | | {"license": "gpl-3.0", "library_name": "gguf", "pipeline_tag": "text-generation"} | text-generation | dranger003/CausalLM-34b-beta-iMat.GGUF | [
"gguf",
"text-generation",
"license:gpl-3.0",
"region:us"
] | 2024-02-15T00:02:43+00:00 | [] | [] | TAGS
#gguf #text-generation #license-gpl-3.0 #region-us
| GGUF importance matrix (imatrix) quants for URL
The importance matrix was trained for 40K tokens (80 batches of 512 tokens) using random data.
From the model author:
*Please do not use wikitext for quantization calibration because all wikitext have been re-aligned on synthetic dataset, and its distribution differs significantly from the original wikitext.*
Layers:
```
60
```
, Context:
```
200000
```
, Template:
```
<|im_start|>system
{instructions}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
{response}
```
| [] | [
"TAGS\n#gguf #text-generation #license-gpl-3.0 #region-us \n"
] | [
22
] | [
"passage: TAGS\n#gguf #text-generation #license-gpl-3.0 #region-us \n"
] | [
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null | null | transformers |
# Model Card for Model ID
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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null | null | null | # Daybreak
This set of LoRAs differ from the crestfall models due to the addition of on-topic SFW content.
The intention is to produce a more intelligent model that is still capable of niche genre ERP. | {} | null | crestf411/daybreak-peft | [
"safetensors",
"region:us"
] | 2024-02-15T00:08:15+00:00 | [] | [] | TAGS
#safetensors #region-us
| # Daybreak
This set of LoRAs differ from the crestfall models due to the addition of on-topic SFW content.
The intention is to produce a more intelligent model that is still capable of niche genre ERP. | [
"# Daybreak\n\nThis set of LoRAs differ from the crestfall models due to the addition of on-topic SFW content.\n\nThe intention is to produce a more intelligent model that is still capable of niche genre ERP."
] | [
"TAGS\n#safetensors #region-us \n",
"# Daybreak\n\nThis set of LoRAs differ from the crestfall models due to the addition of on-topic SFW content.\n\nThe intention is to produce a more intelligent model that is still capable of niche genre ERP."
] | [
11,
47
] | [
"passage: TAGS\n#safetensors #region-us \n# Daybreak\n\nThis set of LoRAs differ from the crestfall models due to the addition of on-topic SFW content.\n\nThe intention is to produce a more intelligent model that is still capable of niche genre ERP."
] | [
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
#base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
base_model: abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
is_llama_derived_model: true
# huggingface repo
datasets:
- path: teknium/OpenHermes-2.5
type: sharegpt
conversation: chatml
train_on_split: train
- path: abhinand/ultrachat_200k_sharegpt
type: sharegpt
conversation: chatml
train_on_split: train
load_in_4bit: false
load_in_8bit: false
bf16: true # require >=ampere
chat_template: chatml
group_by_length: false
val_set_size: 0.0
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
- gate_proj
- down_proj
- up_proj
lora_modules_to_save:
- embed_tokens
- lm_head
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
output_dir: ./trained_models
gradient_accumulation_steps: 4
micro_batch_size: 8
eval_batch_size: 8
num_epochs: 1
logging_steps: 1
save_steps: 50
save_total_limit: 3
save_safetensors: true
gradient_checkpointing: true
lr_scheduler: cosine
optimizer: "adamw_bnb_8bit"
adam_beta2: 0.95
adam_epsilon: 0.00001
weight_decay: 0.1
learning_rate: 0.0005
max_grad_norm: 1.0
#warmup_ratio: 0.05
warmup_steps: 10
flash_attention: true
# Resume from a specific checkpoint dir
resume_from_checkpoint:
