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hlumin/Reinforce-pixelcopter-v0 | hlumin | 2024-03-27T19:18:13Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-03-27T18:20:35Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 18.60 +/- 13.97
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
|
kubernetes-bad/good-robot | kubernetes-bad | 2024-03-27T19:16:39Z | 14 | 2 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:HuggingFaceH4/no_robots",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-12-28T10:04:43Z | ---
datasets:
- HuggingFaceH4/no_robots
language:
- en
license: cc-by-nc-4.0
---
# Good Robot 🤖
> [!NOTE]
> → There is an updated version of this model available, please see [Good Robot 2 →](https://huggingface.co/kubernetes-bad/good-robot-2).
The model "Good Robot" had one simple goal in mind: to be a good instruction-following model that doesn't talk like ChatGPT.
Built upon the Mistral 7b base, this model aims to provide responses that are as human-like as possible, thanks to some DPO training using the (for now, private) `minerva-ai/yes-robots-dpo` dataset.
HuggingFaceH4/no-robots was used as the base for generating a custom dataset to create DPO pairs.
It should follow instructions and be generally as smart as a typical Mistral model - just not as soulless and full of GPT slop.
## Prompt Format:
Alpaca, my beloved ❤️
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{your prompt goes here}
### Response:
```
## Huge Thanks:
- Gryphe for DPO scripts and all the patience 🙏
## Training Data:
- [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots)
- [MinervaAI/yes-robots-dpo](https://huggingface.co/MinervaAI)
- private datasets with common GPTisms
## Limitations:
While I did my best to minimize GPTisms, no model is perfect, and there may still be instances where the generated content has GPT's common phrases - I have a suspicion that's due to them being engrained into Mistral model itself.
## License:
cc-by-nc-4.0
|
togethercomputer/StripedHyena-Hessian-7B | togethercomputer | 2024-03-27T19:16:13Z | 57 | 65 | transformers | [
"transformers",
"safetensors",
"stripedhyena",
"text-generation",
"custom_code",
"en",
"arxiv:2302.10866",
"arxiv:2310.18780",
"arxiv:2311.05908",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
]
| text-generation | 2023-11-21T15:43:25Z | ---
license: apache-2.0
language:
- en
---
## StripedHyena-Hessian-7B (SH 7B)
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/62a1306bbe7fa896d2c8de44/Bfjh77emDsWOY-VmfvU9C.png" width="60%" />
</p>
### About
One of the focus areas at Together Research is new architectures for long context, improved training, and inference performance over the Transformer architecture. Spinning out of a research program from our team and academic collaborators, with roots in **signal processing-inspired sequence models**, we are excited to introduce the **StripedHyena** models. StripedHyena is the **first alternative model competitive with the best open-source Transformers** of similar sizes in short and long-context evaluations.
**StripedHyena-Hessian-7B (SH 7B)** is our **base model** for this release.
- Read more here in [our blog](https://www.together.ai/blog/stripedhyena-7b).
- Play with the model on our [playground](https://api.together.xyz/playground/language/togethercomputer/StripedHyena-Hessian-7B)!
- Dive into the details of our [standalone implementation](https://github.com/togethercomputer/stripedhyena), and our related research: [1](https://arxiv.org/abs/2302.10866), [2](https://arxiv.org/abs/2310.18780), [3](https://arxiv.org/abs/2311.05908).
### Model Architecture
StripedHyena is a hybrid architecture composed of multi-head, grouped-query attention and gated convolutions arranged in [Hyena](https://arxiv.org/abs/2302.10866) blocks, different from traditional decoder-only Transformers.
- Costant memory decoding in Hyena blocks via representation of convolutions as state-space models (modal or canonical form), or as truncated filters.
- Low latency, faster decoding and higher throughput than Transformers.
- Improvement to training and inference-optimal scaling laws, compared to optimized Transformer architectures such as Llama-2.
- Trained on sequences of up to 32k, allowing it to process longer prompts.
### Note
To use StripedHyena outside of the playground, you will need to install custom kernels. Please follow the instructions from the [standalone repository](https://github.com/togethercomputer/stripedhyena).
StripedHyena is a mixed precision model. Make sure to keep your `poles` and `residues` in `float32` precision, especially for longer prompts or training.
## Cite
If you have found the pretrained models or architecture useful for you research or application, consider citing:
```
@software{stripedhyena,
title = {{StripedHyena: Moving Beyond Transformers with Hybrid Signal Processing Models}},
author = { Poli, Michael and Wang, Jue and Massaroli, Stefano and Quesnelle, Jeffrey and Carlow, Ryan and Nguyen, Eric and Thomas, Armin},
month = 12,
year = 2023,
url = { https://github.com/togethercomputer/stripedhyena },
doi = { 10.57967/hf/1595 },
}
``` |
togethercomputer/StripedHyena-Nous-7B | togethercomputer | 2024-03-27T19:15:38Z | 102 | 140 | transformers | [
"transformers",
"pytorch",
"safetensors",
"stripedhyena",
"text-generation",
"custom_code",
"en",
"arxiv:2302.10866",
"arxiv:2310.18780",
"arxiv:2311.05908",
"doi:10.57967/hf/1595",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
]
| text-generation | 2023-12-04T19:56:49Z | ---
license: apache-2.0
language:
- en
---
## StripedHyena-Nous-7B (SH-N 7B)
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/62a1306bbe7fa896d2c8de44/Bfjh77emDsWOY-VmfvU9C.png" width="60%" />
</p>
### About
One of the focus areas at Together Research is new architectures for long context, improved training, and inference performance over the Transformer architecture. Spinning out of a research program from our team and academic collaborators, with roots in **signal processing-inspired sequence models**, we are excited to introduce the **StripedHyena** models. StripedHyena is the **first alternative model competitive with the best open-source Transformers** of similar sizes in short and long-context evaluations.
**StripedHyena-Nous-7B (SH-N 7B)** is our **chat model** for this release, and was developed with our collaborators at [Nous Research](https://nousresearch.com/).
- Read more here in [our blog](https://www.together.ai/blog/stripedhyena-7b).
- Play with the model on our [playground](https://api.together.xyz/playground/chat/togethercomputer/StripedHyena-Nous-7B)!
- Dive into the details of our [standalone implementation](https://github.com/togethercomputer/stripedhyena), and our related research: [1](https://arxiv.org/abs/2302.10866), [2](https://arxiv.org/abs/2310.18780), [3](https://arxiv.org/abs/2311.05908).
### Model Architecture
StripedHyena is a hybrid architecture composed of multi-head, grouped-query attention and gated convolutions arranged in [Hyena](https://arxiv.org/abs/2302.10866) blocks, different from traditional decoder-only Transformers.
- Costant memory decoding in Hyena blocks via representation of convolutions as state-space models (modal or canonical form), or as truncated filters.
- Low latency, faster decoding and higher throughput than Transformers.
- Improvement to training and inference-optimal scaling laws, compared to optimized Transformer architectures such as Llama-2.
- Trained on sequences of up to 32k, allowing it to process longer prompts.
### Prompt Format
StripedHyena-Nous 7B uses this prompt format:
```
### Instruction:\n{prompt}\n\n### Response:\n{response}
```
### Disclaimer
To use StripedHyena outside of the playground, you will need to install custom kernels. Please follow the instructions from the [standalone repository](https://github.com/togethercomputer/stripedhyena).
StripedHyena is a mixed precision model. Make sure to keep your `poles` and `residues` in `float32` precision, especially for longer prompts or training.
## Cite
If you have found the pretrained models or architecture useful for you research or application, consider citing:
```
@software{stripedhyena,
title = {{StripedHyena: Moving Beyond Transformers with Hybrid Signal Processing Models}},
author = { Poli, Michael and Wang, Jue and Massaroli, Stefano and Quesnelle, Jeffrey and Carlow, Ryan and Nguyen, Eric and Thomas, Armin},
month = 12,
year = 2023,
url = { https://github.com/togethercomputer/stripedhyena },
doi = { 10.57967/hf/1595 },
}
``` |
kubernetes-bad/good-robot-2 | kubernetes-bad | 2024-03-27T19:10:32Z | 7 | 3 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"dataset:HuggingFaceH4/no_robots",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T18:59:29Z | ---
datasets:
- HuggingFaceH4/no_robots
language:
- en
license: cc-by-nc-4.0
---
# Good Robot 2 🤖
The model "Good Robot" had one simple goal in mind: to be a good instruction-following model that doesn't talk like ChatGPT.
Built upon the Mistral 7b 0.2 base, this model aims to provide responses that are as human-like as possible, thanks to some DPO training using the (for now, private) `minerva-ai/yes-robots-dpo` dataset.
HuggingFaceH4/no-robots was used as the base for generating a custom dataset to create DPO pairs.
It should follow instructions and be generally as smart as a typical Mistral model - just not as soulless and full of GPT slop.
Changes from the original [good-robot](https://huggingface.co/kubernetes-bad/good-robot) model:
- Mistral 7b-0.2 base (32k native context, no SWA)
- ChatML prompt format
- Trained using GaLore method
## Prompt Format:
ChatML
```
<|im_start|>system
System message
<|im_start|>user
User message<|im_end|>
<|im_start|>assistant
```
## Credits:
Model made in collaboration with [Gryphe](https://huggingface.co/Gryphe).
## Training Data:
- [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots)
- [MinervaAI/yes-robots-dpo](https://huggingface.co/MinervaAI)
- private datasets with common GPTisms
## Limitations:
While I did my best to minimize GPTisms, no model is perfect, and there may still be instances where the generated content has GPT's common phrases - I have a suspicion that's due to them being engrained into Mistral model itself.
## License:
cc-by-nc-4.0
|
Gordon119/qa_test | Gordon119 | 2024-03-27T19:06:43Z | 125 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2024-03-27T18:21:15Z | ---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: google-bert/bert-base-uncased
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. -->
# google-bert/bert-base-uncased
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the squad 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: 2
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
vladim-berezkin/q-FrozenLake-v1-4x4-noSlippery | vladim-berezkin | 2024-03-27T19:05:17Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-03-27T19:05:15Z | ---
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
```python
model = load_from_hub(repo_id="vladim-berezkin/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"])
```
|
drhead/ZeroDiffusion | drhead | 2024-03-27T18:50:32Z | 0 | 15 | null | [
"text-to-image",
"dataset:ChristophSchuhmann/improved_aesthetics_6plus",
"dataset:drhead/laion_hd_21M_deduped",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-07-21T05:32:49Z | ---
license: creativeml-openrail-m
datasets:
- ChristophSchuhmann/improved_aesthetics_6plus
- drhead/laion_hd_21M_deduped
pipeline_tag: text-to-image
---
Currently released models:
**ZeroDiffusion-Base v0.9** (zd_base_v0-9 and zd_base_v0-9_ema) - a base model trained on zero terminal SNR over roughly 20 million samples
**ZeroDiffusion-Inpaint v0.9** (zd_inpaint_v0-9 and zd_inpaint_v0-9_ema) - an experimental finetune of the stable-diffusion-inpainting model, initialized from a merge of ZD 0.9
The intention of this model is to provide a training base for other models, and to provide researchers with a clean model base to test zero terminal SNR with.
For this model to work well, you will probably need CFG rescale, which is implemented in this plugin: https://github.com/Seshelle/CFG_Rescale_webui
You must also download the corresponding YAML file and put it in the folder with the model (assuming you are using A1111's webui or similar). It won't work without it. It will tell webui to use the model in v-prediction mode.
Trained as part of Google's TPU Research Cloud program. |
dhananjay2912/Hermes-2-Pro-Mistral-7B-MEDIQA-CORR-DPO-Fewshot | dhananjay2912 | 2024-03-27T18:46:31Z | 5 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T18:40:40Z | ---
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]
- **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]
|
pouatchaOuanko/POUATCHA_ouanko_belvain | pouatchaOuanko | 2024-03-27T18:40:50Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T15:21:01Z | ---
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]
|
partiallycomplex/pcomplex | partiallycomplex | 2024-03-27T18:38:05Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-03-27T18:33:57Z |
tokenizer = AutoTokenizer.from_pretrained("username/repo_name")
model = AutoModel.from_pretrained("username/repo_name")
|
phanerozoic/Tiny-Pirate-1.1b-v0.1 | phanerozoic | 2024-03-27T18:37:31Z | 119 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"doi:10.57967/hf/1583",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-01-04T14:29:38Z | ---
license: cc-by-nc-4.0
language:
- en
widget:
- text: |
What is best in life?
example_title: "Healthy Eating Tips"
---
# Tiny-Pirate-1.1b-v0.1
Tiny-Pirate-1.1b-v0.1 is a compact and specialized language model designed for generating authentic pirate-themed content. This version is fine-tuned from the TinyLlama-1.1B model, specifically adapted to operate efficiently in CPU-only and resource-limited environments.
- **Developed by**: phanerozoic
- **License**: cc-by-nc-4.0
- **Finetuned from**: TinyLlama-1.1B
### Version Control
Introducing Tiny-Pirate-1.1b-v0.1 to mark the initial release of this specialized language model.
### Performance
The Tiny-Pirate-1.1B model exhibits a robust ability to generate pirate-themed content, demonstrating a strong grasp of pirate vernacular and thematic elements. The responses are notably coherent and contextually appropriate, reflecting the model's adeptness at maintaining a consistent pirate tone. However, there are instances where the responses could benefit from more precise and direct answers to the questions posed, suggesting a potential area for further fine-tuning.
### Direct Use
Ideal for applications requiring thematic language generation in resource-constrained environments, such as edge computing, mobile devices, and lightweight AI applications.
### Training Data
Utilized the same pirate-themed dataset as MistralPirate-7b-v0.3, ensuring rich and diverse inputs for fine-tuning.
### Custom Stopping Strings
To enhance output quality, the following custom stopping strings were employed:
- "},"
- "User:"
- "You:"
- "\nUser"
- "\nUser:"
- "me:"
- ""\n"
### Training Hyperparameters and Fine-Tuning Details
- **LoRA Rank**: 16
- **LoRA Alpha**: 32
- **True Batch Size**: 4
- **Gradient Accumulation Steps**: 1
- **Epochs**: 1
- **Learning Rate**: 3e-4
- **LR Scheduler**: Linear
- **LLaMA Target Projections**: All targets modified
- **Fine-Tuning Approach**: LoRA peft merged back into the base model
### Limitations
While adept at generating pirate-themed content, Tiny-Pirate-v0.1 may not handle highly complex language tasks as larger models do. Its specialization in pirate dialect limits its use in general language applications.
### Compute Infrastructure
Efficiently trained on an RTX 6000 Ada GPU, taking approximately 2-3 minutes, showcasing resource-effective training for specialized models.
### Results
The model successfully produced responses that are thematically aligned with typical pirate lore and language. The outputs are engaging and largely relevant to the queries, showcasing the model's capacity to handle a variety of pirate-related topics from navigation to mythology. The use of pirate dialect is consistent and immersive, contributing to the overall thematic experience. However, the depth of responses varies, indicating room for improvement in handling more complex queries or providing more detailed explanations.
### Summary
Tiny-Pirate-1.1B stands out as an effective tool for generating pirate-themed content, particularly suitable for applications where thematic consistency and lighter computational demands are key. While the model shows competence in creating thematically rich and linguistically coherent outputs, there is potential for enhancing its ability to handle complex scenarios and provide more detailed, context-specific responses. Overall, Tiny-Pirate-1.1B represents a promising step in the realm of specialized, lightweight language models, combining thematic accuracy with operational efficiency.
