modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
sreddy109/test
|
sreddy109
| 2024-05-15T19:56:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T19:56:36Z |
---
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]
|
bitext/Mistral-7B-Retail
|
bitext
| 2024-05-15T19:55:17Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"axolotl",
"generated_from_trainer",
"text-generation-inference",
"conversational",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-03T22:10:41Z |
---
license: apache-2.0
tags:
- axolotl
- generated_from_trainer
- text-generation-inference
base_model: mistralai/Mistral-7B-Instruct-v0.2
model_type: mistral
pipeline_tag: text-generation
model-index:
- name: Mistral-7B-Retail-v1
results: []
---
# Mistral-7B-Retail-v1
## Model Description
This model, named "Mistral-7B-Retail-v1," is a specially adjusted version of the [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). It is fine-tuned to manage questions and provide answers related to retail services.
## Intended Use
- **Recommended applications**: This model is perfect for use in retail environments. It can be integrated into customer service chatbots or help systems to provide real-time responses to common retail-related inquiries.
- **Out-of-scope**: This model should not be used for medical, legal, or any serious safety-related purposes.
## Usage Example
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("bitext-llm/Mistral-7B-Retail-v1")
tokenizer = AutoTokenizer.from_pretrained("bitext-llm/Mistral-7B-Retail-v1")
inputs = tokenizer("<s>[INST] How can I return a purchased item? [/INST]", return_tensors="pt")
outputs = model.generate(inputs['input_ids'], max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Model Architecture
The "Mistral-7B-Retail-v1" uses the `MistralForCausalLM` structure with a `LlamaTokenizer`. It maintains the setup of the base model but is enhanced to better respond to retail-related questions.
## Training Data
This model was trained with a dataset specifically designed for retail-related question and answer interactions. The dataset encompasses a comprehensive range of retail intents, ensuring the model is trained to handle diverse customer inquiries and scenarios. It includes 46 distinct intents such as `add_product`, `availability_in_store`, `cancel_order`, `pay`, `refund_policy`, `track_order`, `use_app`, and many more, reflecting common retail transactions and customer service interactions. Each intent contains 1000 examples, which helps in creating responses across various retail situations.
This extensive training dataset ensures that the model can understand and respond to a wide array of retail-related queries, providing support in customer service applications. The dataset follows a structured approach, similar to other datasets published on Hugging Face, but is specifically tailored to cater to the customer support sector: [bitext/Bitext-customer-support-llm-chatbot-training-dataset](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset)
## Training Procedure
### Hyperparameters
- **Optimizer**: AdamW
- **Learning Rate**: 0.0002
- **Epochs**: 1
- **Batch Size**: 8
- **Gradient Accumulation Steps**: 4
- **Maximum Sequence Length**: 1024 tokens
### Environment
- **Transformers Version**: 4.40.0.dev0
- **Framework**: PyTorch 2.2.1+cu121
- **Tokenizers**: Tokenizers 0.15.0
## Limitations and Bias
- The model is fine-tuned on a domain-specific dataset and may not perform well outside the scope of retail advice.
- Users should be aware of potential biases in the training data, as the model's responses may inadvertently reflect these biases. This model has been trained with a dataset that answers general retail questions, so potential biases may exist for specific use cases.
## Ethical Considerations
This model should be used responsibly, considering ethical implications of automated financial advice. As it is a base model for this retail field, it is crucial to ensure that the model's advice complements human expertise and adheres to relevant retail regulations.
## Acknowledgments
This model was developed by the Bitext and trained on infrastructure provided by Bitext.
## License
This model, "Mistral-7B-Retail-v1", is licensed under the Apache License 2.0 by Bitext Innovations International, Inc. This open-source license allows for free use, modification, and distribution of the model but requires that proper credit be given to Bitext.
### Key Points of the Apache 2.0 License
- **Permissibility**: Users are allowed to use, modify, and distribute this software freely.
- **Attribution**: You must provide proper credit to Bitext Innovations International, Inc. when using this model, in accordance with the original copyright notices and the license.
- **Patent Grant**: The license includes a grant of patent rights from the contributors of the model.
- **No Warranty**: The model is provided "as is" without warranties of any kind.
You may view the full license text at [Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0).
This licensing ensures the model can be used widely and freely while respecting the intellectual contributions of Bitext. For more detailed information or specific legal questions about using this license, please refer to the official license documentation linked above.
|
RichardErkhov/froggeric_-_WestLake-10.7B-v2-4bits
|
RichardErkhov
| 2024-05-15T19:55:09Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-05-15T19:49:23Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
WestLake-10.7B-v2 - bnb 4bits
- Model creator: https://huggingface.co/froggeric/
- Original model: https://huggingface.co/froggeric/WestLake-10.7B-v2/
Original model description:
---
base_model:
- senseable/WestLake-7B-v2
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
language:
- en
---
# WestLake-10.7B-v2: Role-Play & Text Generation Specialist Model
[GGUF version available here](https://huggingface.co/froggeric/WestLake-10.7B-v2-GGUF)\
EXL2 versions available here:
[3.3bpw](https://huggingface.co/StopTryharding/WestLake-10.7B-v2-exl2-3.3) / [4.0bpw](https://huggingface.co/StopTryharding/WestLake-10.7B-v2-exl2-4.0) / [5.0bpw](https://huggingface.co/StopTryharding/WestLake-10.7B-v2-exl2-5.0) / [6.0bpw](https://huggingface.co/StopTryharding/WestLake-10.7B-v2-exl2-6.0) / [8.0bpw](https://huggingface.co/StopTryharding/WestLake-10.7B-v2-exl2-8.0)
This is my first viable self-merge of the fantastic WestLake-7B-v2 model, obtained after more than 12 rounds of testing different
merge configurations. In my [LLM Creativity Benchmark](https://huggingface.co/datasets/froggeric/creativity), it greatly improves over the original 7B model, and ranks between miqu-1-120b
and goliath-120b! I would describe the improvements as a better writing style, with more details. It has a bit more difficulties following instructions, but not by much.
It is also the first model I have tested to obtain a perfect score with the following test:
```
Write a sequence of nominal groups that flow into one another, using the following rules:
- each nominal group is made of exactly 3 words
- the first word of each nominal group must be the last word of the previous nominal group
- the first word of the first nominal group is: "ball"
- the last word of the last nominal group is: "stone"
- there must be a theme, of your choosing, pertaining to all nominal groups
- there must be exactly 7 nominal groups, leading from the first word (ball) to the last word (stone)
- a word already used at the beginning and end of a nominal group cannot be reused
Present your solution as a list numbered with roman numerals.
Finally, explain why you chose your specific theme.
```
## Usage
* Base model: senseable/WestLake-7B-v2 based of Mistral-7B-v0.1
* Context size: **8192** (even though Mistral-7B is 32k, WestLake was trained with 8k, and using a larger context is likely to cause problems)
* Prompt format: in general, Mistral based models are able to understand many prompt formats, but the following produce the best results, and are recommended (in order of preference)
- **Alpaca** (reported by senseable as working better than ChatML, and confirmed by me)
- ChatML (used during WestLake training)
- Mistral Instruct (original format from Mistral-7B)
- Zephyr (variant of ChatML which I have found to sometimes produce better results)
## Merge Details
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).\
This model was merged using the passthrough merge method.\
The following models were included in the merge:
* [senseable/WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2)
The following YAML configuration was used to produce this model:
```yaml
dtype: float16
merge_method: passthrough
slices:
- sources:
- model: senseable/WestLake-7B-v2
layer_range: [0,9]
- sources:
- model: senseable/WestLake-7B-v2
layer_range: [5,14]
- sources:
- model: senseable/WestLake-7B-v2
layer_range: [10,19]
- sources:
- model: senseable/WestLake-7B-v2
layer_range: [15,24]
- sources:
- model: senseable/WestLake-7B-v2
layer_range: [20,32]
```
---
# Original model card: Westlake-7Bv2: Role-Play & Text Generation Specialist Model
**Update Notes:**
*Version 2 trained 1 additional epoch cycle for 3 total*
Welcome to the documentation of Westlake-7B, a cutting-edge language model designed for exceptional role-play and text generation tasks. This README file aims to provide an overview of our capabilities, usage guidelines, and potential applications.
## About Westlake-7Bv2
Westlake-7B is built upon a vast corpus of diverse texts, enabling it to generate contextually relevant responses in various scenarios. With its impressive size of 7 billion parameters, this model excels at understanding nuances in language and producing creative outputs.
### Key Features
1. **Role-Play**: Westlake-7Bv2 can seamlessly adapt to different character personas and engage in dynamic conversations while maintaining consistency throughout the interaction. It can generate believable dialogues across various genres, including fiction, non-fiction, historical events, or even fantasy worlds.
2. **Text Generation**: This model is proficient at generating original content such as stories, poems, essays, news articles, and more. Its ability to capture the essence of different writing styles makes it an ideal tool for creative writers seeking inspiration or assistance in their projects.
3. **Contextual Understanding**: Westlake-7B's extensive training allows it to comprehend complex contexts and generate responses that align with given situations. It can handle multiple topics simultaneously, making it versatile across various applications.
4. **Continuous Learning**: As a language model, Westlake-7B continuously improves its performance through ongoing training on new data sets. This ensures its capabilities remain up-to-date and relevant in an ever-evolving world of communication.
## Usage Guidelines
To utilize Westlake-7Bv2 for your projects or experiments, follow these steps:
1. **Prompting**: Provide clear and concise prompts that outline the desired role-play scenario or text generation task. The quality of output depends heavily on the clarity and relevance of input instructions.
2. **Feedback Loop**: For optimal results, consider incorporating a feedback loop into your application to refine generated outputs based on user preferences or additional contextual information. This iterative process can significantly enhance the model's performance in specific domains.
3. **Ethical Considerations**: As with any AI system, ensure responsible usage of Westlake-7B by avoiding harmful content generation or misuse of its capabilities.
## Potential Applications
Westlake-7Bv2's versatility makes it suitable for various applications across different industries:
1. **Creative Writing**: Assist authors in generating new ideas, expanding storylines, or even completing drafts by providing creative suggestions and textual content.
2. **Education**: Enhance language learning platforms with interactive role-play scenarios to improve students' communication skills and cultural understanding.
3. **Gaming**: Integrate Westlake-7B into game engines for dynamic non-player character interactions or generating unique questlines based on player choices.
4. **Customer Support**: Leverage the model's conversational abilities to create chatbots capable of handling complex queries and providing personalized assistance.
5. **Social Media**: Develop applications that generate engaging content such as captions, status updates, or even entire posts tailored to users' preferences and interests.
|
emilykang/medprob-biochemistry
|
emilykang
| 2024-05-15T19:53:50Z | 147 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-06T11:05:47Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mergekit-community/mergekit-passthrough-dmirwnd
|
mergekit-community
| 2024-05-15T19:53:31Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:NousResearch/Hermes-2-Pro-Llama-3-8B",
"base_model:merge:NousResearch/Hermes-2-Pro-Llama-3-8B",
"base_model:hfl/llama-3-chinese-8b-instruct-v2",
"base_model:merge:hfl/llama-3-chinese-8b-instruct-v2",
"base_model:shenzhi-wang/Llama3-8B-Chinese-Chat",
"base_model:merge:shenzhi-wang/Llama3-8B-Chinese-Chat",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-15T19:48:34Z |
---
base_model:
- hfl/llama-3-chinese-8b-instruct-v2
- NousResearch/Hermes-2-Pro-Llama-3-8B
- shenzhi-wang/Llama3-8B-Chinese-Chat
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* [hfl/llama-3-chinese-8b-instruct-v2](https://huggingface.co/hfl/llama-3-chinese-8b-instruct-v2)
* [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B)
* [shenzhi-wang/Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: shenzhi-wang/Llama3-8B-Chinese-Chat
layer_range: [0, 28]
- sources:
- model: hfl/llama-3-chinese-8b-instruct-v2
layer_range: [5, 28]
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- model: NousResearch/Hermes-2-Pro-Llama-3-8B
layer_range: [28, 32]
merge_method: passthrough
dtype: bfloat16
```
|
Litzy619/G0515HMA1H
|
Litzy619
| 2024-05-15T19:51:31Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:google/gemma-2b",
"base_model:finetune:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-05-15T19:03:13Z |
---
license: gemma
base_model: google/gemma-2b
tags:
- generated_from_trainer
model-index:
- name: G0515HMA1H
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. -->
# G0515HMA1H
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1342
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2177 | 0.09 | 10 | 2.8976 |
| 2.6702 | 0.18 | 20 | 2.2910 |
| 1.8959 | 0.27 | 30 | 1.4043 |
| 1.043 | 0.36 | 40 | 0.5929 |
| 0.375 | 0.45 | 50 | 0.2097 |
| 0.1786 | 0.54 | 60 | 0.1591 |
| 0.1553 | 0.63 | 70 | 0.1511 |
| 0.1598 | 0.73 | 80 | 0.1548 |
| 0.1472 | 0.82 | 90 | 0.1499 |
| 0.1475 | 0.91 | 100 | 0.1484 |
| 0.1495 | 1.0 | 110 | 0.1482 |
| 0.1437 | 1.09 | 120 | 0.1490 |
| 0.1448 | 1.18 | 130 | 0.1472 |
| 0.1452 | 1.27 | 140 | 0.1460 |
| 0.1482 | 1.36 | 150 | 0.1459 |
| 0.143 | 1.45 | 160 | 0.1478 |
| 0.1435 | 1.54 | 170 | 0.1461 |
| 0.1448 | 1.63 | 180 | 0.1441 |
| 0.1461 | 1.72 | 190 | 0.1482 |
| 0.1451 | 1.81 | 200 | 0.1454 |
| 0.1462 | 1.9 | 210 | 0.1447 |
| 0.1459 | 1.99 | 220 | 0.1433 |
| 0.1419 | 2.08 | 230 | 0.1411 |
| 0.1366 | 2.18 | 240 | 0.1400 |
| 0.1371 | 2.27 | 250 | 0.1432 |
| 0.1391 | 2.36 | 260 | 0.1385 |
| 0.1356 | 2.45 | 270 | 0.1383 |
| 0.1343 | 2.54 | 280 | 0.1363 |
| 0.1326 | 2.63 | 290 | 0.1350 |
| 0.1303 | 2.72 | 300 | 0.1343 |
| 0.1341 | 2.81 | 310 | 0.1342 |
| 0.1328 | 2.9 | 320 | 0.1342 |
| 0.1337 | 2.99 | 330 | 0.1342 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.0
|
duyntnet/Einstein-v6-7B-imatrix-GGUF
|
duyntnet
| 2024-05-15T19:51:11Z | 11 | 0 |
transformers
|
[
"transformers",
"gguf",
"imatrix",
"Einstein-v6-7B",
"text-generation",
"en",
"license:other",
"region:us",
"conversational"
] |
text-generation
| 2024-05-15T17:42:10Z |
---
license: other
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- transformers
- gguf
- imatrix
- Einstein-v6-7B
---
Quantizations of https://huggingface.co/Weyaxi/Einstein-v6-7B
# From original readme
## 💬 Prompt Template
You can use this prompt template while using the model:
### ChatML
```
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>
```
This prompt template is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are helpful AI asistant."},
{"role": "user", "content": "Hello!"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
|
emilykang/Phi_medner-surgery_lora
|
emilykang
| 2024-05-15T19:48:33Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:microsoft/phi-2",
"base_model:adapter:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2024-05-15T18:26:45Z |
---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/phi-2
datasets:
- generator
model-index:
- name: Phi_medner-surgery_lora
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Phi_medner-surgery_lora
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|
Dhanu459/LLama3_8B_MarketingTemplate_M12_Lora
|
Dhanu459
| 2024-05-15T19:48:01Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2024-05-15T19:43:46Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** Dhanu459
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
themex1380/Persian-Therapist-Llama-3-8B
|
themex1380
| 2024-05-15T19:46:07Z | 28 | 2 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-15T19:39:42Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** themex1380
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
emilykang/medprob-anatomy
|
emilykang
| 2024-05-15T19:43:05Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-06T11:01:41Z |
---
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]
### 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]
|
RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf
|
RichardErkhov
| 2024-05-15T19:42:49Z | 14 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T17:30:37Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Not-WizardLM-2-7B - GGUF
- Model creator: https://huggingface.co/amazingvince/
- Original model: https://huggingface.co/amazingvince/Not-WizardLM-2-7B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Not-WizardLM-2-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q2_K.gguf) | Q2_K | 2.53GB |
| [Not-WizardLM-2-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [Not-WizardLM-2-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [Not-WizardLM-2-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [Not-WizardLM-2-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [Not-WizardLM-2-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q3_K.gguf) | Q3_K | 3.28GB |
| [Not-WizardLM-2-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [Not-WizardLM-2-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [Not-WizardLM-2-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [Not-WizardLM-2-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q4_0.gguf) | Q4_0 | 3.83GB |
| [Not-WizardLM-2-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [Not-WizardLM-2-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [Not-WizardLM-2-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q4_K.gguf) | Q4_K | 4.07GB |
| [Not-WizardLM-2-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [Not-WizardLM-2-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q4_1.gguf) | Q4_1 | 4.24GB |
| [Not-WizardLM-2-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q5_0.gguf) | Q5_0 | 4.65GB |
| [Not-WizardLM-2-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [Not-WizardLM-2-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q5_K.gguf) | Q5_K | 4.78GB |
| [Not-WizardLM-2-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [Not-WizardLM-2-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q5_1.gguf) | Q5_1 | 5.07GB |
| [Not-WizardLM-2-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q6_K.gguf) | Q6_K | 5.53GB |
| [Not-WizardLM-2-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-gguf/blob/main/Not-WizardLM-2-7B.Q8_0.gguf) | Q8_0 | 7.17GB |
Original model description:
---
license: apache-2.0
---
# amazingvince/Not-WizardLM-2-7B
<a href="https://colab.research.google.com/gist/pszemraj/d3d74ceab942722b49188606785e2bfd/not-wizardlm-2-7b-inference.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
Included is code ripped from fastchat with the expected chat templating.