# If resume_from_checkpoint isn't set and you simply want it to start where it left off.
# Be careful with this being turned on between different models.
# auto_resume_from_checkpoints: true
# wandb configuration if you're using it
# Make sure your `WANDB_API_KEY` environment variable is set (recommended) or you login to wandb with `wandb login`.
wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
wandb_project: "tiny-llama-sft"
wandb_name:
wandb_run_id:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
tokens: # these are delimiters
- "<|im_start|>"
- "<|im_end|>"
```
</details><br>
# trained_models
This model is a fine-tuned version of [abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft](https://huggingface.co/abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.17.0
- Tokenizers 0.15.0 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft", "model-index": [{"name": "trained_models", "results": []}]} | null | sanjay920/cortex-xs | [
"peft",
"tensorboard",
"safetensors",
"llama",
"generated_from_trainer",
"base_model:abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft",
"license:apache-2.0",
"region:us"
] | 2024-02-15T00:10:00+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #llama #generated_from_trainer #base_model-abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft #license-apache-2.0 #region-us
|
<img src="URL alt="Built with Axolotl" width="200" height="32"/>
<details><summary>See axolotl config</summary>
axolotl version: '0.4.0'
</details><br>
# trained_models
This model is a fine-tuned version of abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.17.0
- Tokenizers 0.15.0 | [
"# trained_models\n\nThis model is a fine-tuned version of abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.0.1+cu118\n- Datasets 2.17.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#peft #tensorboard #safetensors #llama #generated_from_trainer #base_model-abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft #license-apache-2.0 #region-us \n",
"# trained_models\n\nThis model is a fine-tuned version of abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.0.1+cu118\n- Datasets 2.17.0\n- Tokenizers 0.15.0"
] | [
66,
48,
6,
12,
8,
3,
128,
4,
44
] | [
"passage: TAGS\n#peft #tensorboard #safetensors #llama #generated_from_trainer #base_model-abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft #license-apache-2.0 #region-us \n# trained_models\n\nThis model is a fine-tuned version of abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 1### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.0.1+cu118\n- Datasets 2.17.0\n- Tokenizers 0.15.0"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | ArthurZ/small-model | [
"transformers",
"safetensors",
"mamba",
"text-generation",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #mamba #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Model Sources [optional]
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- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
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## Technical Specifications [optional]
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### Compute Infrastructure
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APA:
## Glossary [optional]
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] |
null | null | transformers | # OpenHermes-4.6-Dolphin-Mistral
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [cognitivecomputations/dolphin-2.1-mistral-7b](https://huggingface.co/cognitivecomputations/dolphin-2.1-mistral-7b)
* [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: teknium/OpenHermes-2.5-Mistral-7B
layer_range: [0, 32]
- model: cognitivecomputations/dolphin-2.1-mistral-7b
layer_range: [0, 32]
# or, the equivalent models: syntax:
# models:
# - model: psmathur/orca_mini_v3_13b
# - model: garage-bAInd/Platypus2-13B
merge_method: slerp
base_model: teknium/OpenHermes-2.5-Mistral-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5 # fallback for rest of tensors
dtype: float16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["cognitivecomputations/dolphin-2.1-mistral-7b", "teknium/OpenHermes-2.5-Mistral-7B"]} | text-generation | synk/OpenHermes-4.6-Dolphin-Mistral | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:cognitivecomputations/dolphin-2.1-mistral-7b",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-15T00:17:10+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #base_model-cognitivecomputations/dolphin-2.1-mistral-7b #base_model-teknium/OpenHermes-2.5-Mistral-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # OpenHermes-4.6-Dolphin-Mistral
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* cognitivecomputations/dolphin-2.1-mistral-7b
* teknium/OpenHermes-2.5-Mistral-7B
### Configuration
The following YAML configuration was used to produce this model:
| [