### Acknowledgments
Gratitude is extended to the developers of TinyLlama-1.1B for their foundational work, which was instrumental in the creation of Tiny-Pirate-v0.1. |
sharanharsoor/marian-finetuned-kde4-en-to-fr | sharanharsoor | 2024-03-27T18:37:27Z | 104 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2024-03-27T16:52:34Z | ---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.91210143343284
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8554
- Bleu: 52.9121
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
arcee-ai/Saul-Nous-Hermes-2-Mistral-7B-DPO-Ties | arcee-ai | 2024-03-27T18:36:23Z | 14 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"Equall/Saul-Base",
"NousResearch/Nous-Hermes-2-Mistral-7B-DPO",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T18:32:54Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- Equall/Saul-Base
- NousResearch/Nous-Hermes-2-Mistral-7B-DPO
---
# arcee-ai/Saul-Nous-Hermes-2-Mistral-7B-DPO-Ties
arcee-ai/Saul-Nous-Hermes-2-Mistral-7B-DPO-Ties is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [Equall/Saul-Base](https://huggingface.co/Equall/Saul-Base)
* [NousResearch/Nous-Hermes-2-Mistral-7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO)
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
# no parameters necessary for base model
- model: Equall/Saul-Base
parameters:
density: 0.5
weight: 0.5
- model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
parameters:
density: 0.5
weight: 0.5
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
normalize: false
int8_mask: true
dtype: float16
``` |
gjonesQ02/S1_InstructionGenerator | gjonesQ02 | 2024-03-27T18:36:21Z | 112 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T18:30:28Z | ---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: S1_InstructionGenerator
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. -->
# S1_InstructionGenerator
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0900
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 173 | 0.1076 |
| No log | 2.0 | 346 | 0.0987 |
| 0.1211 | 3.0 | 519 | 0.0946 |
| 0.1211 | 4.0 | 692 | 0.0916 |
| 0.1211 | 5.0 | 865 | 0.0905 |
| 0.1044 | 6.0 | 1038 | 0.0900 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
lunarsylph/stablecell_v1 | lunarsylph | 2024-03-27T18:36:00Z | 92 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T18:20:19Z | ---
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] |
mychen76/bio_mistral-7b-cervical_instability_lora_v1 | mychen76 | 2024-03-27T18:25:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"mistral",
"trl",
"en",
"base_model:BioMistral/BioMistral-7B",
"base_model:finetune:BioMistral/BioMistral-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-03-27T17:08:43Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- mistral
- trl
base_model: BioMistral/BioMistral-7B
---
# Uploaded model
- **Developed by:** mychen76
- **License:** apache-2.0
- **Finetuned from model :** BioMistral/BioMistral-7B
|
vtiyyal1/quality_model | vtiyyal1 | 2024-03-27T18:24:42Z | 116 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-27T18:20:56Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: quality_model
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. -->
# quality_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0104
- Mse: 0.0104
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mse |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0154 | 0.05 | 50 | 0.0106 | 0.0106 |
| 0.0172 | 0.11 | 100 | 0.0109 | 0.0109 |
| 0.0166 | 0.16 | 150 | 0.0199 | 0.0199 |
| 0.0132 | 0.22 | 200 | 0.0106 | 0.0106 |
| 0.0153 | 0.27 | 250 | 0.0120 | 0.0120 |
| 0.0131 | 0.32 | 300 | 0.0104 | 0.0104 |
| 0.0127 | 0.38 | 350 | 0.0104 | 0.0104 |
| 0.0143 | 0.43 | 400 | 0.0110 | 0.0110 |
| 0.0146 | 0.48 | 450 | 0.0113 | 0.0113 |
| 0.0119 | 0.54 | 500 | 0.0115 | 0.0115 |
| 0.0172 | 0.59 | 550 | 0.0107 | 0.0107 |
| 0.0111 | 0.65 | 600 | 0.0104 | 0.0104 |
| 0.0114 | 0.7 | 650 | 0.0105 | 0.0105 |
| 0.0219 | 0.75 | 700 | 0.0106 | 0.0106 |
| 0.0118 | 0.81 | 750 | 0.0122 | 0.0122 |
| 0.0184 | 0.86 | 800 | 0.0104 | 0.0104 |
| 0.0176 | 0.92 | 850 | 0.0104 | 0.0104 |
| 0.0137 | 0.97 | 900 | 0.0104 | 0.0104 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
vtiyyal1/empathy_model | vtiyyal1 | 2024-03-27T18:24:16Z | 19,031 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-27T18:20:12Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: empathy_model
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. -->
# empathy_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0043
- Mse: 0.0043
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mse |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0109 | 0.05 | 50 | 0.0050 | 0.0050 |
| 0.0063 | 0.11 | 100 | 0.0092 | 0.0092 |
| 0.0074 | 0.16 | 150 | 0.0045 | 0.0045 |
| 0.0056 | 0.22 | 200 | 0.0060 | 0.0060 |
| 0.0082 | 0.27 | 250 | 0.0046 | 0.0046 |
| 0.0055 | 0.32 | 300 | 0.0056 | 0.0056 |
| 0.0061 | 0.38 | 350 | 0.0045 | 0.0045 |
| 0.0079 | 0.43 | 400 | 0.0060 | 0.0060 |
| 0.0061 | 0.48 | 450 | 0.0043 | 0.0043 |
| 0.0078 | 0.54 | 500 | 0.0046 | 0.0046 |
| 0.0066 | 0.59 | 550 | 0.0043 | 0.0043 |
| 0.0055 | 0.65 | 600 | 0.0044 | 0.0044 |
| 0.0059 | 0.7 | 650 | 0.0043 | 0.0043 |
| 0.0048 | 0.75 | 700 | 0.0056 | 0.0056 |
| 0.0051 | 0.81 | 750 | 0.0043 | 0.0043 |
| 0.0046 | 0.86 | 800 | 0.0043 | 0.0043 |
| 0.0055 | 0.92 | 850 | 0.0043 | 0.0043 |
| 0.0053 | 0.97 | 900 | 0.0043 | 0.0043 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
tsavage68/v1_1000_STEPS_1e6_rate_05_beta_DPO | tsavage68 | 2024-03-27T18:21:33Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T18:17:07Z | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.1
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: v1_1000_STEPS_1e6_rate_05_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. -->
# v1_1000_STEPS_1e6_rate_05_beta_DPO
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1310
- Rewards/chosen: -2.3907
- Rewards/rejected: -3.3587
- Rewards/accuracies: 0.5319
- Rewards/margins: 0.9681
- Logps/rejected: -23.5970
- Logps/chosen: -20.0344
- Logits/rejected: -3.2860
- Logits/chosen: -3.2861
## 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-06
- train_batch_size: 2
- eval_batch_size: 1
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.7136 | 0.05 | 50 | 0.6682 | -0.1718 | -0.2901 | 0.5473 | 0.1184 | -17.4598 | -15.5966 | -3.3833 | -3.3834 |
| 0.8377 | 0.1 | 100 | 0.8534 | -1.2874 | -1.8482 | 0.5495 | 0.5608 | -20.5758 | -17.8278 | -3.3665 | -3.3666 |
| 1.5418 | 0.15 | 150 | 1.2106 | -3.7074 | -3.9590 | 0.5055 | 0.2516 | -24.7976 | -22.6679 | -3.3872 | -3.3874 |
| 0.9966 | 0.2 | 200 | 1.3074 | -2.7550 | -3.0485 | 0.5099 | 0.2935 | -22.9766 | -20.7630 | -3.3239 | -3.3240 |
| 1.631 | 0.24 | 250 | 1.1695 | -2.1801 | -2.7422 | 0.5231 | 0.5621 | -22.3639 | -19.6133 | -3.2748 | -3.2750 |
| 1.4651 | 0.29 | 300 | 1.2408 | -2.1404 | -2.6522 | 0.5033 | 0.5118 | -22.1839 | -19.5338 | -3.3806 | -3.3808 |
| 1.9294 | 0.34 | 350 | 1.2181 | -1.8900 | -2.3214 | 0.5121 | 0.4313 | -21.5223 | -19.0331 | -3.3884 | -3.3885 |
| 1.6417 | 0.39 | 400 | 1.1754 | -1.9580 | -2.4289 | 0.4967 | 0.4710 | -21.7374 | -19.1690 | -3.4056 | -3.4057 |
| 1.0114 | 0.44 | 450 | 1.2146 | -2.0096 | -2.4935 | 0.4879 | 0.4839 | -21.8665 | -19.2723 | -3.3460 | -3.3461 |
| 1.0581 | 0.49 | 500 | 1.2539 | -2.5636 | -3.1382 | 0.5077 | 0.5746 | -23.1559 | -20.3803 | -3.3437 | -3.3439 |
| 1.3239 | 0.54 | 550 | 1.1739 | -2.1012 | -2.8810 | 0.5253 | 0.7798 | -22.6415 | -19.4555 | -3.3313 | -3.3314 |
| 1.2819 | 0.59 | 600 | 1.1770 | -2.3179 | -3.1791 | 0.5407 | 0.8612 | -23.2377 | -19.8889 | -3.3037 | -3.3038 |
| 0.9194 | 0.64 | 650 | 1.1859 | -2.0739 | -2.9235 | 0.5407 | 0.8496 | -22.7266 | -19.4008 | -3.2953 | -3.2955 |
| 1.0744 | 0.68 | 700 | 1.1623 | -2.2911 | -3.1685 | 0.5187 | 0.8773 | -23.2165 | -19.8353 | -3.2851 | -3.2853 |
| 1.3268 | 0.73 | 750 | 1.1441 | -2.3481 | -3.2869 | 0.5231 | 0.9388 | -23.4534 | -19.9493 | -3.2891 | -3.2892 |
| 1.1064 | 0.78 | 800 | 1.1339 | -2.3526 | -3.3046 | 0.5275 | 0.9520 | -23.4888 | -19.9583 | -3.2881 | -3.2882 |
| 1.0456 | 0.83 | 850 | 1.1330 | -2.3878 | -3.3498 | 0.5275 | 0.9620 | -23.5791 | -20.0286 | -3.2864 | -3.2865 |
| 1.4001 | 0.88 | 900 | 1.1333 | -2.3931 | -3.3565 | 0.5275 | 0.9634 | -23.5926 | -20.0393 | -3.2860 | -3.2861 |
| 1.1629 | 0.93 | 950 | 1.1330 | -2.3904 | -3.3570 | 0.5275 | 0.9666 | -23.5936 | -20.0339 | -3.2860 | -3.2861 |
| 0.9777 | 0.98 | 1000 | 1.1310 | -2.3907 | -3.3587 | 0.5319 | 0.9681 | -23.5970 | -20.0344 | -3.2860 | -3.2861 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.0.0+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
|
yz-ictscouts/Reinforce-cart-p-v1 | yz-ictscouts | 2024-03-27T18:10:23Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-03-27T18:10:17Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cart-p-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 237.30 +/- 18.19
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
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
|
0x0mom/nous_r10 | 0x0mom | 2024-03-27T18:05:44Z | 91 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T18:04:40Z | ---
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]
- **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]
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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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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
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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
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]
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[More Information Needed]
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[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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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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gonzalezrostani/my_awesome_wnut_JHs | gonzalezrostani | 2024-03-27T18:03:42Z | 10 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2024-03-22T13:10:28Z | ---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_JHs
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. -->
# my_awesome_wnut_JHs
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0882
- Precision: 0.7944
- Recall: 0.8333
- F1: 0.8134
- Accuracy: 0.9897
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 46 | 0.0399 | 0.7826 | 0.8824 | 0.8295 | 0.9900 |
| No log | 2.0 | 92 | 0.0316 | 0.7830 | 0.8137 | 0.7981 | 0.9884 |
| No log | 3.0 | 138 | 0.0313 | 0.7833 | 0.9216 | 0.8468 | 0.9915 |
| No log | 4.0 | 184 | 0.0290 | 0.8 | 0.8627 | 0.8302 | 0.9912 |
| No log | 5.0 | 230 | 0.0340 | 0.8 | 0.8235 | 0.8116 | 0.9900 |
| No log | 6.0 | 276 | 0.0385 | 0.7982 | 0.8922 | 0.8426 | 0.9912 |
| No log | 7.0 | 322 | 0.0422 | 0.7966 | 0.9216 | 0.8545 | 0.9918 |
| No log | 8.0 | 368 | 0.0442 | 0.8018 | 0.8725 | 0.8357 | 0.9912 |
| No log | 9.0 | 414 | 0.0588 | 0.8022 | 0.7157 | 0.7565 | 0.9866 |
| No log | 10.0 | 460 | 0.0457 | 0.7857 | 0.8627 | 0.8224 | 0.9903 |
| 0.0246 | 11.0 | 506 | 0.0579 | 0.7982 | 0.8529 | 0.8246 | 0.9903 |
| 0.0246 | 12.0 | 552 | 0.0622 | 0.7857 | 0.8627 | 0.8224 | 0.9900 |
| 0.0246 | 13.0 | 598 | 0.0613 | 0.7876 | 0.8725 | 0.8279 | 0.9903 |
| 0.0246 | 14.0 | 644 | 0.0642 | 0.7857 | 0.8627 | 0.8224 | 0.9900 |
| 0.0246 | 15.0 | 690 | 0.0660 | 0.8 | 0.8627 | 0.8302 | 0.9906 |
| 0.0246 | 16.0 | 736 | 0.0674 | 0.7857 | 0.8627 | 0.8224 | 0.9900 |
| 0.0246 | 17.0 | 782 | 0.0697 | 0.8073 | 0.8627 | 0.8341 | 0.9909 |
| 0.0246 | 18.0 | 828 | 0.0714 | 0.7944 | 0.8333 | 0.8134 | 0.9897 |
| 0.0246 | 19.0 | 874 | 0.0700 | 0.7981 | 0.8137 | 0.8058 | 0.9894 |
| 0.0246 | 20.0 | 920 | 0.0655 | 0.7925 | 0.8235 | 0.8077 | 0.9894 |
| 0.0246 | 21.0 | 966 | 0.0659 | 0.7863 | 0.9020 | 0.8402 | 0.9912 |
| 0.0005 | 22.0 | 1012 | 0.0760 | 0.7961 | 0.8039 | 0.8 | 0.9891 |
| 0.0005 | 23.0 | 1058 | 0.0741 | 0.7944 | 0.8333 | 0.8134 | 0.9897 |
| 0.0005 | 24.0 | 1104 | 0.0722 | 0.7788 | 0.8627 | 0.8186 | 0.9897 |
| 0.0005 | 25.0 | 1150 | 0.0832 | 0.8061 | 0.7745 | 0.79 | 0.9887 |
| 0.0005 | 26.0 | 1196 | 0.0758 | 0.7905 | 0.8137 | 0.8019 | 0.9891 |
| 0.0005 | 27.0 | 1242 | 0.0635 | 0.7850 | 0.8235 | 0.8038 | 0.9891 |
| 0.0005 | 28.0 | 1288 | 0.0650 | 0.7928 | 0.8627 | 0.8263 | 0.9903 |
| 0.0005 | 29.0 | 1334 | 0.0718 | 0.7928 | 0.8627 | 0.8263 | 0.9903 |
| 0.0005 | 30.0 | 1380 | 0.0695 | 0.7881 | 0.9118 | 0.8455 | 0.9912 |
| 0.0005 | 31.0 | 1426 | 0.0679 | 0.7966 | 0.9216 | 0.8545 | 0.9915 |
| 0.0005 | 32.0 | 1472 | 0.0702 | 0.8 | 0.8627 | 0.8302 | 0.9906 |
| 0.0004 | 33.0 | 1518 | 0.0697 | 0.7833 | 0.9216 | 0.8468 | 0.9915 |
| 0.0004 | 34.0 | 1564 | 0.0690 | 0.8 | 0.9020 | 0.8479 | 0.9912 |
| 0.0004 | 35.0 | 1610 | 0.0693 | 0.7982 | 0.8529 | 0.8246 | 0.9903 |
| 0.0004 | 36.0 | 1656 | 0.0689 | 0.8018 | 0.8725 | 0.8357 | 0.9909 |
| 0.0004 | 37.0 | 1702 | 0.0695 | 0.8018 | 0.8725 | 0.8357 | 0.9909 |
| 0.0004 | 38.0 | 1748 | 0.0696 | 0.8036 | 0.8824 | 0.8411 | 0.9909 |
| 0.0004 | 39.0 | 1794 | 0.0702 | 0.8053 | 0.8922 | 0.8465 | 0.9912 |
| 0.0004 | 40.0 | 1840 | 0.0756 | 0.8037 | 0.8431 | 0.8230 | 0.9903 |
| 0.0004 | 41.0 | 1886 | 0.0738 | 0.7946 | 0.8725 | 0.8318 | 0.9906 |
| 0.0004 | 42.0 | 1932 | 0.0730 | 0.7966 | 0.9216 | 0.8545 | 0.9921 |
| 0.0004 | 43.0 | 1978 | 0.0740 | 0.8034 | 0.9216 | 0.8584 | 0.9918 |
| 0.0002 | 44.0 | 2024 | 0.0743 | 0.8034 | 0.9216 | 0.8584 | 0.9918 |
| 0.0002 | 45.0 | 2070 | 0.0751 | 0.8034 | 0.9216 | 0.8584 | 0.9918 |
| 0.0002 | 46.0 | 2116 | 0.0749 | 0.8034 | 0.9216 | 0.8584 | 0.9918 |
| 0.0002 | 47.0 | 2162 | 0.0752 | 0.7931 | 0.9020 | 0.8440 | 0.9912 |
| 0.0002 | 48.0 | 2208 | 0.0757 | 0.7913 | 0.8922 | 0.8387 | 0.9909 |
| 0.0002 | 49.0 | 2254 | 0.0760 | 0.7913 | 0.8922 | 0.8387 | 0.9909 |
| 0.0002 | 50.0 | 2300 | 0.0743 | 0.7965 | 0.8824 | 0.8372 | 0.9909 |
| 0.0002 | 51.0 | 2346 | 0.0745 | 0.7965 | 0.8824 | 0.8372 | 0.9909 |
| 0.0002 | 52.0 | 2392 | 0.0757 | 0.8018 | 0.8725 | 0.8357 | 0.9909 |
| 0.0002 | 53.0 | 2438 | 0.0763 | 0.8 | 0.8627 | 0.8302 | 0.9906 |
| 0.0002 | 54.0 | 2484 | 0.0762 | 0.8018 | 0.8725 | 0.8357 | 0.9909 |
| 0.0001 | 55.0 | 2530 | 0.0764 | 0.8018 | 0.8725 | 0.8357 | 0.9909 |
| 0.0001 | 56.0 | 2576 | 0.0833 | 0.7881 | 0.9118 | 0.8455 | 0.9912 |
| 0.0001 | 57.0 | 2622 | 0.0770 | 0.7881 | 0.9118 | 0.8455 | 0.9915 |
| 0.0001 | 58.0 | 2668 | 0.0713 | 0.7965 | 0.8824 | 0.8372 | 0.9906 |
| 0.0001 | 59.0 | 2714 | 0.0753 | 0.7876 | 0.8725 | 0.8279 | 0.9903 |
| 0.0001 | 60.0 | 2760 | 0.0750 | 0.7931 | 0.9020 | 0.8440 | 0.9912 |
| 0.0001 | 61.0 | 2806 | 0.0768 | 0.7838 | 0.8529 | 0.8169 | 0.9897 |
| 0.0001 | 62.0 | 2852 | 0.0758 | 0.7982 | 0.8922 | 0.8426 | 0.9909 |
| 0.0001 | 63.0 | 2898 | 0.0766 | 0.7982 | 0.8922 | 0.8426 | 0.9909 |
| 0.0001 | 64.0 | 2944 | 0.0773 | 0.7895 | 0.8824 | 0.8333 | 0.9906 |
| 0.0001 | 65.0 | 2990 | 0.0779 | 0.7895 | 0.8824 | 0.8333 | 0.9906 |
| 0.0004 | 66.0 | 3036 | 0.0783 | 0.7895 | 0.8824 | 0.8333 | 0.9906 |
| 0.0004 | 67.0 | 3082 | 0.0790 | 0.7895 | 0.8824 | 0.8333 | 0.9906 |
| 0.0004 | 68.0 | 3128 | 0.0795 | 0.7895 | 0.8824 | 0.8333 | 0.9906 |
| 0.0004 | 69.0 | 3174 | 0.0800 | 0.7895 | 0.8824 | 0.8333 | 0.9906 |
| 0.0004 | 70.0 | 3220 | 0.0806 | 0.7895 | 0.8824 | 0.8333 | 0.9906 |
| 0.0004 | 71.0 | 3266 | 0.0810 | 0.7895 | 0.8824 | 0.8333 | 0.9906 |
| 0.0004 | 72.0 | 3312 | 0.0812 | 0.7895 | 0.8824 | 0.8333 | 0.9906 |
| 0.0004 | 73.0 | 3358 | 0.0815 | 0.7895 | 0.8824 | 0.8333 | 0.9906 |
| 0.0004 | 74.0 | 3404 | 0.0818 | 0.7895 | 0.8824 | 0.8333 | 0.9906 |
| 0.0004 | 75.0 | 3450 | 0.0821 | 0.7895 | 0.8824 | 0.8333 | 0.9906 |
| 0.0004 | 76.0 | 3496 | 0.0864 | 0.8 | 0.8235 | 0.8116 | 0.9897 |
| 0.0001 | 77.0 | 3542 | 0.0847 | 0.7944 | 0.8333 | 0.8134 | 0.9897 |
| 0.0001 | 78.0 | 3588 | 0.0849 | 0.7944 | 0.8333 | 0.8134 | 0.9897 |
| 0.0001 | 79.0 | 3634 | 0.0852 | 0.7944 | 0.8333 | 0.8134 | 0.9897 |
| 0.0001 | 80.0 | 3680 | 0.0854 | 0.7890 | 0.8431 | 0.8152 | 0.9897 |
| 0.0001 | 81.0 | 3726 | 0.0855 | 0.7890 | 0.8431 | 0.8152 | 0.9897 |
| 0.0001 | 82.0 | 3772 | 0.0837 | 0.7857 | 0.8627 | 0.8224 | 0.9900 |
| 0.0001 | 83.0 | 3818 | 0.0838 | 0.7857 | 0.8627 | 0.8224 | 0.9900 |
| 0.0001 | 84.0 | 3864 | 0.0840 | 0.7857 | 0.8627 | 0.8224 | 0.9900 |
| 0.0001 | 85.0 | 3910 | 0.0842 | 0.7946 | 0.8725 | 0.8318 | 0.9903 |
| 0.0001 | 86.0 | 3956 | 0.0843 | 0.7857 | 0.8627 | 0.8224 | 0.9900 |
| 0.0001 | 87.0 | 4002 | 0.0845 | 0.7857 | 0.8627 | 0.8224 | 0.9900 |
| 0.0001 | 88.0 | 4048 | 0.0845 | 0.7857 | 0.8627 | 0.8224 | 0.9900 |
| 0.0001 | 89.0 | 4094 | 0.0846 | 0.7946 | 0.8725 | 0.8318 | 0.9903 |
| 0.0001 | 90.0 | 4140 | 0.0847 | 0.7946 | 0.8725 | 0.8318 | 0.9903 |
| 0.0001 | 91.0 | 4186 | 0.0847 | 0.7857 | 0.8627 | 0.8224 | 0.9900 |
| 0.0001 | 92.0 | 4232 | 0.0848 | 0.7857 | 0.8627 | 0.8224 | 0.9900 |
| 0.0001 | 93.0 | 4278 | 0.0894 | 0.8 | 0.8235 | 0.8116 | 0.9897 |
| 0.0001 | 94.0 | 4324 | 0.0895 | 0.8 | 0.8235 | 0.8116 | 0.9897 |
| 0.0001 | 95.0 | 4370 | 0.0895 | 0.8 | 0.8235 | 0.8116 | 0.9897 |
| 0.0001 | 96.0 | 4416 | 0.0895 | 0.8 | 0.8235 | 0.8116 | 0.9897 |
| 0.0001 | 97.0 | 4462 | 0.0894 | 0.8 | 0.8235 | 0.8116 | 0.9897 |
| 0.0001 | 98.0 | 4508 | 0.0893 | 0.8 | 0.8235 | 0.8116 | 0.9897 |
