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| likes
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11.7k
| library_name
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Ricky080811/Test2 | Ricky080811 | 2024-03-11T21:36:27Z | 0 | 0 | null | [
"safetensors",
"autotrain",
"text-generation",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-11T21:36:22Z | ---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# 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)
``` |
yulymur/Micha | yulymur | 2024-03-11T21:34:17Z | 0 | 0 | flair | [
"flair",
"text-generation",
"ru",
"dataset:HuggingFaceTB/cosmopedia",
"license:bsl-1.0",
"region:us"
] | text-generation | 2024-03-11T21:09:53Z | ---
license: bsl-1.0
datasets:
- HuggingFaceTB/cosmopedia
language:
- ru
metrics:
- code_eval
library_name: flair
pipeline_tag: text-generation
--- |
graizelle/grle | graizelle | 2024-03-11T21:29:13Z | 18 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:openrail",
"region:us"
] | text-to-image | 2024-01-31T05:28:54Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '1girl, grle, looking at viewer, long blonde hair, green eyes, red dress, jewelery, masterpiece best quality, realistic, dramatic lighting'
parameter:
negative_prompt: >-
lowres, bad anatomy, bad hands, text, error, missing fingers, extra digits, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry
width=1024,
height=1024,
guidance_scale=5,
num_inference_steps=30
example_title: GrLE
output:
url: images/grle_20240131051416_e000001_01.png
- text: '1girl, grle, looking at viewer, long blonde hair, green eyes, hoodie, shorts, thigh highs, masterpiece best quality, realistic, dramatic lighting'
output:
url: images/grle_20240131051536_e000009_01.png
- text: '1girl, grle, emo, looking at viewer, long blonde hair, basball cap, shorts, masterpiece best quality, realistic, dramatic lighting'
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: null
license: openrail
---
#GrLE
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/graizelle/grle/tree/main) them in the Files & versions tab.
|
whizzzzkid/Bosbonasusmini12 | whizzzzkid | 2024-03-11T21:28:07Z | 90 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-11T21:23:27Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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[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
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[More Information Needed]
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[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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**APA:**
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[More Information Needed] |
Weni/ZeroShot-3.4.3-Mistral-7b-DPO-1.0.0-merged | Weni | 2024-03-11T21:25:37Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-11T20:53:08Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
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### Direct Use
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[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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
smig/leagaleasy-mistral-7b-instruct-v0.2-v1 | smig | 2024-03-11T21:24:38Z | 1 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] | null | 2024-03-06T19:13:41Z | ---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.2
datasets:
- generator
model-index:
- name: leagaleasy-mistral-7b-instruct-v0.2-v1
results: []
---
<!-- 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. -->
# leagaleasy-mistral-7b-instruct-v0.2-v1
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
SimoneJLaudani/trainer4b | SimoneJLaudani | 2024-03-11T21:17:02Z | 93 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-11T21:16:35Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: trainer4b
results: []
---
<!-- 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. -->
# trainer4b
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0036
- Accuracy: 1.0
- F1: 1.0
## 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:
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|
| 0.0001 | 1.2 | 30 | 0.0022 | 1.0 | 1.0 |
| 0.0 | 2.4 | 60 | 0.0023 | 1.0 | 1.0 |
| 0.0 | 3.6 | 90 | 0.0016 | 1.0 | 1.0 |
| 0.0 | 4.8 | 120 | 0.0036 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
OwOOwO/eacc_adhoc_mtest | OwOOwO | 2024-03-11T21:13:50Z | 91 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-10T10:27:28Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
OwOOwO/eacc_6_5_1 | OwOOwO | 2024-03-11T21:11:46Z | 90 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-10T20:51:08Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
enrique2701/cleanrl-ppo-LunarLander-v2-2M | enrique2701 | 2024-03-11T21:07:37Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-11T17:28:07Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 143.99 +/- 61.82
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
|
denysdios/whisper-med-tr-tuned | denysdios | 2024-03-11T21:06:48Z | 9 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"tr",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-03-01T10:11:27Z | ---
language:
- tr
license: apache-2.0
base_model: openai/whisper-medium
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Medium Tr - denysdios
results: []
---
<!-- 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 Medium Tr - denysdios
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 13.0 & Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1618
- Wer: 14.3825
## Model description
The model took about nine hours to train on a single A100 GPU.
## Intended uses & limitations
Absolutely no restrictions additional to whisper models. Increasing the Turkish labeled data in whisper, which was 4333/690k (0.0063), was the primary objective. There are just 49.945 hours of data in the fine-tuning dataset, or about 1.1% of the Turkish dataset that has already been trained.
## Training and evaluation data
Processing...
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1803 | 0.36 | 1000 | 0.2089 | 18.6326 |
| 0.1428 | 0.71 | 2000 | 0.1821 | 16.3912 |
| 0.0535 | 1.07 | 3000 | 0.1693 | 14.9132 |
| 0.0491 | 1.43 | 4000 | 0.1618 | 14.3825 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
sarak7/H4_312_769_v1 | sarak7 | 2024-03-11T21:02:23Z | 181 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-11T21:00:51Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
sweetfelinity/Reinforce-Pixelcopter-PLE-v0 | sweetfelinity | 2024-03-11T21:01:22Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-11T21:01:19Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 22.90 +/- 13.89
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ISTA-DASLab/Llama-2-70b-AQLM-2Bit-2x8-hf | ISTA-DASLab | 2024-03-11T20:55:32Z | 14 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2401.06118",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"aqlm",
"region:us"
] | text-generation | 2024-02-07T14:48:41Z | ---
{}
---
Official [AQLM](https://arxiv.org/abs/2401.06118) quantization of `meta-llama/Llama-2-7b-hf`.
For this quantization, we used 1 codebook of 16 bits.
Selected evaluation results for this and other models:
| Model | AQLM scheme | WikiText 2 PPL | Model size, Gb | Hub link |
|------------|-------------|----------------|----------------|--------------------------------------------------------------------------|
| Llama-2-7b | 1x16 | 5.92 | 2.4 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf) |
| Llama-2-7b | 2x8 | 6.69 | 2.2 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-2x8-hf) |
| Llama-2-7b | 8x8 | 6.61 | 2.2 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-hf) |
| Llama-2-13b| 1x16 | 5.22 | 4.1 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-13b-AQLM-2Bit-1x16-hf)|
| Llama-2-70b| 1x16 | 3.83 | 18.8 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-70b-AQLM-2Bit-1x16-hf)|
| Llama-2-70b (THIS) | 2x8 | 4.21 | 18.2 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-70b-AQLM-2Bit-2x8-hf) |
| Mixtral-8x7b| 1x16 | 3.35 | 12.6 | [Link](https://huggingface.co/ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf)|
| Mixtral-8x7b-Instruct| 1x16 | - | 12.6 | [Link](https://huggingface.co/ISTA-DASLab/Mixtral-8x7B-Instruct-v0_1-AQLM-2Bit-1x16-hf)|
To learn more about the inference, as well as the information on how to quantize models yourself, please refer to the [official GitHub repo](https://github.com/Vahe1994/AQLM). |
ISTA-DASLab/Llama-2-13b-AQLM-2Bit-1x16-hf | ISTA-DASLab | 2024-03-11T20:54:41Z | 12 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2401.06118",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"aqlm",
"region:us"
] | text-generation | 2024-01-31T11:12:34Z | Official [AQLM](https://arxiv.org/abs/2401.06118) quantization of `meta-llama/Llama-2-13b-hf`.
For this quantization, we used 1 codebook of 16 bits.
Selected evaluation results for this and other models:
| Model | AQLM scheme | WikiText 2 PPL | Model size, Gb | Hub link |
|------------|-------------|----------------|----------------|--------------------------------------------------------------------------|
| Llama-2-7b | 1x16 | 5.92 | 2.4 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf) |
| Llama-2-7b | 2x8 | 6.69 | 2.2 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-2x8-hf) |
| Llama-2-7b | 8x8 | 6.61 | 2.2 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-hf) |
| Llama-2-13b (THIS) | 1x16 | 5.22 | 4.1 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-13b-AQLM-2Bit-1x16-hf)|
| Llama-2-70b| 1x16 | 3.83 | 18.8 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-70b-AQLM-2Bit-1x16-hf)|
| Llama-2-70b| 2x8 | 4.21 | 18.2 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-70b-AQLM-2Bit-2x8-hf) |
| Mixtral-8x7b| 1x16 | 3.35 | 12.6 | [Link](https://huggingface.co/ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf)|
| Mixtral-8x7b-Instruct| 1x16 | - | 12.6 | [Link](https://huggingface.co/ISTA-DASLab/Mixtral-8x7B-Instruct-v0_1-AQLM-2Bit-1x16-hf)|
To learn more about the inference, as well as the information on how to quantize models yourself, please refer to the [official GitHub repo](https://github.com/Vahe1994/AQLM). |
ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-hf | ISTA-DASLab | 2024-03-11T20:54:17Z | 12 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2401.06118",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"aqlm",
"region:us"
] | text-generation | 2024-01-30T17:23:36Z | Official [AQLM](https://arxiv.org/abs/2401.06118) quantization of `meta-llama/Llama-2-7b-hf`.
For this quantization, we used 2 codebooks of 8 bits.
Selected evaluation results for this and other models:
| Model | AQLM scheme | WikiText 2 PPL | Model size, Gb | Hub link |
|------------|-------------|----------------|----------------|--------------------------------------------------------------------------|
| Llama-2-7b | 1x16 | 5.92 | 2.4 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf) |
| Llama-2-7b | 2x8 | 6.69 | 2.2 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-2x8-hf) |
| Llama-2-7b (THIS) | 8x8 | 6.61 | 2.2 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-hf) |
| Llama-2-13b| 1x16 | 5.22 | 4.1 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-13b-AQLM-2Bit-1x16-hf)|
| Llama-2-70b| 1x16 | 3.83 | 18.8 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-70b-AQLM-2Bit-1x16-hf)|
| Llama-2-70b| 2x8 | 4.21 | 18.2 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-70b-AQLM-2Bit-2x8-hf) |
| Mixtral-8x7b| 1x16 | 3.35 | 12.6 | [Link](https://huggingface.co/ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf)|
| Mixtral-8x7b-Instruct| 1x16 | - | 12.6 | [Link](https://huggingface.co/ISTA-DASLab/Mixtral-8x7B-Instruct-v0_1-AQLM-2Bit-1x16-hf)|
To learn more about the inference, as well as the information on how to quantize models yourself, please refer to the [official GitHub repo](https://github.com/Vahe1994/AQLM). |
404NotF0und/lunar-llm-phi-2-3epochs | 404NotF0und | 2024-03-11T20:54:06Z | 33 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"autotrain",
"custom_code",
"dataset:404NotF0und/MtG-json-to-ForgeScript",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-08T13:19:14Z | ---
tags:
- autotrain
- text-generation
widget:
- text: >-
Create the Forge script for this magic card { "name": "Wrench", "mana_cost":
"{W}", "type_line": "Artifact— Clue Equipment", "oracle_text": "Equipped
creature gets +1/+1 and has vigilance and "{3}, {T}: Tap target creature."
{2}, Sacrifice CARD_NAME: Draw a card. Equip {2}'"}
license: mit
metrics:
- accuracy
- perplexity
datasets:
- 404NotF0und/MtG-json-to-ForgeScript
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
- Do some installations first
```
pip install transformers datasets matplotlib pandas git-lfs jiwer tqdm numpy
git clone https://huggingface.co/datasets/404NotF0und/MtG-json-to-ForgeScribe
```
The following code are an example of the usage done on a kaggle notebook
```python
import torch
import random
import csv
import pandas as pd
from transformers import AutoTokenizer, AutoModelForCausalLM
from collections.abc import Sequence
# Function to read the CSV files and extract the relevant columns
def read_dataset(file_path):
print(f"Reading dataset from {file_path}")
data = []
with open(file_path, encoding="utf-8") as csv_file:
csv_reader = csv.DictReader(csv_file) # Use DictReader to handle columns by name
for row in csv_reader:
json_input = f"{row['instruction']} {row['input']}" # Assuming 'input' column contains the JSON input
target_dsl = row["output"] # Assuming 'output' column contains the target DSL
data.append((json_input, target_dsl))
return data
# Function to load the model and tokenizer from Hugging Face
def load_model(model_name, read_token, device):
tokenizer = AutoTokenizer.from_pretrained(model_name, token=read_token)
model = AutoModelForCausalLM.from_pretrained(model_name, token=read_token)
return tokenizer, model
# Function to run inference (text generation)
def run_inference(model, tokenizer, prompt, max_length=300):
# Encode the prompt text
input_ids = tokenizer.encode(prompt, return_tensors='pt')
# Generate text using the model
output_sequences = model.generate(
input_ids=input_ids,
max_length=max_length,
temperature=0.5,
top_k=50,
top_p=0.95,
pad_token_id=tokenizer.eos_token_id,
do_sample=True
)
# Decode the generated text
generated_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
print(generated_text.split('###')[1])
return generated_text.split('###')[1]
```
```python
read_token = 'hf_YOUR_TOKEN'
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name = '404NotF0und/lunar-llm-phi-2-3epoch'
# Load the datasets
validation_path = f"MtG-json-to-ForgeScribe/compiled_cards_data_validation.csv"
test_path = f"MtG-json-to-ForgeScribe/compiled_cards_data_test.csv"
train_path = f"MtG-json-to-ForgeScribe/compiled_cards_data_train.csv"
# Read the datasets
validation_data = read_dataset(validation_path)
test_data = read_dataset(test_path)
train_data = read_dataset(test_path)
```
```python
def get_random_prompts(dataset, num_samples=3):
if not isinstance(dataset, Sequence):
dataset = list(dataset)
if len(dataset) < num_samples:
raise ValueError(f"Dataset does not have enough elements to sample {num_samples} items.")
random_elements = random.sample(dataset, num_samples)
# Create a list of dictionaries with 'json_input' and 'max_length' for each selected element
prompts = [
{
'json_input': element[0],
'max_length': len(f"{element[0]}\n### Response: {element[1]}") # Calculate the length of the response
}
for element in random_elements
]
return prompts
# Now you can populate the prompts variable with 6 random elements from each dataset
try:
prompts = [
{
'json_input': "Create the Forge script for this magic card { \"name\": \"Wrench\", \"mana_cost\": \"{W}\", \"type_line\": \"Artifact\u2014 Clue Equipment\", \"oracle_text\": \"Equipped creature gets +1/+1 and has vigilance and \"{3}, {T}: Tap target creature.\"\n{2}, Sacrifice CARD_NAME: Draw a card.\nEquip {2}'\"}",
'max_length': 100
}
]
except ValueError as e:
print(e)
```
```python
# Load the model and tokenizer
tokenizer, model = load_model(model_name, read_token, device)
for prompt in prompts:
print(f"### Question: {prompt['json_input']} \n")
print("\n" + "-"*80 + "\n")
# Run inference (text generation)
generated_text = run_inference(model, tokenizer, prompt['json_input'])
# Print the generated text
# print(generated_text)
print("\n" + "="*80 + "\n") # Separator for readability
```
Lastly this is the example of output you should get
```
### Question: Create the Forge script for this magic card { "name": "Wrench", "mana_cost": "{W}", "type_line": "Artifact— Clue Equipment", "oracle_text": "Equipped creature gets +1/+1 and has vigilance and "{3}, {T}: Tap target creature."
{2}, Sacrifice CARD_NAME: Draw a card.
Equip {2}'"}
--------------------------------------------------------------------------------
Response: Name:Wrench\nManaCost:W\nTypes:Artifact Clue Equipment\nK:Equip:2\nS:Mode$ Continuous | Affected$ Creature.EquippedBy | AddPower$ 1 | AddToughness$ 1 | AddKeyword$ Vigilance | AddAbility$ TrigTap | Description$ Equipped creature gets +1/+1 and has vigilance and "{3}, {T}: Tap target creature."\nSVar:TrigTap:AB$ Tap | Cost$ 3 T | ValidTgts$ Creature | TgtPrompt$ Select target creature | SpellDescription$ Tap target creature.\nA:AB$ Draw | Cost$ 2 Sac<1/CARDNAME> | NumCards$ 1 | SpellDescription$ Draw a card.\nOracle:Equipped creature gets +1/+1 and has vigilance and "{3}, {T}: Tap target creature."\n{2}, Sacrifice Wrench: Draw
================================================================================
``` |
ISTA-DASLab/Llama-2-7b-AQLM-2Bit-2x8-hf | ISTA-DASLab | 2024-03-11T20:53:48Z | 31 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2401.06118",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"aqlm",
"region:us"
] | text-generation | 2024-01-30T10:37:07Z | Official [AQLM](https://arxiv.org/abs/2401.06118) quantization of `meta-llama/Llama-2-7b-hf`.
For this quantization, we used 2 codebooks of 8 bits.
Selected evaluation results for this and other models:
| Model | AQLM scheme | WikiText 2 PPL | Model size, Gb | Hub link |
|------------|-------------|----------------|----------------|--------------------------------------------------------------------------|
| Llama-2-7b | 1x16 | 5.92 | 2.4 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf) |
| Llama-2-7b (THIS) | 2x8 | 6.69 | 2.2 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-2x8-hf) |
| Llama-2-7b | 8x8 | 6.61 | 2.2 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-hf) |
| Llama-2-13b| 1x16 | 5.22 | 4.1 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-13b-AQLM-2Bit-1x16-hf)|
| Llama-2-70b| 1x16 | 3.83 | 18.8 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-70b-AQLM-2Bit-1x16-hf)|
| Llama-2-70b| 2x8 | 4.21 | 18.2 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-70b-AQLM-2Bit-2x8-hf) |
| Mixtral-8x7b| 1x16 | 3.35 | 12.6 | [Link](https://huggingface.co/ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf)|
| Mixtral-8x7b-Instruct| 1x16 | - | 12.6 | [Link](https://huggingface.co/ISTA-DASLab/Mixtral-8x7B-Instruct-v0_1-AQLM-2Bit-1x16-hf)|
**UPD** (20.02.2024).
We applied global finetuning on top of quantized model and improved results compared to first revision.