"# OpenHermes-4.6-Dolphin-Mistral\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* cognitivecomputations/dolphin-2.1-mistral-7b\n* teknium/OpenHermes-2.5-Mistral-7B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #base_model-cognitivecomputations/dolphin-2.1-mistral-7b #base_model-teknium/OpenHermes-2.5-Mistral-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# OpenHermes-4.6-Dolphin-Mistral\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* cognitivecomputations/dolphin-2.1-mistral-7b\n* teknium/OpenHermes-2.5-Mistral-7B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
97,
29,
4,
18,
45,
17
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #base_model-cognitivecomputations/dolphin-2.1-mistral-7b #base_model-teknium/OpenHermes-2.5-Mistral-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# OpenHermes-4.6-Dolphin-Mistral\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the SLERP merge method.### Models Merged\n\nThe following models were included in the merge:\n* cognitivecomputations/dolphin-2.1-mistral-7b\n* teknium/OpenHermes-2.5-Mistral-7B### Configuration\n\nThe following YAML configuration was used to produce this model:"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base_seed42_original_esp-kin-eng_train
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0235
- Spearman Corr: 0.7836
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.81 | 200 | 0.0349 | 0.7407 |
| 0.045 | 3.62 | 400 | 0.0210 | 0.7900 |
| 0.0216 | 5.43 | 600 | 0.0227 | 0.7937 |
| 0.0164 | 7.24 | 800 | 0.0223 | 0.7939 |
| 0.0127 | 9.05 | 1000 | 0.0217 | 0.7902 |
| 0.01 | 10.86 | 1200 | 0.0189 | 0.7783 |
| 0.0084 | 12.67 | 1400 | 0.0188 | 0.7834 |
| 0.0068 | 14.48 | 1600 | 0.0228 | 0.7834 |
| 0.0057 | 16.29 | 1800 | 0.0222 | 0.7867 |
| 0.005 | 18.1 | 2000 | 0.0255 | 0.7847 |
| 0.0045 | 19.91 | 2200 | 0.0216 | 0.7842 |
| 0.0045 | 21.72 | 2400 | 0.0219 | 0.7866 |
| 0.0041 | 23.53 | 2600 | 0.0209 | 0.7850 |
| 0.0038 | 25.34 | 2800 | 0.0235 | 0.7836 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "FacebookAI/xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base_seed42_original_esp-kin-eng_train", "results": []}]} | text-classification | shanhy/xlm-roberta-base_seed42_original_esp-kin-eng_train | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-15T00:18:07+00:00 | [] | [] | TAGS
#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base\_seed42\_original\_esp-kin-eng\_train
======================================================
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0235
* Spearman Corr: 0.7836
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 128
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
68,
141,
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"passage: TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | peft |
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- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Metrics
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### Results
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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## Citation [optional]
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## Model Card Authors [optional]
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### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "codeparrot/codeparrot"} | null | adalib/sqlmodel-cond-gen-codeparrot-prefix | [
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# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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- Carbon Emitted:
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base_seed42_original_esp-hau-eng_train
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0254
- Spearman Corr: 0.7775
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.61 | 200 | 0.0219 | 0.7701 |
| 0.0403 | 3.23 | 400 | 0.0201 | 0.7865 |
| 0.0242 | 4.84 | 600 | 0.0209 | 0.7970 |
| 0.0183 | 6.45 | 800 | 0.0198 | 0.7932 |
| 0.0144 | 8.06 | 1000 | 0.0225 | 0.7858 |
| 0.0144 | 9.68 | 1200 | 0.0248 | 0.7853 |
| 0.0115 | 11.29 | 1400 | 0.0227 | 0.7843 |
| 0.0091 | 12.9 | 1600 | 0.0244 | 0.7801 |
| 0.0073 | 14.52 | 1800 | 0.0243 | 0.7803 |
| 0.0063 | 16.13 | 2000 | 0.0241 | 0.7811 |
| 0.0063 | 17.74 | 2200 | 0.0240 | 0.7780 |
| 0.0055 | 19.35 | 2400 | 0.0254 | 0.7775 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "FacebookAI/xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base_seed42_original_esp-hau-eng_train", "results": []}]} | text-classification | shanhy/xlm-roberta-base_seed42_original_esp-hau-eng_train | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-15T00:25:57+00:00 | [] | [] | TAGS
#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base\_seed42\_original\_esp-hau-eng\_train
======================================================
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0254
* Spearman Corr: 0.7775
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 128
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
68,
141,
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"passage: TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | diffusers |
# DreamBooth trained by AutoTrain
Text encoder was not trained.