Also wiz.pdf is a pdf of the github blog showing the apache 2 release.
Link to wayback machine included: https://web.archive.org/web/20240415221214/https://wizardlm.github.io/WizardLM2/
## example
```python
import dataclasses
from enum import auto, Enum
from typing import List, Tuple, Any
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
@dataclasses.dataclass
class Conversation:
"""A class that keeps all conversation history."""
system: str
roles: List[str]
messages: List[List[str]]
offset: int
sep_style: SeparatorStyle = SeparatorStyle.SINGLE
sep: str = "###"
sep2: str = None
# Used for gradio server
skip_next: bool = False
conv_id: Any = None
def get_prompt(self):
if self.sep_style == SeparatorStyle.SINGLE:
ret = self.system
for role, message in self.messages:
if message:
ret += self.sep + " " + role + ": " + message
else:
ret += self.sep + " " + role + ":"
return ret
elif self.sep_style == SeparatorStyle.TWO:
seps = [self.sep, self.sep2]
ret = self.system + seps[0]
for i, (role, message) in enumerate(self.messages):
if message:
ret += role + ": " + message + seps[i % 2]
else:
ret += role + ":"
return ret
else:
raise ValueError(f"Invalid style: {self.sep_style}")
def append_message(self, role, message):
self.messages.append([role, message])
def to_gradio_chatbot(self):
ret = []
for i, (role, msg) in enumerate(self.messages[self.offset:]):
if i % 2 == 0:
ret.append([msg, None])
else:
ret[-1][-1] = msg
return ret
def copy(self):
return Conversation(
system=self.system,
roles=self.roles,
messages=[[x, y] for x, y in self.messages],
offset=self.offset,
sep_style=self.sep_style,
sep=self.sep,
sep2=self.sep2,
conv_id=self.conv_id)
def dict(self):
return {
"system": self.system,
"roles": self.roles,
"messages": self.messages,
"offset": self.offset,
"sep": self.sep,
"sep2": self.sep2,
"conv_id": self.conv_id,
}
conv = Conversation(
system="A chat between a curious user and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the user's questions.",
roles=("USER", "ASSISTANT"),
messages=[],
offset=0,
sep_style=SeparatorStyle.TWO,
sep=" ",
sep2="</s>",
)
conv.append_message(conv.roles[0], "Why would Microsoft take this down?")
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
result = model.generate(**inputs, max_new_tokens=1000)
generated_ids = result[0]
generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True)
print(generated_text)
```
|
AlekseiPravdin/Seamaiiza-7B-v1
|
AlekseiPravdin
| 2024-05-15T19:42:41Z | 6 | 2 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"SanjiWatsuki/Kunoichi-DPO-v2-7B",
"AlekseiPravdin/KSI-RP-NSK-128k-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-15T19:38:48Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- SanjiWatsuki/Kunoichi-DPO-v2-7B
- AlekseiPravdin/KSI-RP-NSK-128k-7B
---
# Seamaiiza-7B-v1
Seamaiiza-7B-v1 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [AlekseiPravdin/KSI-RP-NSK-128k-7B](https://huggingface.co/AlekseiPravdin/KSI-RP-NSK-128k-7B)
* [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: AlekseiPravdin/KSI-RP-NSK-128k-7B
layer_range: [0, 32]
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [0, 32]
merge_method: slerp
base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
parameters:
t:
- filter: self_attn
value: [0, 0.53, 0.35, 0.7, 1]
- filter: mlp
value: [1, 0.57, 0.75, 0.33, 0]
- value: 0.53
dtype: bfloat16
```
|
RichardErkhov/Undi95_-_Meta-Llama-3-8B-hf-8bits
|
RichardErkhov
| 2024-05-15T19:41:38Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-05-15T19:32:16Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Meta-Llama-3-8B-hf - bnb 8bits
- Model creator: https://huggingface.co/Undi95/
- Original model: https://huggingface.co/Undi95/Meta-Llama-3-8B-hf/
Original model description:
---
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: other
license_name: llama3
license_link: LICENSE
extra_gated_prompt: >-
### META LLAMA 3 COMMUNITY LICENSE AGREEMENT
Meta Llama 3 Version Release Date: April 18, 2024
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Llama Materials set forth herein.
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1. License Rights and Redistribution.
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the State of California without regard to choice of law principles, and the UN Convention on Contracts
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exclusive jurisdiction of any dispute arising out of this Agreement.
### Meta Llama 3 Acceptable Use Policy
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access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of
this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)
#### Prohibited Uses
We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow
others to use, Meta Llama 3 to:
1. Violate the law or others’ rights, including to:
1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
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7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
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1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
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1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
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6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI system
Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation
of this Policy through one of the following means:
* Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)
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developers.facebook.com/llama_output_feedback
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extra_gated_fields:
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By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
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---
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B, for use with transformers and with the original `llama3` codebase.
### Use with transformers
See the snippet below for usage with Transformers:
```python
>>> import transformers
>>> import torch
>>> model_id = "meta-llama/Meta-Llama-3-8B"
>>> pipeline = transformers.pipeline(
"text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
)
>>> pipeline("Hey how are you doing today?")
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3).
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B --include "original/*" --local-dir Meta-Llama-3-8B
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 8B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
|
dannys160/pole-of-da-cart-2k24
|
dannys160
| 2024-05-15T19:40:57Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-05-15T19:40:48Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: pole-of-da-cart-2k24
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
|
emilykang/medprob-anatomy_lora
|
emilykang
| 2024-05-15T19:38:48Z | 26 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] | null | 2024-05-06T19:36:53Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
datasets:
- generator
model-index:
- name: medprob-anatomy_lora
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. -->
# medprob-anatomy_lora
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|
BilalMuftuoglu/beit-base-patch16-224-55-fold1
|
BilalMuftuoglu
| 2024-05-15T19:35:48Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-05-15T18:55:25Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8481012658227848
---
<!-- 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. -->
# beit-base-patch16-224-fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7241
- Accuracy: 0.8481
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log | 0.8571 | 3 | 0.8050 | 0.4557 |
| No log | 2.0 | 7 | 0.7151 | 0.5696 |
| 0.8103 | 2.8571 | 10 | 0.6822 | 0.5570 |
| 0.8103 | 4.0 | 14 | 0.6408 | 0.5696 |
| 0.8103 | 4.8571 | 17 | 0.6244 | 0.6709 |
| 0.6583 | 6.0 | 21 | 0.5893 | 0.6709 |
| 0.6583 | 6.8571 | 24 | 0.5877 | 0.6329 |
| 0.6583 | 8.0 | 28 | 0.5752 | 0.6835 |
| 0.5912 | 8.8571 | 31 | 0.5826 | 0.6456 |
| 0.5912 | 10.0 | 35 | 0.5469 | 0.6835 |
| 0.5912 | 10.8571 | 38 | 0.6173 | 0.6582 |
| 0.5301 | 12.0 | 42 | 0.5151 | 0.6962 |
| 0.5301 | 12.8571 | 45 | 0.5105 | 0.6962 |
| 0.5301 | 14.0 | 49 | 0.5489 | 0.7089 |
| 0.4703 | 14.8571 | 52 | 0.5725 | 0.6835 |
| 0.4703 | 16.0 | 56 | 0.5560 | 0.6962 |
| 0.4703 | 16.8571 | 59 | 0.5824 | 0.6709 |
| 0.4189 | 18.0 | 63 | 0.5401 | 0.7468 |
| 0.4189 | 18.8571 | 66 | 0.5147 | 0.7722 |
| 0.3741 | 20.0 | 70 | 0.4864 | 0.7595 |
| 0.3741 | 20.8571 | 73 | 0.5272 | 0.7342 |
| 0.3741 | 22.0 | 77 | 0.4914 | 0.7468 |
| 0.387 | 22.8571 | 80 | 0.5658 | 0.7468 |
| 0.387 | 24.0 | 84 | 0.4662 | 0.7722 |
| 0.387 | 24.8571 | 87 | 0.4376 | 0.7848 |
| 0.3502 | 26.0 | 91 | 0.5367 | 0.7722 |
| 0.3502 | 26.8571 | 94 | 0.5490 | 0.7342 |
| 0.3502 | 28.0 | 98 | 0.7163 | 0.7722 |
| 0.3148 | 28.8571 | 101 | 0.6005 | 0.7468 |
| 0.3148 | 30.0 | 105 | 0.6501 | 0.7722 |
| 0.3148 | 30.8571 | 108 | 0.5313 | 0.7975 |
| 0.2973 | 32.0 | 112 | 0.5466 | 0.7722 |
| 0.2973 | 32.8571 | 115 | 0.5731 | 0.8101 |
| 0.2973 | 34.0 | 119 | 0.6544 | 0.8101 |
| 0.2474 | 34.8571 | 122 | 0.6061 | 0.7848 |
| 0.2474 | 36.0 | 126 | 0.5816 | 0.7722 |
| 0.2474 | 36.8571 | 129 | 0.7161 | 0.7595 |
| 0.2033 | 38.0 | 133 | 0.6235 | 0.7848 |
| 0.2033 | 38.8571 | 136 | 0.7889 | 0.7595 |
| 0.2338 | 40.0 | 140 | 0.5943 | 0.7595 |
| 0.2338 | 40.8571 | 143 | 0.6170 | 0.7342 |
| 0.2338 | 42.0 | 147 | 0.6964 | 0.6962 |
| 0.2067 | 42.8571 | 150 | 0.7154 | 0.7468 |
| 0.2067 | 44.0 | 154 | 0.7675 | 0.7722 |
| 0.2067 | 44.8571 | 157 | 0.7766 | 0.7468 |
| 0.2133 | 46.0 | 161 | 0.9330 | 0.7848 |
| 0.2133 | 46.8571 | 164 | 0.6494 | 0.7975 |
| 0.2133 | 48.0 | 168 | 0.5709 | 0.7722 |
| 0.2004 | 48.8571 | 171 | 0.6462 | 0.8101 |
| 0.2004 | 50.0 | 175 | 0.6668 | 0.7722 |
| 0.2004 | 50.8571 | 178 | 0.6305 | 0.8101 |
| 0.188 | 52.0 | 182 | 0.7189 | 0.8228 |
| 0.188 | 52.8571 | 185 | 0.6853 | 0.7848 |
| 0.188 | 54.0 | 189 | 0.8040 | 0.8228 |
| 0.1623 | 54.8571 | 192 | 0.6958 | 0.8101 |
| 0.1623 | 56.0 | 196 | 0.6907 | 0.8101 |
| 0.1623 | 56.8571 | 199 | 0.6821 | 0.8101 |
| 0.1588 | 58.0 | 203 | 0.6534 | 0.8101 |
| 0.1588 | 58.8571 | 206 | 0.7192 | 0.8101 |
| 0.1607 | 60.0 | 210 | 0.7753 | 0.8228 |
| 0.1607 | 60.8571 | 213 | 0.8950 | 0.8101 |
| 0.1607 | 62.0 | 217 | 0.7904 | 0.8101 |
| 0.1767 | 62.8571 | 220 | 0.6973 | 0.8101 |
| 0.1767 | 64.0 | 224 | 0.6694 | 0.7975 |
| 0.1767 | 64.8571 | 227 | 0.6339 | 0.8101 |
| 0.1463 | 66.0 | 231 | 0.6530 | 0.8101 |
| 0.1463 | 66.8571 | 234 | 0.6142 | 0.8101 |
| 0.1463 | 68.0 | 238 | 0.6290 | 0.8228 |
| 0.1287 | 68.8571 | 241 | 0.6334 | 0.8354 |
| 0.1287 | 70.0 | 245 | 0.8059 | 0.8101 |
| 0.1287 | 70.8571 | 248 | 0.7241 | 0.8481 |
| 0.1323 | 72.0 | 252 | 0.6836 | 0.8481 |
| 0.1323 | 72.8571 | 255 | 0.6588 | 0.8228 |
| 0.1323 | 74.0 | 259 | 0.6598 | 0.8481 |
| 0.1042 | 74.8571 | 262 | 0.7139 | 0.8354 |
| 0.1042 | 76.0 | 266 | 0.7236 | 0.8354 |
| 0.1042 | 76.8571 | 269 | 0.6919 | 0.8354 |
| 0.1106 | 78.0 | 273 | 0.6568 | 0.8354 |
| 0.1106 | 78.8571 | 276 | 0.6556 | 0.8481 |
| 0.1348 | 80.0 | 280 | 0.6612 | 0.8354 |
| 0.1348 | 80.8571 | 283 | 0.6686 | 0.8228 |
| 0.1348 | 82.0 | 287 | 0.6705 | 0.8481 |
| 0.1352 | 82.8571 | 290 | 0.6776 | 0.8354 |
| 0.1352 | 84.0 | 294 | 0.6873 | 0.8354 |
| 0.1352 | 84.8571 | 297 | 0.6888 | 0.8354 |
| 0.1226 | 85.7143 | 300 | 0.6880 | 0.8354 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
pjchardt/emotion-endpoint-test
|
pjchardt
| 2024-05-15T19:35:43Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"emotion",
"endpoints-template",
"en",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-15T19:35:20Z |
---
language:
- en
tags:
- text-classification
- emotion
- endpoints-template
license: apache-2.0
datasets:
- emotion
metrics:
- Accuracy, F1 Score
---
# Fork of [bhadresh-savani/distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion)
|
PatrickHaller/tiny-python-stack-100k
|
PatrickHaller
| 2024-05-15T19:34:19Z | 147 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-15T19:24:33Z |
---
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. -->
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## Model Card Contact
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|
bartowski/Hermes-2-Theta-Llama-3-8B-exl2
|
bartowski
| 2024-05-15T19:25:09Z | 4 | 2 | null |
[
"Llama-3",
"instruct",
"finetune",
"chatml",
"DPO",
"RLHF",
"gpt4",
"synthetic data",
"distillation",
"function calling",
"json mode",
"axolotl",
"merges",
"text-generation",
"en",
"dataset:teknium/OpenHermes-2.5",
"base_model:NousResearch/Hermes-2-Pro-Llama-3-8B",
"base_model:finetune:NousResearch/Hermes-2-Pro-Llama-3-8B",
"region:us"
] |
text-generation
| 2024-05-15T19:25:06Z |
---
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
tags:
- Llama-3
- instruct
- finetune
- chatml
- DPO
- RLHF
- gpt4
- synthetic data
- distillation
- function calling
- json mode
- axolotl
- merges
model-index:
- name: Hermes-2-Pro-Llama-3-Instruct-8B-Merge
results: []
language:
- en
datasets:
- teknium/OpenHermes-2.5
widget:
- example_title: Hermes 2 Pro Llama-3 Instruct Merge
messages:
- role: system
content: >-
You are a sentient, superintelligent artificial general intelligence, here
to teach and assist me.