To learn more about the inference, as well as the information on how to quantize models yourself, please refer to the [official GitHub repo](https://github.com/Vahe1994/AQLM). |
ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf | ISTA-DASLab | 2024-03-11T20:52:35Z | 44 | 23 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"arxiv:2401.06118",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"aqlm",
"region:us"
] | text-generation | 2024-02-08T12:40:49Z | Official [AQLM](https://arxiv.org/abs/2401.06118) quantization of `mistralai/Mixtral-8x7B-v0.1`.
For this quantization, we used 1 codebook of 16 bits.
Selected evaluation results for this and other models:
| Model | AQLM scheme | WikiText 2 PPL | Model size, Gb | Hub link |
|------------|-------------|----------------|----------------|--------------------------------------------------------------------------|
| Llama-2-7b | 1x16 | 5.92 | 2.4 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf) |
| Llama-2-7b | 2x8 | 6.69 | 2.2 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-2x8-hf) |
| Llama-2-7b | 8x8 | 6.61 | 2.2 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-8x8-hf) |
| Llama-2-13b| 1x16 | 5.22 | 4.1 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-13b-AQLM-2Bit-1x16-hf)|
| Llama-2-70b| 1x16 | 3.83 | 18.8 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-70b-AQLM-2Bit-1x16-hf)|
| Llama-2-70b| 2x8 | 4.21 | 18.2 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-70b-AQLM-2Bit-2x8-hf) |
| Mixtral-8x7b (THIS) | 1x16 | 3.35 | 12.6 | [Link](https://huggingface.co/ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf)|
| Mixtral-8x7b-Instruct| 1x16 | - | 12.6 | [Link](https://huggingface.co/ISTA-DASLab/Mixtral-8x7B-Instruct-v0_1-AQLM-2Bit-1x16-hf)|
To learn more about the inference, as well as the information on how to quantize models yourself, please refer to the [official GitHub repo](https://github.com/Vahe1994/AQLM). |
Weni/ZeroShot-3.4.3-Mistral-7b-DPO-1.0.0 | Weni | 2024-03-11T20:50:47Z | 0 | 0 | trl | [
"trl",
"safetensors",
"DPO",
"ZeroShot",
"en",
"es",
"pt",
"base_model:Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged",
"base_model:finetune:Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged",
"license:mit",
"region:us"
] | null | 2024-03-11T20:06:32Z | ---
license: mit
library_name: "trl"
tags:
- DPO
- ZeroShot
base_model: Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged
model-index:
- name: Weni/ZeroShot-3.4.3-Mistral-7b-DPO-1.0.0
results: []
language: ['en', 'es', 'pt']
---
# Weni/ZeroShot-3.4.3-Mistral-7b-DPO-1.0.0
This model is a fine-tuned version of [Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged] on the dataset Weni/zeroshot-dpo-1.0.0 with the DPO trainer. It is part of the ZeroShot project for [Weni](https://weni.ai/).
It achieves the following results on the evaluation set:
{'eval_loss': 0.20482958853244781, 'eval_runtime': 23.1466, 'eval_samples_per_second': 2.635, 'eval_steps_per_second': 0.346, 'eval_rewards/chosen': 0.035770073533058167, 'eval_rewards/rejected': -7.72912073135376, 'eval_rewards/accuracies': 0.9375, 'eval_rewards/margins': 7.764890670776367, 'eval_logps/rejected': -91.3374252319336, 'eval_logps/chosen': -15.580533981323242, 'eval_logits/rejected': -0.613441526889801, 'eval_logits/chosen': -0.7170205116271973, 'epoch': 5.65}
## Intended uses & limitations
This model has not been trained to avoid specific intructions.
## Training procedure
Finetuning was done on the model Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged with the following prompt:
```
Portuguese:
[INST] Você é muito especialista em classificar a frase do usuário em um chatbot sobre: {context}
Pare, pense bem e responda com APENAS UM ÚNICO \`id\` da classe que melhor represente a intenção para a frase do usuário de acordo com a análise de seu contexto, responda APENAS com o \`id\` da classe só se você tiver muita certeza e não explique o motivo. Na ausência, falta de informações ou caso a frase do usuário não se enquadre em nenhuma classe, classifique como "-1".
# Essas são as Classes com seus Id e Contexto:
{all_classes}
# Frase do usuário: {input}
# Id da Classe: [/INST]
Spanish:
[INST] Eres muy experto en clasificar la frase del usuario en un chatbot sobre: {context}
Deténgase, piense bien y responda con SOLO UN ÚNICO \`id\` de la clase que mejor represente la intención para la frase del usuario de acuerdo con el análisis de su contexto, responda SOLO con el \`id\` de la clase si está muy seguro y no explique el motivo. En ausencia, falta de información o en caso de que la frase del usuario no se ajuste a ninguna clase, clasifique como "-1".
# Estas son las Clases con sus Id y Contexto:
{all_classes}
# Frase del usuario: {input}
# Id de la Clase: [/INST]
English:
[INST] You are very expert in classifying the user sentence in a chatbot about: {context}
Stop, think carefully, and respond with ONLY ONE SINGLE \`id\` of the class that best represents the intention for the user's sentence according to the analysis of its context, respond ONLY with the \`id\` of the class if you are very sure and do not explain the reason. In the absence, lack of information, or if the user's sentence does not fit into any class, classify as "-1".
# These are the Classes and its Context:
{all_classes}
# User's sentence: {input}
# Class Id: [/INST]
Chosen_response:
{chosen_response}
Rejected_response:
{rejected_response}
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- per_device_train_batch_size: 8
- per_device_eval_batch_size: 8
- gradient_accumulation_steps: 4
- num_gpus: 1
- total_train_batch_size: 32
- optimizer: AdamW
- lr_scheduler_type: cosine
- num_steps: 96
- quantization_type: bitsandbytes
- LoRA: ("\n - bits: 4\n - use_exllama: True\n - device_map: auto\n - use_cache: False\n - lora_r: 8\n - lora_alpha: 16\n - lora_dropout: 0.1\n - bias: none\n - target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj']\n - task_type: CAUSAL_LM",)
### Training results
### Framework versions
- transformers==4.38.2
- datasets==2.17.1
- peft==0.8.2
- safetensors==0.4.2
- evaluate==0.4.1
- bitsandbytes==0.42
- huggingface_hub==0.20.3
- seqeval==1.2.2
- optimum==1.17.1
- auto-gptq==0.7.0
- gpustat==1.1.1
- deepspeed==0.13.2
- wandb==0.16.3
- trl==0.7.11
- accelerate==0.27.2
- coloredlogs==15.0.1
- traitlets==5.14.1
- autoawq@https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.0/autoawq-0.2.0+cu118-cp310-cp310-linux_x86_64.whl
### Hardware
- Cloud provided: runpod.io
|
SimoneJLaudani/trainer3b | SimoneJLaudani | 2024-03-11T20:48:55Z | 93 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-11T20:48:26Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: trainer3b
results: []
---
<!-- 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. -->
# trainer3b
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0025
- Accuracy: 1.0
- F1: 1.0
## 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:
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|
| 0.0013 | 1.2 | 30 | 0.0049 | 1.0 | 1.0 |
| 0.0003 | 2.4 | 60 | 0.0025 | 1.0 | 1.0 |
| 0.0002 | 3.6 | 90 | 0.0020 | 1.0 | 1.0 |
| 0.0001 | 4.8 | 120 | 0.0038 | 1.0 | 1.0 |
| 0.0001 | 6.0 | 150 | 0.0031 | 1.0 | 1.0 |
| 0.0001 | 7.2 | 180 | 0.0027 | 1.0 | 1.0 |
| 0.0001 | 8.4 | 210 | 0.0025 | 1.0 | 1.0 |
| 0.0001 | 9.6 | 240 | 0.0025 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
samiksharasaikar/travel-xzg | samiksharasaikar | 2024-03-11T20:48:13Z | 3 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-03-11T20:44:13Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### Travel-XZG Dreambooth model trained by samiksharasaikar following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: I21-15
Sample pictures of this concept:
|
tolgadev/TrendyolMixLLM_v1.1-ties | tolgadev | 2024-03-11T20:47:17Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0",
"Trendyol/Trendyol-LLM-7b-chat-v0.1",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-11T20:42:19Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0
- Trendyol/Trendyol-LLM-7b-chat-v0.1
---
# TrendyolMixLLM_v1.1-ties
TrendyolMixLLM_v1.1-ties is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0](https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0)
* [Trendyol/Trendyol-LLM-7b-chat-v0.1](https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-v0.1)
## 🧩 Configuration
```yaml
models:
- model: Trendyol/Trendyol-LLM-7b-chat-v1.0
# no parameters necessary for base model
- model: Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0
parameters:
density: 0.5
weight: 0.5
- model: Trendyol/Trendyol-LLM-7b-chat-v0.1
parameters:
density: 0.5
weight: 0.3
merge_method: ties
base_model: Trendyol/Trendyol-LLM-7b-chat-v1.0
parameters:
normalize: true
dtype: float16
``` |
tolgadev/Trendyol-LLM-7b-chat-dpo-v1.0-GGUF | tolgadev | 2024-03-11T20:45:41Z | 56 | 2 | transformers | [
"transformers",
"gguf",
"trendyol",
"llama-2",
"turkish",
"text-generation",
"tr",
"en",
"base_model:Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0",
"base_model:quantized:Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0",
"license:apache-2.0",
"region:us",
"conversational"
] | text-generation | 2024-03-11T17:55:28Z | ---
model_name: Trendyol-LLM-7b-chat-dpo-v1.0-gguf
model_creator: Trendyol
base_model: Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0
language:
- tr
- en
pipeline_tag: text-generation
license: apache-2.0
model_type: llama
library_name: transformers
inference: false
tags:
- trendyol
- llama-2
- turkish
quantized_by: tolgadev
---
## Trendyol-LLM-7b-chat-dpo-v1.0 models
----
## Description
This repo contains all types of GGUF formatted model files for [Trendyol-LLM-7b-chat-dpo-v1.0](https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0).
<img src="https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0/resolve/main/trendyol-llm-mistral.jpg"
alt="drawing" width="400"/>
## Quantized LLM models and methods
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [Trendyol-LLM-7b-chat-dpo-v1.0.Q2_K.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-dpo-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-dpo-v1.0.Q2_K.gguf) | Q2_K | 2 | 2.59 GB| 4.88 GB | smallest, significant quality loss - not recommended for most purposes |
| [Trendyol-LLM-7b-chat-dpo-v1.0.Q3_K_S.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-dpo-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-dpo-v1.0.Q3_K_S.gguf) | Q3_K_S | 3 | 3.01 GB| 5.56 GB | very small, high quality loss |
| [Trendyol-LLM-7b-chat-dpo-v1.0.Q3_K_M.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-dpo-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-dpo-v1.0.Q3_K_M.gguf) | Q3_K_M | 3 | 3.36 GB| 5.91 GB | very small, high quality loss |
| [Trendyol-LLM-7b-chat-dpo-v1.0.Q3_K_L.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-dpo-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-dpo-v1.0.Q3_K_L.gguf) | Q3_K_L | 3 | 3.66 GB| 6.20 GB | small, substantial quality loss |
| [Trendyol-LLM-7b-chat-dpo-v1.0.Q4_0.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-dpo-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-dpo-v1.0.Q4_0.gguf) | Q4_0 | 4 | 3.9 GB| 6.45 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Trendyol-LLM-7b-chat-dpo-v1.0.Q4_K_S.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-dpo-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-dpo-v1.0.Q4_K_S.gguf) | Q4_K_S | 4 | 3.93 GB| 6.48 GB | small, greater quality loss |
| [Trendyol-LLM-7b-chat-dpo-v1.0.Q4_K_M.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-dpo-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-dpo-v1.0.Q4_K_M.gguf) | Q4_K_M | 4 | 4.15 GB| 6.69 GB | medium, balanced quality - recommended |
| [Trendyol-LLM-7b-chat-dpo-v1.0.Q5_0.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-dpo-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-dpo-v1.0.Q5_0.gguf) | Q5_0 | 5 | 4.73 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Trendyol-LLM-7b-chat-dpo-v1.0.Q5_K_S.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-dpo-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-dpo-v1.0.Q5_K_S.gguf) | Q5_K_S | 5 | 4.75 GB| 7.27 GB | large, low quality loss - recommended |
| [Trendyol-LLM-7b-chat-dpo-v1.0.Q5_K_M.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-dpo-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-dpo-v1.0.Q5_K_M.gguf) | Q5_K_M | 5 | 4.86 GB| 7.40 GB | large, very low quality loss - recommended |
| [Trendyol-LLM-7b-chat-dpo-v1.0.Q6_K.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-dpo-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-dpo-v1.0.Q6_K.gguf) | Q6_K | 6 | 5.61 GB| 8.15 GB | very large, extremely low quality loss |
The names of the quantization methods follow the naming convention: "q" + the number of bits + the variant used (detailed below). Here is a list of all the models and their corresponding use cases, based on model cards made by [TheBloke](https://huggingface.co/TheBloke/):
* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_s`: Uses Q3_K for all tensors
* `q4_0`: Original quant method, 4-bit.
* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
* `q4_k_s`: Uses Q4_K for all tensors
* `q5_0`: Higher accuracy, higher resource usage and slower inference.
* `q5_1`: Even higher accuracy, resource usage and slower inference.
* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
* `q5_k_s`: Uses Q5_K for all tensors
* `q6_k`: Uses Q8_K for all tensors
**TheBloke recommends using Q5_K_M** as it preserves most of the model's performance.
Alternatively, you can use Q4_K_M if you want to save some memory.
In general, K_M versions are better than K_S versions.
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
## Special thanks to [TheBloke on Huggingface](https://huggingface.co/TheBloke) and [Maxime Labonne on Github](https://github.com/mlabonne/llm-course)
-----
# **Trendyol LLM v1.0 - DPO**
Trendyol LLM v1.0 - DPO is a generative model that is based on Mistral 7B model. DPO training was applied. This is the repository for the chat model.
## Model Details
**Model Developers** Trendyol
**Variations** [base](https://huggingface.co/Trendyol/Trendyol-LLM-7b-base-v1.0), [chat](https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-v1.0), and dpo variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Trendyol LLM is an auto-regressive language model (based on Mistral 7b) that uses an optimized transformer architecture. Huggingface TRL lib was used for training. The DPO version is fine-tuned on 11K sets (prompt-chosen-reject) with the following trainables by using LoRA:
- **lr**=5e-6
- **lora_rank**=64
- **lora_alpha**=128
- **lora_trainable**=q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj
- **lora_dropout**=0.05
- **bf16**=True
- **beta**=0.01
- **max_length**= 1024
- **max_prompt_length**= 512
- **lr_scheduler_type**= cosine
- **torch_dtype**= bfloat16
<img src="https://camo.githubusercontent.com/3e61ca080778f62988b459c7321726fa35bb3776ceb07ecaabf71ebca44f95a7/68747470733a2f2f68756767696e67666163652e636f2f64617461736574732f74726c2d696e7465726e616c2d74657374696e672f6578616d706c652d696d616765732f7265736f6c76652f6d61696e2f696d616765732f74726c5f62616e6e65725f6461726b2e706e67"
alt="drawing" width="600"/>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/lora_diagram.png"
alt="drawing" width="600"/>
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
device_map='auto',
load_in_8bit=True)
sampling_params = dict(do_sample=True, temperature=0.3, top_k=50, top_p=0.9)
pipe = pipeline("text-generation",
model=model,
tokenizer=tokenizer,
device_map="auto",
max_new_tokens=1024,
return_full_text=True,
repetition_penalty=1.1
)
DEFAULT_SYSTEM_PROMPT = "Sen yardımcı bir asistansın ve sana verilen talimatlar doğrultusunda en iyi cevabı üretmeye çalışacaksın.\n"
TEMPLATE = (
"[INST] {system_prompt}\n\n"
"{instruction} [/INST]"
)
def generate_prompt(instruction, system_prompt=DEFAULT_SYSTEM_PROMPT):
return TEMPLATE.format_map({'instruction': instruction,'system_prompt': system_prompt})
def generate_output(user_query, sys_prompt=DEFAULT_SYSTEM_PROMPT):
prompt = generate_prompt(user_query, sys_prompt)
outputs = pipe(prompt,
**sampling_params
)
return outputs[0]["generated_text"].split("[/INST]")[-1]
user_query = "Türkiye'de kaç il var?"
response = generate_output(user_query)
print(response)
```
with chat template:
```python
pipe = pipeline("conversational",
model=model,
tokenizer=tokenizer,
device_map="auto",
max_new_tokens=1024,
repetition_penalty=1.1
)
messages = [
{"role": "user", "content": "Türkiye'de kaç il var?"}
]
outputs = pipe(messages, **sampling_params)
print(outputs)
```
## Limitations, Risks, Bias, and Ethical Considerations
### Limitations and Known Biases
- **Primary Function and Application:** Trendyol LLM, an autoregressive language model, is primarily designed to predict the next token in a text string. While often used for various applications, it is important to note that it has not undergone extensive real-world application testing. Its effectiveness and reliability across diverse scenarios remain largely unverified.
- **Language Comprehension and Generation:** The model is primarily trained in standard English and Turkish. Its performance in understanding and generating slang, informal language, or other languages may be limited, leading to potential errors or misinterpretations.