| 0.0001 | 99.0 | 4554 | 0.0882 | 0.7944 | 0.8333 | 0.8134 | 0.9897 |
| 0.0001 | 100.0 | 4600 | 0.0882 | 0.7944 | 0.8333 | 0.8134 | 0.9897 |
### Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
|
eglkan1/mT5-TextSimp-LT-BatchSize8 | eglkan1 | 2024-03-27T18:01:32Z | 77 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/mt5-base",
"base_model:finetune:google/mt5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-03-27T08:22:45Z | ---
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mT5-TextSimp-LT-BatchSize8
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. -->
# mT5-TextSimp-LT-BatchSize8
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5250
- Rouge1: 0.0062
- Rouge2: 0.0
- Rougel: 0.0062
- Gen Len: 39.0501
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:-------:|
| 16.3791 | 0.96 | 200 | 11.2580 | 0.0041 | 0.0001 | 0.0041 | 512.0 |
| 1.4082 | 1.91 | 400 | 0.9502 | 0.0237 | 0.0002 | 0.0234 | 39.0501 |
| 0.7272 | 2.87 | 600 | 0.6131 | 0.0033 | 0.0 | 0.0032 | 39.0501 |
| 0.6517 | 3.83 | 800 | 0.5775 | 0.0019 | 0.0 | 0.0018 | 39.0501 |
| 0.6625 | 4.78 | 1000 | 0.5559 | 0.0014 | 0.0 | 0.0014 | 39.0501 |
| 0.5423 | 5.74 | 1200 | 0.5359 | 0.0016 | 0.0 | 0.0016 | 39.0501 |
| 0.7486 | 6.7 | 1400 | 0.5289 | 0.007 | 0.0 | 0.0069 | 39.0501 |
| 0.6712 | 7.66 | 1600 | 0.5250 | 0.0062 | 0.0 | 0.0062 | 39.0501 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.1
- Datasets 2.16.1
- Tokenizers 0.15.0
|
kavg/LiLT-SER-ZH-SIN | kavg | 2024-03-27T18:01:08Z | 104 | 0 | transformers | [
"transformers",
"safetensors",
"lilt",
"token-classification",
"generated_from_trainer",
"dataset:xfun",
"base_model:kavg/LiLT-SER-ZH",
"base_model:finetune:kavg/LiLT-SER-ZH",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2024-03-27T17:59:29Z | ---
license: mit
base_model: kavg/LiLT-SER-ZH
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-ZH-SIN
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xfun
type: xfun
config: xfun.sin
split: validation
args: xfun.sin
metrics:
- name: Precision
type: precision
value: 0.7417061611374408
- name: Recall
type: recall
value: 0.770935960591133
- name: F1
type: f1
value: 0.7560386473429951
- name: Accuracy
type: accuracy
value: 0.8558002524898303
---
<!-- 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. -->
# LiLT-SER-ZH-SIN
This model is a fine-tuned version of [kavg/LiLT-SER-ZH](https://huggingface.co/kavg/LiLT-SER-ZH) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2037
- Precision: 0.7417
- Recall: 0.7709
- F1: 0.7560
- Accuracy: 0.8558
## 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: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0013 | 21.74 | 500 | 0.9018 | 0.6843 | 0.7475 | 0.7145 | 0.8599 |
| 0.012 | 43.48 | 1000 | 1.0791 | 0.7115 | 0.7623 | 0.7360 | 0.8561 |
| 0.0002 | 65.22 | 1500 | 1.0060 | 0.7360 | 0.7623 | 0.7489 | 0.8565 |
| 0.03 | 86.96 | 2000 | 1.1521 | 0.7282 | 0.6700 | 0.6979 | 0.8313 |
| 0.0013 | 108.7 | 2500 | 1.1517 | 0.7240 | 0.7463 | 0.7350 | 0.8579 |
| 0.0016 | 130.43 | 3000 | 0.9393 | 0.7319 | 0.7697 | 0.7503 | 0.8732 |
| 0.0021 | 152.17 | 3500 | 0.9972 | 0.7249 | 0.7562 | 0.7402 | 0.8635 |
| 0.0001 | 173.91 | 4000 | 1.0485 | 0.7049 | 0.7796 | 0.7404 | 0.8583 |
| 0.0002 | 195.65 | 4500 | 1.0827 | 0.7055 | 0.7315 | 0.7183 | 0.8433 |
| 0.0 | 217.39 | 5000 | 1.0528 | 0.7354 | 0.7599 | 0.7474 | 0.8586 |
| 0.0001 | 239.13 | 5500 | 1.1183 | 0.7001 | 0.7131 | 0.7065 | 0.8465 |
| 0.0002 | 260.87 | 6000 | 1.1749 | 0.7231 | 0.7685 | 0.7451 | 0.8520 |
| 0.0 | 282.61 | 6500 | 1.1206 | 0.7315 | 0.7685 | 0.7495 | 0.8611 |
| 0.0 | 304.35 | 7000 | 1.2037 | 0.7417 | 0.7709 | 0.7560 | 0.8558 |
| 0.0 | 326.09 | 7500 | 1.3737 | 0.7391 | 0.75 | 0.7445 | 0.8513 |
| 0.0 | 347.83 | 8000 | 1.2926 | 0.7221 | 0.7648 | 0.7428 | 0.8475 |
| 0.0 | 369.57 | 8500 | 1.4108 | 0.6966 | 0.7549 | 0.7246 | 0.8293 |
| 0.0 | 391.3 | 9000 | 1.4346 | 0.7222 | 0.7586 | 0.7399 | 0.8303 |
| 0.0 | 413.04 | 9500 | 1.4146 | 0.7225 | 0.7599 | 0.7407 | 0.8363 |
| 0.0 | 434.78 | 10000 | 1.4097 | 0.7121 | 0.7586 | 0.7346 | 0.8346 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
|
kavg/LiLT-SER-PT-SIN | kavg | 2024-03-27T17:58:58Z | 104 | 0 | transformers | [
"transformers",
"safetensors",
"lilt",
"token-classification",
"generated_from_trainer",
"dataset:xfun",
"base_model:kavg/LiLT-SER-PT",
"base_model:finetune:kavg/LiLT-SER-PT",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2024-03-27T17:57:00Z | ---
license: mit
base_model: kavg/LiLT-SER-PT
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-PT-SIN
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xfun
type: xfun
config: xfun.sin
split: validation
args: xfun.sin
metrics:
- name: Precision
type: precision
value: 0.7639225181598063
- name: Recall
type: recall
value: 0.7770935960591133
- name: F1
type: f1
value: 0.7704517704517705
- name: Accuracy
type: accuracy
value: 0.8626735867583111
---
<!-- 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. -->
# LiLT-SER-PT-SIN
This model is a fine-tuned version of [kavg/LiLT-SER-PT](https://huggingface.co/kavg/LiLT-SER-PT) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2074
- Precision: 0.7639
- Recall: 0.7771
- F1: 0.7705
- Accuracy: 0.8627
## 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: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall |
|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.0124 | 21.74 | 500 | 0.8590 | 0.7403 | 0.8082 | 0.7381 | 0.7426 |
| 0.0017 | 43.48 | 1000 | 0.8301 | 0.7272 | 1.2608 | 0.75 | 0.7057 |
| 0.0004 | 65.22 | 1500 | 0.8694 | 0.7323 | 0.8843 | 0.7098 | 0.7562 |
| 0.0 | 86.96 | 2000 | 0.8617 | 0.7532 | 1.0638 | 0.7419 | 0.7648 |
| 0.0001 | 108.7 | 2500 | 0.8580 | 0.7674 | 1.1504 | 0.7689 | 0.7660 |
| 0.0006 | 130.43 | 3000 | 0.8677 | 0.7479 | 0.9865 | 0.7230 | 0.7746 |
| 0.0 | 152.17 | 3500 | 0.8617 | 0.7558 | 1.1492 | 0.7494 | 0.7623 |
| 0.0001 | 173.91 | 4000 | 0.8385 | 0.7590 | 1.3124 | 0.7485 | 0.7697 |
| 0.0055 | 195.65 | 4500 | 1.1331 | 0.7295 | 0.7869 | 0.7571 | 0.8479 |
| 0.0 | 217.39 | 5000 | 1.2061 | 0.7392 | 0.7611 | 0.7500 | 0.8500 |
| 0.0001 | 239.13 | 5500 | 1.2572 | 0.7253 | 0.7672 | 0.7457 | 0.8482 |
| 0.0 | 260.87 | 6000 | 1.3558 | 0.7494 | 0.7734 | 0.7612 | 0.8569 |
| 0.0 | 282.61 | 6500 | 1.4382 | 0.7598 | 0.7672 | 0.7635 | 0.8589 |
| 0.0 | 304.35 | 7000 | 1.4720 | 0.7537 | 0.7574 | 0.7555 | 0.8533 |
| 0.0 | 326.09 | 7500 | 1.3835 | 0.7524 | 0.7783 | 0.7651 | 0.8579 |
| 0.0 | 347.83 | 8000 | 1.2693 | 0.7534 | 0.7599 | 0.7566 | 0.8599 |
| 0.0 | 369.57 | 8500 | 1.2005 | 0.7417 | 0.7709 | 0.7560 | 0.8600 |
| 0.0 | 391.3 | 9000 | 1.2175 | 0.7560 | 0.7820 | 0.7688 | 0.8601 |
| 0.0 | 413.04 | 9500 | 1.2339 | 0.7556 | 0.7845 | 0.7698 | 0.8601 |
| 0.0 | 434.78 | 10000 | 1.2074 | 0.7639 | 0.7771 | 0.7705 | 0.8627 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
|
weny22/long_text_balanced_smaller_original_text | weny22 | 2024-03-27T17:57:39Z | 104 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:weny22/sum_model_t5_saved",
"base_model:finetune:weny22/sum_model_t5_saved",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-03-26T14:49:05Z | ---
base_model: weny22/sum_model_t5_saved
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: long_text_balanced_smaller_original_text
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. -->
# long_text_balanced_smaller_original_text
The model trained with balanced dataset, without preprocess the training data.
This model is a fine-tuned version of [weny22/sum_model_t5_saved](https://huggingface.co/weny22/sum_model_t5_saved) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3143
- Rouge1: 0.2101
- Rouge2: 0.0804
- Rougel: 0.1705
- Rougelsum: 0.1707
- Gen Len: 18.986
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.002
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 119 | 2.3387 | 0.1849 | 0.0583 | 0.1474 | 0.1475 | 18.98 |
| No log | 2.0 | 238 | 2.1768 | 0.1954 | 0.0647 | 0.1538 | 0.1537 | 18.9707 |
| No log | 3.0 | 357 | 2.1282 | 0.1952 | 0.0637 | 0.1537 | 0.1536 | 18.9947 |
| No log | 4.0 | 476 | 2.1173 | 0.1953 | 0.0683 | 0.1559 | 0.1557 | 18.9813 |
| 2.7944 | 5.0 | 595 | 2.0873 | 0.2022 | 0.0743 | 0.1624 | 0.1623 | 18.976 |
| 2.7944 | 6.0 | 714 | 2.0851 | 0.2054 | 0.0769 | 0.1652 | 0.1653 | 18.9887 |
| 2.7944 | 7.0 | 833 | 2.0948 | 0.2043 | 0.0762 | 0.1633 | 0.1632 | 18.972 |
| 2.7944 | 8.0 | 952 | 2.1123 | 0.1992 | 0.0745 | 0.1607 | 0.1605 | 18.9673 |
| 1.9807 | 9.0 | 1071 | 2.1280 | 0.2067 | 0.0779 | 0.1669 | 0.1669 | 18.9767 |
| 1.9807 | 10.0 | 1190 | 2.1251 | 0.2124 | 0.0801 | 0.1705 | 0.1704 | 18.99 |
| 1.9807 | 11.0 | 1309 | 2.1286 | 0.2069 | 0.0772 | 0.1668 | 0.1668 | 18.9927 |
| 1.9807 | 12.0 | 1428 | 2.1592 | 0.2096 | 0.0786 | 0.1688 | 0.1689 | 18.972 |
| 1.6485 | 13.0 | 1547 | 2.1811 | 0.2069 | 0.0789 | 0.1688 | 0.1689 | 18.9973 |
| 1.6485 | 14.0 | 1666 | 2.2124 | 0.2089 | 0.079 | 0.1686 | 0.1688 | 18.968 |
| 1.6485 | 15.0 | 1785 | 2.2187 | 0.2107 | 0.0797 | 0.1693 | 0.1695 | 18.9893 |
| 1.6485 | 16.0 | 1904 | 2.2438 | 0.2097 | 0.0793 | 0.1695 | 0.1695 | 18.9787 |
| 1.4186 | 17.0 | 2023 | 2.2685 | 0.2092 | 0.0799 | 0.1692 | 0.1693 | 18.99 |
| 1.4186 | 18.0 | 2142 | 2.2733 | 0.2085 | 0.0788 | 0.1684 | 0.1686 | 18.9747 |
| 1.4186 | 19.0 | 2261 | 2.2947 | 0.2087 | 0.0803 | 0.1696 | 0.1696 | 18.9813 |
| 1.4186 | 20.0 | 2380 | 2.3143 | 0.2101 | 0.0804 | 0.1705 | 0.1707 | 18.986 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
tsavage68/v1_1000_STEPS_1e7_rate_05_beta_DPO | tsavage68 | 2024-03-27T17:57:36Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T17:51:47Z | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.1
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: v1_1000_STEPS_1e7_rate_05_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. -->
# v1_1000_STEPS_1e7_rate_05_beta_DPO
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6349
- Rewards/chosen: -0.2276
- Rewards/rejected: -0.4095
- Rewards/accuracies: 0.5890
- Rewards/margins: 0.1819
- Logps/rejected: -17.6986
- Logps/chosen: -15.7083
- Logits/rejected: -3.3433
- Logits/chosen: -3.3435
## 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-07
- train_batch_size: 2
- eval_batch_size: 1
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6758 | 0.1 | 100 | 0.6807 | -0.0108 | -0.0388 | 0.5582 | 0.0280 | -16.9571 | -15.2746 | -3.3527 | -3.3528 |
| 0.648 | 0.2 | 200 | 0.6605 | -0.0898 | -0.1746 | 0.5692 | 0.0849 | -17.2288 | -15.4326 | -3.3470 | -3.3471 |
| 0.6324 | 0.29 | 300 | 0.6498 | -0.1892 | -0.3115 | 0.5802 | 0.1224 | -17.5026 | -15.6314 | -3.3449 | -3.3450 |
| 0.6949 | 0.39 | 400 | 0.6438 | -0.1429 | -0.2881 | 0.5912 | 0.1452 | -17.4557 | -15.5388 | -3.3451 | -3.3452 |
| 0.6848 | 0.49 | 500 | 0.6369 | -0.1735 | -0.3420 | 0.6066 | 0.1685 | -17.5635 | -15.6000 | -3.3438 | -3.3439 |
| 0.6344 | 0.59 | 600 | 0.6375 | -0.2102 | -0.3842 | 0.5846 | 0.1740 | -17.6480 | -15.6735 | -3.3436 | -3.3437 |
| 0.6551 | 0.68 | 700 | 0.6366 | -0.2240 | -0.4017 | 0.5868 | 0.1777 | -17.6829 | -15.7010 | -3.3433 | -3.3434 |
| 0.5891 | 0.78 | 800 | 0.6356 | -0.2274 | -0.4088 | 0.6066 | 0.1813 | -17.6971 | -15.7079 | -3.3433 | -3.3434 |
| 0.6461 | 0.88 | 900 | 0.6348 | -0.2270 | -0.4096 | 0.5956 | 0.1826 | -17.6988 | -15.7070 | -3.3433 | -3.3435 |
| 0.6059 | 0.98 | 1000 | 0.6349 | -0.2276 | -0.4095 | 0.5890 | 0.1819 | -17.6986 | -15.7083 | -3.3433 | -3.3435 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.0.0+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
|
DaJulster/my_awesome_model | DaJulster | 2024-03-27T17:54:05Z | 105 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-25T18:00:09Z | ---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_model
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. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6882
- Accuracy: 0.5794
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 32 | 0.6916 | 0.4953 |
| No log | 2.0 | 64 | 0.6882 | 0.5794 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
|
meseca/messiah-7b-v1.1 | meseca | 2024-03-27T17:49:00Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T17:42:00Z | ---
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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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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#### 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
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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]
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- **Cloud Provider:** [More Information Needed]
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- **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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<!-- 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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0x0son0/nr_m3 | 0x0son0 | 2024-03-27T17:46:17Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-22T12:23:38Z | ---
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]
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[More Information Needed]
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## Model Card Contact
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|
manimaranpa07/my_Ws_extraction_model_27th_mar | manimaranpa07 | 2024-03-27T17:35:56Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2024-03-27T17:33:13Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_Ws_extraction_model_27th_mar
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. -->
# my_Ws_extraction_model_27th_mar
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2730
- Precision: 0.4668
- Recall: 0.4580
- F1: 0.4623
- Accuracy: 0.9046
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 76 | 0.2977 | 0.4502 | 0.4141 | 0.4314 | 0.8999 |
| No log | 2.0 | 152 | 0.2730 | 0.4668 | 0.4580 | 0.4623 | 0.9046 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2
|
PaulTbbr/poca-SoccerTwos00 | PaulTbbr | 2024-03-27T17:34:57Z | 15 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
]
| reinforcement-learning | 2024-03-24T12:15:20Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: PaulTbbr/poca-SoccerTwos00
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
distil-whisper/distil-large-v3-openai | distil-whisper | 2024-03-27T17:19:37Z | 0 | 3 | null | [
"audio",
"automatic-speech-recognition",
"en",
"arxiv:2311.00430",
"license:mit",
"region:us"
]
| automatic-speech-recognition | 2024-03-21T12:04:30Z | ---
language:
- en
tags:
- audio
- automatic-speech-recognition
license: mit
---
# Distil-Whisper: distil-large-v3 for OpenAI Whisper
This repository contains the model weights for [distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3)
converted to [OpenAI Whisper](https://github.com/openai/whisper) format.
Compared to previous Distil-Whisper releases, distil-large-v3 is specifically designed to be compatible
with the OpenAI Whisper long-form transcription algorithm. In our benchmark over 4 out-of-distribution datasets, distil-large-v3
outperformed distil-large-v2 by 5% WER average. Thus, you can expect significant performance gains by switching to this
latest checkpoint.
## Python Usage
To use the model in the original Whisper format, first ensure you have the [`openai-whisper`](https://pypi.org/project/openai-whisper/) package installed.
For this example, we'll also install 🤗 Datasets to load a toy audio dataset from the Hugging Face Hub:
```bash
pip install --upgrade pip
pip install --upgrade openai-whisper datasets[audio]
```
The following code-snippet demonstrates how to transcribe a sample file from the LibriSpeech dataset loaded using
🤗 Datasets:
```python
from huggingface_hub import hf_hub_download
from datasets import load_dataset
from whisper import load_model, transcribe
model_path = hf_hub_download(repo_id="distil-whisper/distil-large-v3-openai", filename="model.bin")
model = load_model(model_path)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
sample = dataset[0]["audio"]["path"]
pred_out = transcribe(model, audio=sample, language="en")
print(pred_out["text"])
```
Note that the model weights will be downloaded and saved to your cache the first time you run the example. Subsequently,
you can re-use the same example, and the weights will be loaded directly from your cache without having to download them
again.
To transcribe a local audio file, simply pass the path to the audio file as the `audio` argument to transcribe:
```python
pred_out = transcribe(model, audio="audio.mp3", language="en")
```
## CLI Usage
The Distil-Whisper model can also be used with the OpenAI Whisper CLI. First, pip install the Hugging Face Hub package:
```bash
pip install --upgrade huggingface_hub
```
Next, download the weights for distil-large-v3 locally:
```bash
huggingface-cli download distil-whisper/distil-large-v3-openai model.bin --local-dir distil-large-v3
```
Finally, use the OpenAI Whisper CLI to transcribe:
```bash
whisper audio.mp3 --model distil-large-v3/model.bin --language en
```
## Model Details
For more information about the distil-large-v3 model, refer to the original [model card](https://huggingface.co/distil-whisper/distil-large-v3).
## License
Distil-Whisper inherits the [MIT license](https://github.com/huggingface/distil-whisper/blob/main/LICENSE) from OpenAI's Whisper model.