| {"tags": ["text-to-image", "diffusers", "autotrain"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "<Cream-on-her-face>", "inference": true} | text-to-image | madmarc/autotrain-aavjn-jvsze | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"has_space",
"region:us"
] | 2024-02-15T00:29:46+00:00 | [] | [] | TAGS
#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us
|
# DreamBooth trained by AutoTrain
Text encoder was not trained.
| [
"# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
] | [
"TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n",
"# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
] | [
45,
19
] | [
"passage: TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base_seed42_original_kin-hau-eng_train
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0234
- Spearman Corr: 0.7912
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.77 | 200 | 0.0248 | 0.7896 |
| 0.0484 | 3.54 | 400 | 0.0191 | 0.7995 |
| 0.0243 | 5.31 | 600 | 0.0227 | 0.8033 |
| 0.0181 | 7.08 | 800 | 0.0202 | 0.8029 |
| 0.0138 | 8.85 | 1000 | 0.0258 | 0.7946 |
| 0.0107 | 10.62 | 1200 | 0.0218 | 0.7996 |
| 0.0088 | 12.39 | 1400 | 0.0219 | 0.7918 |
| 0.0072 | 14.16 | 1600 | 0.0211 | 0.7955 |
| 0.0063 | 15.93 | 1800 | 0.0260 | 0.7909 |
| 0.0063 | 17.7 | 2000 | 0.0234 | 0.7912 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "FacebookAI/xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base_seed42_original_kin-hau-eng_train", "results": []}]} | text-classification | shanhy/xlm-roberta-base_seed42_original_kin-hau-eng_train | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-15T00:32:45+00:00 | [] | [] | TAGS
#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base\_seed42\_original\_kin-hau-eng\_train
======================================================
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0234
* Spearman Corr: 0.7912
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 128
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
68,
141,
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"passage: TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-2-ko-7b-v1.1b-singlegpu_gradient_32_epoch_30_train_batch_size_1_all_data_test_1_1_plus_Feb_14th
This model is a fine-tuned version of [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0+cu121
- Datasets 2.16.0
- Tokenizers 0.13.3
| {"tags": ["generated_from_trainer"], "base_model": "beomi/llama-2-ko-7b", "model-index": [{"name": "llama-2-ko-7b-v1.1b-singlegpu_gradient_32_epoch_30_train_batch_size_1_all_data_test_1_1_plus_Feb_14th", "results": []}]} | text-generation | kim1/test_llama_2_ko_3 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:beomi/llama-2-ko-7b",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-15T00:34:20+00:00 | [] | [] | TAGS
#transformers #pytorch #llama #text-generation #generated_from_trainer #base_model-beomi/llama-2-ko-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# llama-2-ko-7b-v1.1b-singlegpu_gradient_32_epoch_30_train_batch_size_1_all_data_test_1_1_plus_Feb_14th
This model is a fine-tuned version of beomi/llama-2-ko-7b on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0+cu121
- Datasets 2.16.0
- Tokenizers 0.13.3
| [
"# llama-2-ko-7b-v1.1b-singlegpu_gradient_32_epoch_30_train_batch_size_1_all_data_test_1_1_plus_Feb_14th\n\nThis model is a fine-tuned version of beomi/llama-2-ko-7b on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.33.3\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.0\n- Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #generated_from_trainer #base_model-beomi/llama-2-ko-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# llama-2-ko-7b-v1.1b-singlegpu_gradient_32_epoch_30_train_batch_size_1_all_data_test_1_1_plus_Feb_14th\n\nThis model is a fine-tuned version of beomi/llama-2-ko-7b on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.33.3\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.0\n- Tokenizers 0.13.3"
] | [
68,
78,
6,
12,
8,
3,
114,
4,
35
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #generated_from_trainer #base_model-beomi/llama-2-ko-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# llama-2-ko-7b-v1.1b-singlegpu_gradient_32_epoch_30_train_batch_size_1_all_data_test_1_1_plus_Feb_14th\n\nThis model is a fine-tuned version of beomi/llama-2-ko-7b on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30.0### Training results### Framework versions\n\n- Transformers 4.33.3\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.0\n- Tokenizers 0.13.3"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | Kukedlc/TriunviratoPeft | [
"transformers",
"arxiv:1910.09700",
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"1910.09700"