- **Generation of False Information:** Users should be aware that Trendyol LLM may produce inaccurate or misleading information. Outputs should be considered as starting points or suggestions rather than definitive answers.
### Risks and Ethical Considerations
- **Potential for Harmful Use:** There is a risk that Trendyol LLM could be used to generate offensive or harmful language. We strongly discourage its use for any such purposes and emphasize the need for application-specific safety and fairness evaluations before deployment.
- **Unintended Content and Bias:** The model was trained on a large corpus of text data, which was not explicitly checked for offensive content or existing biases. Consequently, it may inadvertently produce content that reflects these biases or inaccuracies.
- **Toxicity:** Despite efforts to select appropriate training data, the model is capable of generating harmful content, especially when prompted explicitly. We encourage the open-source community to engage in developing strategies to minimize such risks.
### Recommendations for Safe and Ethical Usage
- **Human Oversight:** We recommend incorporating a human curation layer or using filters to manage and improve the quality of outputs, especially in public-facing applications. This approach can help mitigate the risk of generating objectionable content unexpectedly.
- **Application-Specific Testing:** Developers intending to use Trendyol LLM should conduct thorough safety testing and optimization tailored to their specific applications. This is crucial, as the model’s responses can be unpredictable and may occasionally be biased, inaccurate, or offensive.
- **Responsible Development and Deployment:** It is the responsibility of developers and users of Trendyol LLM to ensure its ethical and safe application. We urge users to be mindful of the model's limitations and to employ appropriate safeguards to prevent misuse or harmful consequences. |
mariogemoll/bppc-vit | mariogemoll | 2024-03-11T20:44:14Z | 180 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-03-11T12:21:52Z | ---
library_name: transformers
tags: []
---
# Body progress pic classifier
A visual transformer model based on [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k)
to detect if a gym progress pic was taken from the front, from the back, from the left side or from
the right side. There is a [demo](https://huggingface.co/spaces/mariogemoll/bppc), but really this
is just my first dummy project using the transformers library.
|
cnmoro/t5-small-named-entity-recognition | cnmoro | 2024-03-11T20:41:24Z | 117 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-11T04:14:50Z | ---
language:
- en
widget:
- text: "Emma Stone looked genuinely shocked when her name was announced as the best actress winner earlier. “I think I blacked out! I was very shocked,” she says backstage. “I still feel like I’m spinning a little bit. It’s a huge honour and I’m very surprised.” Having experienced a bit of a wardrobe malfunction, she reassures us that all is now well. “They sewed me back in! I genuinely think I busted it during I’m Just Ken! I was so amazed by Ryan Gosling and that number just blew my mind. I was just going for it and things just happen.” She said she learned a lot from playing Bella Baxter in Yorgos Lanthimos’s film."
---
Finetuned on 6.6 million pairs of sentences and named entities. |
nop1006/gte-base-zh-finetuned-emotion | nop1006 | 2024-03-11T20:38:38Z | 92 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:thenlper/gte-base-zh",
"base_model:finetune:thenlper/gte-base-zh",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-07T15:16:58Z | ---
license: mit
base_model: thenlper/gte-base-zh
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: gte-base-zh-finetuned-emotion
results: []
---
<!-- 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. -->
# gte-base-zh-finetuned-emotion
This model is a fine-tuned version of [thenlper/gte-base-zh](https://huggingface.co/thenlper/gte-base-zh) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3958
- Accuracy: 0.8272
- F1: 0.8189
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4103 | 1.0 | 570 | 0.3675 | 0.8333 | 0.8271 |
| 0.3452 | 2.0 | 1140 | 0.3796 | 0.8290 | 0.8180 |
| 0.2784 | 3.0 | 1710 | 0.3930 | 0.8397 | 0.8346 |
| 0.1904 | 4.0 | 2280 | 0.5113 | 0.8364 | 0.8301 |
| 0.1239 | 5.0 | 2850 | 0.6590 | 0.8232 | 0.8100 |
| 0.0828 | 6.0 | 3420 | 0.8153 | 0.8254 | 0.8241 |
| 0.0624 | 7.0 | 3990 | 0.8672 | 0.8250 | 0.8210 |
| 0.0413 | 8.0 | 4560 | 0.9244 | 0.8255 | 0.8159 |
| 0.0303 | 9.0 | 5130 | 1.0888 | 0.8199 | 0.8068 |
| 0.0233 | 10.0 | 5700 | 1.1171 | 0.8250 | 0.8194 |
| 0.0159 | 11.0 | 6270 | 1.2642 | 0.8241 | 0.8115 |
| 0.009 | 12.0 | 6840 | 1.2930 | 0.8265 | 0.8169 |
| 0.0056 | 13.0 | 7410 | 1.3720 | 0.8260 | 0.8150 |
| 0.0019 | 14.0 | 7980 | 1.3878 | 0.8255 | 0.8168 |
| 0.003 | 15.0 | 8550 | 1.3958 | 0.8272 | 0.8189 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
rumeysacelik/turkishReviews-ds-commerce | rumeysacelik | 2024-03-11T20:34:00Z | 0 | 1 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-11T20:33:59Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Litzy619/V0309P8 | Litzy619 | 2024-03-11T20:32:59Z | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2024-03-11T04:34:35Z | ---
license: mit
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: V0309P8
results: []
---
<!-- 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. -->
# V0309P8
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0688
## 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 20
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1598 | 0.09 | 10 | 1.0083 |
| 0.4064 | 0.17 | 20 | 0.1257 |
| 0.1215 | 0.26 | 30 | 0.0774 |
| 0.1055 | 0.34 | 40 | 0.0736 |
| 0.0962 | 0.43 | 50 | 0.0642 |
| 0.0853 | 0.51 | 60 | 0.0657 |
| 0.0804 | 0.6 | 70 | 0.0616 |
| 0.0843 | 0.68 | 80 | 0.0628 |
| 0.0729 | 0.77 | 90 | 0.0615 |
| 0.0704 | 0.85 | 100 | 0.0609 |
| 0.0761 | 0.94 | 110 | 0.0601 |
| 0.0721 | 1.02 | 120 | 0.0648 |
| 0.0697 | 1.11 | 130 | 0.0638 |
| 0.0654 | 1.19 | 140 | 0.0620 |
| 0.0618 | 1.28 | 150 | 0.0608 |
| 0.0632 | 1.37 | 160 | 0.0648 |
| 0.0627 | 1.45 | 170 | 0.0636 |
| 0.0584 | 1.54 | 180 | 0.0622 |
| 0.0621 | 1.62 | 190 | 0.0604 |
| 0.0615 | 1.71 | 200 | 0.0625 |
| 0.0625 | 1.79 | 210 | 0.0594 |
| 0.0606 | 1.88 | 220 | 0.0651 |
| 0.0556 | 1.96 | 230 | 0.0609 |
| 0.0544 | 2.05 | 240 | 0.0641 |
| 0.0462 | 2.13 | 250 | 0.0659 |
| 0.0468 | 2.22 | 260 | 0.0695 |
| 0.043 | 2.3 | 270 | 0.0711 |
| 0.0523 | 2.39 | 280 | 0.0665 |
| 0.051 | 2.47 | 290 | 0.0643 |
| 0.0502 | 2.56 | 300 | 0.0647 |
| 0.0509 | 2.65 | 310 | 0.0661 |
| 0.0434 | 2.73 | 320 | 0.0677 |
| 0.0452 | 2.82 | 330 | 0.0682 |
| 0.0444 | 2.9 | 340 | 0.0686 |
| 0.0477 | 2.99 | 350 | 0.0688 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
6001k1d/dqn-SpaceInvadersNoFrameskip-v4 | 6001k1d | 2024-03-11T20:28:51Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2022-07-16T15:51:34Z | ---
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: 579.50 +/- 324.58
name: mean_reward
verified: false
---
# **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 6001k1d -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 6001k1d -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 6001k1d
```
## Hyperparameters
```python
OrderedDict([('batch_size', 128),
('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', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
tsavage68/mistralit2_1000_STEPS_5e7_rate_03_beta_DPO | tsavage68 | 2024-03-11T20:21:55Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-11T09:38:21Z | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.2
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: mistralit2_1000_STEPS_5e7_rate_03_beta_DPO
results: []
---
<!-- 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. -->
# mistralit2_1000_STEPS_5e7_rate_03_beta_DPO
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0554
- Rewards/chosen: -4.6458
- Rewards/rejected: -7.9897
- Rewards/accuracies: 0.6593
- Rewards/margins: 3.3439
- Logps/rejected: -55.2048
- Logps/chosen: -38.8718
- Logits/rejected: -2.6256
- Logits/chosen: -2.6266
## 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-07
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6053 | 0.1 | 50 | 0.6740 | -0.3080 | -0.4763 | 0.5429 | 0.1682 | -30.1599 | -24.4126 | -2.8583 | -2.8587 |
| 0.6455 | 0.2 | 100 | 0.6888 | -1.9296 | -2.8473 | 0.6110 | 0.9176 | -38.0633 | -29.8180 | -2.7011 | -2.7016 |
| 0.6646 | 0.29 | 150 | 0.8842 | -4.4677 | -5.7716 | 0.5956 | 1.3039 | -47.8112 | -38.2782 | -2.7068 | -2.7076 |
| 0.8576 | 0.39 | 200 | 0.8269 | 0.2095 | -0.3290 | 0.5341 | 0.5385 | -29.6690 | -22.6876 | -2.8074 | -2.8077 |
| 0.9282 | 0.49 | 250 | 0.8715 | -3.3030 | -4.1864 | 0.5758 | 0.8834 | -42.5272 | -34.3958 | -2.8320 | -2.8326 |
| 0.883 | 0.59 | 300 | 0.8491 | -1.6930 | -2.7293 | 0.5846 | 1.0364 | -37.6702 | -29.0290 | -2.8023 | -2.8028 |
| 0.7641 | 0.68 | 350 | 0.8305 | -0.5284 | -1.4934 | 0.5868 | 0.9650 | -33.5504 | -25.1471 | -2.8008 | -2.8013 |
| 0.8485 | 0.78 | 400 | 0.8168 | -1.8042 | -3.2662 | 0.6286 | 1.4620 | -39.4597 | -29.3999 | -2.8978 | -2.8983 |
| 0.6637 | 0.88 | 450 | 0.9089 | -4.1779 | -5.6349 | 0.6220 | 1.4570 | -47.3556 | -37.3123 | -2.7996 | -2.8003 |
| 0.8293 | 0.98 | 500 | 0.7790 | -1.7260 | -3.1768 | 0.6242 | 1.4508 | -39.1617 | -29.1392 | -2.7937 | -2.7943 |
| 0.1061 | 1.07 | 550 | 0.8642 | -2.6748 | -4.9677 | 0.6659 | 2.2929 | -45.1314 | -32.3019 | -2.7609 | -2.7616 |
| 0.1183 | 1.17 | 600 | 1.0052 | -4.2792 | -7.1691 | 0.6527 | 2.8899 | -52.4695 | -37.6498 | -2.6760 | -2.6769 |
| 0.3423 | 1.27 | 650 | 1.0032 | -4.1972 | -7.1444 | 0.6571 | 2.9472 | -52.3871 | -37.3765 | -2.6563 | -2.6572 |
| 0.3015 | 1.37 | 700 | 1.0111 | -4.0263 | -7.1542 | 0.6549 | 3.1280 | -52.4198 | -36.8067 | -2.6518 | -2.6526 |
| 0.0814 | 1.46 | 750 | 1.0416 | -4.3351 | -7.5972 | 0.6484 | 3.2621 | -53.8964 | -37.8360 | -2.6335 | -2.6344 |
| 0.1279 | 1.56 | 800 | 1.0511 | -4.6097 | -7.9321 | 0.6505 | 3.3224 | -55.0127 | -38.7514 | -2.6277 | -2.6287 |
| 0.1507 | 1.66 | 850 | 1.0478 | -4.6393 | -7.9834 | 0.6484 | 3.3441 | -55.1838 | -38.8501 | -2.6262 | -2.6272 |
| 0.2148 | 1.76 | 900 | 1.0515 | -4.6439 | -7.9924 | 0.6527 | 3.3485 | -55.2139 | -38.8655 | -2.6260 | -2.6270 |
| 0.2291 | 1.86 | 950 | 1.0554 | -4.6452 | -7.9877 | 0.6505 | 3.3425 | -55.1980 | -38.8697 | -2.6257 | -2.6267 |
| 0.13 | 1.95 | 1000 | 1.0554 | -4.6458 | -7.9897 | 0.6593 | 3.3439 | -55.2048 | -38.8718 | -2.6256 | -2.6266 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.0.0+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Villian7/01Coder | Villian7 | 2024-03-11T20:19:29Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"code-llm",
"mistral-7b",
"language-model",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-11T15:47:30Z | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
tags:
- code-llm
- mistral-7b
- language-model
---
# Model Card for 01Coder 7B
This model card provides details about a code language model (LLM) based on Mistral 7B architecture. It has been trained on a combination of three datasets: ise-uiuc/Magicoder-OSS-Instruct-75K, HuggingFaceH4/CodeAlpaca_20K, and theblackcat102/evol-codealpaca-v1.
## Model Details
### Model Description
This model is a language model fine-tuned for code generation tasks, leveraging the Mistral 7B base model architecture. It has been trained on a combination of three datasets, namely Magicoder-OSS-Instruct-75K, CodeAlpaca_20K, and evol-codealpaca-v1. The model aims to assist developers in generating code snippets for various programming tasks, ranging from natural language instructions to specific coding prompts.
- **Developed by:** Manoj Athreya A
- **Model type:** Language model (LLM)
- **License:** [Apache 2.0 License]
- **Finetuned from model:** Mistral 7B
## Intended Uses
- Code generation from natural language prompts.
- Assisting developers in completing code snippets.
- Augmenting code-related tasks with automated generation capabilities.
## Limitations and Ethical Considerations
- **Bias:** As with any language model, biases present in the training data may manifest in the generated code snippets.
- **Accuracy:** While the model aims to generate accurate code, it may occasionally produce incorrect or suboptimal solutions, especially for complex tasks.
- **Security:** Generated code should be reviewed for security vulnerabilities, as the model may inadvertently produce insecure implementations.
- **Ethical Use:** Users are encouraged to employ the model responsibly and ethically, avoiding harmful or malicious use cases.
### Recommendations
- Fine-tuning the model on specific domains or tasks may improve its performance.
- Validate generated code in real-world scenarios to ensure its correctness and reliability.
- Provide feedback to continuously improve the model's performance and address any issues encountered during usage.
## License
- The source code in this repo is licensed under the Apache 2.0 license.
## Version History
- 01-Coder-7Bv0.1
|
YASHIKAaa/bert2 | YASHIKAaa | 2024-03-11T20:17:12Z | 194 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-11T20:16:56Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [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
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### 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
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Glossary [optional]
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|
jackshannon/phi-1_5-finetuned-question-generation-merged | jackshannon | 2024-03-11T20:16:20Z | 34 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-11T20:12:26Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
RunDiffusion/Juggernaut-XL-Lightning | RunDiffusion | 2024-03-11T20:09:52Z | 11,409 | 48 | diffusers | [
"diffusers",
"art",
"people",
"diffusion",
"Cinematic",
"Photography",
"Landscape",
"Interior",
"Food",
"Car",
"Wildlife",
"Architecture",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-02-23T20:45:29Z | ---
language:
- en
license: creativeml-openrail-m
library_name: diffusers
tags:
- art
- people
- diffusion
- Cinematic
- Photography
- Landscape
- Interior
- Food
- Car
- Wildlife
- Architecture
thumbnail: https://imagedelivery.net/siANnpeNAc_S2q1M3-eDrA/49a32981-4aa2-410e-a5b1-35835bf20d00/padthumb
base_model: stabilityai/stable-diffusion-xl-base-1.0
---
# Juggernaut XL + RunDiffusion Lightning!
## Want the full version of Juggernaut? Try v9! [Juggernaut v9 + RunDiffusion Photo v2](https://huggingface.co/RunDiffusion/Juggernaut-XL-v9)
This model is not permitted to be used behind API services. Please contact [[email protected]](mailto:[email protected]) for business inquires, commercial licensing, custom models, and consultation.
Juggernaut is available on the new Auto1111 Forge on [RunDiffusion](http://rundiffusion.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=Kandoo)
#Juggernaut XL Lightning is here
Get ready for speed and quality. Who ever said you couldn't have both?! Now you can with the worlds most downloaded model series!
Here are some tips to get you started.
Use this in Automatic1111 and Automatic1111 Forge (Both available on [RunDiffusion](http://rundiffusion.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=Kandoo))
Start with your favorite prompt and negative prompt.