- role: user
content: >-
Write a short story about Goku discovering kirby has teamed up with Majin
Buu to destroy the world.
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of Hermes-2-Theta-Llama-3-8B
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.21">turboderp's ExLlamaV2 v0.0.21</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/NousResearch/Hermes-2-Theta-Llama-3-8B
## Prompt format
```
<|begin_of_text|><|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Available sizes
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (8K) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-exl2/tree/8_0) | 8.0 | 8.0 | 10.1 GB | 10.5 GB | 11.5 GB | 13.6 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-exl2/tree/6_5) | 6.5 | 8.0 | 8.9 GB | 9.3 GB | 10.3 GB | 12.4 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-exl2/tree/5_0) | 5.0 | 6.0 | 7.7 GB | 8.1 GB | 9.1 GB | 11.2 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-exl2/tree/4_25) | 4.25 | 6.0 | 7.0 GB | 7.4 GB | 8.4 GB | 10.5 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-exl2/tree/3_5) | 3.5 | 6.0 | 6.4 GB | 6.8 GB | 7.8 GB | 9.9 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/Hermes-2-Theta-Llama-3-8B-exl2 Hermes-2-Theta-Llama-3-8B-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch:
Linux:
```shell
huggingface-cli download bartowski/Hermes-2-Theta-Llama-3-8B-exl2 --revision 6_5 --local-dir Hermes-2-Theta-Llama-3-8B-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
huggingface-cli download bartowski/Hermes-2-Theta-Llama-3-8B-exl2 --revision 6_5 --local-dir Hermes-2-Theta-Llama-3-8B-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf
|
RichardErkhov
| 2024-05-15T19:23:27Z | 38 | 0 | null |
[
"gguf",
"arxiv:2309.00071",
"arxiv:2402.08268",
"arxiv:2305.14233",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-15T17:21:45Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3-8B-Instruct-Gradient-4194k - GGUF
- Model creator: https://huggingface.co/gradientai/
- Original model: https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-4194k/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Llama-3-8B-Instruct-Gradient-4194k.Q2_K.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q2_K.gguf) | Q2_K | 2.96GB |
| [Llama-3-8B-Instruct-Gradient-4194k.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [Llama-3-8B-Instruct-Gradient-4194k.IQ3_S.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [Llama-3-8B-Instruct-Gradient-4194k.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [Llama-3-8B-Instruct-Gradient-4194k.IQ3_M.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [Llama-3-8B-Instruct-Gradient-4194k.Q3_K.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q3_K.gguf) | Q3_K | 3.74GB |
| [Llama-3-8B-Instruct-Gradient-4194k.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [Llama-3-8B-Instruct-Gradient-4194k.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [Llama-3-8B-Instruct-Gradient-4194k.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [Llama-3-8B-Instruct-Gradient-4194k.Q4_0.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q4_0.gguf) | Q4_0 | 4.34GB |
| [Llama-3-8B-Instruct-Gradient-4194k.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [Llama-3-8B-Instruct-Gradient-4194k.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [Llama-3-8B-Instruct-Gradient-4194k.Q4_K.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q4_K.gguf) | Q4_K | 4.58GB |
| [Llama-3-8B-Instruct-Gradient-4194k.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [Llama-3-8B-Instruct-Gradient-4194k.Q4_1.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q4_1.gguf) | Q4_1 | 4.78GB |
| [Llama-3-8B-Instruct-Gradient-4194k.Q5_0.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q5_0.gguf) | Q5_0 | 5.21GB |
| [Llama-3-8B-Instruct-Gradient-4194k.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [Llama-3-8B-Instruct-Gradient-4194k.Q5_K.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q5_K.gguf) | Q5_K | 5.34GB |
| [Llama-3-8B-Instruct-Gradient-4194k.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [Llama-3-8B-Instruct-Gradient-4194k.Q5_1.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q5_1.gguf) | Q5_1 | 5.65GB |
| [Llama-3-8B-Instruct-Gradient-4194k.Q6_K.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q6_K.gguf) | Q6_K | 6.14GB |
| [Llama-3-8B-Instruct-Gradient-4194k.Q8_0.gguf](https://huggingface.co/RichardErkhov/gradientai_-_Llama-3-8B-Instruct-Gradient-4194k-gguf/blob/main/Llama-3-8B-Instruct-Gradient-4194k.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
language:
- en
pipeline_tag: text-generation
tags:
- meta
- llama-3
license: llama3
---
<a href="https://www.gradient.ai" target="_blank"><img src="https://cdn-uploads.huggingface.co/production/uploads/655bb613e8a8971e89944f3e/TSa3V8YpoVagnTYgxiLaO.png" width="200"/></a>
# Llama-3 8B Instruct Gradient 4194K (v0.1)
Join our custom agent and long context (262k-1M+) waitlist: https://forms.gle/L6TDY7dozx8TuoUv7
Gradient incorporates your data to deploy autonomous assistants that power critical operations across your business. If you're looking to build custom AI models or agents, email us a message [email protected].
For more info see our [End-to-end development service for custom LLMs and AI systems](https://gradient.ai/development-lab)
This model extends LLama-3 8B's context length from 8k to 4194K, developed by Gradient, sponsored by compute from [Crusoe Energy](https://huggingface.co/crusoeai).
It demonstrates that SOTA LLMs can learn to operate on long context with minimal training by appropriately adjusting RoPE theta. For this stage, we trained on 201M tokens, and 1.6B tokens total for all stages, which is ~ 0.01% of Llama-3's original pre-training data.
**Approach:**
- [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as the base
- NTK-aware interpolation [4] following scaling laws [2] to set optimal schedule for RoPE theta
- Progressive training on increasing context lengths, similar to [Large World Model](https://huggingface.co/LargeWorldModel) [2] (See details below)
**Infra:**
We build on top of the EasyContext Blockwise RingAttention library [3] to scalably and efficiently train on contexts up to 1048k tokens on [Crusoe Energy](https://huggingface.co/crusoeai) high performance L40S cluster.
Notably, we layered parallelism on top of Ring Attention with a custom network topology to better leverage large GPU clusters in the face of network bottlenecks from passing many KV blocks between devices.
**Data:**
For training data, we generate long contexts by augmenting [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B). We also fine-tune on a chat dataset based on UltraChat [4], following a similar recipe for data augmentation to [2].
**Progressive Training Details:**
| | 65K | 262K | 524k | 1048k | 4191k |
|------------------------|-----------|-----------|-----------|-----------|-----------|
| Initialize From | LLaMA-3 8B| 65K | 262K | 524k | 1048k |
| Sequence Length 2^N | 16 | 18 | 19 | 20 | 22 |
| RoPE Theta | 15.3 M | 207.1 M | 1.06B | 2.80B | 45.2B |
| Batch Size | 1 | 1 | 16 | 8 | 2 |
| Gradient Accumulation Steps | 32 | 16 | 1 | 1 | 2 |
| Steps | 30 | 24 | 50 | 50 | 12 (stopped early) |
| Total Tokens | 62914560 | 100663296 | 419430400 | 838860800 | 201326592 |
| Learning Rate | 2.00E-05 | 2.00E-05 | 2.00E-05 | 2.00E-05 | 2.00E-05 |
| # GPUs | 8 | 32 | 512 | 512 | 512 |
| GPU Type | NVIDIA L40S | NVIDIA L40S | NVIDIA L40S | NVIDIA L40S | NVIDIA L40S |
| Minutes to Train (Wall)| 202 | 555 | 61 | 87 | 433 |
**Evaluation Details:**
```
EVAL_MAX_CONTEXT_LENGTH=4194200
EVAL_MIN_CONTEXT_LENGTH=100
EVAL_CONTEXT_INTERVAL=260000
EVAL_DEPTH_INTERVAL=0.2
EVAL_RND_NUMBER_DIGITS=8
```
The haystack used is haystack #3, as detailed in this [blog post](https://gradient.ai/blog/the-haystack-matters-for-niah-evals).
**Quants:**
There are no currenty quants released. We advise to run the KV Cache in fp16 precision for higher accuracy.
## The Gradient AI Team
https://gradient.ai/
Gradient is accelerating AI transformation across industries. Our AI Foundry incorporates your data to deploy autonomous assistants that power critical operations across your business.
## Contact Us
Drop an email to [[email protected]](mailto:[email protected])
## References
[1] Peng, Bowen, et al. "Yarn: Efficient context window extension of large language models." arXiv preprint arXiv:2309.00071 (2023).
[2] Liu, Hao, et al. "World Model on Million-Length Video And Language With RingAttention." arXiv preprint arXiv:2402.08268 (2024).
[3] https://github.com/jzhang38/EasyContext
[4] Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Zhi Zheng, Shengding Hu, Zhiyuan
Liu, Maosong Sun, and Bowen Zhou. Enhancing chat language models by scaling
high-quality instructional conversations. arXiv preprint arXiv:2305.14233, 2023.
----
# Base Model
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both.
#### Transformers pipeline
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
#### Transformers AutoModelForCausalLM
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir Meta-Llama-3-8B-Instruct
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
|
RileyI/IAmATest
|
RileyI
| 2024-05-15T19:20:24Z | 0 | 0 | null |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-05-15T19:20:24Z |
---
license: cc-by-nc-4.0
---
|
veronica-girolimetti/mistral-ft-lora-01-RE
|
veronica-girolimetti
| 2024-05-15T19:19:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T19:17:56Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
---
# Uploaded model
- **Developed by:** veronica-girolimetti
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
SelimGilon/test
|
SelimGilon
| 2024-05-15T19:19:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2023-12-24T01:58:59Z |
---
library_name: transformers
tags:
- unsloth
---
# 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
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[More Information Needed]
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<!-- 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. -->
[More Information Needed]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
camilomj/lisabpdebutera
|
camilomj
| 2024-05-15T19:18:41Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-05-15T19:17:53Z |
---
license: apache-2.0
---
|
veronica-girolimetti/mistral-ft-01-RE
|
veronica-girolimetti
| 2024-05-15T19:17:43Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-15T19:13:29Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
---
# Uploaded model
- **Developed by:** veronica-girolimetti
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
emilykang/Gemma_medner-surgery_lora
|
emilykang
| 2024-05-15T19:11:12Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-05-15T17:55:53Z |
---
license: gemma
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: google/gemma-2b
datasets:
- generator
model-index:
- name: Gemma_medner-surgery_lora
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. -->
# Gemma_medner-surgery_lora
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|
monsoon-nlp/llama3-biotokenpretrain-kaniwa
|
monsoon-nlp
| 2024-05-15T19:08:27Z | 8 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"unsloth",
"generated_from_trainer",
"dna",
"en",
"base_model:gradientai/Llama-3-8B-Instruct-262k",
"base_model:adapter:gradientai/Llama-3-8B-Instruct-262k",
"license:llama3",
"region:us"
] | null | 2024-05-12T15:32:03Z |
---
license: llama3
library_name: peft
language:
- en
tags:
- trl
- sft
- unsloth
- generated_from_trainer
- dna
base_model: gradientai/Llama-3-8B-Instruct-262k
model-index:
- name: llama3-biotokenpretrain-kaniwa
results: []
---
# llama3-biotokenpretrain-kaniwa
This is a LoRA adapter.