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#transformers #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- Repository:
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
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null | null | sentence-transformers |
# sreddy109/csbg-nomic-ai-upload-v2run5
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sreddy109/csbg-nomic-ai-upload-v2run5')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sreddy109/csbg-nomic-ai-upload-v2run5)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1560 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CachedMultipleNegativesRankingLoss.CachedMultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 78,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 5e-07
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: NomicBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | sreddy109/csbg-nomic-ai-upload-v2run5 | [
"sentence-transformers",
"safetensors",
"nomic_bert",
"feature-extraction",
"sentence-similarity",
"custom_code",
"endpoints_compatible",
"region:us"
] | 2024-02-15T00:38:01+00:00 | [] | [] | TAGS
#sentence-transformers #safetensors #nomic_bert #feature-extraction #sentence-similarity #custom_code #endpoints_compatible #region-us
|
# sreddy109/csbg-nomic-ai-upload-v2run5
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 1560 with parameters:
Loss:
'sentence_transformers.losses.CachedMultipleNegativesRankingLoss.CachedMultipleNegativesRankingLoss' with parameters:
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
"# sreddy109/csbg-nomic-ai-upload-v2run5\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1560 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CachedMultipleNegativesRankingLoss.CachedMultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #safetensors #nomic_bert #feature-extraction #sentence-similarity #custom_code #endpoints_compatible #region-us \n",
"# sreddy109/csbg-nomic-ai-upload-v2run5\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1560 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CachedMultipleNegativesRankingLoss.CachedMultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
48,
62,
38,
29,
90,
5,
6
] | [
"passage: TAGS\n#sentence-transformers #safetensors #nomic_bert #feature-extraction #sentence-similarity #custom_code #endpoints_compatible #region-us \n# sreddy109/csbg-nomic-ai-upload-v2run5\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1560 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CachedMultipleNegativesRankingLoss.CachedMultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base_seed42_original_amh-hau-eng_train
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0207
- Spearman Corr: 0.8263
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.72 | 200 | 0.0196 | 0.8013 |
| 0.0695 | 3.45 | 400 | 0.0181 | 0.8258 |
| 0.0243 | 5.17 | 600 | 0.0205 | 0.8229 |
| 0.0189 | 6.9 | 800 | 0.0195 | 0.8238 |
| 0.0144 | 8.62 | 1000 | 0.0223 | 0.8295 |
| 0.0112 | 10.34 | 1200 | 0.0248 | 0.8212 |
| 0.0091 | 12.07 | 1400 | 0.0205 | 0.8209 |
| 0.0091 | 13.79 | 1600 | 0.0224 | 0.8242 |
| 0.0078 | 15.52 | 1800 | 0.0208 | 0.8216 |
| 0.0067 | 17.24 | 2000 | 0.0207 | 0.8263 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "FacebookAI/xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base_seed42_original_amh-hau-eng_train", "results": []}]} | text-classification | shanhy/xlm-roberta-base_seed42_original_amh-hau-eng_train | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-15T00:39:39+00:00 | [] | [] | TAGS
#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base\_seed42\_original\_amh-hau-eng\_train
======================================================
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0207
* Spearman Corr: 0.8263
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 128
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
68,
141,
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"passage: TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers | <h1 style="text-align: center">Eros-7B-Test (WIP Name)</h1>
<h2 style="text-align: center">Experimental Roleplay Finetine</h2>
## Model Details
**This is considered an unofficial model**.
An experimental model that uses a new version of PIPPA dataset as the primary base. This PIPPA dataset is the original one we have uploaded that has been refined, augmented and trimmed down for proper model training.
The model is a finetune on the Mistral-7B base with 22K token examples. Eros-7B is primarily designed for ChatRP and with some capabilities to do story generations too. It is trained on the ChatML format.
Due to it being an experimental model, there are some quirks...