## Citation
If you use this model, please consider citing the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430):
```
@misc{gandhi2023distilwhisper,
title={Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling},
author={Sanchit Gandhi and Patrick von Platen and Alexander M. Rush},
year={2023},
eprint={2311.00430},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
b-r-a-n/sloane_LoRA | b-r-a-n | 2024-03-27T17:16:12Z | 2 | 1 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"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 | 2024-03-27T17:15:20Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of SJS young girl
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - b-r-a-n/sloane_LoRA
<Gallery />
## Model description
These are b-r-a-n/sloane_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of SJS young girl to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](b-r-a-n/sloane_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
iamkaikai/FUI-LORA | iamkaikai | 2024-03-27T17:14:32Z | 2 | 1 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"diffusers-training",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-12-25T03:48:23Z | ---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
base_model: runwayml/stable-diffusion-v1-5
inference: true
---
# LoRA text2image fine-tuning - iamkaikai/FUI-LORA
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the iamkaikai/FUI dataset. You can find some example images in the following.
|
nell123/llmHandsOn0 | nell123 | 2024-03-27T17:14:08Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T16:43:22Z | ---
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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed]
|
bartowski/Einstein-v5-v0.2-7B-exl2 | bartowski | 2024-03-27T17:06:42Z | 0 | 0 | null | [
"axolotl",
"generated_from_trainer",
"Mistral",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"science",
"physics",
"chemistry",
"biology",
"math",
"text-generation",
"dataset:allenai/ai2_arc",
"dataset:camel-ai/physics",
"dataset:camel-ai/chemistry",
"dataset:camel-ai/biology",
"dataset:camel-ai/math",
"dataset:metaeval/reclor",
"dataset:openbookqa",
"dataset:mandyyyyii/scibench",
"dataset:derek-thomas/ScienceQA",
"dataset:TIGER-Lab/ScienceEval",
"dataset:jondurbin/airoboros-3.2",
"dataset:LDJnr/Capybara",
"dataset:Cot-Alpaca-GPT4-From-OpenHermes-2.5",
"dataset:STEM-AI-mtl/Electrical-engineering",
"dataset:knowrohit07/saraswati-stem",
"dataset:sablo/oasst2_curated",
"dataset:lmsys/lmsys-chat-1m",
"dataset:TIGER-Lab/MathInstruct",
"dataset:bigbio/med_qa",
"dataset:meta-math/MetaMathQA-40K",
"dataset:piqa",
"dataset:scibench",
"dataset:sciq",
"dataset:Open-Orca/SlimOrca",
"dataset:migtissera/Synthia-v1.3",
"dataset:allenai/WildChat",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:openchat/openchat_sharegpt4_dataset",
"dataset:teknium/GPTeacher-General-Instruct",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"base_model:mistral-community/Mistral-7B-v0.2",
"base_model:finetune:mistral-community/Mistral-7B-v0.2",
"license:other",
"region:us"
]
| text-generation | 2024-03-27T17:06:41Z | ---
license: other
tags:
- axolotl
- generated_from_trainer
- Mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- science
- physics
- chemistry
- biology
- math
base_model: alpindale/Mistral-7B-v0.2-hf
datasets:
- allenai/ai2_arc
- camel-ai/physics
- camel-ai/chemistry
- camel-ai/biology
- camel-ai/math
- metaeval/reclor
- openbookqa
- mandyyyyii/scibench
- derek-thomas/ScienceQA
- TIGER-Lab/ScienceEval
- jondurbin/airoboros-3.2
- LDJnr/Capybara
- Cot-Alpaca-GPT4-From-OpenHermes-2.5
- STEM-AI-mtl/Electrical-engineering
- knowrohit07/saraswati-stem
- sablo/oasst2_curated
- lmsys/lmsys-chat-1m
- TIGER-Lab/MathInstruct
- bigbio/med_qa
- meta-math/MetaMathQA-40K
- openbookqa
- piqa
- metaeval/reclor
- derek-thomas/ScienceQA
- scibench
- sciq
- Open-Orca/SlimOrca
- migtissera/Synthia-v1.3
- TIGER-Lab/ScienceEval
- allenai/WildChat
- microsoft/orca-math-word-problems-200k
- openchat/openchat_sharegpt4_dataset
- teknium/GPTeacher-General-Instruct
- m-a-p/CodeFeedback-Filtered-Instruction
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of Einstein-v5-v0.2-7B
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.16">turboderp's ExLlamaV2 v0.0.16</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/Weyaxi/Einstein-v5-v0.2-7B
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/Einstein-v5-v0.2-7B-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/Einstein-v5-v0.2-7B-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/Einstein-v5-v0.2-7B-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/Einstein-v5-v0.2-7B-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/Einstein-v5-v0.2-7B-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Einstein-v5-v0.2-7B-exl2 Einstein-v5-v0.2-7B-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Einstein-v5-v0.2-7B-exl2`:
```shell
mkdir Einstein-v5-v0.2-7B-exl2
huggingface-cli download bartowski/Einstein-v5-v0.2-7B-exl2 --local-dir Einstein-v5-v0.2-7B-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir Einstein-v5-v0.2-7B-exl2-6_5
huggingface-cli download bartowski/Einstein-v5-v0.2-7B-exl2 --revision 6_5 --local-dir Einstein-v5-v0.2-7B-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir Einstein-v5-v0.2-7B-exl2-6.5
huggingface-cli download bartowski/Einstein-v5-v0.2-7B-exl2 --revision 6_5 --local-dir Einstein-v5-v0.2-7B-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski |
osanseviero/TinyLlama-1.1B-Chat-v1.0-Q4_K_M-GGUF | osanseviero | 2024-03-27T17:01:36Z | 3 | 0 | null | [
"gguf",
"llama-cpp",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2024-03-27T17:01:30Z | ---
language:
- en
license: apache-2.0
tags:
- llama-cpp
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
widget:
- example_title: Fibonacci (Python)
messages:
- role: system
content: You are a chatbot who can help code!
- role: user
content: Write me a function to calculate the first 10 digits of the fibonacci
sequence in Python and print it out to the CLI.
---
# osanseviero/TinyLlama-1.1B-Chat-v1.0-Q4_K_M-GGUF
This model was converted to GGUF format from [`TinyLlama/TinyLlama-1.1B-Chat-v1.0`](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) using llama.cpp.
Refer to the [original model card](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) for more details on the model.
## Use with llama.cpp
```bash
brew install ggerganov/ggerganov/llama.cpp
```
```bash
llama-cli --hf-repo osanseviero/TinyLlama-1.1B-Chat-v1.0-Q4_K_M-GGUF --model tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf -p "The meaning to life and the universe is "
```
```bash
llama-server --hf-repo osanseviero/TinyLlama-1.1B-Chat-v1.0-Q4_K_M-GGUF --model tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf -c 2048
```
|
louisgrc/model_test | louisgrc | 2024-03-27T16:54:37Z | 0 | 0 | null | [
"merge",
"mergekit",
"lazymergekit",
"CultriX/NeuralTrix-bf16",
"AurelPx/Percival_01-7b-slerp",
"license:apache-2.0",
"region:us"
]
| null | 2024-03-27T16:54:36Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- CultriX/NeuralTrix-bf16
- AurelPx/Percival_01-7b-slerp
---
# test
test is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [CultriX/NeuralTrix-bf16](https://huggingface.co/CultriX/NeuralTrix-bf16)
* [AurelPx/Percival_01-7b-slerp](https://huggingface.co/AurelPx/Percival_01-7b-slerp)
## 🧩 Configuration
`yamlslices:
- sources:
- model: CultriX/NeuralTrix-bf16
layer_range: [0, 32]
- model: AurelPx/Percival_01-7b-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: AurelPx/Percival_01-7b-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: float16
` |
Nagase-Kotono/EEVE-LLaVA-Pretrain-2.8B | Nagase-Kotono | 2024-03-27T16:52:05Z | 6 | 0 | transformers | [
"transformers",
"llava_phi",
"text-generation",
"llava",
"custom_code",
"ko",
"dataset:tabtoyou/KoLLaVA-CC3M-Pretrain-595K",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T12:44:54Z | ---
license: apache-2.0
datasets:
- tabtoyou/KoLLaVA-CC3M-Pretrain-595K
language:
- ko
tags:
- llava
---
# EEVE-LLaVA-Pretrain-2.8B
**EEVE-LLaVA-Pretrain-2.8B**
***This model is a pretrained version of the llava multimodal projector.***
**You can use this model to finetune** ***[EEVE-Korean-Instruct-2.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-Instruct-2.8B-v1.0)*** **model.**
### Hardware
**Using 12x RTX 4090**
### Software
**[MoE-LLaVA](https://github.com/PKU-YuanGroup/MoE-LLaVA)** |
iasevoli90/Reinforce-CartPole-v1 | iasevoli90 | 2024-03-27T16:48:23Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-03-27T16:48:13Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
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
|
GowthamYarlagadda/llama-2-7b-chat | GowthamYarlagadda | 2024-03-27T16:43:59Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-10-07T18:07:43Z | These are the converted model weights for Llama-2-7B-chat in Huggingface format.
Courtesy of [Mirage-Studio.io](https://mirage-studio.io), home of MirageGPT: the private ChatGPT alternative.
---
license: other
LLAMA 2 COMMUNITY LICENSE AGREEMENT
Llama 2 Version Release Date: July 18, 2023
"Agreement" means the terms and conditions for use, reproduction, distribution and
modification of the Llama Materials set forth herein.
"Documentation" means the specifications, manuals and documentation
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libraries/llama-downloads/.
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"Llama 2" means the foundational large language models and software and
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elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-
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"Llama Materials" means, collectively, Meta's proprietary Llama 2 and
Documentation (and any portion thereof) made available under this Agreement.
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are an entity, your principal place of business is in the EEA or Switzerland) and Meta
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Llama Materials, you agree to be bound by this Agreement.
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v. You will not use the Llama Materials or any output or results of the
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c. If you institute litigation or other proceedings against Meta or any entity
(including a cross-claim or counterclaim in a lawsuit) alleging that the Llama
Materials or Llama 2 outputs or results, or any portion of any of the foregoing,
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by you, then any licenses granted to you under this Agreement shall terminate as of
the date such litigation or claim is filed or instituted. You will indemnify and hold
harmless Meta from and against any claim by any third party arising out of or related
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6. Term and Termination. The term of this Agreement will commence upon your
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termination of this Agreement.
7. Governing Law and Jurisdiction. This Agreement will be governed and
construed under the laws of the State of California without regard to choice of law
principles, and the UN Convention on Contracts for the International Sale of Goods
does not apply to this Agreement. The courts of California shall have exclusive
jurisdiction of any dispute arising out of this Agreement.
---
|
0x0mom/nous_r9 | 0x0mom | 2024-03-27T16:42:32Z | 90 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T16:41:31Z | ---
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] |
reach-vb/TinyLlama-1.1B-Chat-v1.0-Q8_0-GGUF | reach-vb | 2024-03-27T16:42:18Z | 2 | 0 | null | [
"gguf",
"llama-cpp",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2024-03-27T16:42:06Z | ---
language:
- en
license: apache-2.0
tags:
- llama-cpp
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
widget:
- example_title: Fibonacci (Python)
messages:
- role: system
content: You are a chatbot who can help code!
- role: user
content: Write me a function to calculate the first 10 digits of the fibonacci
sequence in Python and print it out to the CLI.
---
# reach-vb/TinyLlama-1.1B-Chat-v1.0-Q8_0-GGUF
This model was converted to GGUF format from [`TinyLlama/TinyLlama-1.1B-Chat-v1.0`](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) using llama.cpp.
Refer to the [original model card](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) for more details on the model.
## Use with llama.cpp
```bash
brew install ggerganov/ggerganov/llama.cpp
```
```bash
llama-cli --hf-repo reach-vb/TinyLlama-1.1B-Chat-v1.0-Q8_0-GGUF --model tinyllama-1.1b-chat-v1.0.Q8_0.gguf -p "The meaning to life and the universe is "
```
```bash
llama-server --hf-repo reach-vb/TinyLlama-1.1B-Chat-v1.0-Q8_0-GGUF --model tinyllama-1.1b-chat-v1.0.Q8_0.gguf -c 2048
```
|
Elisa/distilbert-finetuned | Elisa | 2024-03-27T16:39:12Z | 106 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-27T16:18:01Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-finetuned
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. -->
# distilbert-finetuned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5026
- Accuracy: 0.8968
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2174 | 1.0 | 8419 | 0.3851 | 0.8876 |
| 0.1536 | 2.0 | 16838 | 0.4623 | 0.8945 |
| 0.0787 | 3.0 | 25257 | 0.5026 | 0.8968 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
calcots/continued-pythia-410m_shuffled_dedup_filtered_with_vis_20k-40k | calcots | 2024-03-27T16:38:50Z | 110 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T16:37:37Z | ---
license: apache-2.0
finetuned_from: EleutherAI/pythia-410m
---
# calcots/continued-pythia-410m_shuffled_dedup_filtered_with_vis_20k-40k
- Checkpoint: `global_step9537`
- Tensorboard: see `./tensorboard`
- Evaluation: see `./eval`
|
wintonYF/SCB3-YOLOv7 | wintonYF | 2024-03-27T16:34:53Z | 2 | 1 | transformers | [
"transformers",
"object-detection",
"endpoints_compatible",
"region:us"
]
| object-detection | 2024-03-26T11:06:29Z | ---
pipeline_tag: object-detection
--- |
Xenova/text-davinci-002 | Xenova | 2024-03-27T16:28:41Z | 0 | 2 | transformers | [
"transformers",
"transformers.js",
"tokenizers",
"endpoints_compatible",
"region:us"
]
| null | 2023-08-04T09:15:25Z | ---
library_name: transformers
tags:
- transformers.js
- tokenizers
---
# text-davinci-002 Tokenizer
A 🤗-compatible version of the **text-davinci-002 tokenizer** (adapted from [openai/tiktoken](https://github.com/openai/tiktoken)). This means it can be used with Hugging Face libraries including [Transformers](https://github.com/huggingface/transformers), [Tokenizers](https://github.com/huggingface/tokenizers), and [Transformers.js](https://github.com/xenova/transformers.js).
## Example usage:
### Transformers/Tokenizers
```py
from transformers import GPT2TokenizerFast
tokenizer = GPT2TokenizerFast.from_pretrained('Xenova/text-davinci-002')
assert tokenizer.encode('hello world') == [31373, 995]
```
### Transformers.js
```js
import { AutoTokenizer } from '@xenova/transformers';
const tokenizer = await AutoTokenizer.from_pretrained('Xenova/text-davinci-002');
const tokens = tokenizer.encode('hello world'); // [31373, 995]
```
|
Xenova/text-embedding-ada-002 | Xenova | 2024-03-27T16:28:08Z | 0 | 68 | transformers | [
"transformers",
"transformers.js",
"tokenizers",
"endpoints_compatible",
"region:us"
]
| null | 2023-08-04T09:17:09Z | ---
library_name: transformers
tags:
- transformers.js
- tokenizers
---
# text-embedding-ada-002 Tokenizer
A 🤗-compatible version of the **text-embedding-ada-002 tokenizer** (adapted from [openai/tiktoken](https://github.com/openai/tiktoken)). This means it can be used with Hugging Face libraries including [Transformers](https://github.com/huggingface/transformers), [Tokenizers](https://github.com/huggingface/tokenizers), and [Transformers.js](https://github.com/xenova/transformers.js).
## Example usage:
### Transformers/Tokenizers
```py
from transformers import GPT2TokenizerFast
tokenizer = GPT2TokenizerFast.from_pretrained('Xenova/text-embedding-ada-002')
assert tokenizer.encode('hello world') == [15339, 1917]
```
### Transformers.js
```js
import { AutoTokenizer } from '@xenova/transformers';
const tokenizer = await AutoTokenizer.from_pretrained('Xenova/text-embedding-ada-002');
const tokens = tokenizer.encode('hello world'); // [15339, 1917]
```
|
dhananjay2912/Hermes-2-Pro-Mistral-7B-MEDIQA-CORR-DPO | dhananjay2912 | 2024-03-27T16:22:11Z | 7 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T16:16:45Z | ---
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]
|
JeanneNdzana/TP_LMN | JeanneNdzana | 2024-03-27T16:21:17Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T11:40:16Z | ---
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]
|
reecursion/xlm-roberta-base-pure-uk-annotations | reecursion | 2024-03-27T16:18:51Z | 105 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-27T16:17:50Z | ---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-pure-uk-annotations
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. -->
# xlm-roberta-base-pure-uk-annotations
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4769
- Accuracy: 0.75
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6036 | 1.0 | 20 | 0.6107 | 0.75 |
| 0.5804 | 2.0 | 40 | 0.5931 | 0.75 |
| 0.5198 | 3.0 | 60 | 0.5423 | 0.75 |
| 0.5039 | 4.0 | 80 | 0.4904 | 0.75 |
| 0.4206 | 5.0 | 100 | 0.4769 | 0.75 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
vaibhavad/llama-enc | vaibhavad | 2024-03-27T16:17:38Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T16:13:52Z | ---
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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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|
reach-vb/phi-2-Q2_K-GGUF | reach-vb | 2024-03-27T16:09:25Z | 1 | 0 | null | [
"gguf",
"nlp",
"code",
"llama-cpp",
"text-generation",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T16:08:43Z | ---
language:
- en
license: mit
tags:
- nlp
- code
- llama-cpp
license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE
pipeline_tag: text-generation
---
# reach-vb/phi-2-Q2_K-GGUF
This model was converted to GGUF format from [`microsoft/phi-2`](https://huggingface.co/microsoft/phi-2) using llama.cpp.
Refer to the [original model card](https://huggingface.co/microsoft/phi-2) for more details on the model.
## Use with llama.cpp
```bash
brew install ggerganov/ggerganov/llama.cpp
```
```bash
llama-cli --hf-repo reach-vb/phi-2-Q2_K-GGUF --model phi-2.Q2_K.gguf -p "The meaning to life and the universe is "
```
|
simonszu0814/gemma-Code-Instruct-Finetune-test4 | simonszu0814 | 2024-03-27T16:06:08Z | 111 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T16:03:15Z | ---
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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[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.
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#### Metrics
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[More Information Needed]
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[More Information Needed]
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<!-- 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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|
data-aces/Llama2-7B-FT-CT | data-aces | 2024-03-27T16:04:14Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"base_model:adapter:NousResearch/Llama-2-7b-chat-hf",
"region:us"
]
| null | 2024-03-27T15:01:18Z | ---
library_name: peft
base_model: NousResearch/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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- **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]
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## Uses
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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.