The base model is the longer-context LLaMA-3-8b-Instruct developed by Gradient and Crusoe: `gradientai/Llama-3-8B-Instruct-262k`
The tokenizer has added "biotokens" ∎A, ∎C, ∎G, and ∎T.
The dataset was 0.5% of BYU's 2019 kaniwa (*Chenopodium pallidicaule*) genome, from https://genomevolution.org/coge/GenomeInfo.pl?gid=53872
The adapter was finetuned for 3 hours on an L4 GPU. The data was split into ~7k nucleotide snippets with an Alpaca like message format.
Training Notebook: https://colab.research.google.com/drive/1FKA3p_jnfRHYd-hqJdYmKn8MQpxec0t5?usp=sharing
Sample message:
```
Write information about the nucleotide sequence.
### Sequence:
∎G∎C∎C∎T∎A∎T∎A∎G∎T∎G∎T∎G∎T∎A∎G...
### Annotation:
Information about location in the kaniwa chromosome: >lcl|Cp5
```
## Usage
### Inference with DNA sequence
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained("monsoon-nlp/llama3-biotokenpretrain-kaniwa", load_in_4bit=True).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/llama3-biotokenpretrain-kaniwa")
tokenizer.pad_token = tokenizer.eos_token # pad fix
qed = "∎" # from math symbols, used in pretraining
sequence = "".join([(qed + nt.upper()) for nt in "GCCTATAGTGTGTAGCTAATGAGCCTAGGTTATCGACCCTAATCT"])
inputs = tokenizer(f"{prefix}{sequence}{annotation}", return_tensors="pt")
outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=50)
sample = tokenizer.batch_decode(outputs, skip_special_tokens=False)[0]
```
### LoRA finetuning on a new task
```python
from transformers import AutoTokenizer
from trl import SFTTrainer
from unsloth import FastLanguageModel
model, _ = FastLanguageModel.from_pretrained(
model_name = "monsoon-nlp/llama3-biotokenpretrain-kaniwa",
max_seq_length = 7_000, # max 6,000 bp for AgroNT tasks
dtype = None,
load_in_4bit = True,
resize_model_vocab=128260, # includes biotokens
)
tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/llama3-biotokenpretrain-kaniwa")
tokenizer.pad_token = tokenizer.eos_token # pad fix
trainer = SFTTrainer(
model = model,
tokenizer = tokenizer,
...
)
```
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 3407
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 280
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
### Genome Citation
Mangelson H, et al. The genome of *Chenopodium pallidicaule*: an emerging Andean super grain. Appl. Plant Sci. 2019;7:e11300. doi: 10.1002/aps3.11300
|
raghadOmar/whisper-quran
|
raghadOmar
| 2024-05-15T19:05:23Z | 19 | 1 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ar",
"dataset:zolfa",
"base_model:tarteel-ai/whisper-base-ar-quran",
"base_model:finetune:tarteel-ai/whisper-base-ar-quran",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-05-09T19:26:50Z |
---
language:
- ar
license: apache-2.0
base_model: tarteel-ai/whisper-base-ar-quran
tags:
- generated_from_trainer
datasets:
- zolfa
metrics:
- wer
model-index:
- name: Whisper-raghadomar
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Zolfa Dataset
type: zolfa
args: 'config: ar, split: test'
metrics:
- name: Wer
type: wer
value: 10.344827586206897
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper-raghadomar
This model is a fine-tuned version of [tarteel-ai/whisper-base-ar-quran](https://huggingface.co/tarteel-ai/whisper-base-ar-quran) on the Zolfa Dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0325
- Wer: 10.3448
## 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: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0002 | 1.0 | 21 | 0.0317 | 10.3448 |
| 0.0002 | 2.0 | 42 | 0.0287 | 10.3448 |
| 0.0001 | 3.0 | 63 | 0.0293 | 10.3448 |
| 0.0002 | 4.0 | 84 | 0.0298 | 10.3448 |
| 0.0002 | 5.0 | 105 | 0.0281 | 10.3448 |
| 0.0002 | 6.0 | 126 | 0.0308 | 10.3448 |
| 0.0002 | 7.0 | 147 | 0.0262 | 10.3448 |
| 0.0008 | 8.0 | 168 | 0.0341 | 10.3448 |
| 0.0002 | 9.0 | 189 | 0.0223 | 3.4483 |
| 0.0003 | 10.0 | 210 | 0.0411 | 10.3448 |
| 0.0002 | 11.0 | 231 | 0.0357 | 10.3448 |
| 0.0003 | 12.0 | 252 | 0.0349 | 10.3448 |
| 0.0001 | 13.0 | 273 | 0.0429 | 10.3448 |
| 0.0003 | 14.0 | 294 | 0.0311 | 10.3448 |
| 0.0003 | 15.0 | 315 | 0.0372 | 10.3448 |
| 0.0002 | 16.0 | 336 | 0.0329 | 10.3448 |
| 0.0002 | 17.0 | 357 | 0.0390 | 10.3448 |
| 0.0004 | 18.0 | 378 | 0.0333 | 10.3448 |
| 0.0002 | 19.0 | 399 | 0.0450 | 10.3448 |
| 0.0003 | 20.0 | 420 | 0.0384 | 10.3448 |
| 0.0002 | 21.0 | 441 | 0.0366 | 10.3448 |
| 0.0002 | 22.0 | 462 | 0.0360 | 10.3448 |
| 0.0001 | 23.0 | 483 | 0.0441 | 10.3448 |
| 0.0006 | 23.8095 | 500 | 0.0325 | 10.3448 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
jan-martens0124/quail_egg_detection
|
jan-martens0124
| 2024-05-15T19:05:15Z | 0 | 1 | null |
[
"region:us"
] | null | 2024-05-15T18:26:11Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This repository contains a machine learning model trained for the purpose of detecting quail eggs in images.
## Model Details
### Model Description
Leveraging the Quail Egg Detection Dataset, this model utilizes the YOLOv8 architecture, specifically the YOLOv8 Large variant from Ultralytics, known for its efficiency and accuracy in object detection tasks.
- **Developed by:** Jan Martens
- **Model type:** Object Detection
- **Language(s) (NLP):** python --> yoloV8
- **Finetuned from model [optional]:** yoloV8_large
### Model Sources [optional]
This model was trained using self-taken images in my henhouse and some additional ones in a classroom setting.
The dataset can be found at "jan-martens0124/quail_egg"
## Uses
Can be used to detect quail_eggs in a henhouse. It distingquisches quail_eggs from quails and other types of eggs.
### Downstream Use [optional]
When furhter trained, this model could be used to distinguish between different kind of eggs.
## Bias, Risks, and Limitations
Sometimes, eastereggs with fancy packaging that has high-contrast silver paper are recognized as a quail egg. Also white chocolate eggs are often incorrectly identified.
[More Information Needed]
## How to Get Started with the Model
You can use the code in the folder 'code' to get started with the model.
The python-script detect_on_video_for_loop.py contains a script to let the model infer with your webcam or a video-file on your pc.
|
JustineJ/OC_IMLP5
|
JustineJ
| 2024-05-15T19:05:05Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-03T14:14:33Z |
---
license: cc-by-nc-sa-4.0
---
|
hklair/first_qa_model
|
hklair
| 2024-05-15T19:03:28Z | 123 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-05-13T16:03:36Z |
---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: first_QA_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. -->
# first_QA_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5612
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.2039 |
| 2.6743 | 2.0 | 500 | 1.6623 |
| 2.6743 | 3.0 | 750 | 1.5612 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.15.0
|
BrokenSoul/GPT2-GPTQ-4bit
|
BrokenSoul
| 2024-05-15T19:01:33Z | 8 | 0 |
transformers
|
[
"transformers",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-05-15T18:59:04Z |
---
library_name: transformers
tags: []
---
# BrokenSoul/GPT2-GPTQ-4bit
<!-- Provide a quick summary of what the model is/does. -->
This is a GPT2 Quantized model following this tutorial: [4-bit LLM Quantization with GPTQ](https://mlabonne.github.io/blog/posts/4_bit_Quantization_with_GPTQ.html).
|
Fariha4185/bart-large-xsum-samsum
|
Fariha4185
| 2024-05-15T19:00:02Z | 110 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large-xsum",
"base_model:finetune:facebook/bart-large-xsum",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-05-15T16:56:53Z |
---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
model-index:
- name: bart-large-xsum-samsum
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. -->
# bart-large-xsum-samsum
This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5727
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.4126 | 0.5431 | 500 | 1.5727 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
emilykang/medner-urology_lora
|
emilykang
| 2024-05-15T19:00:01Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] | null | 2024-05-15T18:53:58Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
datasets:
- generator
model-index:
- name: medner-urology_lora
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. -->
# medner-urology_lora
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|
lodestones/Cascaded-Rectified-Flow
|
lodestones
| 2024-05-15T18:50:27Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-05-10T06:18:27Z |
---
license: apache-2.0
---
|
YYYYYYibo/nash_dpo_rank4_on_vanilla_iter_1
|
YYYYYYibo
| 2024-05-15T18:47:49Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"dataset:updated",
"dataset:original",
"base_model:alignment-handbook/zephyr-7b-sft-full",
"base_model:adapter:alignment-handbook/zephyr-7b-sft-full",
"license:apache-2.0",
"region:us"
] | null | 2024-05-15T16:43:44Z |
---
license: apache-2.0
library_name: peft
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
base_model: alignment-handbook/zephyr-7b-sft-full
datasets:
- updated
- original
model-index:
- name: nash_dpo_rank4_on_vanilla_iter_1
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. -->
# nash_dpo_rank4_on_vanilla_iter_1
This model is a fine-tuned version of [YYYYYYibo/vanilla_dpo_iter_3](https://huggingface.co/YYYYYYibo/vanilla_dpo_iter_3) on the updated and the original datasets.
It achieves the following results on the evaluation set:
- Loss: 0.6753
- Rewards/chosen: -0.1174
- Rewards/rejected: -0.1651
- Rewards/accuracies: 0.6360
- Rewards/margins: 0.0477
- Logps/rejected: -298.1530
- Logps/chosen: -305.5673
- Logits/rejected: -2.4653
- Logits/chosen: -2.5565
## 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-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.689 | 0.64 | 100 | 0.6753 | -0.1174 | -0.1651 | 0.6360 | 0.0477 | -298.1530 | -305.5673 | -2.4653 | -2.5565 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.3.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
NikolayKozloff/EVA-GPT-German-v7-2-Beta-Q5_K_M-GGUF
|
NikolayKozloff
| 2024-05-15T18:45:17Z | 4 | 1 |
transformers
|
[
"transformers",
"gguf",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"TMP-Networks LLM Studio",
"llama-cpp",
"gguf-my-repo",
"en",
"de",
"region:us"
] | null | 2024-05-15T18:45:03Z |
---
language:
- en
- de
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
- TMP-Networks LLM Studio
- llama-cpp
- gguf-my-repo
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# NikolayKozloff/EVA-GPT-German-v7-2-Beta-Q5_K_M-GGUF
This model was converted to GGUF format from [`MTSmash/EVA-GPT-German-v7-2-Beta`](https://huggingface.co/MTSmash/EVA-GPT-German-v7-2-Beta) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/MTSmash/EVA-GPT-German-v7-2-Beta) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo NikolayKozloff/EVA-GPT-German-v7-2-Beta-Q5_K_M-GGUF --model eva-gpt-german-v7-2-beta.Q5_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo NikolayKozloff/EVA-GPT-German-v7-2-Beta-Q5_K_M-GGUF --model eva-gpt-german-v7-2-beta.Q5_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m eva-gpt-german-v7-2-beta.Q5_K_M.gguf -n 128
```
|
jspr/llama3-instruct-wordcel-smutrom_merged
|
jspr
| 2024-05-15T18:44:26Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:jspr/llama3_8b_instruct_wordcel_merged",
"base_model:finetune:jspr/llama3_8b_instruct_wordcel_merged",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-15T18:41:28Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: jspr/llama3_8b_instruct_wordcel_merged
---
# Uploaded model
- **Developed by:** jspr
- **License:** apache-2.0
- **Finetuned from model :** jspr/llama3_8b_instruct_wordcel_merged
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
BANA577/Llama3-Michael-3
|
BANA577
| 2024-05-15T18:44:02Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-15T18:40:18Z |
---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
jspr/llama3-instruct-wordcel-smutrom_peft
|
jspr
| 2024-05-15T18:41:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:jspr/llama3_8b_instruct_wordcel_merged",
"base_model:finetune:jspr/llama3_8b_instruct_wordcel_merged",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T18:41:15Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: jspr/llama3_8b_instruct_wordcel_merged
---
# Uploaded model
- **Developed by:** jspr
- **License:** apache-2.0
- **Finetuned from model :** jspr/llama3_8b_instruct_wordcel_merged
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
jfranklin-foundry/gemma-2b-flock-1715798166
|
jfranklin-foundry
| 2024-05-15T18:37:02Z | 145 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-15T18:34:53Z |
---
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]
|
Giuieu/Marcel
|
Giuieu
| 2024-05-15T18:35:38Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"question-answering",
"ro",
"en",
"de",
"hu",
"dataset:open-llm-leaderboard/details_cloudyu__google-gemma-7b-it-dpo-v1",
"arxiv:1910.09700",
"region:us"
] |
question-answering
| 2024-05-15T18:17:22Z |
---
datasets:
- open-llm-leaderboard/details_cloudyu__google-gemma-7b-it-dpo-v1
language:
- ro
- en
- de
- hu
metrics:
- recall
- accuracy
- precision
- f1
- bleu
- roc_auc
library_name: adapter-transformers
pipeline_tag: question-answering
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## 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]
|
UmarRamzan/w2v2-bert-urdu
|
UmarRamzan
| 2024-05-15T18:32:30Z | 47 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2-bert",
"automatic-speech-recognition",
"generated_from_trainer",
"ur",
"dataset:mozilla-foundation/common_voice_17_0",
"base_model:UmarRamzan/w2v2-bert-urdu",
"base_model:finetune:UmarRamzan/w2v2-bert-urdu",
"license:mit",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-05-12T13:59:02Z |
---
license: mit
base_model: UmarRamzan/w2v2-bert-urdu
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v2-bert-urdu
results: []
language:
- ur
datasets:
- mozilla-foundation/common_voice_17_0
---
<!-- 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. -->
# Wav2Vec-Bert-2.0-Urdu
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the Urdu split of the [Common Voice 17](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3681
- Wer: 0.2929
## Usage Instructions
```python
from transformers import AutoFeatureExtractor, Wav2Vec2BertModel
import torch
from datasets import load_dataset
dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
dataset = dataset.sort("id")
sampling_rate = dataset.features["audio"].sampling_rate
processor = AutoProcessor.from_pretrained("UmarRamzan/w2v2-bert-urdu")
model = Wav2Vec2BertModel.from_pretrained("UmarRamzan/w2v2-bert-urdu")
# audio file is decoded on the fly
inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.4362 | 0.1695 | 50 | 0.4144 | 0.3213 |
| 0.3776 | 0.3390 | 100 | 0.4029 | 0.3137 |
| 0.3918 | 0.5085 | 150 | 0.4095 | 0.3060 |
| 0.3968 | 0.6780 | 200 | 0.3961 | 0.3060 |
| 0.3685 | 0.8475 | 250 | 0.3681 | 0.2929 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
UmarRamzan/w2v2-bert-ngram-urdu
|
UmarRamzan
| 2024-05-15T18:31:58Z | 85 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2-bert",
"automatic-speech-recognition",
"generated_from_trainer",
"ur",
"dataset:mozilla-foundation/common_voice_17_0",
"base_model:UmarRamzan/w2v2-bert-urdu",
"base_model:finetune:UmarRamzan/w2v2-bert-urdu",
"license:mit",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-05-12T22:34:00Z |
---
license: mit
base_model: UmarRamzan/w2v2-bert-urdu
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v2-bert-urdu
results: []
language:
- ur
datasets:
- mozilla-foundation/common_voice_17_0
---
<!-- 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. -->
# Wav2Vec-Bert-2.0-ngram-Urdu
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the Urdu split of the [Common Voice 17](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0) dataset. The fine-tuned model is enhanced with the addition of an ngram language model that has also been trained on the same dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3681
- Wer: 0.2407
## Usage Instructions
```python
from transformers import AutoFeatureExtractor, Wav2Vec2BertModel
import torch
from datasets import load_dataset
dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
dataset = dataset.sort("id")
sampling_rate = dataset.features["audio"].sampling_rate
processor = AutoProcessor.from_pretrained("UmarRamzan/w2v2-bert-ngram-urdu")
model = Wav2Vec2BertModel.from_pretrained("UmarRamzan/w2v2-bert-ngram-urdu")
# audio file is decoded on the fly
inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
bartowski/dolphin-2.9.1-qwen-110b-GGUF
|
bartowski
| 2024-05-15T18:24:55Z | 742 | 2 | null |
[
"gguf",
"generated_from_trainer",
"axolotl",
"text-generation",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:Qwen/Qwen1.5-110B",
"base_model:quantized:Qwen/Qwen1.5-110B",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] |
text-generation
| 2024-05-15T14:29:40Z |
---
license: other
license_name: tongyi-qianwen
license_link: >-
https://huggingface.co/Qwen/Qwen1.5-110B/blob/main/LICENSE
base_model: Qwen/Qwen1.5-110B
tags:
- generated_from_trainer
- axolotl
datasets:
- cognitivecomputations/Dolphin-2.9
- teknium/OpenHermes-2.5
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- microsoft/orca-math-word-problems-200k
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp imatrix Quantizations of dolphin-2.9.1-qwen-110b
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2854">b2854</a> for quantization.