- Rare occasion to misspell words
- Rare occasion to have random formatting artifact at the end of generations
- Tendencies to use the same phrase when generating (e.g. *she was always smiling* variants persisting in multi-turn conversations)
- Not very smart but highly creative due to a lack of logic/reasoning dataset
While this model is not good enough to be deemed as an official release model under the PygmalionAI name, I feel like it is a good stepping point to give this to the public under this account. Any feedback is appreciated. The above mentioned issues will be fixed in the next training attempt of models.
## Prompting Details
**This is under the assumption this model is used with [SillyTavern](https://github.com/SillyTavern/SillyTavern), please note it may not cover other existing application use cases.**
Use the ChatML Instruct Settings
<img src="https://files.catbox.moe/6318gp.png" alt="sillytavernsettings" width="350" height="500">
Use these settings for consistent generations
<img src="https://files.catbox.moe/ayos28.png" alt="sillytavernsettings" width="350" height="500">
**Note**: Temperature, and Min P values can be adjusted to greater or lower values depending on generation preferences.
## Limitations and biases
The intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope.
As such, it was **not** fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading.
| {"language": ["en"], "tags": ["text-generation-inference", "instruct"], "pipeline_tag": "text-generation"} | text-generation | tavtav/eros-7b-test | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"instruct",
"conversational",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2024-02-15T00:40:57+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #text-generation-inference #instruct #conversational #en #autotrain_compatible #endpoints_compatible #has_space #region-us
| <h1 style="text-align: center">Eros-7B-Test (WIP Name)</h1>
<h2 style="text-align: center">Experimental Roleplay Finetine</h2>
## Model Details
This is considered an unofficial model.
An experimental model that uses a new version of PIPPA dataset as the primary base. This PIPPA dataset is the original one we have uploaded that has been refined, augmented and trimmed down for proper model training.
The model is a finetune on the Mistral-7B base with 22K token examples. Eros-7B is primarily designed for ChatRP and with some capabilities to do story generations too. It is trained on the ChatML format.
Due to it being an experimental model, there are some quirks...
- Rare occasion to misspell words
- Rare occasion to have random formatting artifact at the end of generations
- Tendencies to use the same phrase when generating (e.g. *she was always smiling* variants persisting in multi-turn conversations)
- Not very smart but highly creative due to a lack of logic/reasoning dataset
While this model is not good enough to be deemed as an official release model under the PygmalionAI name, I feel like it is a good stepping point to give this to the public under this account. Any feedback is appreciated. The above mentioned issues will be fixed in the next training attempt of models.
## Prompting Details
This is under the assumption this model is used with SillyTavern, please note it may not cover other existing application use cases.
Use the ChatML Instruct Settings
<img src="URL alt="sillytavernsettings" width="350" height="500">
Use these settings for consistent generations
<img src="URL alt="sillytavernsettings" width="350" height="500">
Note: Temperature, and Min P values can be adjusted to greater or lower values depending on generation preferences.
## Limitations and biases
The intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope.
As such, it was not fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading.
| [
"## Model Details\nThis is considered an unofficial model. \n\nAn experimental model that uses a new version of PIPPA dataset as the primary base. This PIPPA dataset is the original one we have uploaded that has been refined, augmented and trimmed down for proper model training. \nThe model is a finetune on the Mistral-7B base with 22K token examples. Eros-7B is primarily designed for ChatRP and with some capabilities to do story generations too. It is trained on the ChatML format. \n\nDue to it being an experimental model, there are some quirks...\n\n- Rare occasion to misspell words\n- Rare occasion to have random formatting artifact at the end of generations\n- Tendencies to use the same phrase when generating (e.g. *she was always smiling* variants persisting in multi-turn conversations)\n- Not very smart but highly creative due to a lack of logic/reasoning dataset\n\nWhile this model is not good enough to be deemed as an official release model under the PygmalionAI name, I feel like it is a good stepping point to give this to the public under this account. Any feedback is appreciated. The above mentioned issues will be fixed in the next training attempt of models.",
"## Prompting Details\nThis is under the assumption this model is used with SillyTavern, please note it may not cover other existing application use cases.\n\nUse the ChatML Instruct Settings\n<img src=\"URL alt=\"sillytavernsettings\" width=\"350\" height=\"500\">\nUse these settings for consistent generations\n<img src=\"URL alt=\"sillytavernsettings\" width=\"350\" height=\"500\">\n\nNote: Temperature, and Min P values can be adjusted to greater or lower values depending on generation preferences.",
"## Limitations and biases\nThe intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope.\n\nAs such, it was not fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading."