[More Information Needed]
## Training Details
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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]
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- **Carbon Emitted:** [More Information Needed]
## 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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[More Information Needed]
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### Framework versions
- PEFT 0.10.0 |
HorcruxNo13/segformer-b0-finetuned-segments-toolwear | HorcruxNo13 | 2024-03-27T16:03:34Z | 40 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"segformer",
"generated_from_trainer",
"base_model:nvidia/mit-b0",
"base_model:finetune:nvidia/mit-b0",
"license:other",
"endpoints_compatible",
"region:us"
]
| null | 2023-09-27T15:13:27Z | ---
license: other
base_model: nvidia/mit-b0
tags:
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-toolwear
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. -->
# segformer-b0-finetuned-segments-toolwear
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0491
- Mean Iou: 0.3531
- Mean Accuracy: 0.7062
- Overall Accuracy: 0.7062
- Accuracy Unlabeled: nan
- Accuracy Mass: 0.7062
- Iou Unlabeled: 0.0
- Iou Mass: 0.7062
## 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: 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: 45
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Mass | Iou Unlabeled | Iou Mass |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:--------:|
| 0.3512 | 1.25 | 20 | 0.3893 | 0.0773 | 0.1545 | 0.1545 | nan | 0.1545 | 0.0 | 0.1545 |
| 0.2286 | 2.5 | 40 | 0.2047 | 0.1937 | 0.3874 | 0.3874 | nan | 0.3874 | 0.0 | 0.3874 |
| 0.1657 | 3.75 | 60 | 0.1423 | 0.2491 | 0.4982 | 0.4982 | nan | 0.4982 | 0.0 | 0.4982 |
| 0.1581 | 5.0 | 80 | 0.1117 | 0.2649 | 0.5299 | 0.5299 | nan | 0.5299 | 0.0 | 0.5299 |
| 0.1063 | 6.25 | 100 | 0.0943 | 0.3327 | 0.6653 | 0.6653 | nan | 0.6653 | 0.0 | 0.6653 |
| 0.0829 | 7.5 | 120 | 0.0782 | 0.2983 | 0.5966 | 0.5966 | nan | 0.5966 | 0.0 | 0.5966 |
| 0.0808 | 8.75 | 140 | 0.0740 | 0.3257 | 0.6515 | 0.6515 | nan | 0.6515 | 0.0 | 0.6515 |
| 0.0694 | 10.0 | 160 | 0.0725 | 0.3503 | 0.7005 | 0.7005 | nan | 0.7005 | 0.0 | 0.7005 |
| 0.0589 | 11.25 | 180 | 0.0663 | 0.2629 | 0.5259 | 0.5259 | nan | 0.5259 | 0.0 | 0.5259 |
| 0.0473 | 12.5 | 200 | 0.0604 | 0.3685 | 0.7369 | 0.7369 | nan | 0.7369 | 0.0 | 0.7369 |
| 0.0433 | 13.75 | 220 | 0.0569 | 0.3055 | 0.6109 | 0.6109 | nan | 0.6109 | 0.0 | 0.6109 |
| 0.0511 | 15.0 | 240 | 0.0546 | 0.3572 | 0.7145 | 0.7145 | nan | 0.7145 | 0.0 | 0.7145 |
| 0.04 | 16.25 | 260 | 0.0536 | 0.3234 | 0.6467 | 0.6467 | nan | 0.6467 | 0.0 | 0.6467 |
| 0.0365 | 17.5 | 280 | 0.0555 | 0.3086 | 0.6171 | 0.6171 | nan | 0.6171 | 0.0 | 0.6171 |
| 0.0314 | 18.75 | 300 | 0.0505 | 0.3595 | 0.7191 | 0.7191 | nan | 0.7191 | 0.0 | 0.7191 |
| 0.0295 | 20.0 | 320 | 0.0536 | 0.3079 | 0.6159 | 0.6159 | nan | 0.6159 | 0.0 | 0.6159 |
| 0.0337 | 21.25 | 340 | 0.0490 | 0.3446 | 0.6891 | 0.6891 | nan | 0.6891 | 0.0 | 0.6891 |
| 0.0325 | 22.5 | 360 | 0.0489 | 0.3946 | 0.7891 | 0.7891 | nan | 0.7891 | 0.0 | 0.7891 |
| 0.0314 | 23.75 | 380 | 0.0514 | 0.3184 | 0.6368 | 0.6368 | nan | 0.6368 | 0.0 | 0.6368 |
| 0.0267 | 25.0 | 400 | 0.0485 | 0.3572 | 0.7144 | 0.7144 | nan | 0.7144 | 0.0 | 0.7144 |
| 0.0321 | 26.25 | 420 | 0.0490 | 0.3787 | 0.7573 | 0.7573 | nan | 0.7573 | 0.0 | 0.7573 |
| 0.025 | 27.5 | 440 | 0.0474 | 0.3615 | 0.7230 | 0.7230 | nan | 0.7230 | 0.0 | 0.7230 |
| 0.0225 | 28.75 | 460 | 0.0472 | 0.3660 | 0.7319 | 0.7319 | nan | 0.7319 | 0.0 | 0.7319 |
| 0.0247 | 30.0 | 480 | 0.0502 | 0.3488 | 0.6976 | 0.6976 | nan | 0.6976 | 0.0 | 0.6976 |
| 0.0216 | 31.25 | 500 | 0.0483 | 0.3536 | 0.7072 | 0.7072 | nan | 0.7072 | 0.0 | 0.7072 |
| 0.0195 | 32.5 | 520 | 0.0508 | 0.3289 | 0.6578 | 0.6578 | nan | 0.6578 | 0.0 | 0.6578 |
| 0.0259 | 33.75 | 540 | 0.0496 | 0.3846 | 0.7692 | 0.7692 | nan | 0.7692 | 0.0 | 0.7692 |
| 0.0242 | 35.0 | 560 | 0.0487 | 0.3464 | 0.6928 | 0.6928 | nan | 0.6928 | 0.0 | 0.6928 |
| 0.0217 | 36.25 | 580 | 0.0503 | 0.3325 | 0.6650 | 0.6650 | nan | 0.6650 | 0.0 | 0.6650 |
| 0.0204 | 37.5 | 600 | 0.0502 | 0.3429 | 0.6858 | 0.6858 | nan | 0.6858 | 0.0 | 0.6858 |
| 0.0204 | 38.75 | 620 | 0.0507 | 0.3457 | 0.6913 | 0.6913 | nan | 0.6913 | 0.0 | 0.6913 |
| 0.0191 | 40.0 | 640 | 0.0494 | 0.3494 | 0.6988 | 0.6988 | nan | 0.6988 | 0.0 | 0.6988 |
| 0.0204 | 41.25 | 660 | 0.0503 | 0.3426 | 0.6852 | 0.6852 | nan | 0.6852 | 0.0 | 0.6852 |
| 0.019 | 42.5 | 680 | 0.0485 | 0.3616 | 0.7232 | 0.7232 | nan | 0.7232 | 0.0 | 0.7232 |
| 0.0198 | 43.75 | 700 | 0.0494 | 0.3504 | 0.7008 | 0.7008 | nan | 0.7008 | 0.0 | 0.7008 |
| 0.0212 | 45.0 | 720 | 0.0491 | 0.3531 | 0.7062 | 0.7062 | nan | 0.7062 | 0.0 | 0.7062 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
balakhonoff/solidity_security_model_v6 | balakhonoff | 2024-03-27T15:57:23Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
]
| null | 2024-03-27T14:55:49Z | ---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# 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. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **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]
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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]
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[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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### Framework versions
- PEFT 0.10.0 |
Samuael/asr-amharic-phoneme-based-37-6 | Samuael | 2024-03-27T15:50:24Z | 110 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:Samuael/asr-amharic-phoneme-based-37-6",
"base_model:finetune:Samuael/asr-amharic-phoneme-based-37-6",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2024-01-25T20:27:57Z | ---
license: apache-2.0
tags:
- generated_from_trainer
base_model: Samuael/asr-amharic-phoneme-based-37-6
model-index:
- name: asr-amharic-phoneme-based-37-6
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. -->
# asr-amharic-phoneme-based-37-6
This model is a fine-tuned version of [Samuael/asr-amharic-phoneme-based-37-6](https://huggingface.co/Samuael/asr-amharic-phoneme-based-37-6) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2685
- eval_wer: 0.2816
- eval_phoneme_cer: 0.0532
- eval_cer: 0.0767
- eval_runtime: 25.2246
- eval_samples_per_second: 14.232
- eval_steps_per_second: 1.784
- epoch: 6.27
- step: 5000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
kemtho/Etudiant | kemtho | 2024-03-27T15:48:39Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T09:22:41Z | ---
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]
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#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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**BibTeX:**
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**APA:**
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## 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 Contact
[More Information Needed]
|
Coffee-Gym/COFFEEPOTS-critic | Coffee-Gym | 2024-03-27T15:44:11Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-03-27T14:59:30Z | ---
pipeline_tag: text2text-generation
---
This is the official checkpoint of COFFEEPOTS-critic model.
This model generates natural language feedback given an erroneous code.
You may want to use this model with [COFFEEPOTS-editor](https://huggingface.co/Anonymous-COFFEE/COFFEEPOTS-editor).
For further detials, please see our paper and the official repository.
Official repo: https://anonymous.4open.science/status/COFFEE_official-83E6 |
wangyuhao/dqn-SpaceInvadersNoFrameskip-v4-1 | wangyuhao | 2024-03-27T15:43:42Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-03-27T15:43:11Z | ---
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: 532.00 +/- 72.91
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 wangyuhao -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 wangyuhao -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 wangyuhao
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
saransh03sharma/mintrec-mistral-2-7b-150 | saransh03sharma | 2024-03-27T15:43:05Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T12:59:04Z | ---
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.
- **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] |
tedad09/PolizzeDonut-Cluster4di5-ftPDF-3Epochs | tedad09 | 2024-03-27T15:39:55Z | 48 | 0 | transformers | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:tedad09/PolizzeDonut-SoloPDF-3Epochs-tot",
"base_model:finetune:tedad09/PolizzeDonut-SoloPDF-3Epochs-tot",
"license:mit",
"endpoints_compatible",
"region:us"
]
| image-text-to-text | 2024-03-27T15:33:16Z | ---
license: mit
base_model: tedad09/PolizzeDonut-SoloPDF-3Epochs-tot
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: PolizzeDonut-Cluster4di5-ftPDF-3Epochs
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. -->
# PolizzeDonut-Cluster4di5-ftPDF-3Epochs
This model is a fine-tuned version of [tedad09/PolizzeDonut-SoloPDF-3Epochs-tot](https://huggingface.co/tedad09/PolizzeDonut-SoloPDF-3Epochs-tot) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3031
## 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: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.546 | 1.0 | 15 | 0.3631 |
| 0.4095 | 2.0 | 30 | 0.3205 |
| 0.3741 | 3.0 | 45 | 0.3031 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
lemon-mint/gemma-ko-7b-it-v0.33 | lemon-mint | 2024-03-27T15:32:51Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"korean",
"pytorch",
"conversational",
"ko",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T15:20:58Z | ---
library_name: transformers
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
language:
- ko
- en
tags:
- korean
- gemma
- pytorch
pipeline_tag: text-generation
---
# Gemma Ko 7B Instruct v0.33
- Eval Loss: `1.012`
- lr: `1.5e-5`
- optimizer: adamw
- lr_scheduler_type: cosine
## Model Details
### Model Description
The Gemma 7B Ko Instruct v0.33 model is designed for generating human-like text in the Korean language.
It can be used for a variety of natural language processing tasks, such as language translation, text summarization, question answering, and conversation generation.
This model is particularly well-suited for applications that require high-quality, coherent, and contextually relevant Korean text generation.
- **Developed by:** `lemon-mint`
- **Model type:** Gemma
- **Language(s) (NLP):** Korean, English
- **License:** [gemma-terms-of-use](https://ai.google.dev/gemma/terms)
- **Finetuned from model:** [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it)
# Limitations and Ethical Considerations
As Gemma Ko 7B has been trained on extensive web data, biases present in the training data may be reflected in the model. Additionally, there is a possibility that it may generate sentences containing errors or incorrect information. Therefore, rather than blindly trusting the model's output, it is necessary to refer to it with caution.
|
weny22/extract_long_text_unbalanced_smaller_6 | weny22 | 2024-03-27T15:28:43Z | 108 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:weny22/sum_model_t5_saved",
"base_model:finetune:weny22/sum_model_t5_saved",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-03-27T15:03:08Z | ---
base_model: weny22/sum_model_t5_saved
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: extract_long_text_unbalanced_smaller_6
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. -->
# extract_long_text_unbalanced_smaller_6
Use this , this is for the small unbalanced dataset with extracted text.
This model is a fine-tuned version of [weny22/sum_model_t5_saved](https://huggingface.co/weny22/sum_model_t5_saved) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4469
- Rouge1: 0.202
- Rouge2: 0.0715
- Rougel: 0.1621
- Rougelsum: 0.1621
- Gen Len: 18.9807
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.002
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 72 | 2.4359 | 0.1821 | 0.0587 | 0.1461 | 0.1461 | 18.9967 |
| No log | 2.0 | 144 | 2.3350 | 0.1934 | 0.0625 | 0.1529 | 0.1532 | 19.0 |
| No log | 3.0 | 216 | 2.2535 | 0.1907 | 0.0618 | 0.1515 | 0.1516 | 18.9947 |
| No log | 4.0 | 288 | 2.2242 | 0.1915 | 0.0619 | 0.1515 | 0.1517 | 18.9913 |
| No log | 5.0 | 360 | 2.2027 | 0.196 | 0.0646 | 0.1544 | 0.1545 | 18.9973 |
| No log | 6.0 | 432 | 2.2339 | 0.1894 | 0.0619 | 0.1501 | 0.1502 | 18.9967 |
| 2.7907 | 7.0 | 504 | 2.1934 | 0.1949 | 0.0649 | 0.155 | 0.155 | 18.9847 |
| 2.7907 | 8.0 | 576 | 2.2615 | 0.1976 | 0.0669 | 0.1574 | 0.1575 | 18.982 |
| 2.7907 | 9.0 | 648 | 2.2664 | 0.2033 | 0.0726 | 0.1623 | 0.1622 | 18.9827 |
| 2.7907 | 10.0 | 720 | 2.2514 | 0.2025 | 0.0713 | 0.1609 | 0.161 | 18.984 |
| 2.7907 | 11.0 | 792 | 2.2772 | 0.1982 | 0.071 | 0.1591 | 0.1591 | 18.9847 |
| 2.7907 | 12.0 | 864 | 2.3114 | 0.2056 | 0.0731 | 0.1635 | 0.1637 | 18.9753 |
| 2.7907 | 13.0 | 936 | 2.3120 | 0.2011 | 0.071 | 0.1602 | 0.1602 | 18.9867 |
| 1.8632 | 14.0 | 1008 | 2.3276 | 0.2044 | 0.0733 | 0.1636 | 0.1638 | 18.9687 |
| 1.8632 | 15.0 | 1080 | 2.3733 | 0.201 | 0.072 | 0.161 | 0.1611 | 18.9847 |
| 1.8632 | 16.0 | 1152 | 2.3852 | 0.2021 | 0.0719 | 0.1627 | 0.1627 | 18.9773 |
| 1.8632 | 17.0 | 1224 | 2.4101 | 0.1999 | 0.0705 | 0.1608 | 0.1608 | 18.9787 |
| 1.8632 | 18.0 | 1296 | 2.4123 | 0.1999 | 0.0709 | 0.1604 | 0.1605 | 18.9833 |
| 1.8632 | 19.0 | 1368 | 2.4414 | 0.2 | 0.0704 | 0.1605 | 0.1604 | 18.9753 |
| 1.8632 | 20.0 | 1440 | 2.4469 | 0.202 | 0.0715 | 0.1621 | 0.1621 | 18.9807 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
simonszu0814/gemma-Code-Instruct-Finetune-test2 | simonszu0814 | 2024-03-27T15:25:36Z | 109 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T15:22:40Z | ---
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]
- **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]
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|
0x0mom/nous_r7 | 0x0mom | 2024-03-27T15:20:54Z | 90 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T15:19:46Z | ---
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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KGsteven/0327Exchange-rate-related-distilroberta-finetuned-banking77 | KGsteven | 2024-03-27T15:06:22Z | 163 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-27T15:06:07Z | ---
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.
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## How to Get Started with the Model
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[More Information Needed]
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|
weny22/extract_long_text_unbalanced_smaller_5 | weny22 | 2024-03-27T15:01:44Z | 106 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:weny22/sum_model_t5_saved",
"base_model:finetune:weny22/sum_model_t5_saved",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-03-27T14:36:47Z | ---
base_model: weny22/sum_model_t5_saved
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: extract_long_text_unbalanced_smaller_5
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. -->
# extract_long_text_unbalanced_smaller_5
This model is a fine-tuned version of [weny22/sum_model_t5_saved](https://huggingface.co/weny22/sum_model_t5_saved) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2242
- Rouge1: 0.2008
- Rouge2: 0.0688
- Rougel: 0.1593
- Rougelsum: 0.1594
- Gen Len: 18.9847
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 72 | 2.3970 | 0.1842 | 0.0572 | 0.1461 | 0.1458 | 18.98 |
| No log | 2.0 | 144 | 2.2826 | 0.1923 | 0.0623 | 0.1516 | 0.1515 | 19.0 |
| No log | 3.0 | 216 | 2.2308 | 0.1945 | 0.0634 | 0.1529 | 0.1527 | 18.9953 |
| No log | 4.0 | 288 | 2.1962 | 0.1944 | 0.0636 | 0.1528 | 0.1527 | 18.9967 |
| No log | 5.0 | 360 | 2.1940 | 0.1948 | 0.0633 | 0.1529 | 0.1528 | 18.9953 |
| No log | 6.0 | 432 | 2.1734 | 0.1882 | 0.0628 | 0.1492 | 0.1491 | 18.99 |
| 3.0387 | 7.0 | 504 | 2.1584 | 0.1964 | 0.0663 | 0.156 | 0.1559 | 18.992 |
| 3.0387 | 8.0 | 576 | 2.1588 | 0.197 | 0.068 | 0.1563 | 0.1562 | 18.9847 |
| 3.0387 | 9.0 | 648 | 2.1852 | 0.1967 | 0.0669 | 0.156 | 0.1559 | 18.9793 |
| 3.0387 | 10.0 | 720 | 2.1859 | 0.201 | 0.0685 | 0.159 | 0.1587 | 18.982 |
| 3.0387 | 11.0 | 792 | 2.1760 | 0.1936 | 0.0643 | 0.1534 | 0.1531 | 18.9953 |
| 3.0387 | 12.0 | 864 | 2.2081 | 0.1978 | 0.0672 | 0.1566 | 0.1564 | 18.9753 |
| 3.0387 | 13.0 | 936 | 2.2030 | 0.1991 | 0.068 | 0.1584 | 0.158 | 18.9833 |
| 2.204 | 14.0 | 1008 | 2.2029 | 0.1981 | 0.0686 | 0.1578 | 0.1578 | 18.9867 |
| 2.204 | 15.0 | 1080 | 2.2076 | 0.2016 | 0.0694 | 0.1595 | 0.1592 | 18.9773 |
| 2.204 | 16.0 | 1152 | 2.2172 | 0.203 | 0.0716 | 0.1617 | 0.1617 | 18.9893 |
| 2.204 | 17.0 | 1224 | 2.2136 | 0.2018 | 0.0697 | 0.1604 | 0.1603 | 18.9827 |
| 2.204 | 18.0 | 1296 | 2.2147 | 0.2016 | 0.0695 | 0.1601 | 0.1599 | 18.988 |
| 2.204 | 19.0 | 1368 | 2.2224 | 0.2007 | 0.0687 | 0.1592 | 0.1592 | 18.9847 |
| 2.204 | 20.0 | 1440 | 2.2242 | 0.2008 | 0.0688 | 0.1593 | 0.1594 | 18.9847 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
tedad09/PolizzeDonut-Cluster1di5-ftPDF-3Epochs | tedad09 | 2024-03-27T14:58:02Z | 50 | 0 | transformers | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:tedad09/PolizzeDonut-SoloPDF-3Epochs-tot",
"base_model:finetune:tedad09/PolizzeDonut-SoloPDF-3Epochs-tot",
"license:mit",
"endpoints_compatible",
"region:us"
]
| image-text-to-text | 2024-03-27T14:37:17Z | ---
license: mit
base_model: tedad09/PolizzeDonut-SoloPDF-3Epochs-tot
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: PolizzeDonut-Cluster1di5-ftPDF-3Epochs
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. -->
# PolizzeDonut-Cluster1di5-ftPDF-3Epochs
This model is a fine-tuned version of [tedad09/PolizzeDonut-SoloPDF-3Epochs-tot](https://huggingface.co/tedad09/PolizzeDonut-SoloPDF-3Epochs-tot) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1428
## 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: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2178 | 1.0 | 24 | 0.1333 |
| 0.1286 | 2.0 | 48 | 0.1479 |
| 0.0954 | 3.0 | 72 | 0.1428 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
pasttheboundaries/bert-tokenizer-med-240327 | pasttheboundaries | 2024-03-27T14:58:02Z | 0 | 0 | transformers | [
"transformers",
"pl",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-03-27T14:55:37Z | ---
library_name: transformers
language:
- pl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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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.