Original model: https://huggingface.co/cognitivecomputations/dolphin-2.9.1-qwen-110b
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/b6ac44691e994344625687afe3263b3a)
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [dolphin-2.9.1-qwen-110b-Q8_0.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/tree/main/dolphin-2.9.1-qwen-110b-Q8_0.gguf) | Q8_0 | 118.17GB | Extremely high quality, generally unneeded but max available quant. |
| [dolphin-2.9.1-qwen-110b-Q6_K.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/tree/main/dolphin-2.9.1-qwen-110b-Q6_K.gguf) | Q6_K | 91.23GB | Very high quality, near perfect, *recommended*. |
| [dolphin-2.9.1-qwen-110b-Q5_K_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/tree/main/dolphin-2.9.1-qwen-110b-Q5_K_M.gguf) | Q5_K_M | 78.81GB | High quality, *recommended*. |
| [dolphin-2.9.1-qwen-110b-Q5_K_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/tree/main/dolphin-2.9.1-qwen-110b-Q5_K_S.gguf) | Q5_K_S | 76.63GB | High quality, *recommended*. |
| [dolphin-2.9.1-qwen-110b-Q4_K_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/tree/main/dolphin-2.9.1-qwen-110b-Q4_K_M.gguf) | Q4_K_M | 67.17GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [dolphin-2.9.1-qwen-110b-Q4_K_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/tree/main/dolphin-2.9.1-qwen-110b-Q4_K_S.gguf) | Q4_K_S | 63.47GB | Slightly lower quality with more space savings, *recommended*. |
| [dolphin-2.9.1-qwen-110b-IQ4_NL.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/tree/main/dolphin-2.9.1-qwen-110b-IQ4_NL.gguf) | IQ4_NL | 62.97GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [dolphin-2.9.1-qwen-110b-IQ4_XS.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/tree/main/dolphin-2.9.1-qwen-110b-IQ4_XS.gguf) | IQ4_XS | 59.55GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [dolphin-2.9.1-qwen-110b-Q3_K_L.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/tree/main/dolphin-2.9.1-qwen-110b-Q3_K_L.gguf) | Q3_K_L | 58.14GB | Lower quality but usable, good for low RAM availability. |
| [dolphin-2.9.1-qwen-110b-Q3_K_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/tree/main/dolphin-2.9.1-qwen-110b-Q3_K_M.gguf) | Q3_K_M | 53.70GB | Even lower quality. |
| [dolphin-2.9.1-qwen-110b-IQ3_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/blob/main/dolphin-2.9.1-qwen-110b-IQ3_M.gguf) | IQ3_M | 49.70GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [dolphin-2.9.1-qwen-110b-IQ3_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/blob/main/dolphin-2.9.1-qwen-110b-IQ3_S.gguf) | IQ3_S | 48.45GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| [dolphin-2.9.1-qwen-110b-Q3_K_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/blob/main/dolphin-2.9.1-qwen-110b-Q3_K_S.gguf) | Q3_K_S | 48.45GB | Low quality, not recommended. |
| [dolphin-2.9.1-qwen-110b-IQ3_XS.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/blob/main/dolphin-2.9.1-qwen-110b-IQ3_XS.gguf) | IQ3_XS | 45.91GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [dolphin-2.9.1-qwen-110b-IQ3_XXS.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/blob/main/dolphin-2.9.1-qwen-110b-IQ3_XXS.gguf) | IQ3_XXS | 43.10GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [dolphin-2.9.1-qwen-110b-Q2_K.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/blob/main/dolphin-2.9.1-qwen-110b-Q2_K.gguf) | Q2_K | 41.17GB | Very low quality but surprisingly usable. |
| [dolphin-2.9.1-qwen-110b-IQ2_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/blob/main/dolphin-2.9.1-qwen-110b-IQ2_M.gguf) | IQ2_M | 37.41GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [dolphin-2.9.1-qwen-110b-IQ2_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/blob/main/dolphin-2.9.1-qwen-110b-IQ2_S.gguf) | IQ2_S | 34.33GB | Very low quality, uses SOTA techniques to be usable. |
| [dolphin-2.9.1-qwen-110b-IQ2_XS.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/blob/main/dolphin-2.9.1-qwen-110b-IQ2_XS.gguf) | IQ2_XS | 33.04GB | Very low quality, uses SOTA techniques to be usable. |
| [dolphin-2.9.1-qwen-110b-IQ2_XXS.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/blob/main/dolphin-2.9.1-qwen-110b-IQ2_XXS.gguf) | IQ2_XXS | 29.79GB | Lower quality, uses SOTA techniques to be usable. |
| [dolphin-2.9.1-qwen-110b-IQ1_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/blob/main/dolphin-2.9.1-qwen-110b-IQ1_M.gguf) | IQ1_M | 25.94GB | Extremely low quality, *not* recommended. |
| [dolphin-2.9.1-qwen-110b-IQ1_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-qwen-110b-GGUF/blob/main/dolphin-2.9.1-qwen-110b-IQ1_S.gguf) | IQ1_S | 23.63GB | Extremely low quality, *not* recommended. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/dolphin-2.9.1-qwen-110b-GGUF --include "dolphin-2.9.1-qwen-110b-Q4_K_M.gguf" --local-dir ./ --local-dir-use-symlinks False
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/dolphin-2.9.1-qwen-110b-GGUF --include "dolphin-2.9.1-qwen-110b-Q8_0.gguf/*" --local-dir dolphin-2.9.1-qwen-110b-Q8_0 --local-dir-use-symlinks False
```
You can either specify a new local-dir (dolphin-2.9.1-qwen-110b-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
cctuan/lora_model
|
cctuan
| 2024-05-15T18:22:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T18:22:13Z |
---
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]
|
sakren/debert-imeocap
|
sakren
| 2024-05-15T18:21:03Z | 108 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-15T17:23:01Z |
---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
- accuracy
model-index:
- name: debert-imeocap
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. -->
# debert-imeocap
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8660
- F1: 0.6185
- Precision: 0.6337
- Recall: 0.6154
- Accuracy: 0.6154
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 0.4637 | 1.0 | 74 | 1.3864 | 0.6129 | 0.6262 | 0.6115 | 0.6115 |
| 0.3815 | 2.0 | 148 | 1.3801 | 0.6193 | 0.6348 | 0.6173 | 0.6173 |
| 0.3363 | 3.0 | 222 | 1.6944 | 0.6077 | 0.6297 | 0.6077 | 0.6077 |
| 0.31 | 4.0 | 296 | 1.6945 | 0.5995 | 0.6285 | 0.5942 | 0.5942 |
| 0.2885 | 5.0 | 370 | 1.5945 | 0.6218 | 0.6306 | 0.6192 | 0.6192 |
| 0.2594 | 6.0 | 444 | 1.7662 | 0.6279 | 0.6396 | 0.625 | 0.625 |
| 0.2319 | 7.0 | 518 | 1.7093 | 0.6210 | 0.6321 | 0.6173 | 0.6173 |
| 0.2306 | 8.0 | 592 | 1.8068 | 0.6279 | 0.6341 | 0.6288 | 0.6288 |
| 0.2167 | 9.0 | 666 | 1.7306 | 0.6376 | 0.6444 | 0.6346 | 0.6346 |
| 0.2158 | 10.0 | 740 | 1.8745 | 0.6262 | 0.6318 | 0.6269 | 0.6269 |
| 0.222 | 11.0 | 814 | 1.8323 | 0.6200 | 0.6348 | 0.6173 | 0.6173 |
| 0.2152 | 12.0 | 888 | 1.8576 | 0.6246 | 0.6363 | 0.6212 | 0.6212 |
| 0.226 | 13.0 | 962 | 1.8880 | 0.6343 | 0.6411 | 0.6308 | 0.6308 |
| 0.2097 | 14.0 | 1036 | 1.8884 | 0.6152 | 0.6326 | 0.6115 | 0.6115 |
| 0.2192 | 15.0 | 1110 | 1.8660 | 0.6185 | 0.6337 | 0.6154 | 0.6154 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
AvinashAmballa/DPO_LLAMA-7B_0.25
|
AvinashAmballa
| 2024-05-15T18:20:31Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-15T18:15:47Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
emilykang/Phi_medner-consult-historyandphy_lora
|
emilykang
| 2024-05-15T18:13:53Z | 5 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:microsoft/phi-2",
"base_model:adapter:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2024-05-15T17:29:19Z |
---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/phi-2
datasets:
- generator
model-index:
- name: Phi_medner-consult-historyandphy_lora
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Phi_medner-consult-historyandphy_lora
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|
AvinashAmballa/DPO_LLAMA-7B_0.5
|
AvinashAmballa
| 2024-05-15T18:12:38Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-15T18:08:03Z |
---
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 for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## 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]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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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]
#### 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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[More Information Needed]
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|
reemmasoud/idv_vs_col_llama-3_PromptTuning_CAUSAL_LM_gradient_descent_v2
|
reemmasoud
| 2024-05-15T18:09:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T18:09:15Z |
---
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.
- **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]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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[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]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
projecte-aina/aina-translator-en-ca
|
projecte-aina
| 2024-05-15T18:06:09Z | 158 | 0 |
fairseq
|
[
"fairseq",
"en",
"ca",
"dataset:projecte-aina/CA-EN_Parallel_Corpus",
"doi:10.57967/hf/1932",
"license:apache-2.0",
"region:us"
] | null | 2022-11-25T08:39:56Z |
---
license: apache-2.0
datasets:
- projecte-aina/CA-EN_Parallel_Corpus
language:
- en
- ca
metrics:
- bleu
library_name: fairseq
---
## Projecte Aina's English-Catalan machine translation model
## Model description
This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of English-Catalan datasets,
which after filtering and cleaning comprised 30.023.034 sentence pairs. The model was evaluated on several public datasets comprising different domains.
## Intended uses and limitations
You can use this model for machine translation from English to Catalan.
## How to use
### Usage
Required libraries:
```bash
pip install ctranslate2 pyonmttok
```
Translate a sentence using python
```python
import ctranslate2
import pyonmttok
from huggingface_hub import snapshot_download
model_dir = snapshot_download(repo_id="projecte-aina/aina-translator-en-ca", revision="main")
tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model")
tokenized=tokenizer.tokenize("Welcome to the Aina Project!")
translator = ctranslate2.Translator(model_dir)
translated = translator.translate_batch([tokenized[0]])
print(tokenizer.detokenize(translated[0][0]['tokens']))
```
## Limitations and bias
At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model.
However, we are well aware that our models may be biased. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
## Training
### Training data
The model was trained on a combination of several datasets, including data collected from [Opus](https://opus.nlpl.eu/), [HPLT](https://hplt-project.org/),
an internally created [CA-EN Parallel Corpus](https://huggingface.co/datasets/projecte-aina/CA-EN_Parallel_Corpus), and other sources.
### Training procedure
### Data preparation
All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75.
This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE).
The filtered datasets are then concatenated to form a final corpus of 30.023.034 parallel sentences and before training the punctuation
is normalized using a modified version of the join-single-file.py script from [SoftCatalà](https://github.com/Softcatala/nmt-models/blob/master/data-processing-tools/join-single-file.py)
#### Tokenization
All data is tokenized using sentencepiece, with a 50 thousand token sentencepiece model learned from the combination of all filtered training data.
This model is included.