- Set the sampler to: DPM++ SDE or DPM++ SDE Karras
- Set the steps between 5 and 7
- Set the CFG between 1.5 and 2
- Set the resolution to >= 1024x1024
 |
brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity | brucethemoose | 2024-03-11T20:09:21Z | 1,392 | 11 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"merge",
"en",
"license:other",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-09T07:18:23Z | ---
language:
- en
license: other
library_name: transformers
tags:
- text-generation-inference
- merge
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
pipeline_tag: text-generation
model-index:
- name: CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 67.41
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 85.77
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 77.44
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 57.84
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 83.11
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 61.33
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity
name: Open LLM Leaderboard
---
### Possibly obsolete, replaced by https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v5
Old model description below:
***
**Dolphin-2.2-yi-34b-200k**, **Nous-Capybara-34B**, **Tess-M-v1.4**, **Airoboros-3_1-yi-34b-200k**, **PlatYi-34B-200K-Q**, and **Una-xaberius-34b-v1beta** merged with a new, experimental implementation of "dare ties" via mergekit. See:
> [Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch](https://github.com/yule-BUAA/MergeLM)
> https://github.com/cg123/mergekit/tree/dare
This variant is merged with a "higher than recommended" density with with the following config, and the tokenizer from chargoddard's Yi-Llama:
```
models:
- model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
# no parameters necessary for base model
- model: /home/alpha/Storage/Models/Raw/migtissera_Tess-34B-v1.4
parameters:
weight: 0.19
density: 0.6
- model: /home/alpha//Storage/Models/Raw/bhenrym14_airoboros-3_1-yi-34b-200k
parameters:
weight: 0.14
density: 0.5
- model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B
parameters:
weight: 0.19
density: 0.6
- model: /home/alpha/Storage/Models/Raw/kyujinpy_PlatYi-34B-200K-Q
parameters:
weight: 0.14
density: 0.5
- model: /home/alpha/FastModels/ehartford_dolphin-2.2-yi-34b-200k
parameters:
weight: 0.19
density: 0.6
- model: /home/alpha/FastModels/fblgit_una-xaberius-34b-v1beta
parameters:
weight: 0.15
density: 0.08
merge_method: dare_ties
base_model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
parameters:
int8_mask: true
dtype: bfloat16
```
***
## Prompt template: Orca-Vicuna?
```
SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
```
It might recognize ChatML from Dolphin+Xaberius, and Llama-chat from Airoboros.
Sometimes the model "spells out" the stop token as `</s>` like Capybara, so you may need to add `</s>` as an additional stopping condition.
***
## Running
Being a Yi model, try disabling the BOS token and/or running a lower temperature with 0.05-0.13 MinP, a little repitition penalty, and no other samplers. Yi tends to run "hot" by default.
24GB GPUs can run Yi-34B-200K models at **45K-75K context** with exllamav2. I go into more detail in this [post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/)
I recommend exl2 quantizations profiled on data similar to the desired task. It is especially sensitive to the quantization data at low bpw! I published my own quantizations on vicuuna chat + fiction writing here: [4bpw](https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-34B-200K-exl2-4bpw-fiction) [3.1bpw](https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-34B-200K-exl2-4bpw-fiction)
To load this in full-context backends like transformers and vllm, you *must* change `max_position_embeddings` in config.json to a lower value than 200,000, otherwise you will OOM!
***
## Testing Notes
Various densities were tested with perplexity tests and long context prompts. Relatively high densities seem to perform better, contrary to the findings of the Super Mario paper.
This particular version is merged with more than the "recommended" max density of 0.5. It seems to result in even better perplexity, and a much higher position on the hf leaderboard, but I'm not sure if this translates to better output.
Weights that add up to 1 seems to be optimal.
Dare Ties is also resulting in seemingly better, lower perplexity merges than a regular ties merge, task arithmetic or a slerp merge.
Xaberuis is not a 200K model, hence it was merged at a very low density to try and preserve Yi 200K's long context performance while still inheriting some of Xaberius's performance.
I chose not to include other finetunes because they aren't trained on the 200K base. If any other 200K finetunes pop up, let me know.
***
## Credits:
https://github.com/cg123/mergekit/tree/dare
https://huggingface.co/ehartford/dolphin-2.2-yi-34b-200k
https://huggingface.co/kyujinpy/PlatYi-34B-200K-Q
https://huggingface.co/NousResearch/Nous-Capybara-34B/
https://huggingface.co/bhenrym14/airoboros-3_1-yi-34b-200k
https://huggingface.co/migtissera/Tess-M-v1.4
https://huggingface.co/fblgit/una-xaberius-34b-v1beta
https://huggingface.co/chargoddard/Yi-34B-200K-Llama
https://huggingface.co/01-ai/Yi-34B-200K
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity)
| Metric |Value|
|---------------------------------|----:|
|Avg. |72.15|
|AI2 Reasoning Challenge (25-Shot)|67.41|
|HellaSwag (10-Shot) |85.77|
|MMLU (5-Shot) |77.44|
|TruthfulQA (0-shot) |57.84|
|Winogrande (5-shot) |83.11|
|GSM8k (5-shot) |61.33|
|
brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity | brucethemoose | 2024-03-11T20:09:17Z | 1,397 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"license:other",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-09T16:39:14Z | ---
license: other
tags:
- merge
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
model-index:
- name: CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 66.89
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 85.69
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 77.35
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 57.63
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 82.0
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 59.82
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity
name: Open LLM Leaderboard
---
Just a test of a very high density DARE ties merge, for benchmarking on the open llm leaderboard.
You probably shouldn't use this model, use this one instead: https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity
mergekit config:
```
models:
- model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
# no parameters necessary for base model
- model: /home/alpha/Storage/Models/Raw/migtissera_Tess-34B-v1.4
parameters:
weight: 0.19
density: 0.83
- model: /home/alpha//Storage/Models/Raw/bhenrym14_airoboros-3_1-yi-34b-200k
parameters:
weight: 0.14
density: 0.6
- model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B
parameters:
weight: 0.19
density: 0.83
- model: /home/alpha/Storage/Models/Raw/kyujinpy_PlatYi-34B-200K-Q
parameters:
weight: 0.14
density: 0.6
- model: /home/alpha/FastModels/ehartford_dolphin-2.2-yi-34b-200k
parameters:
weight: 0.19
density: 0.83
- model: /home/alpha/FastModels/fblgit_una-xaberius-34b-v1beta
parameters:
weight: 0.15
density: 0.08
merge_method: dare_ties
base_model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
parameters:
int8_mask: true
dtype: bfloat16
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-ExtremeDensity)
| Metric |Value|
|---------------------------------|----:|
|Avg. |71.57|
|AI2 Reasoning Challenge (25-Shot)|66.89|
|HellaSwag (10-Shot) |85.69|
|MMLU (5-Shot) |77.35|
|TruthfulQA (0-shot) |57.63|
|Winogrande (5-shot) |82.00|
|GSM8k (5-shot) |59.82|
|
RunDiffusion/Juggernaut-XL-v6 | RunDiffusion | 2024-03-11T20:08:41Z | 260,317 | 3 | diffusers | [
"diffusers",
"art",
"people",
"diffusion",
"Cinematic",
"Photography",
"Landscape",
"Interior",
"Food",
"Car",
"Wildlife",
"Architecture",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-02-22T00:14:34Z | ---
language:
- en
license: creativeml-openrail-m
library_name: diffusers
tags:
- art
- people
- diffusion
- Cinematic
- Photography
- Landscape
- Interior
- Food
- Car
- Wildlife
- Architecture
thumbnail: https://imagedelivery.net/siANnpeNAc_S2q1M3-eDrA/a38aa9e8-e3cf-4d43-afbd-fd1de0896500/padthumb
base_model: stabilityai/stable-diffusion-xl-base-1.0
---
# Juggernaut XL v6 + RunDiffusion Photo v1 Official
## Juggernaut v9 is here! [Juggernaut v9 + RunDiffusion Photo v2](https://huggingface.co/RunDiffusion/Juggernaut-XL-v9)
This model is not permitted to be used behind API services. Please contact [[email protected]](mailto:[email protected]) for business inquires, commercial licensing, custom models, and consultation.
Juggernaut is available on the new Auto1111 Forge on [RunDiffusion](http://rundiffusion.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=Kandoo)
A big thanks for Version 6 goes to [RunDiffusion](http://rundiffusion.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=Kandoo) ([Photo Model](https://rundiffusion.com/rundiffusion-photo/?utm_source=huggingface&utm_medium=referral&utm_campaign=Kandoo)) and [Adam](https://twitter.com/Colorblind_Adam), who diligently helped me test :) (Leave some love for them ;) )
For business inquires, commercial licensing, custom models, and consultation contact me under [email protected]
|
Sukanth07/gemma-2b-plm-ft | Sukanth07 | 2024-03-11T20:08:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-11T20:07:26Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
RunDiffusion/Juggernaut-XL-v7-fp16-vae-fix | RunDiffusion | 2024-03-11T20:07:02Z | 61 | 1 | diffusers | [
"diffusers",
"art",
"people",
"diffusion",
"Cinematic",
"Photography",
"Landscape",
"Interior",
"Food",
"Car",
"Wildlife",
"Architecture",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-02-22T00:14:59Z | ---
language:
- en
license: creativeml-openrail-m
library_name: diffusers
tags:
- art
- people
- diffusion
- Cinematic
- Photography
- Landscape
- Interior
- Food
- Car
- Wildlife
- Architecture
thumbnail: https://imagedelivery.net/siANnpeNAc_S2q1M3-eDrA/dfaf8264-1355-413a-504d-eb792e69da00/padthumb
base_model: stabilityai/stable-diffusion-xl-base-1.0
---
# Juggernaut XL v7 FP16 VAE Fix + RunDiffusion Photo v1 Official
## Juggernaut v9 is here! [Juggernaut v9 + RunDiffusion Photo v2](https://huggingface.co/RunDiffusion/Juggernaut-XL-v9)
This model is not permitted to be used behind API services. Please contact [[email protected]](mailto:[email protected]) for business inquires, commercial licensing, custom models, and consultation.
Juggernaut is available on the new Auto1111 Forge on [RunDiffusion](http://rundiffusion.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=Kandoo)
A big thanks for Version v7 FP16 VAE Fix goes to [RunDiffusion](http://rundiffusion.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=Kandoo) ([Photo Model](https://rundiffusion.com/rundiffusion-photo/?utm_source=huggingface&utm_medium=referral&utm_campaign=Kandoo)) and [Adam](https://twitter.com/Colorblind_Adam), who diligently helped me test :) (Leave some love for them ;) )
For business inquires, commercial licensing, custom models, and consultation contact me under [email protected]
|
RunDiffusion/Juggernaut-XL-v5 | RunDiffusion | 2024-03-11T20:06:36Z | 60 | 0 | diffusers | [
"diffusers",
"art",
"people",
"diffusion",
"Cinematic",
"Photography",
"Landscape",
"Interior",
"Food",
"Car",
"Wildlife",
"Architecture",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-02-22T00:58:09Z | ---
language:
- en
license: creativeml-openrail-m
library_name: diffusers
tags:
- art
- people
- diffusion
- Cinematic
- Photography
- Landscape
- Interior
- Food
- Car
- Wildlife
- Architecture
thumbnail: https://imagedelivery.net/siANnpeNAc_S2q1M3-eDrA/e93ca50b-aadc-4645-2aa6-2931b5a26900/padthumb
base_model: stabilityai/stable-diffusion-xl-base-1.0
---
# Juggernaut XL v5 Official
## Juggernaut v9 is here! [Juggernaut v9 + RunDiffusion Photo v2](https://huggingface.co/RunDiffusion/Juggernaut-XL-v9)
This model is not permitted to be used behind API services. Please contact [[email protected]](mailto:[email protected]) for business inquires, commercial licensing, custom models, and consultation.
Juggernaut is available on the new Auto1111 Forge on [RunDiffusion](http://rundiffusion.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=Kandoo)
For business inquires, commercial licensing, custom models, and consultation contact me under [email protected]
|
brucethemoose/Yi-34B-200K-DARE-megamerge-v8 | brucethemoose | 2024-03-11T20:05:56Z | 176 | 27 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"Yi",
"en",
"arxiv:2311.03099",
"arxiv:2306.01708",
"license:other",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-14T18:13:39Z | ---
language:
- en
license: other
library_name: transformers
tags:
- mergekit
- merge
- Yi
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
base_model: []
model-index:
- name: Yi-34B-200K-DARE-megamerge-v8
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 67.75
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-megamerge-v8
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 86.06
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-megamerge-v8
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 77.03
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-megamerge-v8
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 56.31
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-megamerge-v8
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 82.79
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-megamerge-v8
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.43
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-megamerge-v8
name: Open LLM Leaderboard
---
# Yi 34B 200K DARE Merge v8
A merge of many Yi 34B 200K models using the new DARE Ties method via mergekit. The goal is to create a merge model that excels at 32K+ context performance, without any additional finetuning.
## Prompt template: Orca-Vicuna
```
SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
```
It might recognize ChatML, and possibly Alpaca-like formats. Raw prompting as described here is also effective: https://old.reddit.com/r/LocalLLaMA/comments/18zqy4s/the_secret_to_writing_quality_stories_with_llms/
## Running
Being a Yi model, run a lower temperature with 0.1 or higher MinP, a little repetition penalty, maybe mirostat with a low tau, and no other samplers. Yi tends to run "hot" by default, and it really needs a low temperature + MinP to cull Yi's huge vocabulary. See the explanation here: https://github.com/ggerganov/llama.cpp/pull/3841
24GB GPUs can efficiently run Yi-34B-200K models at **40K-90K context** with exllamav2, and performant UIs like [exui](https://github.com/turboderp/exui). I go into more detail in this [post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/). 16GB GPUs can still run the high context with aggressive quantization.
Lonestriker has also uploaded general purpose quantizations here: https://huggingface.co/models?sort=trending&search=LoneStriker+Yi-34B-200K-DARE-megamerge-v8
Additionally, TheBloke has uploaded experimental GGUFs using llama.cpp's new imatrix quantization feature, profiled on VMware open-instruct: https://huggingface.co/TheBloke/Yi-34B-200K-DARE-megamerge-v8-GGUF
To load/train this in full-context backends like transformers, you *must* change `max_position_embeddings` in config.json to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends like exllamav2, litellm or unsloth.
## Testing Notes
See: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v5#testing-notes
An intermediate merge model was created to try and extend the context of several 4k models before adding them to the main merge, as seen in the "megamerge" recipe below. I can upload this upon request
In addition, the weight gradients are biased towards Vicuna-format models in the first few layers to try and "emphasize" the Orca-Vicuna prompt template. How sucessful this is remains to be seen.
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.
### Models Merged
The following models were included in the merge:
* https://huggingface.co/kyujinpy/PlatYi-34B-200k-Q-FastChat
* https://huggingface.co/jondurbin/bagel-34b-v0.2
* https://huggingface.co/migtissera/Tess-M-Creative-v1.0
* https://huggingface.co/brucethemoose/SUS-Bagel-200K-DARE-Test
* https://huggingface.co/Mihaiii/Pallas-0.5
* https://huggingface.co/bhenrym14/airoboros-3_1-yi-34b-200k
* https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-v2
* https://huggingface.co/migtissera/Tess-34B-v1.4
* https://huggingface.co/SUSTech/SUS-Chat-34B
* https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2
* https://huggingface.co/bhenrym14/platypus-yi-34b
* https://huggingface.co/Weyaxi/Nous-Hermes-2-SUS-Chat-34B-Slerp
* https://huggingface.co/TriadParty/deepsex-34b
* https://huggingface.co/TriadParty/deepmoney-34b-200k-base
* https://huggingface.co/chargoddard/Yi-34B-200K-Llama
* https://huggingface.co/chargoddard/Yi-34B-Llama
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /home/alpha/Models/Raw/chargoddard_Yi-34B-Llama
# No parameters necessary for base model
- model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
#200K base to extend the context of 4K models, max density as we *want* it to 'interfere'
parameters:
weight: 0.33
density: 1
- model: /home/alpha/Models/Raw/Weyaxi_Nous-Hermes-2-SUS-Chat-34B-Slerp
parameters:
weight: 0.15
density: 0.36
- model: /home/alpha/Models/Raw/jondurbin_bagel-dpo-34b-v0.2
#Mix dpo with sft to tone down dpo
parameters:
weight: 0.06
density: 0.36
- model: /home/alpha/Models/Raw/jondurbin_bagel-34b-v0.2
parameters:
weight: 0.06
density: 0.41
- model: /home/alpha/Models/Raw/bhenrym14_platypus-yi-34b
#Vicuna format
parameters:
weight: 0.19
density: 0.41
# - model: /home/alpha/Models/Raw/01-ai_Yi-34B-Chat #+/home/alpha/Models/Raw/Doctor-Shotgun_limarpv3-yi-llama-34b-lora
# #Can't get lora OR base model to work without erroring out?
# parameters:
# weight: 0.04
# density: 0.36
- model: /home/alpha/Models/Raw/TriadParty_deepsex-34b
#Base model with no prompt
parameters:
weight: 0.21
density: 0.39
merge_method: dare_ties
tokenizer_source: union
base_model: /home/alpha/Models/Raw/chargoddard_Yi-34B-Llama
parameters:
int8_mask: true
dtype: bfloat16
name: 4kmerge-v2
---
models:
- model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
# No parameters necessary for base model
- model: /home/alpha/Storage/Models/Raw/migtissera_Tess-34B-v1.4
#Emphasize the beginning of Vicuna format models
parameters:
weight: [0.22, 0.113, 0.113, 0.113, 0.113, 0.113]
density: 0.61
- model: /home/alpha/Models/Raw/Mihaiii_Pallas-0.5
# Vicuna format
parameters:
weight: [0.22, 0.113, 0.113, 0.113, 0.113, 0.113]
density: 0.61
- model: /home/alpha//Storage/Models/Raw/bhenrym14_airoboros-3_1-yi-34b-200k
parameters:
weight: [0.02, 0.081, 0.081, 0.081, 0.081, 0.081]
density: 0.59
- model: /home/alpha/Storage/Models/Raw/jondurbin_bagel-34b-v0.2
#Only the SFT in the main merge since the DPO version seems to have no long context ability at all, and some overfitting(?) issues
parameters:
weight: [0.02, 0.093, 0.093, 0.093, 0.093, 0.093]
density: 0.4
- model: /home/alpha/Storage/Models/Raw/kyujinpy_PlatYi-34B-200k-Q-FastChat
parameters:
weight: [0.02, 0.081, 0.081, 0.081, 0.081, 0.081]
density: 0.59
#- model: /home/alpha/Storage/Models/Raw/ehartford_dolphin-2.2-yi-34b-200k
# Dolphin 200K seems to be funky according to multiple leaderboards and perplexity tests?
# parameters:
# weight: 0.15
# density: 0.6
- model: /home/alpha/Models/Raw/adamo1139_Yi-34B-200K-AEZAKMI-v2
parameters:
weight: [0.02, 0.096, 0.096, 0.096, 0.096, 0.096]
density: 0.59
- model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B
parameters:
weight: [0.21, 0.115, 0.115, 0.115, 0.115, 0.115]
density: 0.59
- model: 4kmerge-v2
#Previous merge
parameters:
weight: [0.02, 0.115, 0.115, 0.115, 0.115, 0.115]
density: 0.4
- model: /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0
# Vicuna format
parameters:
weight: [0.21, 0.09, 0.09, 0.09, 0.09, 0.09]
density: 0.61
- model: /home/alpha/Models/Raw/TriadParty_deepmoney-34b-200k-base
# No prompt format, native long context full finetune
parameters:
weight: [0.04, 0.103, 0.103, 0.103, 0.103, 0.103]
density: 0.61
merge_method: dare_ties
tokenizer_source: union
base_model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
parameters:
int8_mask: true
dtype: bfloat16
```
## Self Promotion
I'm part of a AI startup called Holocene AI!