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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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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
#### Metrics
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[More Information Needed]
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[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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Eeyoung/model5 | Eeyoung | 2024-03-27T14:57:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-03-27T12:03:23Z | ---
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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<!-- 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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<!-- 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
### 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]
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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. -->
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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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
minatoyuichiro/NeuralPipe-7B-slerp | minatoyuichiro | 2024-03-27T14:47:27Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"OpenPipe/mistral-ft-optimized-1218",
"mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:OpenPipe/mistral-ft-optimized-1218",
"base_model:merge:OpenPipe/mistral-ft-optimized-1218",
"base_model:mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T14:43:22Z | ---
tags:
- merge
- mergekit
- lazymergekit
- OpenPipe/mistral-ft-optimized-1218
- mlabonne/NeuralHermes-2.5-Mistral-7B
base_model:
- OpenPipe/mistral-ft-optimized-1218
- mlabonne/NeuralHermes-2.5-Mistral-7B
---
# NeuralPipe-7B-slerp
NeuralPipe-7B-slerp 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/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1218
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
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 = "minatoyuichiro/NeuralPipe-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"])
``` |
hoangduy0610/uit-cs221-sentiment-analysis | hoangduy0610 | 2024-03-27T14:47:23Z | 106 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-27T14:37:47Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: uit-cs221-sentiment-analysis
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
- name: F1
type: f1
value: 0.8684210526315789
language:
- en
---
<!-- 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. -->
# uit-cs221-sentiment-analysis
## Model description
This model is used for sentiment analysis. It is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3057
- Accuracy: 0.8667
- F1: 0.8684
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
- Loss: 0.3057
- Accuracy: 0.8667
- F1: 0.8684
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2 |
Oneeb/Humanised-LLMv4 | Oneeb | 2024-03-27T14:45:40Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T14:36:35Z | ---
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]
|
dfurman/deberta-v3-base-imdb | dfurman | 2024-03-27T14:45:00Z | 108 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"dataset:stanfordnlp/imdb",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-25T05:34:43Z | ---
license: mit
base_model: microsoft/deberta-v3-base
datasets: stanfordnlp/imdb
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deberta-v3-base-imdb
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. -->
# deberta-v3-base-imdb
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the [imdb](https://huggingface.co/datasets/stanfordnlp/imdb) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3594
- Accuracy: 0.9577
- F1: 0.9579
- Precision: 0.9530
- Recall: 0.9629
## 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 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3108 | 1.0 | 12500 | 0.2634 | 0.9530 | 0.9529 | 0.9557 | 0.9502 |
| 0.2322 | 2.0 | 25000 | 0.2629 | 0.9546 | 0.9552 | 0.9437 | 0.9670 |
| 0.1119 | 3.0 | 37500 | 0.2944 | 0.9546 | 0.9550 | 0.9467 | 0.9634 |
| 0.0292 | 4.0 | 50000 | 0.3694 | 0.9557 | 0.9564 | 0.9422 | 0.9710 |
| 0.0191 | 5.0 | 62500 | 0.3594 | 0.9577 | 0.9579 | 0.9530 | 0.9629 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
dfurman/distilbert-base-uncased-imdb | dfurman | 2024-03-27T14:44:08Z | 283 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:stanfordnlp/imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-27T01:36:32Z | ---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
datasets:
- stanfordnlp/imdb
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-uncased-imdb
results: []
---
# distilbert-base-uncased-imdb
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the [imdb](https://huggingface.co/datasets/stanfordnlp/imdb) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4367
- Accuracy: 0.9327
- F1: 0.9336
- Precision: 0.9212
- Recall: 0.9463
## 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 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2601 | 1.0 | 3125 | 0.3550 | 0.8857 | 0.8744 | 0.9709 | 0.7953 |
| 0.1842 | 2.0 | 6250 | 0.2355 | 0.9327 | 0.9327 | 0.9328 | 0.9326 |
| 0.1191 | 3.0 | 9375 | 0.3287 | 0.9311 | 0.9303 | 0.9417 | 0.9191 |
| 0.0452 | 4.0 | 12500 | 0.4053 | 0.9331 | 0.9337 | 0.9256 | 0.942 |
| 0.0299 | 5.0 | 15625 | 0.4367 | 0.9327 | 0.9336 | 0.9212 | 0.9463 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
k-andreev/ppo-LunarLander-v2 | k-andreev | 2024-03-27T14:41:23Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-03-27T14:09:54Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 273.78 +/- 19.44
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
prajwalJumde/27_Mar24_QA_layout3 | prajwalJumde | 2024-03-27T14:40:43Z | 48 | 0 | transformers | [
"transformers",
"safetensors",
"layoutlmv3",
"document-question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| document-question-answering | 2024-03-27T14:35:46Z | ---
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]
|
Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12 | Lin-Chen | 2024-03-27T14:40:37Z | 3,022 | 17 | transformers | [
"transformers",
"pytorch",
"image-feature-extraction",
"arxiv:2311.12793",
"region:us"
]
| image-feature-extraction | 2023-11-21T13:06:12Z | ---
inference: false
pipeline_tag: image-feature-extraction
---
<br>
<br>
# ShareGPT4V Model Card
## Model details
**Model type:**
This is the vision tower of ShareGPT4V-7B fine-tuned with our [ShareGPT4V dataset](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V).
**Model date:**
This vision tower was trained in Nov 2023.
**Paper or resources for more information:**
[[Project](https://ShareGPT4V.github.io/)] [[Paper](https://huggingface.co/papers/2311.12793)] [[Code](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V)]
## License
Llama 2 is licensed under the LLAMA 2 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved.
## Intended use
**Primary intended uses:**
The primary use of this vision tower is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
- 1.2M high-quality image-text pairs |
CunCunnel/cartpole-v1 | CunCunnel | 2024-03-27T14:36:07Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-03-27T14:35:58Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
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
|
balakhonoff/solidity_security_model_v5 | balakhonoff | 2024-03-27T14:31:25Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
]
| null | 2024-03-27T14:30:43Z | ---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# 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. -->
- **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]
### Framework versions
- PEFT 0.10.0 |
CunCunnel/ppo-SnowballTarget | CunCunnel | 2024-03-27T14:26:27Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2024-03-27T14:26:25Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: CunCunnel/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
magjico/ppo-SnowballTarget | magjico | 2024-03-27T14:25:40Z | 6 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2024-03-27T14:25:38Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: magjico/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
b43646/test_trainer | b43646 | 2024-03-27T14:20:51Z | 159 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-27T11:30:54Z | ---
license: apache-2.0
base_model: google-bert/bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test_trainer
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. -->
# test_trainer
This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0804
- Accuracy: 0.566
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 125 | 1.1481 | 0.478 |
| No log | 2.0 | 250 | 1.0505 | 0.56 |
| No log | 3.0 | 375 | 1.0804 | 0.566 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.1
- Datasets 2.14.6
- Tokenizers 0.15.1
|
AlignmentResearch/robust_llm_pythia-imdb-1b-mz-ada-v3-nd | AlignmentResearch | 2024-03-27T14:10:18Z | 64 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b",
"base_model:finetune:EleutherAI/pythia-1b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-27T14:08:18Z | ---
license: apache-2.0
tags:
- generated_from_trainer
base_model: EleutherAI/pythia-1b
model-index:
- name: robust_llm_pythia-imdb-1b-mz-ada-v3-nd
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. -->
# robust_llm_pythia-imdb-1b-mz-ada-v3-nd
This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) 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: 8
- eval_batch_size: 64
- 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
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
|
Tchaptchet/your_repos_hub | Tchaptchet | 2024-03-27T14:08:55Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-27T13:58:06Z | ---
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]
|
balakhonoff/solidity_security_model_v4 | balakhonoff | 2024-03-27T14:08:54Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
]
| null | 2024-03-26T23:28:49Z | ---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# 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. -->
- **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]
### Framework versions
- PEFT 0.10.0 |
moatazmohamed/araT5-1500context-sample-epoch-1-v1 | moatazmohamed | 2024-03-27T14:07:53Z | 107 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-03-27T14:06:48Z | ---
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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
[More Information Needed]
#### Software
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## 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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
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|
xverse/XVERSE-7B-Chat-GPTQ-Int4 | xverse | 2024-03-27T14:07:02Z | 16 | 0 | transformers | [
"transformers",
"xverse",
"text-generation",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"4-bit",
"gptq",
"region:us"
]
| text-generation | 2024-03-25T11:42:30Z | ---
license: apache-2.0
inference: false
---
# XVERSE-7B-Chat-GPTQ-Int4
## 模型介绍
**XVERSE-7B-Chat**为[**XVERSE-7B**](https://huggingface.co/xverse/XVERSE-7B)模型对齐后的版本。
**XVERSE-7B** 是由深圳元象科技自主研发的支持多语言的大语言模型（Large Language Model），参数规模为 70 亿，主要特点如下：
- **模型结构**：XVERSE-7B 使用主流 Decoder-only 的标准 Transformer 网络结构，支持 8K 的上下文长度（Context Length），能满足更长的多轮对话、知识问答与摘要等需求，模型应用场景更广泛。
- **训练数据**：构建了 2.6 万亿 token 的高质量、多样化的数据对模型进行充分训练，包含中、英、俄、西等 40 多种语言，通过精细化设置不同类型数据的采样比例，使得中英两种语言表现优异，也能兼顾其他语言效果。
- **分词**：基于 BPE（Byte-Pair Encoding）算法，使用上百 GB 语料训练了一个词表大小为 100,534 的分词器，能够同时支持多语言，而无需额外扩展词表。
- **训练框架**：自主研发多项关键技术，包括高效算子、显存优化、并行调度策略、数据-计算-通信重叠、平台和框架协同等，让训练效率更高，模型稳定性强，在千卡集群上的峰值算力利用率可达到 58.5%，位居业界前列。
## Model Introduction
**XVERSE-7B-Chat** is the aligned version of model [**XVERSE-7B**](https://huggingface.co/xverse/XVERSE-7B)
**XVERSE-7B** is a multilingual large language model, independently developed by Shenzhen Yuanxiang Technology. Its key features are as follows:
- **Model Structure**: XVERSE-7B uses the mainstream Decoder-only Transformer network structure, supports 8k context length, which can meet the need of longer multi-round dialogues, knowledge question-answering, and summarization. This makes the model more versatile in application scenarios.
- **Training Data**: The model has been thoroughly trained on a diversified and high-quality dataset consisting of 2.6 trillion of tokens, including more than 40 languages such as Chinese, English, Russian, and Spanish. The sampling ratio of different types of data is finely set, which makes the performance of Chinese and English excellent, and also takes into account the effect of other languages.
- **Tokenization**: Based on the BPE (Byte-Pair Encoding) algorithm, a tokenizer with a vocabulary size of 100,534 has been trained using hundreds of gigabytes of language data. This tokenizer is capable of supporting multilingual without the need for additional vocabulary expansion.
- **Training Framework**: Several key technologies have also been independently developed, including efficient operators, memory optimization, parallel scheduling strategies, overlap of data-computation-communication, and synergy between platforms and frameworks. These advancements enhance training efficiency and model stability. With these technologies, the peak computational power utilization rate on a thousand-card cluster can reach 58.5%, ranking at the forefront of the industry.
## 环境准备
我们建议您克隆[`vllm`](https://github.com/vllm-project/vllm.git)并按照官方指南进行安装。
## Environment Setup
We advise you to clone [`vllm`](https://github.com/vllm-project/vllm.git) and install it following the official guide.
## 使用方法
我们演示了如何使用 vLLM 来运行XVERSE-7B-Chat-GPTQ-Int4量化模型:
```python
from vllm import LLM, SamplingParams
model_dir = "xverse/XVERSE-7B-Chat-GPTQ-Int4/"
# Create an LLM.
llm = LLM(model_dir,
trust_remote_code=True)
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.5, top_p=0.85, max_tokens=2048, repetition_penalty=1.1)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
prompts = ["Human: 请你写一篇关于环保的文章，题材是从个人做起。\n\nAssistant: ",]
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Generated text:\n{generated_text}")
```
## Usage
We demonstrated how to use vLLM to run the XVERSE-7B-Chat-GPTQ-Int4 quantization model:
```python
from vllm import LLM, SamplingParams
model_dir = "xverse/XVERSE-7B-Chat-GPTQ-Int4/"
# Create an LLM.
llm = LLM(model_dir,
trust_remote_code=True)
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.5, top_p=0.85, max_tokens=2048, repetition_penalty=1.1)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
prompts = ["Human: 请你写一篇关于环保的文章，题材是从个人做起。\n\nAssistant: ",]
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Generated text:\n{generated_text}")
```
## 局限性与免责申明
XVERSE-7B-Chat 与其他所有 LLM 一样，在某些情况下可能会产生不准确、有偏见或其他令人反感的内容。因此，请谨慎使用模型生成的内容，请勿将生成的有害内容进行传播，在部署任何 XVERSE-7B-Chat 的应用之前，开发人员应根据其具体应用对模型进行安全测试和调优。
我们强烈警告不要将 XVERSE-7B-Chat 模型用于制造或传播有害信息，或进行任何可能损害公众、国家、社会安全或违反法规的活动。如果使用 XVERSE-7B-Chat 模型产生任何问题，无论是数据安全问题、公共舆论风险，还是模型被误解、滥用、传播或不合规使用所引发的任何风险和问题，我们将不承担任何责任。
## Limitations and Disclaimer
Like all other Large Language Models (LLMs), XVERSE-7B-Chat may produce inaccurate, biased, or otherwise offensive content under certain circumstances. Therefore, please use the content generated by the model with caution and refrain from disseminating harmful content. Before deploying any application of XVERSE-7B-Chat, developers should conduct safety tests and optimization of the model according to its specific application.
We strongly warn against the use of the XVERSE-7B-Chat model for producing or spreading harmful information, or conducting any activities that might harm the public, national, or social security, or violate regulations. We assume no responsibility for any problems arising from the use of the XVERSE-7B-Chat model, whether it be data security issues, public opinion risks, or any risks and issues caused by misunderstanding, misuse, dissemination, or non-compliance with the model.
## 模型开源协议
使用本仓库的源码需要遵循 [Apache-2.0](https://github.com/xverse-ai/XVERSE-7B/blob/main/LICENSE) 开源协议，使用 XVERSE-7B-Chat 的模型权重则需要遵循[模型许可协议](https://github.com/xverse-ai/XVERSE-7B/blob/main/MODEL_LICENSE.pdf)。
XVERSE-7B-Chat 模型权重对学术研究**完全开放**，并且支持**免费商用**。如需申请商业许可证，请填写【[申请表](https://chat.xverse.cn/home/business.html)】，如有其他问题或合作，请联系 <[email protected]>。
## Open Source License
The use of the source code in this repository must follow the [Apache-2.0](https://github.com/xverse-ai/XVERSE-7B/blob/main/LICENSE) open-source license, while the use of the model weights of XVERSE-7B-Chat needs to adhere to the [Model License Agreement](https://github.com/xverse-ai/XVERSE-7B/blob/main/MODEL_LICENSE.pdf).
The XVERSE-7B-Chat model weights are **fully open** to academic research and support **free commercial use**. To apply for a commercial license, please fill in the [application form](https://chat.xverse.cn/home/business.html). For other questions or collaborations, please contact <[email protected]>.