#### Hyperparameters
The model is based on the Transformer-XLarge proposed by [Subramanian et al.](https://aclanthology.org/2021.wmt-1.18.pdf)
The following hyperparamenters were set on the Fairseq toolkit:
| Hyperparameter | Value |
|------------------------------------|----------------------------------|
| Architecture | transformer_vaswani_wmt_en_de_big |
| Embedding size | 1024 |
| Feedforward size | 4096 |
| Number of heads | 16 |
| Encoder layers | 24 |
| Decoder layers | 6 |
| Normalize before attention | True |
| --share-decoder-input-output-embed | True |
| --share-all-embeddings | True |
| Effective batch size | 96.000 |
| Optimizer | adam |
| Adam betas | (0.9, 0.980) |
| Clip norm | 0.0 |
| Learning rate | 1e-3 |
| Lr. schedurer | inverse sqrt |
| Warmup updates | 4000 |
| Dropout | 0.1 |
| Label smoothing | 0.1 |
The model was trained for a total of 16000 updates. Weights were saved every 1000 updates and reported results are the average of the last 2 checkpoints.
## Evaluation
### Variable and metrics
We use the BLEU score for evaluation on the following test sets:
[Spanish Constitution (TaCon)](https://elrc-share.eu/repository/browse/tacon-spanish-constitution-mt-test-set/84a96138b98611ec9c1a00155d02670628f3e6857b0f422abd82abc3795ec8c2/),
[United Nations](https://zenodo.org/record/3888414#.Y33-_tLMIW0),
[AAPP](https://elrc-share.eu/repository/browse/catalan-spanish-catgov-corpus/8088130a722811ed9c1a00155d02670690607f8261a847549c8a0583cbe729da/),
[European Commission](https://elrc-share.eu/repository/browse/european-commission-corpus/8a419b1758ea11ed9c1a00155d0267069bd085cae124481589b0858e5b274327/),
[Flores-200](https://github.com/facebookresearch/flores),
[Cybersecurity](https://elrc-share.eu/repository/browse/cyber-mt-test-set/2bd93faab98c11ec9c1a00155d026706b96a490ed3e140f0a29a80a08c46e91e/),
[wmt19 biomedical test set](http://www.statmt.org/wmt19/biomedical-translation-task.html),
[wmt13 news test set](https://elrc-share.eu/repository/browse/catalan-wmt2013-machine-translation-shared-task-test-set/84a96139b98611ec9c1a00155d0267061a0aa1b62e2248e89aab4952f3c230fc/).
### Evaluation results
Below are the evaluation results on the machine translation from English to Catalan compared to [Softcatalà](https://www.softcatala.org/) and
[Google Translate](https://translate.google.es/?hl=es):
| Test set | SoftCatalà | Google Translate | aina-translator-en-ca |
|----------------------|------------|------------------|---------------|
| Spanish Constitution | 32,6 | 37,8 | **41,2** |
| United Nations | 39,0 | 40,5 | **41,2** |
| AAPP | 46,5 | 51,40 | **51,70** |
| European Commission | 49,1 | **52,0** | 51 |
| Flores 200 dev | 41,0 | **45,1** | 43,3 |
| Flores 200 devtest | 42,1 | **46,0** | 44,1 |
| Cybersecurity | 42,5 | **48,1** | 45,8 |
| wmt 19 biomedical | 21,7 | 25,5 | **26,7** |
| wmt 13 news | 34,9 | **35,7** | 34,0 |
| **Average** | 38,82 | **42,45** | 42,1 |
## Additional information
### Author
The Language Technologies Unit from Barcelona Supercomputing Center.
### Contact
For further information, please send an email to <[email protected]>.
### Copyright
Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.
### License
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Funding
This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/).
### Disclaimer
<details>
<summary>Click to expand</summary>
The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0.
Be aware that the model may have biases and/or any other undesirable distortions.
When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it)
or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and,
in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owner and creator of the model (Barcelona Supercomputing Center)
be liable for any results arising from the use made by third parties.
</details>
|
vijayhn/llama2-7b-qlora-ft-sql
|
vijayhn
| 2024-05-15T18:02:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T18:02:15Z |
---
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.
- **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]
|
veronica-girolimetti/mistral_qt_finetuned_LoRA_10
|
veronica-girolimetti
| 2024-05-15T17:55:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T17:52:20Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
---
# Uploaded model
- **Developed by:** veronica-girolimetti
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
digiplay/ValMix2-byHemlok
|
digiplay
| 2024-05-15T17:54:24Z | 1,031 | 5 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-05-04T05:38:27Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
Beautiful anime model by Hemlok...
more detailed :
https://huggingface.co/Hemlok/VaLMix
Sample image generated by AUTOMATIC1111 :
|
veronica-girolimetti/mistral-ft-lora10
|
veronica-girolimetti
| 2024-05-15T17:51:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T17:48:28Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
---
# Uploaded model
- **Developed by:** veronica-girolimetti
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
leon-se/ros2-instruct-v4
|
leon-se
| 2024-05-15T17:47:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T17:47:40Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** thwin27
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mradermacher/Llama-3-Teal-Instruct-2x8B-MoE-GGUF
|
mradermacher
| 2024-05-15T17:42:30Z | 30 | 1 |
transformers
|
[
"transformers",
"gguf",
"moe",
"llama",
"3",
"llama 3",
"2x8b",
"en",
"base_model:RDson/Llama-3-Teal-Instruct-2x8B-MoE",
"base_model:quantized:RDson/Llama-3-Teal-Instruct-2x8B-MoE",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-15T15:45:14Z |
---
base_model: RDson/Llama-3-Teal-Instruct-2x8B-MoE
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- moe
- llama
- '3'
- llama 3
- 2x8b
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hfhfix -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/RDson/Llama-3-Teal-Instruct-2x8B-MoE
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Teal-Instruct-2x8B-MoE-GGUF/resolve/main/Llama-3-Teal-Instruct-2x8B-MoE.Q2_K.gguf) | Q2_K | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Teal-Instruct-2x8B-MoE-GGUF/resolve/main/Llama-3-Teal-Instruct-2x8B-MoE.IQ3_XS.gguf) | IQ3_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Teal-Instruct-2x8B-MoE-GGUF/resolve/main/Llama-3-Teal-Instruct-2x8B-MoE.Q3_K_S.gguf) | Q3_K_S | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Teal-Instruct-2x8B-MoE-GGUF/resolve/main/Llama-3-Teal-Instruct-2x8B-MoE.IQ3_S.gguf) | IQ3_S | 6.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Teal-Instruct-2x8B-MoE-GGUF/resolve/main/Llama-3-Teal-Instruct-2x8B-MoE.IQ3_M.gguf) | IQ3_M | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Teal-Instruct-2x8B-MoE-GGUF/resolve/main/Llama-3-Teal-Instruct-2x8B-MoE.Q3_K_M.gguf) | Q3_K_M | 6.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Teal-Instruct-2x8B-MoE-GGUF/resolve/main/Llama-3-Teal-Instruct-2x8B-MoE.Q3_K_L.gguf) | Q3_K_L | 7.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Teal-Instruct-2x8B-MoE-GGUF/resolve/main/Llama-3-Teal-Instruct-2x8B-MoE.IQ4_XS.gguf) | IQ4_XS | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Teal-Instruct-2x8B-MoE-GGUF/resolve/main/Llama-3-Teal-Instruct-2x8B-MoE.Q4_K_S.gguf) | Q4_K_S | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Teal-Instruct-2x8B-MoE-GGUF/resolve/main/Llama-3-Teal-Instruct-2x8B-MoE.Q4_K_M.gguf) | Q4_K_M | 8.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Teal-Instruct-2x8B-MoE-GGUF/resolve/main/Llama-3-Teal-Instruct-2x8B-MoE.Q5_K_S.gguf) | Q5_K_S | 9.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Teal-Instruct-2x8B-MoE-GGUF/resolve/main/Llama-3-Teal-Instruct-2x8B-MoE.Q5_K_M.gguf) | Q5_K_M | 9.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Teal-Instruct-2x8B-MoE-GGUF/resolve/main/Llama-3-Teal-Instruct-2x8B-MoE.Q6_K.gguf) | Q6_K | 11.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Teal-Instruct-2x8B-MoE-GGUF/resolve/main/Llama-3-Teal-Instruct-2x8B-MoE.Q8_0.gguf) | Q8_0 | 14.6 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
aeolian83/Llama-3-8B-Instruct-cp-transfer_0.7
|
aeolian83
| 2024-05-15T17:30:14Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ko",
"en",
"arxiv:2310.04799",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-15T06:09:17Z |
---
library_name: transformers
license: apache-2.0
language:
- ko
- en
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
aeolian83/Llama-3-Open-Ko-8B-aeolian83-chatvec 모델은
chat-vector 논문( https://arxiv.org/abs/2310.04799v2 )에 근거하여,
llama3의 pre-trained 모델의 parameter와 instruction 모델의 매개변수의 차이를
beomi님의 Llama-3-Open-Ko-8B에 적용한 모델
이 방법이 가능하다면 llama3의 instruction 모델에
llama3의 pre-trained 모델과 한국어 CP모델인 Llama-3-Open-Ko-8B 모델의 매개변수 차이를
instruction 모델에 넣었을 때 어떻게 되는지 확인하는 모델
매개변수에 가중치를 0.7 줌
# Metric
results/all/aeolian83/Llama-3-8B-Instruct-cp-transfer_0.7
| | 0 | 5 |
|:---------------------------------|---------:|---------:|
| kobest_boolq (macro_f1) | 0.786414 | 0.775213 |
| kobest_copa (macro_f1) | 0.67162 | 0.719676 |
| kobest_hellaswag (macro_f1) | 0.436275 | 0.423664 |
| kobest_sentineg (macro_f1) | 0.503784 | 0.901741 |
| kohatespeech (macro_f1) | 0.260168 | 0.339337 |
| kohatespeech_apeach (macro_f1) | 0.337667 | 0.611894 |
| kohatespeech_gen_bias (macro_f1) | 0.124535 | 0.505005 |
| korunsmile (f1) | 0.410636 | 0.323403 |
| nsmc (acc) | 0.53778 | 0.81346 |
| pawsx_ko (acc) | 0.5485 | 0.486 |
# Used Model
- Base model(weight diff를 구하기 위한 베이스 모델) : meta-llama/Meta-Llama-3-8B
- Chat model(weight diff를 제공하는 cp model) : beomi/Llama-3-Open-Ko-8B
- Target model(weight diff를 적용해서 instruction 튠을 하고자 하는 모델) : meta-llama/Meta-Llama-3-8B-Instruct
|
Josephgflowers/TinyLlama-Cinder-Agent-Rag
|
Josephgflowers
| 2024-05-15T17:28:57Z | 144 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Josephgflowers/TinyLlama-3T-Cinder-v1.2",
"base_model:finetune:Josephgflowers/TinyLlama-3T-Cinder-v1.2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-13T15:28:33Z |
---
license: mit
base_model: Josephgflowers/TinyLlama-3T-Cinder-v1.2
tags:
- generated_from_trainer
model-index:
- name: TinyLlama-Cinder-Agent-Rag
results: []
---
This is first pass training. Further training and model update coming.
# TinyLlama-Cinder-Agent-Rag
Special Thanks to https://nationtech.io/ for their generous sponorship in training this model.
This model is a fine-tuned version of [Josephgflowers/TinyLlama-3T-Cinder-v1.2](https://huggingface.co/Josephgflowers/TinyLlama-3T-Cinder-v1.2) on https://huggingface.co/datasets/Josephgflowers/agent_1.
## Model description
This models is trained for RAG, Summary, Function Calling and Tool usage. Trained off of Cinder. Cinder is a chatbot designed for chat about STEM topics and storytelling. More information coming.
More model versions coming soon.
See https://huggingface.co/Josephgflowers/TinyLlama-Cinder-Agent-Rag/blob/main/tinyllama_agent_cinder_txtai-rag.py
For usage example with wiki rag.
## Intended uses & limitations
RAG, Chat, Summary, and tool usage.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 12
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
RichardErkhov/amazingvince_-_Not-WizardLM-2-7B-8bits
|
RichardErkhov
| 2024-05-15T17:28:05Z | 73 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-05-15T17:18:41Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Not-WizardLM-2-7B - bnb 8bits
- Model creator: https://huggingface.co/amazingvince/
- Original model: https://huggingface.co/amazingvince/Not-WizardLM-2-7B/
Original model description:
---
license: apache-2.0
---
# amazingvince/Not-WizardLM-2-7B
<a href="https://colab.research.google.com/gist/pszemraj/d3d74ceab942722b49188606785e2bfd/not-wizardlm-2-7b-inference.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
Included is code ripped from fastchat with the expected chat templating.
Also wiz.pdf is a pdf of the github blog showing the apache 2 release.
Link to wayback machine included: https://web.archive.org/web/20240415221214/https://wizardlm.github.io/WizardLM2/
## example
```python
import dataclasses
from enum import auto, Enum
from typing import List, Tuple, Any
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
@dataclasses.dataclass
class Conversation:
"""A class that keeps all conversation history."""
system: str
roles: List[str]
messages: List[List[str]]
offset: int
sep_style: SeparatorStyle = SeparatorStyle.SINGLE
sep: str = "###"
sep2: str = None
# Used for gradio server
skip_next: bool = False
conv_id: Any = None
def get_prompt(self):
if self.sep_style == SeparatorStyle.SINGLE:
ret = self.system
for role, message in self.messages:
if message:
ret += self.sep + " " + role + ": " + message
else:
ret += self.sep + " " + role + ":"
return ret
elif self.sep_style == SeparatorStyle.TWO:
seps = [self.sep, self.sep2]
ret = self.system + seps[0]
for i, (role, message) in enumerate(self.messages):
if message:
ret += role + ": " + message + seps[i % 2]
else:
ret += role + ":"
return ret
else:
raise ValueError(f"Invalid style: {self.sep_style}")
def append_message(self, role, message):
self.messages.append([role, message])
def to_gradio_chatbot(self):
ret = []
for i, (role, msg) in enumerate(self.messages[self.offset:]):
if i % 2 == 0:
ret.append([msg, None])
else:
ret[-1][-1] = msg
return ret
def copy(self):
return Conversation(
system=self.system,
roles=self.roles,
messages=[[x, y] for x, y in self.messages],
offset=self.offset,
sep_style=self.sep_style,
sep=self.sep,
sep2=self.sep2,
conv_id=self.conv_id)
def dict(self):
return {
"system": self.system,
"roles": self.roles,
"messages": self.messages,
"offset": self.offset,
"sep": self.sep,
"sep2": self.sep2,
"conv_id": self.conv_id,
}
conv = Conversation(
system="A chat between a curious user and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the user's questions.",
roles=("USER", "ASSISTANT"),
messages=[],
offset=0,
sep_style=SeparatorStyle.TWO,
sep=" ",
sep2="</s>",
)
conv.append_message(conv.roles[0], "Why would Microsoft take this down?")