We're new, busy, and still setting things up. But if you have any business inquiries, want a job, or just want some consultation, feel free to shoot me an email. We have expertise in RAG applications and llama/embeddings model finetuning, and absolutely *none* of the nonsense of scammy AI startups.
Contact me at: [email protected]
I also set up a Ko-Fi! I want to run some (personal) training/LASERing as well, at 100K context or so. If you'd like to buy me 10 minutes on an A100 (or 5 seconds on an MI300X), I'd appreciate it: https://ko-fi.com/alphaatlas
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__Yi-34B-200K-DARE-megamerge-v8)
| Metric |Value|
|---------------------------------|----:|
|Avg. |72.56|
|AI2 Reasoning Challenge (25-Shot)|67.75|
|HellaSwag (10-Shot) |86.06|
|MMLU (5-Shot) |77.03|
|TruthfulQA (0-shot) |56.31|
|Winogrande (5-shot) |82.79|
|GSM8k (5-shot) |65.43|
|
RunDiffusion/Juggernaut-XL | RunDiffusion | 2024-03-11T20:05:53Z | 185 | 1 | diffusers | [
"diffusers",
"art",
"people",
"diffusion",
"Cinematic",
"Photography",
"Landscape",
"Interior",
"Food",
"Car",
"Wildlife",
"Architecture",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-02-22T00:43:28Z | ---
language:
- en
license: creativeml-openrail-m
library_name: diffusers
tags:
- art
- people
- diffusion
- Cinematic
- Photography
- Landscape
- Interior
- Food
- Car
- Wildlife
- Architecture
thumbnail: https://imagedelivery.net/siANnpeNAc_S2q1M3-eDrA/def40db4-42d0-4e45-2baf-bebed29ae000/padthumb
base_model: stabilityai/stable-diffusion-xl-base-1.0
---
# Juggernaut XL v2 Official
## Juggernaut v9 is here! [Juggernaut v9 + RunDiffusion Photo v2](https://huggingface.co/RunDiffusion/Juggernaut-XL-v9)
Version 2 is technically the best version from the first four versions and should be used.
This model is not permitted to be used behind API services. Please contact [[email protected]](mailto:[email protected]) for business inquires, commercial licensing, custom models, and consultation.
Juggernaut is available on the new Auto1111 Forge on [RunDiffusion](http://rundiffusion.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=Kandoo)
For business inquires, commercial licensing, custom models, and consultation contact me under [email protected]
|
Holarissun/zephyr3b-aisft-gsm8k-seq | Holarissun | 2024-03-11T20:01:55Z | 1 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:stabilityai/stablelm-zephyr-3b",
"base_model:adapter:stabilityai/stablelm-zephyr-3b",
"license:other",
"region:us"
] | null | 2024-03-11T20:01:51Z | ---
license: other
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: stabilityai/stablelm-zephyr-3b
model-index:
- name: zephyr3b-aisft-gsm8k-seq
results: []
---
<!-- 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. -->
# zephyr3b-aisft-gsm8k-seq
This model is a fine-tuned version of [stabilityai/stablelm-zephyr-3b](https://huggingface.co/stabilityai/stablelm-zephyr-3b) 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: 5e-05
- train_batch_size: 1
- 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
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
Holarissun/zephyr3b-aisft-gsm8k-rand | Holarissun | 2024-03-11T19:59:11Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:stabilityai/stablelm-zephyr-3b",
"base_model:adapter:stabilityai/stablelm-zephyr-3b",
"license:other",
"region:us"
] | null | 2024-03-11T19:59:08Z | ---
license: other
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: stabilityai/stablelm-zephyr-3b
model-index:
- name: zephyr3b-aisft-gsm8k-rand
results: []
---
<!-- 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. -->
# zephyr3b-aisft-gsm8k-rand
This model is a fine-tuned version of [stabilityai/stablelm-zephyr-3b](https://huggingface.co/stabilityai/stablelm-zephyr-3b) 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: 5e-05
- train_batch_size: 1
- 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
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
mehmettozlu/multilingual-xlm-roberta-for-ner | mehmettozlu | 2024-03-11T19:57:21Z | 91 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-03-11T19:20:51Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: multilingual-xlm-roberta-for-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8625248226950355
---
<!-- 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. -->
# multilingual-xlm-roberta-for-ner
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1350
- F1: 0.8625
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2657 | 1.0 | 525 | 0.1631 | 0.8156 |
| 0.1275 | 2.0 | 1050 | 0.1370 | 0.8521 |
| 0.0797 | 3.0 | 1575 | 0.1350 | 0.8625 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
|
tolgadev/Trendyol-LLM-7b-chat-v1.0-GGUF | tolgadev | 2024-03-11T19:52:06Z | 90 | 1 | transformers | [
"transformers",
"gguf",
"trendyol",
"llama-2",
"turkish",
"text-generation",
"tr",
"en",
"base_model:Trendyol/Trendyol-LLM-7b-chat-v1.0",
"base_model:quantized:Trendyol/Trendyol-LLM-7b-chat-v1.0",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-03-11T14:21:33Z | ---
model_name: Trendyol-LLM-7b-chat-v1.0-gguf
model_creator: Trendyol
base_model: Trendyol/Trendyol-LLM-7b-chat-v1.0
language:
- tr
- en
pipeline_tag: text-generation
license: apache-2.0
model_type: llama
library_name: transformers
inference: false
tags:
- trendyol
- llama-2
- turkish
quantized_by: tolgadev
---
## Trendyol-LLM-7b-chat-v1.0-gguf models
----
## Description
This repo contains all types of GGUF formatted model files for [Trendyol-LLM-7b-chat-v1.0](https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-v1.0).
<img src="https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-v1.0/resolve/main/trendyol-llm-mistral.jpg"
alt="drawing" width="400"/>
## Quantized LLM models and methods
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [Trendyol-LLM-7b-chat-v1.0.Q2_K.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-v1.0.Q2_K.gguf) | Q2_K | 2 | 2.59 GB| 4.88 GB | smallest, significant quality loss - not recommended for most purposes |
| [Trendyol-LLM-7b-chat-v1.0.Q3_K_S.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-v1.0.Q3_K_S.gguf) | Q3_K_S | 3 | 3.01 GB| 5.56 GB | very small, high quality loss |
| [Trendyol-LLM-7b-chat-v1.0.Q3_K_M.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-v1.0.Q3_K_M.gguf) | Q3_K_M | 3 | 3.36 GB| 5.91 GB | very small, high quality loss |
| [Trendyol-LLM-7b-chat-v1.0.Q3_K_L.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-v1.0.Q3_K_L.gguf) | Q3_K_L | 3 | 3.66 GB| 6.20 GB | small, substantial quality loss |
| [Trendyol-LLM-7b-chat-v1.0.Q4_0.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-v1.0.Q4_0.gguf) | Q4_0 | 4 | 3.9 GB| 6.45 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Trendyol-LLM-7b-chat-v1.0.Q4_K_S.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-v1.0.Q4_K_S.gguf) | Q4_K_S | 4 | 3.93 GB| 6.48 GB | small, greater quality loss |
| [Trendyol-LLM-7b-chat-v1.0.Q4_K_M.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-v1.0.Q4_K_M.gguf) | Q4_K_M | 4 | 4.15 GB| 6.69 GB | medium, balanced quality - recommended |
| [Trendyol-LLM-7b-chat-v1.0.Q5_0.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-v1.0.Q5_0.gguf) | Q5_0 | 5 | 4.73 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Trendyol-LLM-7b-chat-v1.0.Q5_K_S.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-v1.0.Q5_K_S.gguf) | Q5_K_S | 5 | 4.75 GB| 7.27 GB | large, low quality loss - recommended |
| [Trendyol-LLM-7b-chat-v1.0.Q5_K_M.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-v1.0.Q5_K_M.gguf) | Q5_K_M | 5 | 4.86 GB| 7.40 GB | large, very low quality loss - recommended |
| [Trendyol-LLM-7b-chat-v1.0.Q6_K.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-chat-v1.0-GGUF/blob/main/trendyol-llm-7b-chat-v1.0.Q6_K.gguf) | Q6_K | 6 | 5.61 GB| 8.15 GB | very large, extremely low quality loss |
The names of the quantization methods follow the naming convention: "q" + the number of bits + the variant used (detailed below). Here is a list of all the models and their corresponding use cases, based on model cards made by [TheBloke](https://huggingface.co/TheBloke/):
* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_s`: Uses Q3_K for all tensors
* `q4_0`: Original quant method, 4-bit.
* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
* `q4_k_s`: Uses Q4_K for all tensors
* `q5_0`: Higher accuracy, higher resource usage and slower inference.
* `q5_1`: Even higher accuracy, resource usage and slower inference.
* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
* `q5_k_s`: Uses Q5_K for all tensors
* `q6_k`: Uses Q8_K for all tensors
**TheBloke recommends using Q5_K_M** as it preserves most of the model's performance.
Alternatively, you can use Q4_K_M if you want to save some memory.
In general, K_M versions are better than K_S versions.
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
## Special thanks to [TheBloke on Huggingface](https://huggingface.co/TheBloke) and [Maxime Labonne on Github](https://github.com/mlabonne/llm-course)
-----
# **Trendyol LLM v1.0 - DPO**
Trendyol LLM v1.0 - DPO is a generative model that is based on Mistral 7B model. DPO training was applied. This is the repository for the chat model.
## Model Details
**Model Developers** Trendyol
**Variations** [base](https://huggingface.co/Trendyol/Trendyol-LLM-7b-base-v1.0), [chat](https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-v1.0), and dpo variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Trendyol LLM is an auto-regressive language model (based on Mistral 7b) that uses an optimized transformer architecture. Huggingface TRL lib was used for training. The DPO version is fine-tuned on 11K sets (prompt-chosen-reject) with the following trainables by using LoRA:
- **lr**=5e-6
- **lora_rank**=64
- **lora_alpha**=128
- **lora_trainable**=q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj
- **lora_dropout**=0.05
- **bf16**=True
- **beta**=0.01
- **max_length**= 1024
- **max_prompt_length**= 512
- **lr_scheduler_type**= cosine
- **torch_dtype**= bfloat16
<img src="https://camo.githubusercontent.com/3e61ca080778f62988b459c7321726fa35bb3776ceb07ecaabf71ebca44f95a7/68747470733a2f2f68756767696e67666163652e636f2f64617461736574732f74726c2d696e7465726e616c2d74657374696e672f6578616d706c652d696d616765732f7265736f6c76652f6d61696e2f696d616765732f74726c5f62616e6e65725f6461726b2e706e67"
alt="drawing" width="600"/>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/lora_diagram.png"
alt="drawing" width="600"/>
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "Trendyol/Trendyol-LLM-7b-chat-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
device_map='auto',
load_in_8bit=True)
sampling_params = dict(do_sample=True, temperature=0.3, top_k=50, top_p=0.9)
pipe = pipeline("text-generation",
model=model,
tokenizer=tokenizer,
device_map="auto",
max_new_tokens=1024,
return_full_text=True,
repetition_penalty=1.1
)
DEFAULT_SYSTEM_PROMPT = "Sen yardımcı bir asistansın ve sana verilen talimatlar doğrultusunda en iyi cevabı üretmeye çalışacaksın.\n"
TEMPLATE = (
"[INST] {system_prompt}\n\n"
"{instruction} [/INST]"
)
def generate_prompt(instruction, system_prompt=DEFAULT_SYSTEM_PROMPT):
return TEMPLATE.format_map({'instruction': instruction,'system_prompt': system_prompt})
def generate_output(user_query, sys_prompt=DEFAULT_SYSTEM_PROMPT):
prompt = generate_prompt(user_query, sys_prompt)
outputs = pipe(prompt,
**sampling_params
)
return outputs[0]["generated_text"].split("[/INST]")[-1]
user_query = "Türkiye'de kaç il var?"
response = generate_output(user_query)
print(response)
```
with chat template:
```python
pipe = pipeline("conversational",
model=model,
tokenizer=tokenizer,
device_map="auto",
max_new_tokens=1024,
repetition_penalty=1.1
)
messages = [
{"role": "user", "content": "Türkiye'de kaç il var?"}
]
outputs = pipe(messages, **sampling_params)
print(outputs)
```
## Limitations, Risks, Bias, and Ethical Considerations
### Limitations and Known Biases
- **Primary Function and Application:** Trendyol LLM, an autoregressive language model, is primarily designed to predict the next token in a text string. While often used for various applications, it is important to note that it has not undergone extensive real-world application testing. Its effectiveness and reliability across diverse scenarios remain largely unverified.
- **Language Comprehension and Generation:** The model is primarily trained in standard English and Turkish. Its performance in understanding and generating slang, informal language, or other languages may be limited, leading to potential errors or misinterpretations.
- **Generation of False Information:** Users should be aware that Trendyol LLM may produce inaccurate or misleading information. Outputs should be considered as starting points or suggestions rather than definitive answers.
### Risks and Ethical Considerations
- **Potential for Harmful Use:** There is a risk that Trendyol LLM could be used to generate offensive or harmful language. We strongly discourage its use for any such purposes and emphasize the need for application-specific safety and fairness evaluations before deployment.
- **Unintended Content and Bias:** The model was trained on a large corpus of text data, which was not explicitly checked for offensive content or existing biases. Consequently, it may inadvertently produce content that reflects these biases or inaccuracies.
- **Toxicity:** Despite efforts to select appropriate training data, the model is capable of generating harmful content, especially when prompted explicitly. We encourage the open-source community to engage in developing strategies to minimize such risks.
### Recommendations for Safe and Ethical Usage
- **Human Oversight:** We recommend incorporating a human curation layer or using filters to manage and improve the quality of outputs, especially in public-facing applications. This approach can help mitigate the risk of generating objectionable content unexpectedly.
- **Application-Specific Testing:** Developers intending to use Trendyol LLM should conduct thorough safety testing and optimization tailored to their specific applications. This is crucial, as the model’s responses can be unpredictable and may occasionally be biased, inaccurate, or offensive.
- **Responsible Development and Deployment:** It is the responsibility of developers and users of Trendyol LLM to ensure its ethical and safe application. We urge users to be mindful of the model's limitations and to employ appropriate safeguards to prevent misuse or harmful consequences. |
mHossain/ml_sum_v3 | mHossain | 2024-03-11T19:47:57Z | 94 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:mHossain/ml_sum_v2",
"base_model:finetune:mHossain/ml_sum_v2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-09T17:39:19Z | ---
license: apache-2.0
base_model: mHossain/ml_sum_v2
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: ml_sum_v3
results: []
---
<!-- 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. -->
# ml_sum_v3
This model is a fine-tuned version of [mHossain/ml_sum_v2](https://huggingface.co/mHossain/ml_sum_v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 0.0
## 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:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: linear
- lr_scheduler_warmup_steps: 5000
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 312 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.9648 | 2.0 | 625 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.9648 | 3.0 | 936 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
papahawk/falcon-40b | papahawk | 2024-03-11T19:44:24Z | 15 | 1 | transformers | [
"transformers",
"pytorch",
"RefinedWeb",
"text-generation",
"custom_code",
"en",
"de",
"es",
"fr",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2205.14135",
"arxiv:1911.02150",
"arxiv:2101.00027",
"arxiv:2005.14165",
"arxiv:2104.09864",
"arxiv:2306.01116",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-07-04T22:06:07Z | ---
datasets:
- tiiuae/falcon-refinedweb
language:
- en
- de
- es
- fr
pipeline_tag: text-generation
inference: false
license: apache-2.0
---
<h1 style='text-align: center '>🚀 Falcon-40B</h1>
<h1 style='text-align: center '><em>fork of tiiuae/falcon-40b</em> </h1>
<h2 style='text-align: center '><em>Technology Innovation Institute (TII) LLM</em> </h2>
<h3 style='text-align: center '>All credit and thanks to TII for their work!</h3>
<img src="https://alt-web.xyz/images/rainbow.png" alt="Rainbow Solutions" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
**Falcon-40B is a 40B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 1,000B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. It is made available under the Apache 2.0 license.**
*Paper coming soon 😊.*
# Call for Proposals : Falcon 40B - World's Top Ranked AI Model Empowers Exceptional Use Cases with Training Compute Power in Call for Proposals
We get it. AI is everywhere! Is it taking over?