|
samhitmantrala/prc4 | samhitmantrala | 2024-03-27T14:06:47Z | 112 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-24T12:25:39Z | ---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: prc4
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. -->
# prc4
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 1.4282 |
| No log | 2.0 | 2 | 0.6417 |
| No log | 3.0 | 3 | 0.5086 |
| No log | 4.0 | 4 | 0.2781 |
| No log | 5.0 | 5 | 0.1540 |
| No log | 6.0 | 6 | 0.0654 |
| No log | 7.0 | 7 | 0.0348 |
| No log | 8.0 | 8 | 0.0263 |
| No log | 9.0 | 9 | 0.0198 |
| No log | 10.0 | 10 | 0.0129 |
| No log | 11.0 | 11 | 0.0074 |
| No log | 12.0 | 12 | 0.0037 |
| No log | 13.0 | 13 | 0.0017 |
| No log | 14.0 | 14 | 0.0008 |
| No log | 15.0 | 15 | 0.0004 |
| No log | 16.0 | 16 | 0.0002 |
| No log | 17.0 | 17 | 0.0001 |
| No log | 18.0 | 18 | 0.0001 |
| No log | 19.0 | 19 | 0.0001 |
| No log | 20.0 | 20 | 0.0001 |
| No log | 21.0 | 21 | 0.0001 |
| No log | 22.0 | 22 | 0.0001 |
| No log | 23.0 | 23 | 0.0001 |
| No log | 24.0 | 24 | 0.0000 |
| No log | 25.0 | 25 | 0.0000 |
| No log | 26.0 | 26 | 0.0000 |
| No log | 27.0 | 27 | 0.0000 |
| No log | 28.0 | 28 | 0.0000 |
| No log | 29.0 | 29 | 0.0000 |
| No log | 30.0 | 30 | 0.0000 |
| No log | 31.0 | 31 | 0.0000 |
| No log | 32.0 | 32 | 0.0000 |
| No log | 33.0 | 33 | 0.0000 |
| No log | 34.0 | 34 | 0.0000 |
| No log | 35.0 | 35 | 0.0000 |
| No log | 36.0 | 36 | 0.0000 |
| No log | 37.0 | 37 | 0.0000 |
| No log | 38.0 | 38 | 0.0000 |
| No log | 39.0 | 39 | 0.0000 |
| No log | 40.0 | 40 | 0.0000 |
| No log | 41.0 | 41 | 0.0000 |
| No log | 42.0 | 42 | 0.0000 |
| No log | 43.0 | 43 | 0.0000 |
| No log | 44.0 | 44 | 0.0000 |
| No log | 45.0 | 45 | 0.0000 |
| No log | 46.0 | 46 | 0.0000 |
| No log | 47.0 | 47 | 0.0000 |
| No log | 48.0 | 48 | 0.0000 |
| No log | 49.0 | 49 | 0.0000 |
| No log | 50.0 | 50 | 0.0000 |
| No log | 51.0 | 51 | 0.0000 |
| No log | 52.0 | 52 | 0.0000 |
| No log | 53.0 | 53 | 0.0000 |
| No log | 54.0 | 54 | 0.0000 |
| No log | 55.0 | 55 | 0.0000 |
| No log | 56.0 | 56 | 0.0000 |
| No log | 57.0 | 57 | 0.0001 |
| No log | 58.0 | 58 | 0.0000 |
| No log | 59.0 | 59 | 0.0000 |
| No log | 60.0 | 60 | 0.0000 |
| No log | 61.0 | 61 | 0.0000 |
| No log | 62.0 | 62 | 0.0000 |
| No log | 63.0 | 63 | 0.0000 |
| No log | 64.0 | 64 | 0.0000 |
| No log | 65.0 | 65 | 0.0000 |
| No log | 66.0 | 66 | 0.0000 |
| No log | 67.0 | 67 | 0.0000 |
| No log | 68.0 | 68 | 0.0000 |
| No log | 69.0 | 69 | 0.0000 |
| No log | 70.0 | 70 | 0.0000 |
| No log | 71.0 | 71 | 0.0000 |
| No log | 72.0 | 72 | 0.0000 |
| No log | 73.0 | 73 | 0.0000 |
| No log | 74.0 | 74 | 0.0000 |
| No log | 75.0 | 75 | 0.0000 |
| No log | 76.0 | 76 | 0.0000 |
| No log | 77.0 | 77 | 0.0000 |
| No log | 78.0 | 78 | 0.0000 |
| No log | 79.0 | 79 | 0.0000 |
| No log | 80.0 | 80 | 0.0000 |
| No log | 81.0 | 81 | 0.0000 |
| No log | 82.0 | 82 | 0.0000 |
| No log | 83.0 | 83 | 0.0000 |
| No log | 84.0 | 84 | 0.0000 |
| No log | 85.0 | 85 | 0.0000 |
| No log | 86.0 | 86 | 0.0000 |
| No log | 87.0 | 87 | 0.0000 |
| No log | 88.0 | 88 | 0.0000 |
| No log | 89.0 | 89 | 0.0000 |
| No log | 90.0 | 90 | 0.0000 |
| No log | 91.0 | 91 | 0.0000 |
| No log | 92.0 | 92 | 0.0000 |
| No log | 93.0 | 93 | 0.0000 |
| No log | 94.0 | 94 | 0.0000 |
| No log | 95.0 | 95 | 0.0000 |
| No log | 96.0 | 96 | 0.0000 |
| No log | 97.0 | 97 | 0.0000 |
| No log | 98.0 | 98 | 0.0000 |
| No log | 99.0 | 99 | 0.0000 |
| No log | 100.0 | 100 | 0.0000 |
| No log | 101.0 | 101 | 0.0000 |
| No log | 102.0 | 102 | 0.0000 |
| No log | 103.0 | 103 | 0.0000 |
| No log | 104.0 | 104 | 0.0000 |
| No log | 105.0 | 105 | 0.0000 |
| No log | 106.0 | 106 | 0.0000 |
| No log | 107.0 | 107 | 0.0000 |
| No log | 108.0 | 108 | 0.0000 |
| No log | 109.0 | 109 | 0.0000 |
| No log | 110.0 | 110 | 0.0000 |
| No log | 111.0 | 111 | 0.0000 |
| No log | 112.0 | 112 | 0.0000 |
| No log | 113.0 | 113 | 0.0000 |
| No log | 114.0 | 114 | 0.0000 |
| No log | 115.0 | 115 | 0.0000 |
| No log | 116.0 | 116 | 0.0000 |
| No log | 117.0 | 117 | 0.0000 |
| No log | 118.0 | 118 | 0.0000 |
| No log | 119.0 | 119 | 0.0000 |
| No log | 120.0 | 120 | 0.0000 |
| No log | 121.0 | 121 | 0.0000 |
| No log | 122.0 | 122 | 0.0000 |
| No log | 123.0 | 123 | 0.0001 |
| No log | 124.0 | 124 | 0.0001 |
| No log | 125.0 | 125 | 0.0002 |
| No log | 126.0 | 126 | 0.0002 |
| No log | 127.0 | 127 | 0.0002 |
| No log | 128.0 | 128 | 0.0002 |
| No log | 129.0 | 129 | 0.0001 |
| No log | 130.0 | 130 | 0.0001 |
| No log | 131.0 | 131 | 0.0001 |
| No log | 132.0 | 132 | 0.0001 |
| No log | 133.0 | 133 | 0.0000 |
| No log | 134.0 | 134 | 0.0001 |
| No log | 135.0 | 135 | 0.0001 |
| No log | 136.0 | 136 | 0.0001 |
| No log | 137.0 | 137 | 0.0001 |
| No log | 138.0 | 138 | 0.0001 |
| No log | 139.0 | 139 | 0.0001 |
| No log | 140.0 | 140 | 0.0001 |
| No log | 141.0 | 141 | 0.0001 |
| No log | 142.0 | 142 | 0.0001 |
| No log | 143.0 | 143 | 0.0001 |
| No log | 144.0 | 144 | 0.0001 |
| No log | 145.0 | 145 | 0.0001 |
| No log | 146.0 | 146 | 0.0000 |
| No log | 147.0 | 147 | 0.0000 |
| No log | 148.0 | 148 | 0.0000 |
| No log | 149.0 | 149 | 0.0000 |
| No log | 150.0 | 150 | 0.0000 |
| No log | 151.0 | 151 | 0.0000 |
| No log | 152.0 | 152 | 0.0000 |
| No log | 153.0 | 153 | 0.0000 |
| No log | 154.0 | 154 | 0.0000 |
| No log | 155.0 | 155 | 0.0000 |
| No log | 156.0 | 156 | 0.0000 |
| No log | 157.0 | 157 | 0.0000 |
| No log | 158.0 | 158 | 0.0000 |
| No log | 159.0 | 159 | 0.0000 |
| No log | 160.0 | 160 | 0.0000 |
| No log | 161.0 | 161 | 0.0000 |
| No log | 162.0 | 162 | 0.0000 |
| No log | 163.0 | 163 | 0.0000 |
| No log | 164.0 | 164 | 0.0000 |
| No log | 165.0 | 165 | 0.0000 |
| No log | 166.0 | 166 | 0.0000 |
| No log | 167.0 | 167 | 0.0000 |
| No log | 168.0 | 168 | 0.0000 |
| No log | 169.0 | 169 | 0.0000 |
| No log | 170.0 | 170 | 0.0000 |
| No log | 171.0 | 171 | 0.0000 |
| No log | 172.0 | 172 | 0.0000 |
| No log | 173.0 | 173 | 0.0000 |
| No log | 174.0 | 174 | 0.0000 |
| No log | 175.0 | 175 | 0.0000 |
| No log | 176.0 | 176 | 0.0000 |
| No log | 177.0 | 177 | 0.0000 |
| No log | 178.0 | 178 | 0.0000 |
| No log | 179.0 | 179 | 0.0000 |
| No log | 180.0 | 180 | 0.0000 |
| No log | 181.0 | 181 | 0.0000 |
| No log | 182.0 | 182 | 0.0000 |
| No log | 183.0 | 183 | 0.0000 |
| No log | 184.0 | 184 | 0.0000 |
| No log | 185.0 | 185 | 0.0000 |
| No log | 186.0 | 186 | 0.0000 |
| No log | 187.0 | 187 | 0.0000 |
| No log | 188.0 | 188 | 0.0000 |
| No log | 189.0 | 189 | 0.0000 |
| No log | 190.0 | 190 | 0.0000 |
| No log | 191.0 | 191 | 0.0000 |
| No log | 192.0 | 192 | 0.0000 |
| No log | 193.0 | 193 | 0.0000 |
| No log | 194.0 | 194 | 0.0000 |
| No log | 195.0 | 195 | 0.0000 |
| No log | 196.0 | 196 | 0.0000 |
| No log | 197.0 | 197 | 0.0000 |
| No log | 198.0 | 198 | 0.0000 |
| No log | 199.0 | 199 | 0.0000 |
| No log | 200.0 | 200 | 0.0000 |
| No log | 201.0 | 201 | 0.0000 |
| No log | 202.0 | 202 | 0.0000 |
| No log | 203.0 | 203 | 0.0000 |
| No log | 204.0 | 204 | 0.0000 |
| No log | 205.0 | 205 | 0.0000 |
| No log | 206.0 | 206 | 0.0000 |
| No log | 207.0 | 207 | 0.0000 |
| No log | 208.0 | 208 | 0.0000 |
| No log | 209.0 | 209 | 0.0000 |
| No log | 210.0 | 210 | 0.0000 |
| No log | 211.0 | 211 | 0.0000 |
| No log | 212.0 | 212 | 0.0000 |
| No log | 213.0 | 213 | 0.0000 |
| No log | 214.0 | 214 | 0.0000 |
| No log | 215.0 | 215 | 0.0000 |
| No log | 216.0 | 216 | 0.0000 |
| No log | 217.0 | 217 | 0.0000 |
| No log | 218.0 | 218 | 0.0000 |
| No log | 219.0 | 219 | 0.0000 |
| No log | 220.0 | 220 | 0.0000 |
| No log | 221.0 | 221 | 0.0000 |
| No log | 222.0 | 222 | 0.0000 |
| No log | 223.0 | 223 | 0.0000 |
| No log | 224.0 | 224 | 0.0000 |
| No log | 225.0 | 225 | 0.0000 |
| No log | 226.0 | 226 | 0.0000 |
| No log | 227.0 | 227 | 0.0000 |
| No log | 228.0 | 228 | 0.0000 |
| No log | 229.0 | 229 | 0.0000 |
| No log | 230.0 | 230 | 0.0000 |
| No log | 231.0 | 231 | 0.0000 |
| No log | 232.0 | 232 | 0.0000 |
| No log | 233.0 | 233 | 0.0000 |
| No log | 234.0 | 234 | 0.0000 |
| No log | 235.0 | 235 | 0.0000 |
| No log | 236.0 | 236 | 0.0000 |
| No log | 237.0 | 237 | 0.0000 |
| No log | 238.0 | 238 | 0.0000 |
| No log | 239.0 | 239 | 0.0000 |
| No log | 240.0 | 240 | 0.0000 |
| No log | 241.0 | 241 | 0.0000 |
| No log | 242.0 | 242 | 0.0000 |
| No log | 243.0 | 243 | 0.0000 |
| No log | 244.0 | 244 | 0.0000 |
| No log | 245.0 | 245 | 0.0000 |
| No log | 246.0 | 246 | 0.0000 |
| No log | 247.0 | 247 | 0.0000 |
| No log | 248.0 | 248 | 0.0000 |
| No log | 249.0 | 249 | 0.0000 |
| No log | 250.0 | 250 | 0.0000 |
| No log | 251.0 | 251 | 0.0000 |
| No log | 252.0 | 252 | 0.0000 |
| No log | 253.0 | 253 | 0.0000 |
| No log | 254.0 | 254 | 0.0000 |
| No log | 255.0 | 255 | 0.0000 |
| No log | 256.0 | 256 | 0.0000 |
| No log | 257.0 | 257 | 0.0000 |
| No log | 258.0 | 258 | 0.0000 |
| No log | 259.0 | 259 | 0.0000 |
| No log | 260.0 | 260 | 0.0000 |
| No log | 261.0 | 261 | 0.0000 |
| No log | 262.0 | 262 | 0.0000 |
| No log | 263.0 | 263 | 0.0000 |
| No log | 264.0 | 264 | 0.0000 |
| No log | 265.0 | 265 | 0.0000 |
| No log | 266.0 | 266 | 0.0000 |
| No log | 267.0 | 267 | 0.0000 |
| No log | 268.0 | 268 | 0.0000 |
| No log | 269.0 | 269 | 0.0000 |
| No log | 270.0 | 270 | 0.0000 |
| No log | 271.0 | 271 | 0.0000 |
| No log | 272.0 | 272 | 0.0000 |
| No log | 273.0 | 273 | 0.0000 |
| No log | 274.0 | 274 | 0.0000 |
| No log | 275.0 | 275 | 0.0000 |
| No log | 276.0 | 276 | 0.0000 |
| No log | 277.0 | 277 | 0.0000 |
| No log | 278.0 | 278 | 0.0000 |
| No log | 279.0 | 279 | 0.0000 |
| No log | 280.0 | 280 | 0.0000 |
| No log | 281.0 | 281 | 0.0000 |
| No log | 282.0 | 282 | 0.0000 |
| No log | 283.0 | 283 | 0.0000 |
| No log | 284.0 | 284 | 0.0000 |
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| No log | 332.0 | 332 | 0.0000 |
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| No log | 336.0 | 336 | 0.0000 |
| No log | 337.0 | 337 | 0.0000 |
| No log | 338.0 | 338 | 0.0000 |
| No log | 339.0 | 339 | 0.0000 |
| No log | 340.0 | 340 | 0.0000 |
| No log | 341.0 | 341 | 0.0000 |
| No log | 342.0 | 342 | 0.0000 |
| No log | 343.0 | 343 | 0.0000 |
| No log | 344.0 | 344 | 0.0000 |
| No log | 345.0 | 345 | 0.0000 |
| No log | 346.0 | 346 | 0.0000 |
| No log | 347.0 | 347 | 0.0000 |
| No log | 348.0 | 348 | 0.0000 |
| No log | 349.0 | 349 | 0.0000 |
| No log | 350.0 | 350 | 0.0000 |
| No log | 351.0 | 351 | 0.0000 |
| No log | 352.0 | 352 | 0.0000 |
| No log | 353.0 | 353 | 0.0000 |
| No log | 354.0 | 354 | 0.0000 |
| No log | 355.0 | 355 | 0.0000 |
| No log | 356.0 | 356 | 0.0000 |
| No log | 357.0 | 357 | 0.0000 |
| No log | 358.0 | 358 | 0.0000 |
| No log | 359.0 | 359 | 0.0000 |
| No log | 360.0 | 360 | 0.0000 |
| No log | 361.0 | 361 | 0.0000 |
| No log | 362.0 | 362 | 0.0000 |
| No log | 363.0 | 363 | 0.0000 |
| No log | 364.0 | 364 | 0.0000 |
| No log | 365.0 | 365 | 0.0000 |
| No log | 366.0 | 366 | 0.0000 |
| No log | 367.0 | 367 | 0.0000 |
| No log | 368.0 | 368 | 0.0000 |
| No log | 369.0 | 369 | 0.0000 |
| No log | 370.0 | 370 | 0.0000 |
| No log | 371.0 | 371 | 0.0000 |
| No log | 372.0 | 372 | 0.0000 |
| No log | 373.0 | 373 | 0.0000 |
| No log | 374.0 | 374 | 0.0000 |
| No log | 375.0 | 375 | 0.0000 |
| No log | 376.0 | 376 | 0.0000 |
| No log | 377.0 | 377 | 0.0000 |
| No log | 378.0 | 378 | 0.0000 |
| No log | 379.0 | 379 | 0.0000 |
| No log | 380.0 | 380 | 0.0000 |
| No log | 381.0 | 381 | 0.0000 |
| No log | 382.0 | 382 | 0.0000 |
| No log | 383.0 | 383 | 0.0000 |
| No log | 384.0 | 384 | 0.0000 |
| No log | 385.0 | 385 | 0.0000 |
| No log | 386.0 | 386 | 0.0000 |
| No log | 387.0 | 387 | 0.0000 |
| No log | 388.0 | 388 | 0.0000 |
| No log | 389.0 | 389 | 0.0000 |
| No log | 390.0 | 390 | 0.0000 |
| No log | 391.0 | 391 | 0.0000 |
| No log | 392.0 | 392 | 0.0000 |
| No log | 393.0 | 393 | 0.0000 |
| No log | 394.0 | 394 | 0.0000 |
| No log | 395.0 | 395 | 0.0000 |
| No log | 396.0 | 396 | 0.0000 |
| No log | 397.0 | 397 | 0.0000 |
| No log | 398.0 | 398 | 0.0000 |
| No log | 399.0 | 399 | 0.0000 |
| No log | 400.0 | 400 | 0.0000 |
| No log | 401.0 | 401 | 0.0000 |
| No log | 402.0 | 402 | 0.0000 |
| No log | 403.0 | 403 | 0.0000 |
| No log | 404.0 | 404 | 0.0000 |
| No log | 405.0 | 405 | 0.0000 |
| No log | 406.0 | 406 | 0.0000 |
| No log | 407.0 | 407 | 0.0000 |
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| No log | 409.0 | 409 | 0.0000 |
| No log | 410.0 | 410 | 0.0000 |
| No log | 411.0 | 411 | 0.0000 |
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| No log | 413.0 | 413 | 0.0000 |
| No log | 414.0 | 414 | 0.0000 |
| No log | 415.0 | 415 | 0.0000 |
| No log | 416.0 | 416 | 0.0000 |
| No log | 417.0 | 417 | 0.0000 |
| No log | 418.0 | 418 | 0.0000 |
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| No log | 420.0 | 420 | 0.0000 |
| No log | 421.0 | 421 | 0.0000 |
| No log | 422.0 | 422 | 0.0000 |
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| No log | 424.0 | 424 | 0.0000 |
| No log | 425.0 | 425 | 0.0000 |
| No log | 426.0 | 426 | 0.0000 |
| No log | 427.0 | 427 | 0.0000 |
| No log | 428.0 | 428 | 0.0000 |
| No log | 429.0 | 429 | 0.0000 |
| No log | 430.0 | 430 | 0.0000 |
| No log | 431.0 | 431 | 0.0000 |
| No log | 432.0 | 432 | 0.0000 |
| No log | 433.0 | 433 | 0.0000 |
| No log | 434.0 | 434 | 0.0000 |
| No log | 435.0 | 435 | 0.0000 |
| No log | 436.0 | 436 | 0.0000 |
| No log | 437.0 | 437 | 0.0000 |
| No log | 438.0 | 438 | 0.0000 |
| No log | 439.0 | 439 | 0.0000 |
| No log | 440.0 | 440 | 0.0000 |
| No log | 441.0 | 441 | 0.0000 |
| No log | 442.0 | 442 | 0.0000 |
| No log | 443.0 | 443 | 0.0000 |
| No log | 444.0 | 444 | 0.0000 |
| No log | 445.0 | 445 | 0.0000 |
| No log | 446.0 | 446 | 0.0000 |
| No log | 447.0 | 447 | 0.0000 |
| No log | 448.0 | 448 | 0.0000 |
| No log | 449.0 | 449 | 0.0000 |
| No log | 450.0 | 450 | 0.0000 |
| No log | 451.0 | 451 | 0.0000 |
| No log | 452.0 | 452 | 0.0000 |
| No log | 453.0 | 453 | 0.0000 |
| No log | 454.0 | 454 | 0.0000 |
| No log | 455.0 | 455 | 0.0000 |
| No log | 456.0 | 456 | 0.0000 |
| No log | 457.0 | 457 | 0.0000 |
| No log | 458.0 | 458 | 0.0000 |
| No log | 459.0 | 459 | 0.0000 |
| No log | 460.0 | 460 | 0.0000 |
| No log | 461.0 | 461 | 0.0000 |
| No log | 462.0 | 462 | 0.0000 |
| No log | 463.0 | 463 | 0.0000 |
| No log | 464.0 | 464 | 0.0000 |
| No log | 465.0 | 465 | 0.0000 |
| No log | 466.0 | 466 | 0.0000 |
| No log | 467.0 | 467 | 0.0000 |
| No log | 468.0 | 468 | 0.0000 |
| No log | 469.0 | 469 | 0.0000 |
| No log | 470.0 | 470 | 0.0000 |
| No log | 471.0 | 471 | 0.0000 |
| No log | 472.0 | 472 | 0.0000 |
| No log | 473.0 | 473 | 0.0000 |
| No log | 474.0 | 474 | 0.0000 |
| No log | 475.0 | 475 | 0.0000 |
| No log | 476.0 | 476 | 0.0000 |
| No log | 477.0 | 477 | 0.0000 |
| No log | 478.0 | 478 | 0.0000 |
| No log | 479.0 | 479 | 0.0000 |
| No log | 480.0 | 480 | 0.0000 |
| No log | 481.0 | 481 | 0.0000 |
| No log | 482.0 | 482 | 0.0000 |
| No log | 483.0 | 483 | 0.0000 |
| No log | 484.0 | 484 | 0.0000 |
| No log | 485.0 | 485 | 0.0000 |
| No log | 486.0 | 486 | 0.0000 |
| No log | 487.0 | 487 | 0.0000 |
| No log | 488.0 | 488 | 0.0000 |
| No log | 489.0 | 489 | 0.0000 |
| No log | 490.0 | 490 | 0.0000 |
| No log | 491.0 | 491 | 0.0000 |
| No log | 492.0 | 492 | 0.0000 |
| No log | 493.0 | 493 | 0.0000 |
| No log | 494.0 | 494 | 0.0000 |
| No log | 495.0 | 495 | 0.0000 |
| No log | 496.0 | 496 | 0.0000 |
| No log | 497.0 | 497 | 0.0000 |
| No log | 498.0 | 498 | 0.0000 |
| No log | 499.0 | 499 | 0.0000 |
| 0.01 | 500.0 | 500 | 0.0000 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
xverse/XVERSE-7B-Chat-GPTQ-Int8 | xverse | 2024-03-27T14:06:13Z | 20 | 0 | transformers | [
"transformers",
"xverse",
"text-generation",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"8-bit",
"gptq",
"region:us"
]
| text-generation | 2024-03-25T13:04:48Z | ---
license: apache-2.0
inference: false
---
# XVERSE-7B-Chat-GPTQ-Int8
## 模型介绍
**XVERSE-7B-Chat**为[**XVERSE-7B**](https://huggingface.co/xverse/XVERSE-7B)模型对齐后的版本。
**XVERSE-7B** 是由深圳元象科技自主研发的支持多语言的大语言模型（Large Language Model），参数规模为 70 亿，主要特点如下：
- **模型结构**：XVERSE-7B 使用主流 Decoder-only 的标准 Transformer 网络结构，支持 8K 的上下文长度（Context Length），能满足更长的多轮对话、知识问答与摘要等需求，模型应用场景更广泛。
- **训练数据**：构建了 2.6 万亿 token 的高质量、多样化的数据对模型进行充分训练，包含中、英、俄、西等 40 多种语言，通过精细化设置不同类型数据的采样比例，使得中英两种语言表现优异，也能兼顾其他语言效果。
- **分词**：基于 BPE（Byte-Pair Encoding）算法，使用上百 GB 语料训练了一个词表大小为 100,534 的分词器，能够同时支持多语言，而无需额外扩展词表。
- **训练框架**：自主研发多项关键技术，包括高效算子、显存优化、并行调度策略、数据-计算-通信重叠、平台和框架协同等，让训练效率更高，模型稳定性强，在千卡集群上的峰值算力利用率可达到 58.5%，位居业界前列。
## Model Introduction
**XVERSE-7B-Chat** is the aligned version of model [**XVERSE-7B**](https://huggingface.co/xverse/XVERSE-7B)
**XVERSE-7B** is a multilingual large language model, independently developed by Shenzhen Yuanxiang Technology. Its key features are as follows:
- **Model Structure**: XVERSE-7B uses the mainstream Decoder-only Transformer network structure, supports 8k context length, which can meet the need of longer multi-round dialogues, knowledge question-answering, and summarization. This makes the model more versatile in application scenarios.