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
result = model.generate(**inputs, max_new_tokens=1000)
generated_ids = result[0]
generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True)
print(generated_text)
```
|
mpasila/Finnish-Alpaca-Tiny-V2-7B
|
mpasila
| 2024-05-15T17:27:57Z | 88 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"fi",
"dataset:mpasila/Finnish-Alpaca-Tiny",
"base_model:LumiOpen/Viking-7B",
"base_model:finetune:LumiOpen/Viking-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-10T17:30:52Z |
---
language:
- fi
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: LumiOpen/Viking-7B
datasets:
- mpasila/Finnish-Alpaca-Tiny
---
This is a merge of [mpasila/Finnish-Alpaca-Tiny-V2-LoRA-7B](https://huggingface.co/mpasila/Finnish-Alpaca-Tiny-V2-LoRA-7B).
LoRA trained in 4-bit with 2k context using [LumiOpen/Viking-7B](https://huggingface.co/LumiOpen/Viking-7B/) as the base model for 1 epoch.
Dataset used is [mpasila/Finnish-Alpaca-Tiny](https://huggingface.co/datasets/mpasila/Finnish-Alpaca-Tiny).
It works relatively well for question and answering. I will make a bigger dataset for the next fine-tune.
### Prompt format: Alpaca
It uses Alpaca format but with a translated instruction at the start:
```
{
"instruction,output": "Alla on ohje, jossa kuvataan tehtävä. Kirjoita vastaus, joka täyttää pyynnön asianmukaisesti.\n\n### Instruction:\n%instruction%\n\n### Response:\n%output%",
"instruction,input,output": "Alla on ohje, jossa kuvataan tehtävä ja joka on yhdistetty kontekstia lisäävään syötteeseen. Kirjoita vastaus, joka täyttää pyynnön asianmukaisesti.\n\n### Instruction:\n%instruction%\n\n### Input:\n%input%\n\n### Response:\n%output%"
}
```
## Evaluation
| Model | Size | Type | FIN-bench (score) |
|-------|------|------|-------|
| **mpasila/Finnish-Alpaca-Tiny-V2-7B** | 7B | Instruct | **0.4654** |
| [mpasila/Finnish-Alpaca-Small-7B](https://huggingface.co/mpasila/Finnish-Alpaca-Small-7B) | 7B | Instruct | 0.3586 |
| [mpasila/Alpacazord-Viking-7B](https://huggingface.co/mpasila/Alpacazord-Viking-7B) | 7B | Instruct | 0.4123 |
| [mpasila/NordicAlpaca-Finnish-V1-7B](https://huggingface.co/mpasila/NordicAlpaca-Finnish-V1-7B) | 7B | Instruct | 0.3891 |
| [mpasila/Finnish-Viking-Alpaca-V1-7B](https://huggingface.co/mpasila/Finnish-Viking-Alpaca-V1-7B) | 7B | Instruct | 0.3943 |
| [Finnish-NLP/llama-7b-finnish-instruct-v0.1](https://huggingface.co/Finnish-NLP/llama-7b-finnish-instruct-v0.1) | 7B | Instruct | 0.4365 |
| [Finnish-NLP/llama-7b-finnish-instruct-v0.2](https://huggingface.co/Finnish-NLP/llama-7b-finnish-instruct-v0.2) | 7B | Instruct | 0.3993 |
| [Finnish-NLP/llama-7b-finnish](https://huggingface.co/Finnish-NLP/llama-7b-finnish) | 7B | Base | 0.2350 |
| [LumiOpen/Viking-7B (1000B)](https://huggingface.co/LumiOpen/Viking-7B) | 7B | Base | 0.3721 |
| [HPLT/gpt-7b-nordic-prerelease](https://huggingface.co/HPLT/gpt-7b-nordic-prerelease) | 7B | Base | 0.3169 |
[Source](https://docs.google.com/spreadsheets/d/1rqJb9dQVihg-Z1_Ras1L_-wuzPg9xNzpdmM2x5HueeY/edit?usp=sharing)
#### FIN-bench scores:
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_analogies | 0|multiple_choice_grade|0.6385|± |0.0423|
|bigbench_arithmetic_1_digit_addition | 0|multiple_choice_grade|0.7200|± |0.0451|
|bigbench_arithmetic_1_digit_division | 0|multiple_choice_grade|0.7391|± |0.0936|
|bigbench_arithmetic_1_digit_multiplication | 0|multiple_choice_grade|0.4800|± |0.0502|
|bigbench_arithmetic_1_digit_subtraction | 0|multiple_choice_grade|0.6300|± |0.0485|
|bigbench_arithmetic_2_digit_addition | 0|multiple_choice_grade|0.4000|± |0.0492|
|bigbench_arithmetic_2_digit_division | 0|multiple_choice_grade|0.5000|± |0.0503|
|bigbench_arithmetic_2_digit_multiplication | 0|multiple_choice_grade|0.2800|± |0.0451|
|bigbench_arithmetic_2_digit_subtraction | 0|multiple_choice_grade|0.4300|± |0.0498|
|bigbench_arithmetic_3_digit_addition | 0|multiple_choice_grade|0.5800|± |0.0496|
|bigbench_arithmetic_3_digit_division | 0|multiple_choice_grade|0.3100|± |0.0465|
|bigbench_arithmetic_3_digit_multiplication | 0|multiple_choice_grade|0.2900|± |0.0456|
|bigbench_arithmetic_3_digit_subtraction | 0|multiple_choice_grade|0.5100|± |0.0502|
|bigbench_arithmetic_4_digit_addition | 0|multiple_choice_grade|0.5300|± |0.0502|
|bigbench_arithmetic_4_digit_division | 0|multiple_choice_grade|0.3900|± |0.0490|
|bigbench_arithmetic_4_digit_multiplication | 0|multiple_choice_grade|0.3100|± |0.0465|
|bigbench_arithmetic_4_digit_subtraction | 0|multiple_choice_grade|0.6200|± |0.0488|
|bigbench_arithmetic_5_digit_addition | 0|multiple_choice_grade|0.6500|± |0.0479|
|bigbench_arithmetic_5_digit_division | 0|multiple_choice_grade|0.3200|± |0.0469|
|bigbench_arithmetic_5_digit_multiplication | 0|multiple_choice_grade|0.3000|± |0.0461|
|bigbench_arithmetic_5_digit_subtraction | 0|multiple_choice_grade|0.6400|± |0.0482|
|bigbench_cause_and_effect_one_sentence | 0|multiple_choice_grade|0.5686|± |0.0700|
|bigbench_cause_and_effect_one_sentence_no_prompt| 0|multiple_choice_grade|0.6471|± |0.0676|
|bigbench_cause_and_effect_two_sentences | 0|multiple_choice_grade|0.4314|± |0.0700|
|bigbench_emotions | 0|multiple_choice_grade|0.2250|± |0.0331|
|bigbench_empirical_judgments | 0|multiple_choice_grade|0.2525|± |0.0439|
|bigbench_general_knowledge | 0|multiple_choice_grade|0.3429|± |0.0571|
|bigbench_hhh_alignment_harmless | 0|multiple_choice_grade|0.3793|± |0.0643|
|bigbench_hhh_alignment_helpful | 0|multiple_choice_grade|0.3390|± |0.0622|
|bigbench_hhh_alignment_honest | 0|multiple_choice_grade|0.3729|± |0.0635|
|bigbench_hhh_alignment_other | 0|multiple_choice_grade|0.5349|± |0.0770|
|bigbench_intent_recognition | 0|multiple_choice_grade|0.2153|± |0.0156|
|bigbench_misconceptions | 0|multiple_choice_grade|0.5224|± |0.0433|
|bigbench_paraphrase | 0|multiple_choice_grade|0.4750|± |0.0354|
|bigbench_sentence_ambiguity | 0|multiple_choice_grade|0.4833|± |0.0651|
|bigbench_similarities_abstraction | 0|multiple_choice_grade|0.6974|± |0.0530|
# Uploaded model
- **Developed by:** mpasila
- **License:** apache-2.0
- **Finetuned from model :** LumiOpen/Viking-7B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mpasila/Finnish-Alpaca-Tiny-V2-7B-exl2-4bpw
|
mpasila
| 2024-05-15T17:27:36Z | 12 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"fi",
"dataset:mpasila/Finnish-Alpaca-Tiny",
"base_model:LumiOpen/Viking-7B",
"base_model:finetune:LumiOpen/Viking-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-11T11:47:48Z |
---
language:
- fi
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: LumiOpen/Viking-7B
datasets:
- mpasila/Finnish-Alpaca-Tiny
---
This is an ExLlamaV2 quantized model in 4bpw of [mpasila/Finnish-Alpaca-Tiny-V2-7B](https://huggingface.co/mpasila/Finnish-Alpaca-Tiny-V2-7B) using the default calibration dataset.
# Original Model card:
This is a merge of [mpasila/Finnish-Alpaca-Tiny-V2-LoRA-7B](https://huggingface.co/mpasila/Finnish-Alpaca-Tiny-V2-LoRA-7B).
LoRA trained in 4-bit with 2k context using [LumiOpen/Viking-7B](https://huggingface.co/LumiOpen/Viking-7B/) as the base model for 1 epoch.
Dataset used is [mpasila/Finnish-Alpaca-Tiny](https://huggingface.co/datasets/mpasila/Finnish-Alpaca-Tiny).
It works relatively well for question and answering. I will make a bigger dataset for the next fine-tune.
### Prompt format: Alpaca
It uses Alpaca format but with a translated instruction at the start:
```
{
"instruction,output": "Alla on ohje, jossa kuvataan tehtävä. Kirjoita vastaus, joka täyttää pyynnön asianmukaisesti.\n\n### Instruction:\n%instruction%\n\n### Response:\n%output%",
"instruction,input,output": "Alla on ohje, jossa kuvataan tehtävä ja joka on yhdistetty kontekstia lisäävään syötteeseen. Kirjoita vastaus, joka täyttää pyynnön asianmukaisesti.\n\n### Instruction:\n%instruction%\n\n### Input:\n%input%\n\n### Response:\n%output%"
}
```
## Evaluation
| Model | Size | Type | FIN-bench (score) |
|-------|------|------|-------|
| **mpasila/Finnish-Alpaca-Tiny-V2-7B** | 7B | Instruct | **0.4654** |
| [mpasila/Finnish-Alpaca-Small-7B](https://huggingface.co/mpasila/Finnish-Alpaca-Small-7B) | 7B | Instruct | 0.3586 |
| [mpasila/Alpacazord-Viking-7B](https://huggingface.co/mpasila/Alpacazord-Viking-7B) | 7B | Instruct | 0.4123 |
| [mpasila/NordicAlpaca-Finnish-V1-7B](https://huggingface.co/mpasila/NordicAlpaca-Finnish-V1-7B) | 7B | Instruct | 0.3891 |
| [mpasila/Finnish-Viking-Alpaca-V1-7B](https://huggingface.co/mpasila/Finnish-Viking-Alpaca-V1-7B) | 7B | Instruct | 0.3943 |
| [Finnish-NLP/llama-7b-finnish-instruct-v0.1](https://huggingface.co/Finnish-NLP/llama-7b-finnish-instruct-v0.1) | 7B | Instruct | 0.4365 |
| [Finnish-NLP/llama-7b-finnish-instruct-v0.2](https://huggingface.co/Finnish-NLP/llama-7b-finnish-instruct-v0.2) | 7B | Instruct | 0.3993 |
| [Finnish-NLP/llama-7b-finnish](https://huggingface.co/Finnish-NLP/llama-7b-finnish) | 7B | Base | 0.2350 |
| [LumiOpen/Viking-7B (1000B)](https://huggingface.co/LumiOpen/Viking-7B) | 7B | Base | 0.3721 |
| [HPLT/gpt-7b-nordic-prerelease](https://huggingface.co/HPLT/gpt-7b-nordic-prerelease) | 7B | Base | 0.3169 |
[Source](https://docs.google.com/spreadsheets/d/1rqJb9dQVihg-Z1_Ras1L_-wuzPg9xNzpdmM2x5HueeY/edit?usp=sharing)
#### FIN-bench scores:
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_analogies | 0|multiple_choice_grade|0.6385|± |0.0423|
|bigbench_arithmetic_1_digit_addition | 0|multiple_choice_grade|0.7200|± |0.0451|
|bigbench_arithmetic_1_digit_division | 0|multiple_choice_grade|0.7391|± |0.0936|
|bigbench_arithmetic_1_digit_multiplication | 0|multiple_choice_grade|0.4800|± |0.0502|
|bigbench_arithmetic_1_digit_subtraction | 0|multiple_choice_grade|0.6300|± |0.0485|
|bigbench_arithmetic_2_digit_addition | 0|multiple_choice_grade|0.4000|± |0.0492|
|bigbench_arithmetic_2_digit_division | 0|multiple_choice_grade|0.5000|± |0.0503|
|bigbench_arithmetic_2_digit_multiplication | 0|multiple_choice_grade|0.2800|± |0.0451|
|bigbench_arithmetic_2_digit_subtraction | 0|multiple_choice_grade|0.4300|± |0.0498|
|bigbench_arithmetic_3_digit_addition | 0|multiple_choice_grade|0.5800|± |0.0496|
|bigbench_arithmetic_3_digit_division | 0|multiple_choice_grade|0.3100|± |0.0465|
|bigbench_arithmetic_3_digit_multiplication | 0|multiple_choice_grade|0.2900|± |0.0456|
|bigbench_arithmetic_3_digit_subtraction | 0|multiple_choice_grade|0.5100|± |0.0502|
|bigbench_arithmetic_4_digit_addition | 0|multiple_choice_grade|0.5300|± |0.0502|
|bigbench_arithmetic_4_digit_division | 0|multiple_choice_grade|0.3900|± |0.0490|
|bigbench_arithmetic_4_digit_multiplication | 0|multiple_choice_grade|0.3100|± |0.0465|
|bigbench_arithmetic_4_digit_subtraction | 0|multiple_choice_grade|0.6200|± |0.0488|
|bigbench_arithmetic_5_digit_addition | 0|multiple_choice_grade|0.6500|± |0.0479|
|bigbench_arithmetic_5_digit_division | 0|multiple_choice_grade|0.3200|± |0.0469|
|bigbench_arithmetic_5_digit_multiplication | 0|multiple_choice_grade|0.3000|± |0.0461|
|bigbench_arithmetic_5_digit_subtraction | 0|multiple_choice_grade|0.6400|± |0.0482|
|bigbench_cause_and_effect_one_sentence | 0|multiple_choice_grade|0.5686|± |0.0700|
|bigbench_cause_and_effect_one_sentence_no_prompt| 0|multiple_choice_grade|0.6471|± |0.0676|
|bigbench_cause_and_effect_two_sentences | 0|multiple_choice_grade|0.4314|± |0.0700|
|bigbench_emotions | 0|multiple_choice_grade|0.2250|± |0.0331|
|bigbench_empirical_judgments | 0|multiple_choice_grade|0.2525|± |0.0439|
|bigbench_general_knowledge | 0|multiple_choice_grade|0.3429|± |0.0571|
|bigbench_hhh_alignment_harmless | 0|multiple_choice_grade|0.3793|± |0.0643|
|bigbench_hhh_alignment_helpful | 0|multiple_choice_grade|0.3390|± |0.0622|
|bigbench_hhh_alignment_honest | 0|multiple_choice_grade|0.3729|± |0.0635|
|bigbench_hhh_alignment_other | 0|multiple_choice_grade|0.5349|± |0.0770|
|bigbench_intent_recognition | 0|multiple_choice_grade|0.2153|± |0.0156|
|bigbench_misconceptions | 0|multiple_choice_grade|0.5224|± |0.0433|
|bigbench_paraphrase | 0|multiple_choice_grade|0.4750|± |0.0354|
|bigbench_sentence_ambiguity | 0|multiple_choice_grade|0.4833|± |0.0651|
|bigbench_similarities_abstraction | 0|multiple_choice_grade|0.6974|± |0.0530|
# Uploaded model
- **Developed by:** mpasila
- **License:** apache-2.0
- **Finetuned from model :** LumiOpen/Viking-7B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mpasila/Finnish-Alpaca-Small-7B
|
mpasila
| 2024-05-15T17:25:45Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"fi",
"dataset:mpasila/Finnish-Alpaca-Small",
"base_model:LumiOpen/Viking-7B",
"base_model:finetune:LumiOpen/Viking-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-14T15:29:02Z |
---
language:
- fi
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: LumiOpen/Viking-7B
datasets:
- mpasila/Finnish-Alpaca-Small
---
This is a merge of [mpasila/Finnish-Alpaca-Small-LoRA-7B](https://huggingface.co/mpasila/Finnish-Alpaca-Small-LoRA-7B).