Before we debate the scant likelihood of a cyborg assassin from the future terminating humanity, let’s get to know the newbie that has soared to top-spot on the leaderboard – Falcon 40B.
Falcon 40B is the UAE’s and the Middle East’s first home-grown, open-source large language model (LLM) with 40 billion parameters trained on one trillion tokens. The brainchild of the Technology Innovation Institute (TII), Falcon 40B has generated a tremendous amount of global interest and intrigue, but what really sweetens the deal is its transparent, open-source feature.
TII is now calling for proposals from users worldwide to submit their most creative ideas for Falcon 40B’s deployment – allowing them to share their knowledge, enhance the software, and potentially transform their ideas into reality! Take that, ChatGPT!
Worth checking out? Give it a go and see for yourself!
Submit your proposal today! https://falconllm.tii.ae/call-for-proposal.php
🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)!
## Why use Falcon-40B?
* **It is the best open-source model currently available.** Falcon-40B outperforms [LLaMA](https://github.com/facebookresearch/llama), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1), [MPT](https://huggingface.co/mosaicml/mpt-7b), etc. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
* **It is made available under a permissive Apache 2.0 license allowing for commercial use**, without any royalties or restrictions.
*
⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.** If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct).
💸 **Looking for a smaller, less expensive model?** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) is Falcon-40B's little brother!
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-40b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon).
You will need **at least 85-100GB of memory** to swiftly run inference with Falcon-40B.
# Model Card for Falcon-40B
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish);
- **License:** Apache 2.0 license.
### Model Source
- **Paper:** *coming soon*.
## Uses
### Direct Use
Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.)
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
Falcon-40B is trained mostly on English, German, Spanish, French, with limited capabilities also in in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish. It will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of Falcon-40B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-40b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Details
### Training Data
Falcon-40B was trained on 1,000B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. Significant components from our curated copora were inspired by The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)).
| **Data source** | **Fraction** | **Tokens** | **Sources** |
|--------------------|--------------|------------|-----------------------------------|
| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 75% | 750B | massive web crawl |
| RefinedWeb-Europe | 7% | 70B | European massive web crawl |
| Books | 6% | 60B | |
| Conversations | 5% | 50B | Reddit, StackOverflow, HackerNews |
| Code | 5% | 50B | |
| Technical | 2% | 20B | arXiv, PubMed, USPTO, etc. |
RefinedWeb-Europe is made of the following languages:
| **Language** | **Fraction of multilingual data** | **Tokens** |
|--------------|-----------------------------------|------------|
| German | 26% | 18B |
| Spanish | 24% | 17B |
| French | 23% | 16B |
| _Italian_ | 7% | 5B |
| _Portuguese_ | 4% | 3B |
| _Polish_ | 4% | 3B |
| _Dutch_ | 4% | 3B |
| _Romanian_ | 3% | 2B |
| _Czech_ | 3% | 2B |
| _Swedish_ | 2% | 1B |
The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
### Training Procedure
Falcon-40B was trained on 384 A100 40GB GPUs, using a 3D parallelism strategy (TP=8, PP=4, DP=12) combined with ZeRO.
#### Training Hyperparameters
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|------------|-------------------------------------------|
| Precision | `bfloat16` | |
| Optimizer | AdamW | |
| Learning rate | 1.85e-4 | 4B tokens warm-up, cosine decay to 1.85e-5 |
| Weight decay | 1e-1 | |
| Z-loss | 1e-4 | |
| Batch size | 1152 | 100B tokens ramp-up |
#### Speeds, Sizes, Times
Training started in December 2022 and took two months.
## Evaluation
*Paper coming soon.*
See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
## Technical Specifications
### Model Architecture and Objective
Falcon-40B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
* **Decoder-block:** parallel attention/MLP with a two layer norms.
For multiquery, we are using an internal variant which uses independent key and values per tensor parallel degree.
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| Layers | 60 | |
| `d_model` | 8192 | |
| `head_dim` | 64 | Reduced to optimise for FlashAttention |
| Vocabulary | 65024 | |
| Sequence length | 2048 | |
### Compute Infrastructure
#### Hardware
Falcon-40B was trained on AWS SageMaker, on 384 A100 40GB GPUs in P4d instances.
#### Software
Falcon-40B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
## Citation
*Paper coming soon* 😊. In the meanwhile, you can use the following information to cite:
```
@article{falcon40b,
title={{Falcon-40B}: an open large language model with state-of-the-art performance},
author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
year={2023}
}
```
To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116).
```
@article{refinedweb,
title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
journal={arXiv preprint arXiv:2306.01116},
eprint={2306.01116},
eprinttype = {arXiv},
url={https://arxiv.org/abs/2306.01116},
year={2023}
}
```
## License
Falcon-40B is made available under the Apache 2.0 license.
## Contact
[email protected]
|
Thang203/us-only-mar11 | Thang203 | 2024-03-11T19:39:43Z | 1 | 0 | bertopic | [
"bertopic",
"text-classification",
"region:us"
] | text-classification | 2024-03-11T19:39:41Z |
---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---
# us-only-mar11
This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
## Usage
To use this model, please install BERTopic:
```
pip install -U bertopic
```
You can use the model as follows:
```python
from bertopic import BERTopic
topic_model = BERTopic.load("Thang203/us-only-mar11")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 20
* Number of training documents: 1908
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| -1 | models - language - model - language models - llms | 10 | -1_models_language_model_language models |
| 0 | models - language - reasoning - language models - large | 616 | 0_models_language_reasoning_language models |
| 1 | code - llms - language - models - programming | 467 | 1_code_llms_language_models |
| 2 | learning - reinforcement - reinforcement learning - planning - rl | 139 | 2_learning_reinforcement_reinforcement learning_planning |
| 3 | clinical - medical - models - language - data | 92 | 3_clinical_medical_models_language |
| 4 | language - models - language models - llms - scaling | 86 | 4_language_models_language models_llms |
| 5 | summarization - event - generation - events - text | 75 | 5_summarization_event_generation_events |
| 6 | dialogue - dialog - systems - conversational - conversations | 59 | 6_dialogue_dialog_systems_conversational |
| 7 | text - adversarial - attacks - detection - models | 58 | 7_text_adversarial_attacks_detection |
| 8 | bias - biases - social - gender - models | 52 | 8_bias_biases_social_gender |
| 9 | ai - chatgpt - ethical - artificial intelligence - intelligence | 49 | 9_ai_chatgpt_ethical_artificial intelligence |
| 10 | education - students - programming - educational - questions | 49 | 10_education_students_programming_educational |
| 11 | privacy - private - federated - attacks - models | 37 | 11_privacy_private_federated_attacks |
| 12 | speech - audio - asr - speech recognition - recognition | 21 | 12_speech_audio_asr_speech recognition |
| 13 | materials - chemistry - chemical - molecular - model | 20 | 13_materials_chemistry_chemical_molecular |
| 14 | recommendation - user - item - reviews - news | 20 | 14_recommendation_user_item_reviews |
| 15 | financial - sentiment - stock - data - market | 17 | 15_financial_sentiment_stock_data |
| 16 | game - games - state - generate - state information | 15 | 16_game_games_state_generate |
| 17 | legal - law - argumentative - court - standards | 14 | 17_legal_law_argumentative_court |
| 18 | metadata - language - keyphrase - large - user intents | 12 | 18_metadata_language_keyphrase_large |
</details>
## Training hyperparameters
* calculate_probabilities: False
* language: english
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: 20
* seed_topic_list: None
* top_n_words: 10
* verbose: True
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None
## Framework versions
* Numpy: 1.25.2
* HDBSCAN: 0.8.33
* UMAP: 0.5.5
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.5.1
* Transformers: 4.38.2
* Numba: 0.58.1
* Plotly: 5.15.0
* Python: 3.10.12
|
adeebkm/EmotionDetectionModel | adeebkm | 2024-03-11T19:23:42Z | 0 | 0 | tensorflow | [
"tensorflow",
"tf",
"text-classification",
"bert",
"en",
"license:apache-2.0",
"region:us"
] | text-classification | 2024-03-11T18:55:52Z | ---
language: en
license: apache-2.0
tags:
- text-classification
- tensorflow
- bert
library_name: tensorflow
---
# BERT Sentiment Classifier
This model is a fine-tuned version of BERT (Bidirectional Encoder Representations from Transformers) designed to classify text sentiment into positive or negative. It's trained on a large corpus of movie reviews and can be adapted for similar natural language processing tasks.
## Requirements
To use this model, you need the following packages:
- TensorFlow 2.x
- ktrain
## Installation
First, ensure you have Python 3.6 or newer installed. Then, install the required packages using pip:
```bash
pip install tensorflow ktrain
```
## Loading the Predictor
To load the predictor, use the following code snippet. Ensure the model directory ('./model') is correctly specified to the location where you've downloaded the model files.
```python
import ktrain
predictor = ktrain.load_predictor('./model')
```
## Making Predictions
You can make predictions with the model as follows:
```python
text = "I absolutely loved this movie! The acting was great and the story was compelling."
prediction = predictor.predict(text)
print("Sentiment:", "Positive" if prediction[0] == 1 else "Negative")
```
## Model Files
This model repository includes the following files:
- `tf_model.h5`: The model weights.
- `tf_model.preproc`: The preprocessing data for the model inputs, ensuring input data is in the correct format for prediction.
## Additional Notes
This model is intended for educational and research purposes. It may require further tuning for optimal performance on specific tasks.
For any questions or issues, please open an issue in the repository or contact the model maintainers.
|
jackshannon/phi-1_5-finetuned-question-generation | jackshannon | 2024-03-11T19:22:46Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:microsoft/phi-1_5",
"base_model:adapter:microsoft/phi-1_5",
"license:mit",
"region:us"
] | null | 2024-03-11T17:05:21Z | ---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/phi-1_5
model-index:
- name: phi-1_5-finetuned-question-generation
results: []
---
<!-- 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-1_5-finetuned-question-generation
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9170
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 2
- 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: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9836 | 0.09 | 100 | 2.8641 |
| 2.8536 | 0.17 | 200 | 2.7929 |
| 2.8051 | 0.26 | 300 | 2.7567 |
| 2.7782 | 0.35 | 400 | 2.7092 |
| 2.7542 | 0.44 | 500 | 2.6946 |
| 2.6978 | 0.52 | 600 | 2.6719 |
| 2.6833 | 0.61 | 700 | 2.6497 |
| 2.6504 | 0.7 | 800 | 2.6172 |
| 2.6228 | 0.78 | 900 | 2.6008 |
| 2.6219 | 0.87 | 1000 | 2.5802 |
| 2.5629 | 0.96 | 1100 | 2.5519 |
| 2.5315 | 1.05 | 1200 | 2.5255 |
| 2.4813 | 1.13 | 1300 | 2.5156 |
| 2.4539 | 1.22 | 1400 | 2.4884 |
| 2.4466 | 1.31 | 1500 | 2.4660 |
| 2.4205 | 1.39 | 1600 | 2.4431 |
| 2.3937 | 1.48 | 1700 | 2.4238 |
| 2.3686 | 1.57 | 1800 | 2.4069 |
| 2.3209 | 1.66 | 1900 | 2.3826 |
| 2.3409 | 1.74 | 2000 | 2.3606 |
| 2.2874 | 1.83 | 2100 | 2.3453 |
| 2.309 | 1.92 | 2200 | 2.3222 |
| 2.2676 | 2.01 | 2300 | 2.2981 |
| 2.1734 | 2.09 | 2400 | 2.2892 |
| 2.1495 | 2.18 | 2500 | 2.2549 |
| 2.1163 | 2.27 | 2600 | 2.2401 |
| 2.1 | 2.35 | 2700 | 2.2317 |
| 2.1046 | 2.44 | 2800 | 2.2153 |
| 2.1138 | 2.53 | 2900 | 2.1938 |
| 2.0691 | 2.62 | 3000 | 2.1775 |
| 2.0945 | 2.7 | 3100 | 2.1563 |
| 2.045 | 2.79 | 3200 | 2.1408 |
| 2.0212 | 2.88 | 3300 | 2.1229 |
| 2.0011 | 2.96 | 3400 | 2.1156 |
| 1.983 | 3.05 | 3500 | 2.0942 |
| 1.9309 | 3.14 | 3600 | 2.0769 |
| 1.8844 | 3.23 | 3700 | 2.0709 |
| 1.9085 | 3.31 | 3800 | 2.0589 |
| 1.8827 | 3.4 | 3900 | 2.0405 |
| 1.8511 | 3.49 | 4000 | 2.0310 |
| 1.8807 | 3.57 | 4100 | 2.0170 |
| 1.8437 | 3.66 | 4200 | 2.0045 |
| 1.8667 | 3.75 | 4300 | 2.0036 |
| 1.8081 | 3.84 | 4400 | 1.9886 |
| 1.8688 | 3.92 | 4500 | 1.9767 |
| 1.8187 | 4.01 | 4600 | 1.9652 |
| 1.7511 | 4.1 | 4700 | 1.9592 |
| 1.7384 | 4.18 | 4800 | 1.9558 |
| 1.7843 | 4.27 | 4900 | 1.9474 |
| 1.7389 | 4.36 | 5000 | 1.9412 |
| 1.7465 | 4.45 | 5100 | 1.9346 |
| 1.7483 | 4.53 | 5200 | 1.9290 |
| 1.7149 | 4.62 | 5300 | 1.9246 |
| 1.7154 | 4.71 | 5400 | 1.9211 |
| 1.7637 | 4.8 | 5500 | 1.9188 |
| 1.7559 | 4.88 | 5600 | 1.9181 |
| 1.7204 | 4.97 | 5700 | 1.9170 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
ardasamet/mistral-7b-fake-ft | ardasamet | 2024-03-11T19:19:30Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"license:apache-2.0",
"region:us"
] | null | 2024-03-11T19:04:39Z | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
model-index:
- name: ardasamet/mistral-7b-fake-ft
results: []
---
<!-- 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. -->
# ardasamet/mistral-7b-fake-ft
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8977
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.5918 | 0.92 | 3 | 3.9531 |
| 4.0342 | 1.85 | 6 | 3.4187 |
| 3.4573 | 2.77 | 9 | 2.9750 |
| 2.2651 | 4.0 | 13 | 2.5798 |
| 2.7052 | 4.92 | 16 | 2.3497 |
| 2.4053 | 5.85 | 19 | 2.1773 |
| 2.1965 | 6.77 | 22 | 2.0325 |
| 1.5431 | 8.0 | 26 | 1.9541 |
| 2.0077 | 8.92 | 29 | 1.9105 |
| 1.3889 | 9.23 | 30 | 1.8977 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
dyumat/phi-1_5-arxiv-physics | dyumat | 2024-03-11T19:19:22Z | 35 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-11T19:16:21Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Nexesenex/Undi95_Miqu-70B-Alpaca-DPO-iMat.GGUF | Nexesenex | 2024-03-11T19:18:46Z | 94 | 3 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-02-08T19:30:57Z | GGUF Quants with iMatrix for : https://huggingface.co/Undi95/Miqu-70B-Alpaca-DPO
Q3_K_M to be uploaded shortly.
Q3_K_S, IQ3_XXS, Q2_K, Q2_K_S, IQ2_XS, IQ2_XXS to follow.
LlamaCPP Benchs on the Q3_K_M with iMatrix shared here :
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,Hellaswag,84.5,,400,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,Hellaswag,83.6,,1000,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,Arc-Challenge,58.52842809,,299,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,Arc-Easy,77.36842105,,570,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,MMLU,49.84025559,,313,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,Thruthful-QA,42.83965728,,817,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,Winogrande,78.7687,,1267,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,wikitext,4.2963,512,512,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,81
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,wikitext,3.8397,512,512,2024-02-07 00:00:00,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,655
LlamaCPP Benchs on a non iMatrix Q3_K_M released by Undi95 :
- Miqu-70B-DPO.q3_k_m.gguf,-,Hellaswag,84.5,400,,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,Hellaswag,83.8,1000,,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,Arc-Challenge,57.85953177,,299,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,Arc-Easy,77.36842105,,570,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,MMLU,50.15974441,,313,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,Thruthful-QA,42.47246022,,817,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,Winogrande,78.7687,,1267,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,
- Miqu-70B-DPO.q3_k_m.gguf,-,wikitext,4.3018,512,512,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,81
- Miqu-70B-DPO.q3_k_m.gguf,-,wikitext,3.8469,512,512,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,NeverSleep,655
Quite convincing compared to the original Miqu.. with iMatrix :
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Arc-Challenge,57.19063545,,299,2024-01-29 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Arc-Easy,77.19298246,,570,2024-01-29 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,MMLU,50.15974441,,313,2024-01-29 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Thruthful-QA,41.49326805,,817,2024-01-29 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,Winogrande,78.8477,,1267,2024-01-29 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,wikitext,4.2957,512,512,2024-01-29 00:00:00,RBF1000000,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,81
- Miqu-1-70b-Requant-b1989-iMat-c32_ch400-Q3_K_M.gguf,-,wikitext,3.8380,512,512,2024-01-29 00:00:00,RBF1000000,70b,Mistral_Medium,32768,,,GGUF,- Miqudev,Nexesenex,655
The TQA shows a slight bonus, thanks to the DPO training I believe.
The slightly bonified ARC benchs (a rare thing on DPO releases!) and the respected perplexity show that the model was not dumbified by the DPO training.