- **Training Data**: The model has been thoroughly trained on a diversified and high-quality dataset consisting of 2.6 trillion of tokens, including more than 40 languages such as Chinese, English, Russian, and Spanish. The sampling ratio of different types of data is finely set, which makes the performance of Chinese and English excellent, and also takes into account the effect of other languages.
- **Tokenization**: Based on the BPE (Byte-Pair Encoding) algorithm, a tokenizer with a vocabulary size of 100,534 has been trained using hundreds of gigabytes of language data. This tokenizer is capable of supporting multilingual without the need for additional vocabulary expansion.
- **Training Framework**: Several key technologies have also been independently developed, including efficient operators, memory optimization, parallel scheduling strategies, overlap of data-computation-communication, and synergy between platforms and frameworks. These advancements enhance training efficiency and model stability. With these technologies, the peak computational power utilization rate on a thousand-card cluster can reach 58.5%, ranking at the forefront of the industry.
## 环境准备
我们建议您克隆[`vllm`](https://github.com/vllm-project/vllm.git)并按照官方指南进行安装。
## Environment Setup
We advise you to clone [`vllm`](https://github.com/vllm-project/vllm.git) and install it following the official guide.
## 使用方法
我们演示了如何使用 vLLM 来运行XVERSE-7B-Chat-GPTQ-Int8量化模型:
```python
from vllm import LLM, SamplingParams
model_dir = "xverse/XVERSE-7B-Chat-GPTQ-Int8/"
# Create an LLM.
llm = LLM(model_dir,
trust_remote_code=True)
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.5, top_p=0.85, max_tokens=2048, repetition_penalty=1.1)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
prompts = ["Human: 请你写一篇关于环保的文章，题材是从个人做起。\n\nAssistant: ",]
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Generated text:\n{generated_text}")
```
## Usage
We demonstrated how to use vLLM to run the XVERSE-7B-Chat-GPTQ-Int8 quantization model:
```python
from vllm import LLM, SamplingParams
model_dir = "xverse/XVERSE-7B-Chat-GPTQ-Int8/"
# Create an LLM.
llm = LLM(model_dir,
trust_remote_code=True)
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.5, top_p=0.85, max_tokens=2048, repetition_penalty=1.1)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
prompts = ["Human: 请你写一篇关于环保的文章，题材是从个人做起。\n\nAssistant: ",]
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Generated text:\n{generated_text}")
```
## 局限性与免责申明
XVERSE-7B-Chat 与其他所有 LLM 一样，在某些情况下可能会产生不准确、有偏见或其他令人反感的内容。因此，请谨慎使用模型生成的内容，请勿将生成的有害内容进行传播，在部署任何 XVERSE-7B-Chat 的应用之前，开发人员应根据其具体应用对模型进行安全测试和调优。
我们强烈警告不要将 XVERSE-7B-Chat 模型用于制造或传播有害信息，或进行任何可能损害公众、国家、社会安全或违反法规的活动。如果使用 XVERSE-7B-Chat 模型产生任何问题，无论是数据安全问题、公共舆论风险，还是模型被误解、滥用、传播或不合规使用所引发的任何风险和问题，我们将不承担任何责任。
## Limitations and Disclaimer
Like all other Large Language Models (LLMs), XVERSE-7B-Chat may produce inaccurate, biased, or otherwise offensive content under certain circumstances. Therefore, please use the content generated by the model with caution and refrain from disseminating harmful content. Before deploying any application of XVERSE-7B-Chat, developers should conduct safety tests and optimization of the model according to its specific application.
We strongly warn against the use of the XVERSE-7B-Chat model for producing or spreading harmful information, or conducting any activities that might harm the public, national, or social security, or violate regulations. We assume no responsibility for any problems arising from the use of the XVERSE-7B-Chat model, whether it be data security issues, public opinion risks, or any risks and issues caused by misunderstanding, misuse, dissemination, or non-compliance with the model.
## 模型开源协议
使用本仓库的源码需要遵循 [Apache-2.0](https://github.com/xverse-ai/XVERSE-7B/blob/main/LICENSE) 开源协议，使用 XVERSE-7B-Chat 的模型权重则需要遵循[模型许可协议](https://github.com/xverse-ai/XVERSE-7B/blob/main/MODEL_LICENSE.pdf)。
XVERSE-7B-Chat 模型权重对学术研究**完全开放**，并且支持**免费商用**。如需申请商业许可证，请填写【[申请表](https://chat.xverse.cn/home/business.html)】，如有其他问题或合作，请联系 <[email protected]>。
## Open Source License
The use of the source code in this repository must follow the [Apache-2.0](https://github.com/xverse-ai/XVERSE-7B/blob/main/LICENSE) open-source license, while the use of the model weights of XVERSE-7B-Chat needs to adhere to the [Model License Agreement](https://github.com/xverse-ai/XVERSE-7B/blob/main/MODEL_LICENSE.pdf).
The XVERSE-7B-Chat model weights are **fully open** to academic research and support **free commercial use**. To apply for a commercial license, please fill in the [application form](https://chat.xverse.cn/home/business.html). For other questions or collaborations, please contact <[email protected]>.
|
trinhxuankhai/external_vehicle_rewrite | trinhxuankhai | 2024-03-27T14:06:13Z | 1 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:Qwen/Qwen-VL-Chat",
"base_model:adapter:Qwen/Qwen-VL-Chat",
"region:us"
]
| null | 2024-03-27T14:05:36Z | ---
library_name: peft
base_model: Qwen/Qwen-VL-Chat
---
# 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. -->
- **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
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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
- **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. -->
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#### 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]
### Framework versions
- PEFT 0.8.2 |
trinhxuankhai/external_vehicle_action | trinhxuankhai | 2024-03-27T14:03:46Z | 1 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:Qwen/Qwen-VL-Chat",
"base_model:adapter:Qwen/Qwen-VL-Chat",
"region:us"
]
| null | 2024-03-27T14:03:12Z | ---
library_name: peft
base_model: Qwen/Qwen-VL-Chat
---
# 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. -->
- **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]
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## Uses
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### Downstream Use [optional]
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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
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Summary
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[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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## Glossary [optional]
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### Framework versions
- PEFT 0.8.2 |
trinhxuankhai/external_vehicle_location | trinhxuankhai | 2024-03-27T14:02:44Z | 1 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:Qwen/Qwen-VL-Chat",
"base_model:adapter:Qwen/Qwen-VL-Chat",
"region:us"
]
| null | 2024-03-27T14:02:06Z | ---
library_name: peft
base_model: Qwen/Qwen-VL-Chat
---
# 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. -->
- **Developed by:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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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
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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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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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
<!-- This should link to a Dataset Card if possible. -->
[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]
- **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]
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[More Information Needed]
#### Software
[More Information Needed]
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**BibTeX:**
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## More Information [optional]
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## Model Card Contact
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### Framework versions
- PEFT 0.8.2 |
magjico/cartpole-v1 | magjico | 2024-03-27T14:02:18Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-03-17T16:01:47Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 428.70 +/- 66.01
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
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
|
xverse/XVERSE-65B-Chat-GPTQ-Int8 | xverse | 2024-03-27T14:01:33Z | 2 | 0 | transformers | [
"transformers",
"xverse",
"text-generation",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"8-bit",
"gptq",
"region:us"
]
| text-generation | 2024-03-26T07:59:51Z | ---
license: apache-2.0
inference: false
---
# XVERSE-65B-Chat-GPTQ-Int8
## 更新信息
- **[2024/03/25]** 发布XVERSE-65B-Chat-GPTQ-Int8量化模型，支持vLLM推理XVERSE-65B-Chat量化模型。
- **[2023/12/08]** 发布 **XVERSE-65B-2** 底座模型，该模型在前一版本的基础上进行了 **Continual Pre-Training**，训练总 token 量达到 **3.2** 万亿；模型各方面的能力均得到提升，尤其是数学和代码能力，在 GSM8K 上提升 **20**%，HumanEval 上提升 **41**%。
- **[2023/11/29]** 更新模型架构及更多底座数据的相关信息。
- **[2023/11/24]** 更新预训练数据的相关信息。
- **[2023/11/06]** 发布 65B 尺寸的 XVERSE-65B 底座模型。
## Update Information
- **[2024/03/25]** Release the XVERSE-65B-Chat-GPTQ-Int8 quantification model, supporting vLLM inference for the XVERSE-65B-Chat quantification model.
- **[2023/12/08]** Released the **XVERSE-65B-2** base model. This model builds upon its predecessor through **Continual Pre-Training**, reaching a total training volume of **3.2** trillion tokens. It exhibits enhancements in all capabilities, particularly in mathematics and coding skills, with a **20%** improvement on the GSM8K benchmark and a **41%** increase on HumanEval.
- **[2023/11/29]** Update model architecture and additional pre-training data information.
- **[2023/11/24]** Update the related information of the pre-training data.
- **[2023/11/06]** Released the XVERSE-65B base model.
## 模型介绍
**XVERSE-65B** 是由深圳元象科技自主研发的支持多语言的大语言模型（Large Language Model），参数规模为 650 亿，本次开源的模型为底座模型 **XVERSE-65B**，主要特点如下：
- **模型结构**：XVERSE-65B 使用主流 Decoder-only 的标准 Transformer 网络结构，支持 16K 的上下文长度（Context Length），能满足更长的多轮对话、知识问答与摘要等需求，模型应用场景更广泛。
- **训练数据**：构建了 2.6 万亿 token 的高质量、多样化的数据对模型进行充分训练，包含中、英、俄、西等 40 多种语言，通过精细化设置不同类型数据的采样比例，使得中英两种语言表现优异，也能兼顾其他语言效果。
- **分词**：基于 BPE（Byte-Pair Encoding）算法，使用上百 GB 语料训练了一个词表大小为 100,534 的分词器，能够同时支持多语言，而无需额外扩展词表。
- **训练框架**：训练中采用 FlashAttention2 加速计算，3D 并行基础上采用虚拟流水线（virtual pipeline）技术，降低较长流水线和 16k 上下文窗口产生的过高气泡率，在千卡集群的峰值算力利用率达到业界前列。同时通过集群基础设施运营、资源调度、训练框架和调度平台协同等持续优化，打造出高稳定、低中断、强容错的训练系统，将每周有效训练率提升至 98.6%。
**XVERSE-65B**的模型大小、架构和学习率如下：
| params | d_model | n_heads | n_layers | d_ff | learning rate |
|:------:|:-------:|:-------:|:--------:|:-----:|:-------------:|
| 65B | 8192 | 64 | 80 | 22016 | 1.5e−4 |
## Model Introduction
**XVERSE-65B** is a multilingual large language model, independently developed by Shenzhen Yuanxiang Technology. The models released this time is the base model **XVERSE-65B**. Its key features are as follows:
- **Model Structure**: XVERSE-65B uses the mainstream Decoder-only Transformer network structure, supports 16k context length, which can meet the need of longer multi-round dialogues, knowledge question-answering, and summarization. This makes the model more versatile in application scenarios.
- **Training Data**: The model has been thoroughly trained on a diversified and high-quality dataset consisting of 2.6 trillion of tokens, including more than 40 languages such as Chinese, English, Russian, and Spanish. The sampling ratio of different types of data is finely set, which makes the performance of Chinese and English excellent, and also takes into account the effect of other languages.
- **Tokenization**: Based on the BPE (Byte-Pair Encoding) algorithm, a tokenizer with a vocabulary size of 100,534 has been trained using hundreds of gigabytes of language data. This tokenizer is capable of supporting multilingual without the need for additional vocabulary expansion.
- **Training Framework**: The training utilizes FlashAttention2 for accelerated computation, and on top of 3D parallelism, virtual pipeline technology is applied to reduce the excessive bubble rate caused by longer pipelines and 16k context windows. This achieves a peak computational efficiency within the industry-leading range in the petaflop-scale cluster. Concurrently, through continuous optimization of cluster infrastructure operations, resource scheduling, training frameworks, and the scheduling platform, a highly stable, low-interruption, and robust fault-tolerant training system has been developed, enhancing the effective weekly training rate to 98.6%.
The models sizes, architectures and learning rate of **XVERSE-65B** are showed as follows:
| params | d_model | n_heads | n_layers | d_ff | learning rate |
|:------:|:-------:|:-------:|:--------:|:-----:|:-------------:|
| 65B | 8192 | 64 | 80 | 22016 | 1.5e−4 |
## 环境准备
我们建议您克隆[`vllm`](https://github.com/vllm-project/vllm.git)并按照官方指南进行安装。
## Environment Setup
We advise you to clone [`vllm`](https://github.com/vllm-project/vllm.git) and install it following the official guide.
## 使用方法
由于上传的safetensors文件大小超出50GB的最大文件限制，因此我们将safetensors文件切分为3个，因此您可以将它们连接起来以获得整个文件：
```bash
cat gptq_model-8bit-128g.safetensors.* > gptq_model-8bit-128g.safetensors
```
我们演示了如何使用 vLLM 来运行XVERSE-65B-Chat-GPTQ-Int8量化模型:
```python
from vllm import LLM, SamplingParams
model_dir = "xverse/XVERSE-65B-Chat-GPTQ-Int8/"
# Create an LLM.
llm = LLM(model_dir,
trust_remote_code=True)
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.85, top_p=0.85, max_tokens=2048, repetition_penalty=1.1)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
prompts = ["Human: 请你写一篇关于环保的文章，题材是从个人做起。\n\nAssistant: ",]
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Generated text:\n{generated_text}")
```
## Usage
Due to the uploaded safetensors file size exceeding the maximum file limit of 50GB,
we have divided the safetensors file into three parts, so you can connect them together to obtain the entire file:
```bash
cat gptq_model-8bit-128g.safetensors.* > gptq_model-8bit-128g.safetensors
```
We demonstrated how to use vLLM to run the XVERSE-65B-Chat-GPTQ-Int8 quantization model:
```python
from vllm import LLM, SamplingParams
model_dir = "xverse/XVERSE-65B-Chat-GPTQ-Int8/"
# Create an LLM.
llm = LLM(model_dir,
trust_remote_code=True)
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.85, top_p=0.85, max_tokens=2048, repetition_penalty=1.1)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
prompts = ["Human: 请你写一篇关于环保的文章，题材是从个人做起。\n\nAssistant: ",]
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Generated text:\n{generated_text}")
```
## 局限性与免责申明
XVERSE-65B 与其他所有 LLM 一样，在某些情况下可能会产生不准确、有偏见或其他令人反感的内容。因此，请谨慎使用模型生成的内容，请勿将生成的有害内容进行传播，在部署任何 XVERSE-65B 的应用之前，开发人员应根据其具体应用对模型进行安全测试和调优。
我们强烈警告不要将 XVERSE-65B 模型用于制造或传播有害信息，或进行任何可能损害公众、国家、社会安全或违反法规的活动。如果使用 XVERSE-65B 模型产生任何问题，无论是数据安全问题、公共舆论风险，还是模型被误解、滥用、传播或不合规使用所引发的任何风险和问题，我们将不承担任何责任。
## Limitations and Disclaimer
Like all other Large Language Models (LLMs), XVERSE-65B may produce inaccurate, biased, or otherwise offensive content under certain circumstances. Therefore, please use the content generated by the model with caution and refrain from disseminating harmful content. Before deploying any application of XVERSE-65B, developers should conduct safety tests and optimization of the model according to its specific application.
We strongly warn against the use of the XVERSE-65B model for producing or spreading harmful information, or conducting any activities that might harm the public, national, or social security, or violate regulations. We assume no responsibility for any problems arising from the use of the XVERSE-65B model, whether it be data security issues, public opinion risks, or any risks and issues caused by misunderstanding, misuse, dissemination, or non-compliance with the model.
## 模型开源协议
使用本仓库的源码需要遵循 [Apache-2.0](https://github.com/xverse-ai/XVERSE-65B/blob/main/LICENSE) 开源协议，使用 XVERSE-65B 的模型权重则需要遵循[模型许可协议](https://github.com/xverse-ai/XVERSE-65B/blob/main/MODEL_LICENSE.pdf)。
XVERSE-65B 模型权重对学术研究**完全开放**，并且支持**免费商用**。如需申请商业许可证，请填写【[申请表](https://chat.xverse.cn/home/business.html)】，如有其他问题或合作，请联系 <[email protected]>。
## Open Source License
The use of the source code in this repository must follow the [Apache-2.0](https://github.com/xverse-ai/XVERSE-65B/blob/main/LICENSE) open-source license, while the use of the model weights of XVERSE-65B needs to adhere to the [Model License Agreement](https://github.com/xverse-ai/XVERSE-65B/blob/main/MODEL_LICENSE.pdf).
The XVERSE-65B model weights are **fully open** to academic research and support **free commercial use**. To apply for a commercial license, please fill in the [application form](https://chat.xverse.cn/home/business.html). For other questions or collaborations, please contact <[email protected]>.
|
minhah/videomae-base-finetuned-crema-d8-finetuned-elder-creama-d-pretuned | minhah | 2024-03-27T14:00:54Z | 61 | 0 | transformers | [
"transformers",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:minhah/videomae-base-finetuned-crema-d8",
"base_model:finetune:minhah/videomae-base-finetuned-crema-d8",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
]
| video-classification | 2024-03-27T05:59:15Z | ---
license: cc-by-nc-4.0
base_model: minhah/videomae-base-finetuned-crema-d8
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-crema-d8-finetuned-elder-creama-d-pretuned
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. -->
# videomae-base-finetuned-crema-d8-finetuned-elder-creama-d-pretuned
This model is a fine-tuned version of [minhah/videomae-base-finetuned-crema-d8](https://huggingface.co/minhah/videomae-base-finetuned-crema-d8) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6702
- Accuracy: 0.3389
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1440
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6909 | 0.1 | 145 | 1.6484 | 0.3352 |
| 1.5944 | 1.1 | 290 | 1.6735 | 0.2798 |
| 1.5776 | 2.1 | 435 | 1.6654 | 0.3212 |
| 1.6768 | 3.1 | 580 | 1.7330 | 0.1858 |
| 1.6108 | 4.1 | 725 | 1.6919 | 0.2504 |
| 1.5103 | 5.1 | 870 | 1.6524 | 0.2805 |
| 1.5447 | 6.1 | 1015 | 1.6767 | 0.3086 |
| 1.5237 | 7.1 | 1160 | 1.7329 | 0.2553 |
| 1.4397 | 8.1 | 1305 | 1.7293 | 0.2475 |
| 1.4544 | 9.09 | 1440 | 1.7368 | 0.2518 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Subsets and Splits