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #text-generation-inference #instruct #conversational #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Model Details\nThis is considered an unofficial model. \n\nAn experimental model that uses a new version of PIPPA dataset as the primary base. This PIPPA dataset is the original one we have uploaded that has been refined, augmented and trimmed down for proper model training. \nThe model is a finetune on the Mistral-7B base with 22K token examples. Eros-7B is primarily designed for ChatRP and with some capabilities to do story generations too. It is trained on the ChatML format. \n\nDue to it being an experimental model, there are some quirks...\n\n- Rare occasion to misspell words\n- Rare occasion to have random formatting artifact at the end of generations\n- Tendencies to use the same phrase when generating (e.g. *she was always smiling* variants persisting in multi-turn conversations)\n- Not very smart but highly creative due to a lack of logic/reasoning dataset\n\nWhile this model is not good enough to be deemed as an official release model under the PygmalionAI name, I feel like it is a good stepping point to give this to the public under this account. Any feedback is appreciated. The above mentioned issues will be fixed in the next training attempt of models.",
"## Prompting Details\nThis is under the assumption this model is used with SillyTavern, please note it may not cover other existing application use cases.\n\nUse the ChatML Instruct Settings\n<img src=\"URL alt=\"sillytavernsettings\" width=\"350\" height=\"500\">\nUse these settings for consistent generations\n<img src=\"URL alt=\"sillytavernsettings\" width=\"350\" height=\"500\">\n\nNote: Temperature, and Min P values can be adjusted to greater or lower values depending on generation preferences.",
"## Limitations and biases\nThe intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope.\n\nAs such, it was not fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading."
] | [
60,
279,
123,
130
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #text-generation-inference #instruct #conversational #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n## Model Details\nThis is considered an unofficial model. \n\nAn experimental model that uses a new version of PIPPA dataset as the primary base. This PIPPA dataset is the original one we have uploaded that has been refined, augmented and trimmed down for proper model training. \nThe model is a finetune on the Mistral-7B base with 22K token examples. Eros-7B is primarily designed for ChatRP and with some capabilities to do story generations too. It is trained on the ChatML format. \n\nDue to it being an experimental model, there are some quirks...\n\n- Rare occasion to misspell words\n- Rare occasion to have random formatting artifact at the end of generations\n- Tendencies to use the same phrase when generating (e.g. *she was always smiling* variants persisting in multi-turn conversations)\n- Not very smart but highly creative due to a lack of logic/reasoning dataset\n\nWhile this model is not good enough to be deemed as an official release model under the PygmalionAI name, I feel like it is a good stepping point to give this to the public under this account. Any feedback is appreciated. The above mentioned issues will be fixed in the next training attempt of models.## Prompting Details\nThis is under the assumption this model is used with SillyTavern, please note it may not cover other existing application use cases.\n\nUse the ChatML Instruct Settings\n<img src=\"URL alt=\"sillytavernsettings\" width=\"350\" height=\"500\">\nUse these settings for consistent generations\n<img src=\"URL alt=\"sillytavernsettings\" width=\"350\" height=\"500\">\n\nNote: Temperature, and Min P values can be adjusted to greater or lower values depending on generation preferences."