In ST, the models performs beautifully. |
crncskn/radiovers16v | crncskn | 2024-03-11T19:15:19Z | 177 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit_mae",
"pretraining",
"masked-auto-encoding",
"generated_from_trainer",
"dataset:imagefolder",
"endpoints_compatible",
"region:us"
] | null | 2024-03-11T17:04:25Z | ---
tags:
- masked-auto-encoding
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: radiovers16v
results: []
---
<!-- 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. -->
# radiovers16v
This model is a fine-tuned version of [](https://huggingface.co/) on the /kaggle/radioai/radiology_ai dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4036
## 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: 3.125e-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: 40.0
### Training results
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Takekazuchi/Caracam | Takekazuchi | 2024-03-11T19:09:16Z | 177 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-01-19T05:24:47Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-vit-base-patch16
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5851995594482614
---
# Caracam (gen 1)
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9156
- Accuracy: 0.5852
## Model description
First generation of my AI that tells you what car you took a picture of. \
More versions coming soon with accuracy ratings of 85% and higher! Trained on 70+ brands with 2700+ cars going from 1945-2024. \
***App coming soon (also called Caracam) to Android and IOS*** \
(Late March - Early April 2024).
In the future I will take user opinion into account on what brands to add. The app will be updated semi-yearly with user-suggested car brands! \
if you wish to support project Caracam please visit my [Patreon](https://www.patreon.com/Caracam) or my [Cashapp](https://cash.app/$Clippayy)!!
## Intended uses & limitations
***NOT FOR COMMERCIAL USE OUTSIDE OF OFFICIAL CARACAM MOBILE APP***
## 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: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 4.0308 | 1.0 | 5362 | 3.6948 | 0.2491 |
| 2.694 | 2.0 | 10725 | 2.2586 | 0.5199 |
| 2.4475 | 3.0 | 16086 | 1.9156 | 0.5852 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cpu
- Datasets 2.16.1
- Tokenizers 0.15.0
|
cdillinger/cnn_news_summary_model_trained_on_reduced_data | cdillinger | 2024-03-11T19:08:49Z | 91 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-04T13:10:26Z | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: cnn_news_summary_model_trained_on_reduced_data
results: []
---
<!-- 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. -->
# cnn_news_summary_model_trained_on_reduced_data
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6040
- Rouge1: 0.2179
- Rouge2: 0.094
- Rougel: 0.184
- Rougelsum: 0.184
- Generated Length: 19.0
## 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:
- 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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Generated Length |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------------:|
| No log | 1.0 | 431 | 1.6239 | 0.2175 | 0.0934 | 0.1831 | 0.183 | 19.0 |
| 1.92 | 2.0 | 862 | 1.6075 | 0.2169 | 0.0933 | 0.1829 | 0.1827 | 19.0 |
| 1.8221 | 3.0 | 1293 | 1.6040 | 0.2179 | 0.094 | 0.184 | 0.184 | 19.0 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_sign_acc_ef_signal_it_273 | furrutiav | 2024-03-11T19:06:23Z | 91 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-03-11T19:01:57Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Repository:** [More Information Needed]
- **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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
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|
watersplash/waste-classification | watersplash | 2024-03-11T18:56:21Z | 351 | 1 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-02-24T22:44:32Z | ---
library_name: transformers
metrics:
- accuracy
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
An Image Classifier model fine-tuned on ViT. This model can classify garbage images.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Finetuned from model :** ViT
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/KomaliValluru/waste-classification
## 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. -->
- Target classes: Battery, Biological, Brown-grass, Cardboard, Clothes, Green-Glass, Metal, Paper, Plastic, Shoes, Trash, White-Glass
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
https://www.kaggle.com/datasets/mostafaabla/garbage-classification
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
Accuracy
### Results
Accuracy: 98%
#### Summary
- **Hours used:** 1 hour 30 minutes
- **References:** Based on the model yangy50/garbage-classification
|
dchatca/vistral_economics_summarization_v4.2 | dchatca | 2024-03-11T18:52:46Z | 62 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-03-11T18:31:45Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ralux3/sdxl-lora | ralux3 | 2024-03-11T18:45:52Z | 26 | 2 | diffusers | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"diffusers-training",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-03-06T16:56:47Z | ---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- diffusers-training
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: 'a <s0><s1> blue bedroom, in the style of <s0><s1>'
output:
url:
"image_0.png"
- text: 'a <s0><s1> blue bedroom, in the style of <s0><s1>'
output:
url:
"image_1.png"
- text: 'a <s0><s1> blue bedroom, in the style of <s0><s1>'
output:
url:
"image_2.png"
- text: 'a <s0><s1> blue bedroom, in the style of <s0><s1>'
output:
url:
"image_3.png"
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: room in the style of <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - ralux3/sdxl-lora
<Gallery />
## Model description
### These are ralux3/sdxl-lora LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`sdxl-lora.safetensors` here 💾](/ralux3/sdxl-lora/blob/main/sdxl-lora.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:sdxl-lora:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`sdxl-lora_emb.safetensors` here 💾](/ralux3/sdxl-lora/blob/main/sdxl-lora_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `sdxl-lora_emb` to your prompt. For example, `room in the style of sdxl-lora_emb`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('ralux3/sdxl-lora', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='ralux3/sdxl-lora', filename='sdxl-lora_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('a <s0><s1> blue bedroom, in the style of <s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/ralux3/sdxl-lora/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
khizer-kt/Embeded_llama2_7b_chat_UG_Handbook | khizer-kt | 2024-03-11T18:45:27Z | 63 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-03-11T18:42:16Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
edgilr/clasificador-rotten-tomatoes-funnel-transfomer | edgilr | 2024-03-11T18:43:45Z | 77 | 0 | transformers | [
"transformers",
"safetensors",
"funnel",
"text-classification",
"classification",
"generated_from_trainer",
"base_model:funnel-transformer/small-base",
"base_model:finetune:funnel-transformer/small-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-11T18:43:27Z | ---
license: apache-2.0
base_model: funnel-transformer/small-base
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-rotten-tomatoes-funnel-transfomer
results: []
---
<!-- 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. -->
# clasificador-rotten-tomatoes-funnel-transfomer
This model is a fine-tuned version of [funnel-transformer/small-base](https://huggingface.co/funnel-transformer/small-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4962
- Accuracy: 0.8856
## 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
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4326 | 1.0 | 1067 | 0.4819 | 0.8471 |
| 0.2963 | 2.0 | 2134 | 0.4710 | 0.8856 |
| 0.1752 | 3.0 | 3201 | 0.4962 | 0.8856 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Holarissun/gptj6b-aisft-gsm8k-seq | Holarissun | 2024-03-11T18:43:29Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:EleutherAI/gpt-j-6b",
"base_model:adapter:EleutherAI/gpt-j-6b",
"license:apache-2.0",
"region:us"
] | null | 2024-03-11T18:43:09Z | ---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: EleutherAI/gpt-j-6b
model-index:
- name: gptj6b-aisft-gsm8k-seq
results: []
---
<!-- 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. -->
# gptj6b-aisft-gsm8k-seq
This model is a fine-tuned version of [EleutherAI/gpt-j-6b](https://huggingface.co/EleutherAI/gpt-j-6b) 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: 5e-05
- train_batch_size: 1
- 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
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
EncryptedBinary/results_modified_7b_p1_5epoch | EncryptedBinary | 2024-03-11T18:40:31Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-11-16T08:22:46Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
pmu/my-vase-acb | pmu | 2024-03-11T18:33:49Z | 0 | 1 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-03-11T18:25:20Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Vase-acb Dreambooth model trained by pmu following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 4MC22IS075
Sample pictures of this concept:
.png)
|
gate369/Blurred-Beagle-7b-slerp | gate369 | 2024-03-11T18:33:47Z | 26 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"alnrg2arg/blockchainlabs_7B_merged_test2_4",
"222gate/BrurryDog-7b-v0.1",
"conversational",
"base_model:alnrg2arg/blockchainlabs_7B_merged_test2_4",
"base_model:merge:alnrg2arg/blockchainlabs_7B_merged_test2_4",
"base_model:gate369/BrurryDog-7b-v0.1",
"base_model:merge:gate369/BrurryDog-7b-v0.1",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-20T20:00:25Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- alnrg2arg/blockchainlabs_7B_merged_test2_4
- 222gate/BrurryDog-7b-v0.1
base_model:
- alnrg2arg/blockchainlabs_7B_merged_test2_4
- 222gate/BrurryDog-7b-v0.1
model-index:
- name: Blurred-Beagle-7b-slerp
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 72.78
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/Blurred-Beagle-7b-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 88.58
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/Blurred-Beagle-7b-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.95
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/Blurred-Beagle-7b-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 69.39
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/Blurred-Beagle-7b-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 83.19
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/Blurred-Beagle-7b-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 69.9
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/Blurred-Beagle-7b-slerp
name: Open LLM Leaderboard
---
# Blurred-Beagle-7b-slerp
Blurred-Beagle-7b-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [alnrg2arg/blockchainlabs_7B_merged_test2_4](https://huggingface.co/alnrg2arg/blockchainlabs_7B_merged_test2_4)
* [222gate/BrurryDog-7b-v0.1](https://huggingface.co/222gate/BrurryDog-7b-v0.1)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: alnrg2arg/blockchainlabs_7B_merged_test2_4
layer_range: [0, 32]
- model: 222gate/BrurryDog-7b-v0.1
layer_range: [0, 32]
merge_method: slerp
base_model: alnrg2arg/blockchainlabs_7B_merged_test2_4
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
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "222gate/Blurred-Beagle-7b-slerp"
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"])
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_222gate__Blurred-Beagle-7b-slerp)
| Metric |Value|
|---------------------------------|----:|
|Avg. |74.80|
|AI2 Reasoning Challenge (25-Shot)|72.78|
|HellaSwag (10-Shot) |88.58|
|MMLU (5-Shot) |64.95|
|TruthfulQA (0-shot) |69.39|
|Winogrande (5-shot) |83.19|
|GSM8k (5-shot) |69.90|
|
liminerity/Blur-7B-slerp-v0.1 | liminerity | 2024-03-11T18:33:40Z | 1,381 | 2 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"OpenPipe/mistral-ft-optimized-1218",
"mlabonne/Marcoro14-7B-slerp",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-14T22:13:06Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- OpenPipe/mistral-ft-optimized-1218
- mlabonne/Marcoro14-7B-slerp
model-index:
- name: Blur-7B-slerp-v0.1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 68.77
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Blur-7B-slerp-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 86.58
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Blur-7B-slerp-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.18
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Blur-7B-slerp-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 60.64
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Blur-7B-slerp-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 81.14
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Blur-7B-slerp-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 72.1
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Blur-7B-slerp-v0.1
name: Open LLM Leaderboard
---
things are bout' to get blurry
# Blur-7B-slerp-v0.1
Blur-7B-slerp-v0.1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)
* [mlabonne/Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1218
layer_range: [0, 32]
- model: mlabonne/Marcoro14-7B-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/Marcoro14-7B-slerp
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
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "222gate/Blur-7B-slerp-v0.1"
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"])
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__Blur-7B-slerp-v0.1)
| Metric |Value|
|---------------------------------|----:|
|Avg. |72.40|
|AI2 Reasoning Challenge (25-Shot)|68.77|
|HellaSwag (10-Shot) |86.58|
|MMLU (5-Shot) |65.18|
|TruthfulQA (0-shot) |60.64|
|Winogrande (5-shot) |81.14|
|GSM8k (5-shot) |72.10|
|
liminerity/dhbacmes-3b-slerp | liminerity | 2024-03-11T18:33:27Z | 137 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"liminerity/herbaccbaccules-3b-slerp",
"KnutJaegersberg/Deita-2b",
"conversational",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-25T04:26:39Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- liminerity/herbaccbaccules-3b-slerp
- KnutJaegersberg/Deita-2b
model-index:
- name: dhbacmes-3b-slerp
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 45.22
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/dhbacmes-3b-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 70.77
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/dhbacmes-3b-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 52.94
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/dhbacmes-3b-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 40.41
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/dhbacmes-3b-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.11
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/dhbacmes-3b-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 43.67
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/dhbacmes-3b-slerp
name: Open LLM Leaderboard
---
# dhbacmes-3b-slerp
dhbacmes-3b-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [liminerity/herbaccbaccules-3b-slerp](https://huggingface.co/liminerity/herbaccbaccules-3b-slerp)
* [KnutJaegersberg/Deita-2b](https://huggingface.co/KnutJaegersberg/Deita-2b)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: liminerity/herbaccbaccules-3b-slerp
layer_range: [0, 40]
- model: KnutJaegersberg/Deita-2b
layer_range: [0, 40]
merge_method: slerp
base_model: liminerity/herbaccbaccules-3b-slerp
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
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__dhbacmes-3b-slerp)
| Metric |Value|
|---------------------------------|----:|
|Avg. |53.02|
|AI2 Reasoning Challenge (25-Shot)|45.22|
|HellaSwag (10-Shot) |70.77|
|MMLU (5-Shot) |52.94|
|TruthfulQA (0-shot) |40.41|
|Winogrande (5-shot) |65.11|
|GSM8k (5-shot) |43.67|
|
liminerity/Neurotic-Jomainotrik-7b-slerp | liminerity | 2024-03-11T18:32:40Z | 58 | 2 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"liminerity/merge",
"bardsai/jaskier-7b-dpo-v5.6",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-25T21:00:27Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- liminerity/merge
- bardsai/jaskier-7b-dpo-v5.6
model-index:
- name: Neurotic-Jomainotrik-7b-slerp
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 72.95
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Neurotic-Jomainotrik-7b-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 89.15
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Neurotic-Jomainotrik-7b-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.28
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Neurotic-Jomainotrik-7b-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 77.64
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Neurotic-Jomainotrik-7b-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 85.4
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Neurotic-Jomainotrik-7b-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 68.99
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Neurotic-Jomainotrik-7b-slerp
name: Open LLM Leaderboard
---
# Neurotic-Jomainotrik-7b-slerp
Neurotic-Jomainotrik-7b-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [liminerity/merge](https://huggingface.co/liminerity/merge)
* [bardsai/jaskier-7b-dpo-v5.6](https://huggingface.co/bardsai/jaskier-7b-dpo-v5.6)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: liminerity/merge
layer_range: [0, 32]
- model: bardsai/jaskier-7b-dpo-v5.6
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/merge
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
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__Neurotic-Jomainotrik-7b-slerp)
| Metric |Value|
|---------------------------------|----:|
|Avg. |76.40|
|AI2 Reasoning Challenge (25-Shot)|72.95|
|HellaSwag (10-Shot) |89.15|
|MMLU (5-Shot) |64.28|
|TruthfulQA (0-shot) |77.64|
|Winogrande (5-shot) |85.40|
|GSM8k (5-shot) |68.99|
|
liminerity/mm4-3b | liminerity | 2024-03-11T18:32:26Z | 227 | 3 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"dataset:teknium/GPT4-LLM-Cleaned",
"dataset:vicgalle/alpaca-gpt4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-27T02:20:46Z | ---
license: apache-2.0
datasets:
- teknium/GPT4-LLM-Cleaned
- vicgalle/alpaca-gpt4
model-index:
- name: mm4-3b
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 44.8
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/mm4-3b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 70.41
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/mm4-3b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 50.9
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/mm4-3b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 43.2
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/mm4-3b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.22
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/mm4-3b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 43.82
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/mm4-3b
name: Open LLM Leaderboard
---
MM4-3b
a llama based model i made thru extensive training and merging
ill explain later i literally made so many models today
Title: Divergent Knowledge Enhancement through Retrograde Merging Strategies: Redefining Accuracy Perspectives in Language Model Evolution
Abstract: Have you picked up any bad habits, or have you ever learned to do something incorrectly, only to realize you must completly relearn whatever it is you're trying to accomplish? In this proposal, we present an innovative and unconventional approach to enhancing the performance and knowledge base of natural language models. Our proposed method, titled 'Divergent Knowledge Enhancement through Retrograde Merging Strategies' (DKE-RS), aims to challenge traditional practices in model development by incorporating a deliberate back-and-forth merger between high and low accuracy language models.
The initial conceptualization of DKE-RS stemmed from the realization that learning often encompasses both acquisition and unlearning, as encapsulated by the quote, "learning is just as sacred as unlearning." The proposed technique commences with a baseline model, 'blur-7b,' attaining an accuracy rate of 72.1%, subsequently merged with a Mistral fine-tuned model on the Dolphin dataset, only achieving a 46% accuracy level.
By deliberately merging with less accurate models and retracing the evolutionary process, DKE-RS aims to broaden the knowledge base of the resulting model. This strategy, dubbed 'making the bad good,' intentionally degrades the initial accuracy in an effort to refine it, thus breaking conventional iterative improvements for innovative progression.
image/png
The DKE-RS method challenges the status quo by not solely relying on a linear enhancement trajectory, instead adopting a more holistic and diverse approach. We anticipate that this non-linear merger process will further diversify the model's knowledge base, thereby creating a more resilient and well-rounded language generation tool, capable of handling complex contexts with a broader understanding.
Through thorough experimentation and analysis, we plan to assess the effectiveness and potential drawbacks of DKE-RS, comparing it to traditional merging techniques. The results from such evaluations will provide valuable insights into the efficacy of this divergent strategy in the landscape of natural language model development.
We posit that the Divergent Knowledge Enhancement through Retrograde Merging Strategies approach contributes a significant and compelling step forward in the field, provoking thought-provoking discourse about the nature of accuracy refinement and model progression.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__mm4-3b)
| Metric |Value|
|---------------------------------|----:|
|Avg. |53.22|
|AI2 Reasoning Challenge (25-Shot)|44.80|
|HellaSwag (10-Shot) |70.41|
|MMLU (5-Shot) |50.90|
|TruthfulQA (0-shot) |43.20|
|Winogrande (5-shot) |66.22|
|GSM8k (5-shot) |43.82|
|
gate369/BrurryDog-7b-v0.1 | gate369 | 2024-03-11T18:31:21Z | 11 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"udkai/Turdus",
"leveldevai/TurdusBeagle-7B",
"liminerity/Blur-7b-v1.21",
"base_model:leveldevai/TurdusBeagle-7B",
"base_model:merge:leveldevai/TurdusBeagle-7B",
"base_model:liminerity/Blur-7b-v1.21",
"base_model:merge:liminerity/Blur-7b-v1.21",
"base_model:udkai/Turdus",
"base_model:merge:udkai/Turdus",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-20T00:40:44Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- udkai/Turdus
- leveldevai/TurdusBeagle-7B
- liminerity/Blur-7b-v1.21
base_model:
- udkai/Turdus
- leveldevai/TurdusBeagle-7B
- liminerity/Blur-7b-v1.21
model-index:
- name: BrurryDog-7b-v0.1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 72.53
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/BrurryDog-7b-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 88.37
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/BrurryDog-7b-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.74
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/BrurryDog-7b-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 70.05
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/BrurryDog-7b-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 82.87
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/BrurryDog-7b-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.87
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/BrurryDog-7b-v0.1
name: Open LLM Leaderboard
---
# BrurryDog-7b-v0.1
BrurryDog-7b-v0.1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [udkai/Turdus](https://huggingface.co/udkai/Turdus)
* [leveldevai/TurdusBeagle-7B](https://huggingface.co/leveldevai/TurdusBeagle-7B)
* [liminerity/Blur-7b-v1.21](https://huggingface.co/liminerity/Blur-7b-v1.21)
## 🧩 Configuration
```yaml
models:
- model: udkai/Turdus
parameters:
density: [1, 0.7, 0.1] # density gradient
weight: 1.0
- model: leveldevai/TurdusBeagle-7B
parameters:
density: 0.5
weight: [0, 0.3, 0.7, 1] # weight gradient
- model: liminerity/Blur-7b-v1.21
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0
merge_method: ties
base_model: udkai/Turdus
parameters:
normalize: true
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "222gate/BrurryDog-7b-v0.1"
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"])
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_222gate__BrurryDog-7b-v0.1)
| Metric |Value|
|---------------------------------|----:|
|Avg. |74.24|
|AI2 Reasoning Challenge (25-Shot)|72.53|
|HellaSwag (10-Shot) |88.37|
|MMLU (5-Shot) |64.74|
|TruthfulQA (0-shot) |70.05|
|Winogrande (5-shot) |82.87|
|GSM8k (5-shot) |66.87|
|
liminerity/Blur-7b-v1.21 | liminerity | 2024-03-11T18:30:17Z | 49 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"udkai/Turdus",
"decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP",
"liminerity/Blur-7b-v1.2",
"base_model:decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP",
"base_model:merge:decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP",
"base_model:liminerity/Blur-7b-v1.2",
"base_model:merge:liminerity/Blur-7b-v1.2",
"base_model:udkai/Turdus",
"base_model:merge:udkai/Turdus",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-18T04:30:23Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- udkai/Turdus
- decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP
- liminerity/Blur-7b-v1.2
base_model:
- udkai/Turdus
- decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP
- liminerity/Blur-7b-v1.2
model-index:
- name: Blur-7b-v1.21
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 70.82
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Blur-7b-v1.21
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 88.07
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Blur-7b-v1.21
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.85
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Blur-7b-v1.21
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 67.99
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Blur-7b-v1.21
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 83.82
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Blur-7b-v1.21
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 69.52
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Blur-7b-v1.21
name: Open LLM Leaderboard
---
# Blur-7b-v1.21
Blur-7b-v1.21 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [udkai/Turdus](https://huggingface.co/udkai/Turdus)
* [decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP](https://huggingface.co/decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP)
* [liminerity/Blur-7b-v1.2](https://huggingface.co/liminerity/Blur-7b-v1.2)
## 🧩 Configuration
```yaml
models:
- model: udkai/Turdus
parameters:
density: [1, 0.7, 0.1] # density gradient
weight: 1.0
- model: decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP
parameters:
density: 0.5
weight: [0, 0.3, 0.7, 1] # weight gradient
- model: liminerity/Blur-7b-v1.2
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0
merge_method: ties
base_model: fblgit/UNA-TheBeagle-7b-v1
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/Blur-7b-v1.21"
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"])
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__Blur-7b-v1.21)
| Metric |Value|
|---------------------------------|----:|
|Avg. |74.18|
|AI2 Reasoning Challenge (25-Shot)|70.82|
|HellaSwag (10-Shot) |88.07|
|MMLU (5-Shot) |64.85|
|TruthfulQA (0-shot) |67.99|
|Winogrande (5-shot) |83.82|
|GSM8k (5-shot) |69.52|
|
franklee1015/q-FrozenLake-v1-4x4-noSlippery | franklee1015 | 2024-03-11T18:24:40Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-28T12:12:11Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
model = load_from_hub(repo_id="franklee1015/q-FrozenLake-v1-4x4-noSlippery", 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"])
|
ferrazzipietro/Qwen1.5-7B-Chat__adapters_en.layer1_8_torch.bfloat16_64_64_0.01_4_0.0002 | ferrazzipietro | 2024-03-11T18:19:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-11T18:19:05Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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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[More Information Needed]
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## Glossary [optional]
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|
suban244/muRIL-squad-nep-translated-squad | suban244 | 2024-03-11T18:15:48Z | 27 | 1 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:suban244/muRIL-squad",
"base_model:finetune:suban244/muRIL-squad",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-12-11T08:57:32Z | ---
base_model: suban244/muRIL-squad
tags:
- generated_from_trainer
model-index:
- name: muRIL-squad-nep-translated-squad
results: []
---
<!-- 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. -->
# muRIL-squad-nep-translated-squad
This model is a fine-tuned version of [suban244/muRIL-squad](https://huggingface.co/suban244/muRIL-squad) 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: 1e-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: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|
franklee1015/dqn-SpaceInvadersNoFrameskip-v4 | franklee1015 | 2024-03-11T18:15:19Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-07T02:59:47Z | ---
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
model-index:
- name: dqn-SpaceInvadersNoFrameskip-v4
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 877.00 +/- 176.35
name: mean_reward
verified: false
library_name: stable-baselines3
---
# **Deep Q Learning** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **Deep Q Learning** agent playing **SpaceInvadersNoFrameskip-v4** . |
shubham-krishna/peft-gemma-2b-dolly | shubham-krishna | 2024-03-11T18:14:45Z | 1 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"license:other",
"region:us"
] | null | 2024-03-11T18:14:38Z | ---
license: other
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: google/gemma-2b
datasets:
- generator
model-index:
- name: peft-gemma-2b-dolly
results: []
---
<!-- 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. -->
# peft-gemma-2b-dolly
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- PEFT 0.8.2
- Transformers 4.39.0.dev0
- Pytorch 2.3.0
- Datasets 2.17.1
- Tokenizers 0.15.2 |
Artefact2/Mixtral-8x7B-v0.1-GGUF | Artefact2 | 2024-03-11T18:12:59Z | 118 | 2 | null | [
"gguf",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-02-26T16:39:22Z | ---
language:
- en
license: apache-2.0
---
These are GGUF quantized versions of [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1).
The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using `wiki.train.raw`.
Some model files above 50GB are split into smaller files. To concatenate them, use the `cat` command (on Windows, use PowerShell): `cat foo-Q6_K.gguf.* > foo-Q6_K.gguf`
* What quant do I need? See https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
* Quant requests? Just open a discussion in the community tabs. |
ferrazzipietro/Qwen1.5-7B-Chat__adapters_en.layer1_8_torch.bfloat16_64_64_0.01_2_0.0002 | ferrazzipietro | 2024-03-11T18:12:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-11T18:11:22Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
automerger/Experiment24Yam-7B | automerger | 2024-03-11T18:04:40Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:mayacinka/yam-jom-7B",
"base_model:merge:mayacinka/yam-jom-7B",
"base_model:yam-peleg/Experiment24-7B",
"base_model:merge:yam-peleg/Experiment24-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-08T16:52:03Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
base_model:
- yam-peleg/Experiment24-7B
- mayacinka/yam-jom-7B
---
# Experiment24Yam-7B
Experiment24Yam-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
* [yam-peleg/Experiment24-7B](https://huggingface.co/yam-peleg/Experiment24-7B)
* [mayacinka/yam-jom-7B](https://huggingface.co/mayacinka/yam-jom-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: yam-peleg/Experiment24-7B
layer_range: [0, 32]
- model: mayacinka/yam-jom-7B
layer_range: [0, 32]
merge_method: slerp
base_model: yam-peleg/Experiment24-7B
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
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/Experiment24Yam-7B"
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"])
``` |
mouss217/mistral-7b-chatgptprompts | mouss217 | 2024-03-11T18:03:50Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-11T17:54:47Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
aegunal/FT_IPD_gemma7b | aegunal | 2024-03-11T18:02:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-11T18:02:50Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Draichi/Taxi-v3-Qlearning | Draichi | 2024-03-11T18:01:15Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-11T18:01:13Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3-Qlearning
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **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="Draichi/Taxi-v3-Qlearning", 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"])
```
|
NikolayKozloff/occiglot-7b-es-en-GGUF | NikolayKozloff | 2024-03-11T17:59:12Z | 2 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-03-11T17:36:26Z | GGUF for this model: https://huggingface.co/occiglot/occiglot-7b-es-en
Occiglot-7B-ES-EN is a generative language model with 7B parameters for Spanish and English and trained by the Occiglot Research Collective. It is based on Mistral-7B-v0.1 and trained on 112B tokens of additional multilingual and code data with a block size of 8,192 tokens per sample. Note that the model is a general-purpose base model and was not instruction-fine-tuned nor optimized for chat or other applications.
|
sunilregmi/wav2vec2-base-openslr43-colab | sunilregmi | 2024-03-11T17:58:16Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-11T17:30:04Z | ---
library_name: transformers
tags: []
---
# 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. -->
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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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]
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|
ferrazzipietro/Qwen1.5-7B-Chat__adapters_en.layer1_8_torch.bfloat16_64_32_0.01_4_0.0002 | ferrazzipietro | 2024-03-11T17:56:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-11T17:56:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## 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:** [More Information Needed]
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## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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]
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## Model Card Contact
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|
ibunescu/Phi-2_GDPR_chapter_classifier_v5_adapter | ibunescu | 2024-03-11T17:55:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-11T17:54:40Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
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[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
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[More Information Needed]
## Training Details
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[More Information Needed]
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#### Preprocessing [optional]
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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]
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|
Dharil/Llama-2-7b-finetune-legal-data | Dharil | 2024-03-11T17:52:18Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2024-03-11T17:51:37Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
Thomstr/Taxi-v3 | Thomstr | 2024-03-11T17:50:36Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-11T17:36:06Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **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="Thomstr/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"])
```
|
lkk688/detr-resnet-50_finetuned_coco | lkk688 | 2024-03-11T17:50:07Z | 177 | 0 | transformers | [
"transformers",
"safetensors",
"detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | object-detection | 2024-03-11T06:58:52Z | ---
library_name: transformers
tags: []
---
# 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. -->
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Dharil/Llama-2-fine-tuned-on-legal-data | Dharil | 2024-03-11T17:49:25Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2024-03-11T17:48:34Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
Thomstr/q-q-FrozenLake-v1-4x4-noSlippery_test | Thomstr | 2024-03-11T17:49:00Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-11T15:55:24Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-q-FrozenLake-v1-4x4-noSlippery_test
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Thomstr/q-q-FrozenLake-v1-4x4-noSlippery_test", 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"])
```
|
grianzeno/Aunt-Itoe | grianzeno | 2024-03-11T17:43:00Z | 0 | 0 | null | [
"region:us"
] | null | 2024-03-11T17:34:53Z | - text: >-
AzumaFubuki, 1girl, mature female, completely nude, bedroom, standing,
cowboy shot, perfect hands, perfect face, masterpiece, best quality,
absurdres, long hair, hair over one eye, hair ribbon, straight-on, hands
on hips, smile, curvy, mature female, thick thighs
output:
url: main/23401652.png |
Weni/ZeroShot-3.4.2-Mistral-7b-DPO-1.0.0 | Weni | 2024-03-11T17:42:44Z | 0 | 0 | trl | [
"trl",
"safetensors",
"DPO",
"ZeroShot",
"en",
"es",
"pt",
"base_model:Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged",
"base_model:finetune:Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged",
"license:mit",
"region:us"
] | null | 2024-03-11T17:06:46Z | ---
license: mit
library_name: "trl"
tags:
- DPO
- ZeroShot
base_model: Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged
model-index:
- name: Weni/ZeroShot-3.4.2-Mistral-7b-DPO-1.0.0
results: []
language: ['en', 'es', 'pt']
---
# Weni/ZeroShot-3.4.2-Mistral-7b-DPO-1.0.0
This model is a fine-tuned version of [Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged] on the dataset Weni/zeroshot-dpo-1.0.0 with the DPO trainer. It is part of the ZeroShot project for [Weni](https://weni.ai/).
It achieves the following results on the evaluation set:
{'eval_loss': 0.10210147500038147, 'eval_runtime': 27.5135, 'eval_samples_per_second': 2.217, 'eval_steps_per_second': 0.291, 'eval_rewards/chosen': 0.792843222618103, 'eval_rewards/rejected': -3.810342311859131, 'eval_rewards/accuracies': 0.953125, 'eval_rewards/margins': 4.603185176849365, 'eval_logps/rejected': -51.665706634521484, 'eval_logps/chosen': -8.38036823272705, 'eval_logits/rejected': -1.3307629823684692, 'eval_logits/chosen': -1.3801817893981934, 'epoch': 2.82}
## Intended uses & limitations
This model has not been trained to avoid specific intructions.
## Training procedure
Finetuning was done on the model Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged with the following prompt:
```
Portuguese:
[INST] Você é muito especialista em classificar a frase do usuário em um chatbot sobre: {context}
Pare, pense bem e responda com APENAS UM ÚNICO \`id\` da classe que melhor represente a intenção para a frase do usuário de acordo com a análise de seu contexto, responda APENAS com o \`id\` da classe só se você tiver muita certeza e não explique o motivo. Na ausência, falta de informações ou caso a frase do usuário não se enquadre em nenhuma classe, classifique como "-1".
# Essas são as Classes com seus Id e Contexto:
{all_classes}
# Frase do usuário: {input}
# Id da Classe: [/INST]
Spanish:
[INST] Eres muy experto en clasificar la frase del usuario en un chatbot sobre: {context}
Deténgase, piense bien y responda con SOLO UN ÚNICO \`id\` de la clase que mejor represente la intención para la frase del usuario de acuerdo con el análisis de su contexto, responda SOLO con el \`id\` de la clase si está muy seguro y no explique el motivo. En ausencia, falta de información o en caso de que la frase del usuario no se ajuste a ninguna clase, clasifique como "-1".
# Estas son las Clases con sus Id y Contexto:
{all_classes}
# Frase del usuario: {input}
# Id de la Clase: [/INST]
English:
[INST] You are very expert in classifying the user sentence in a chatbot about: {context}
Stop, think carefully, and respond with ONLY ONE SINGLE \`id\` of the class that best represents the intention for the user's sentence according to the analysis of its context, respond ONLY with the \`id\` of the class if you are very sure and do not explain the reason. In the absence, lack of information, or if the user's sentence does not fit into any class, classify as "-1".
# These are the Classes and its Context:
{all_classes}
# User's sentence: {input}
# Class Id: [/INST]
Chosen_response:
{chosen_response}
Rejected_response:
{rejected_response}
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- per_device_train_batch_size: 8
- per_device_eval_batch_size: 8
- gradient_accumulation_steps: 4
- num_gpus: 1
- total_train_batch_size: 32
- optimizer: AdamW
- lr_scheduler_type: cosine
- num_steps: 48
- quantization_type: bitsandbytes
- LoRA: ("\n - bits: 4\n - use_exllama: True\n - device_map: auto\n - use_cache: False\n - lora_r: 8\n - lora_alpha: 16\n - lora_dropout: 0.1\n - bias: none\n - target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj']\n - task_type: CAUSAL_LM",)
### Training results
### Framework versions
- transformers==4.38.2
- datasets==2.17.1
- peft==0.8.2
- safetensors==0.4.2
- evaluate==0.4.1
- bitsandbytes==0.42
- huggingface_hub==0.20.3
- seqeval==1.2.2
- optimum==1.17.1
- auto-gptq==0.7.0
- gpustat==1.1.1
- deepspeed==0.13.2
- wandb==0.16.3
- trl==0.7.11
- accelerate==0.27.2
- coloredlogs==15.0.1
- traitlets==5.14.1
- autoawq@https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.0/autoawq-0.2.0+cu118-cp310-cp310-linux_x86_64.whl
### Hardware
- Cloud provided: runpod.io
|
spar-ai/henry-LLM-epoch6-50dia-4bit | spar-ai | 2024-03-11T17:42:27Z | 64 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] | text-generation | 2024-03-11T17:38:42Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
### Model Description
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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:** [More Information Needed]
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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 Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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[More Information Needed] |
StaAhmed/llama_lora_QA | StaAhmed | 2024-03-11T17:29:29Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"base_model:finetune:NousResearch/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-03-11T06:55:54Z | ---
base_model: NousResearch/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: llama_lora_QA
results: []
---
<!-- 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_lora_QA
This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.13.3
|
LarryAIDraw/Natsumi__Adult_Ver__-000017 | LarryAIDraw | 2024-03-11T17:28:02Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-03-11T17:13:24Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/343915/natsumi-adult-ver-date-a-live-lora |
LarryAIDraw/CHAR-KirikoYukoku | LarryAIDraw | 2024-03-11T17:27:44Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-03-11T17:11:38Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/342027/kiriko-yukoku-4-outfits-or-the-idolmster-shiny-colors |
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