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null | null | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [aipart/chatjob_sft_from_llama2_0.1_ALL](https://huggingface.co/aipart/chatjob_sft_from_llama2_0.1_ALL)
* [aipart/chatjob_llama2_0.2_type2](https://huggingface.co/aipart/chatjob_llama2_0.2_type2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: aipart/chatjob_sft_from_llama2_0.1_ALL
layer_range: [0, 32]
- model: aipart/chatjob_llama2_0.2_type2
layer_range: [0, 32]
merge_method: slerp
base_model: aipart/chatjob_sft_from_llama2_0.1_ALL
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["aipart/chatjob_sft_from_llama2_0.1_ALL", "aipart/chatjob_llama2_0.2_type2"]} | text-generation | eastjin/all_type2_merge | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"base_model:aipart/chatjob_sft_from_llama2_0.1_ALL",
"base_model:aipart/chatjob_llama2_0.2_type2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-15T00:41:54+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-aipart/chatjob_sft_from_llama2_0.1_ALL #base_model-aipart/chatjob_llama2_0.2_type2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* aipart/chatjob_sft_from_llama2_0.1_ALL
* aipart/chatjob_llama2_0.2_type2
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* aipart/chatjob_sft_from_llama2_0.1_ALL\n* aipart/chatjob_llama2_0.2_type2",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-aipart/chatjob_sft_from_llama2_0.1_ALL #base_model-aipart/chatjob_llama2_0.2_type2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* aipart/chatjob_sft_from_llama2_0.1_ALL\n* aipart/chatjob_llama2_0.2_type2",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
96,
18,
4,
18,
49,
17
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-aipart/chatjob_sft_from_llama2_0.1_ALL #base_model-aipart/chatjob_llama2_0.2_type2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# merge\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the SLERP merge method.### Models Merged\n\nThe following models were included in the merge:\n* aipart/chatjob_sft_from_llama2_0.1_ALL\n* aipart/chatjob_llama2_0.2_type2### Configuration\n\nThe following YAML configuration was used to produce this model:"
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null | null | transformers |
# Uploaded model
- **Developed by:** sanjay920
- **License:** apache-2.0
- **Finetuned from model :** sanjay920/cortex-xs
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "sanjay920/cortex-xs"} | null | sanjay920/cortex-xs-GGUF | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:sanjay920/cortex-xs",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-15T00:46:17+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #llama #text-generation-inference #unsloth #en #base_model-sanjay920/cortex-xs #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: sanjay920
- License: apache-2.0
- Finetuned from model : sanjay920/cortex-xs
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: sanjay920\n- License: apache-2.0\n- Finetuned from model : sanjay920/cortex-xs\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #gguf #llama #text-generation-inference #unsloth #en #base_model-sanjay920/cortex-xs #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: sanjay920\n- License: apache-2.0\n- Finetuned from model : sanjay920/cortex-xs\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
62,
78
] | [
"passage: TAGS\n#transformers #gguf #llama #text-generation-inference #unsloth #en #base_model-sanjay920/cortex-xs #license-apache-2.0 #endpoints_compatible #region-us \n# Uploaded model\n\n- Developed by: sanjay920\n- License: apache-2.0\n- Finetuned from model : sanjay920/cortex-xs\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base_seed42_original_kin-amh-eng_train
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0167
- Spearman Corr: 0.8481
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.95 | 200 | 0.0180 | 0.8098 |
| 0.0401 | 3.9 | 400 | 0.0152 | 0.8402 |
| 0.0212 | 5.85 | 600 | 0.0183 | 0.8493 |
| 0.0155 | 7.8 | 800 | 0.0166 | 0.8535 |
| 0.0116 | 9.76 | 1000 | 0.0206 | 0.8508 |
| 0.0097 | 11.71 | 1200 | 0.0155 | 0.8459 |
| 0.008 | 13.66 | 1400 | 0.0159 | 0.8481 |
| 0.0068 | 15.61 | 1600 | 0.0153 | 0.8467 |
| 0.0058 | 17.56 | 1800 | 0.0196 | 0.8481 |
| 0.0053 | 19.51 | 2000 | 0.0167 | 0.8481 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "FacebookAI/xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base_seed42_original_kin-amh-eng_train", "results": []}]} | text-classification | shanhy/xlm-roberta-base_seed42_original_kin-amh-eng_train | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-15T00:46:32+00:00 | [] | [] | TAGS
#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base\_seed42\_original\_kin-amh-eng\_train
======================================================
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0167
* Spearman Corr: 0.8481
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 128
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
68,
141,
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"passage: TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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Subsets and Splits