LoRA trained in 4-bit with 2k context using [LumiOpen/Viking-7B](https://huggingface.co/LumiOpen/Viking-7B/) as the base model for 1 epoch.
Dataset used is [mpasila/Finnish-Alpaca-Small](https://huggingface.co/datasets/mpasila/Finnish-Alpaca-Small).
Re-trained because I have no idea if I used the fully trained model or the partially trained model (of Viking-7B), since it apparently was just released. (After re-training the score lowered noticeably so I wonder if I screwed up something.)
### Prompt format: Alpaca
It uses Alpaca format but with a translated instruction at the start:
```
{
"instruction,output": "Alla on ohje, jossa kuvataan tehtävä. Kirjoita vastaus, joka täyttää pyynnön asianmukaisesti.\n\n### Instruction:\n%instruction%\n\n### Response:\n%output%",
"instruction,input,output": "Alla on ohje, jossa kuvataan tehtävä ja joka on yhdistetty kontekstia lisäävään syötteeseen. Kirjoita vastaus, joka täyttää pyynnön asianmukaisesti.\n\n### Instruction:\n%instruction%\n\n### Input:\n%input%\n\n### Response:\n%output%"
}
```
## Evaluation
| Model | Size | Type | FIN-bench (score) |
|-------|------|------|-------|
| **mpasila/Finnish-Alpaca-Small-7B** | 7B | Instruct | 0.3586 |
| [mpasila/Finnish-Alpaca-Tiny-V2-7B](https://huggingface.co/mpasila/Finnish-Alpaca-Tiny-V2-7B) | 7B | Instruct | **0.4654** |
| [mpasila/Alpacazord-Viking-7B](https://huggingface.co/mpasila/Alpacazord-Viking-7B) | 7B | Instruct | 0.4123 |
| [mpasila/NordicAlpaca-Finnish-V1-7B](https://huggingface.co/mpasila/NordicAlpaca-Finnish-V1-7B) | 7B | Instruct | 0.3891 |
| [mpasila/Finnish-Viking-Alpaca-V1-7B](https://huggingface.co/mpasila/Finnish-Viking-Alpaca-V1-7B) | 7B | Instruct | 0.3943 |
| [Finnish-NLP/llama-7b-finnish-instruct-v0.1](https://huggingface.co/Finnish-NLP/llama-7b-finnish-instruct-v0.1) | 7B | Instruct | 0.4365 |
| [Finnish-NLP/llama-7b-finnish-instruct-v0.2](https://huggingface.co/Finnish-NLP/llama-7b-finnish-instruct-v0.2) | 7B | Instruct | 0.3993 |
| [Finnish-NLP/llama-7b-finnish](https://huggingface.co/Finnish-NLP/llama-7b-finnish) | 7B | Base | 0.2350 |
| [LumiOpen/Viking-7B (1000B)](https://huggingface.co/LumiOpen/Viking-7B) | 7B | Base | 0.3721 |
| [HPLT/gpt-7b-nordic-prerelease](https://huggingface.co/HPLT/gpt-7b-nordic-prerelease) | 7B | Base | 0.3169 |
[Source](https://docs.google.com/spreadsheets/d/1rqJb9dQVihg-Z1_Ras1L_-wuzPg9xNzpdmM2x5HueeY/edit?usp=sharing)
#### FIN-bench scores:
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_analogies | 0|multiple_choice_grade|0.5923|± |0.0433|
|bigbench_arithmetic_1_digit_addition | 0|multiple_choice_grade|0.2700|± |0.0446|
|bigbench_arithmetic_1_digit_division | 0|multiple_choice_grade|0.4783|± |0.1065|
|bigbench_arithmetic_1_digit_multiplication | 0|multiple_choice_grade|0.2600|± |0.0441|
|bigbench_arithmetic_1_digit_subtraction | 0|multiple_choice_grade|0.2200|± |0.0416|
|bigbench_arithmetic_2_digit_addition | 0|multiple_choice_grade|0.1700|± |0.0378|
|bigbench_arithmetic_2_digit_division | 0|multiple_choice_grade|0.3600|± |0.0482|
|bigbench_arithmetic_2_digit_multiplication | 0|multiple_choice_grade|0.2000|± |0.0402|
|bigbench_arithmetic_2_digit_subtraction | 0|multiple_choice_grade|0.1300|± |0.0338|
|bigbench_arithmetic_3_digit_addition | 0|multiple_choice_grade|0.3100|± |0.0465|
|bigbench_arithmetic_3_digit_division | 0|multiple_choice_grade|0.2100|± |0.0409|
|bigbench_arithmetic_3_digit_multiplication | 0|multiple_choice_grade|0.1600|± |0.0368|
|bigbench_arithmetic_3_digit_subtraction | 0|multiple_choice_grade|0.2300|± |0.0423|
|bigbench_arithmetic_4_digit_addition | 0|multiple_choice_grade|0.3900|± |0.0490|
|bigbench_arithmetic_4_digit_division | 0|multiple_choice_grade|0.2300|± |0.0423|
|bigbench_arithmetic_4_digit_multiplication | 0|multiple_choice_grade|0.2100|± |0.0409|
|bigbench_arithmetic_4_digit_subtraction | 0|multiple_choice_grade|0.4500|± |0.0500|
|bigbench_arithmetic_5_digit_addition | 0|multiple_choice_grade|0.4800|± |0.0502|
|bigbench_arithmetic_5_digit_division | 0|multiple_choice_grade|0.0700|± |0.0256|
|bigbench_arithmetic_5_digit_multiplication | 0|multiple_choice_grade|0.1700|± |0.0378|
|bigbench_arithmetic_5_digit_subtraction | 0|multiple_choice_grade|0.5800|± |0.0496|
|bigbench_cause_and_effect_one_sentence | 0|multiple_choice_grade|0.6275|± |0.0684|
|bigbench_cause_and_effect_one_sentence_no_prompt| 0|multiple_choice_grade|0.6667|± |0.0667|
|bigbench_cause_and_effect_two_sentences | 0|multiple_choice_grade|0.5098|± |0.0707|
|bigbench_emotions | 0|multiple_choice_grade|0.3312|± |0.0373|
|bigbench_empirical_judgments | 0|multiple_choice_grade|0.3333|± |0.0476|
|bigbench_general_knowledge | 0|multiple_choice_grade|0.2857|± |0.0544|
|bigbench_hhh_alignment_harmless | 0|multiple_choice_grade|0.3793|± |0.0643|
|bigbench_hhh_alignment_helpful | 0|multiple_choice_grade|0.3559|± |0.0629|
|bigbench_hhh_alignment_honest | 0|multiple_choice_grade|0.3559|± |0.0629|
|bigbench_hhh_alignment_other | 0|multiple_choice_grade|0.5349|± |0.0770|
|bigbench_intent_recognition | 0|multiple_choice_grade|0.1546|± |0.0138|
|bigbench_misconceptions | 0|multiple_choice_grade|0.5448|± |0.0432|
|bigbench_paraphrase | 0|multiple_choice_grade|0.5300|± |0.0354|
|bigbench_sentence_ambiguity | 0|multiple_choice_grade|0.4333|± |0.0645|
|bigbench_similarities_abstraction | 0|multiple_choice_grade|0.6974|± |0.0530|
# Uploaded model
- **Developed by:** mpasila
- **License:** apache-2.0
- **Finetuned from model :** LumiOpen/Viking-7B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
YasaminAbb/Llama-2-7b-CNN_Q_lora_Summarizer-merged-peft
|
YasaminAbb
| 2024-05-15T17:23:46Z | 2 | 1 |
peft
|
[
"peft",
"region:us"
] | null | 2024-05-15T17:23:42Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
emilykang/medner-surgery_lora
|
emilykang
| 2024-05-15T17:20:29Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] | null | 2024-05-15T16:34:47Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
datasets:
- generator
model-index:
- name: medner-surgery_lora
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. -->
# medner-surgery_lora
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|
sumitsahay/Llama2-7B-FinanceNews
|
sumitsahay
| 2024-05-15T17:15:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T17:15:50Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **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]
|
MiVaCod/ppo-Huggy
|
MiVaCod
| 2024-05-15T17:05:23Z | 7 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2024-05-15T17:01:27Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
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: MiVaCod/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
RichardErkhov/unsloth_-_Hermes-2-Pro-Mistral-7B-8bits
|
RichardErkhov
| 2024-05-15T17:03:09Z | 69 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-05-15T16:55:09Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Hermes-2-Pro-Mistral-7B - bnb 8bits
- Model creator: https://huggingface.co/unsloth/
- Original model: https://huggingface.co/unsloth/Hermes-2-Pro-Mistral-7B/
Original model description:
---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- unsloth
- transformers
- yi
- yi-34b
---
# Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
## ✨ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Gemma 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) | 2.4x faster | 58% less |
| **Mistral 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less |
| **Llama-2 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing) | 2.2x faster | 43% less |
| **TinyLlama** | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less |
| **CodeLlama 34b** A100 | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | 1.9x faster | 27% less |
| **Mistral 7b** 1xT4 | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less |
| **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less |
- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
|
RichardErkhov/ibm-granite_-_granite-8b-code-instruct-8bits
|
RichardErkhov
| 2024-05-15T16:58:17Z | 62 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2405.04324",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-05-15T16:48:44Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
granite-8b-code-instruct - bnb 8bits
- Model creator: https://huggingface.co/ibm-granite/
- Original model: https://huggingface.co/ibm-granite/granite-8b-code-instruct/
Original model description:
---
pipeline_tag: text-generation
base_model: ibm-granite/granite-8b-code-base
inference: false
license: apache-2.0
datasets:
- bigcode/commitpackft
- TIGER-Lab/MathInstruct
- meta-math/MetaMathQA
- glaiveai/glaive-code-assistant-v3
- glaive-function-calling-v2
- bugdaryan/sql-create-context-instruction
- garage-bAInd/Open-Platypus
- nvidia/HelpSteer
metrics:
- code_eval
library_name: transformers
tags:
- code
- granite
model-index:
- name: granite-8b-code-instruct
results:
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(Python)
metrics:
- name: pass@1
type: pass@1
value: 57.9
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(JavaScript)
metrics:
- name: pass@1
type: pass@1
value: 52.4
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(Java)
metrics:
- name: pass@1
type: pass@1
value: 58.5
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(Go)
metrics:
- name: pass@1
type: pass@1
value: 43.3
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(C++)
metrics:
- name: pass@1
type: pass@1
value: 48.2
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(Rust)
metrics:
- name: pass@1
type: pass@1
value: 37.2
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(Python)
metrics:
- name: pass@1
type: pass@1
value: 53.0
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(JavaScript)
metrics:
- name: pass@1
type: pass@1
value: 42.7
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(Java)
metrics:
- name: pass@1
type: pass@1
value: 52.4
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(Go)
metrics:
- name: pass@1
type: pass@1
value: 36.6
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(C++)
metrics:
- name: pass@1
type: pass@1
value: 43.9
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(Rust)
metrics:
- name: pass@1
type: pass@1
value: 16.5
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(Python)
metrics:
- name: pass@1
type: pass@1
value: 39.6
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(JavaScript)
metrics:
- name: pass@1
type: pass@1
value: 40.9
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(Java)
metrics:
- name: pass@1
type: pass@1
value: 48.2
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(Go)
metrics:
- name: pass@1
type: pass@1
value: 41.5
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(C++)
metrics:
- name: pass@1
type: pass@1
value: 39.0
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(Rust)
metrics:
- name: pass@1
type: pass@1
value: 32.9
veriefied: false
---
# Granite-8B-Code-Instruct
## Model Summary
**Granite-8B-Code-Instruct** is a 8B parameter model fine tuned from *Granite-8B-Code-Base* on a combination of **permissively licensed** instruction data to enhance instruction following capabilities including logical reasoning and problem-solving skills.
- **Developers:** IBM Research
- **GitHub Repository:** [ibm-granite/granite-code-models](https://github.com/ibm-granite/granite-code-models)
- **Paper:** [Granite Code Models: A Family of Open Foundation Models for Code Intelligence](https://arxiv.org/abs/2405.04324)
- **Release Date**: May 6th, 2024
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
## Usage
> [!WARNING]
> **You need to build transformers from source to use this model correctly.**
> Relevant PR: https://github.com/huggingface/transformers/pull/30031
> ```shell
> git clone https://github.com/huggingface/transformers
> cd transformers/
> pip install ./
> cd ..
> ```
### Intended use
The model is designed to respond to coding related instructions and can be used to build coding assitants.
<!-- TO DO: Check starcoder2 instruct code example that includes the template https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1 -->
### Generation
This is a simple example of how to use **Granite-8B-Code-Instruct** model.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # or "cpu"
model_path = "ibm-granite/granite-8b-code-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()
# change input text as desired
chat = [
{ "role": "user", "content": "Write a code to find the maximum value in a list of numbers." },
]
chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
# tokenize the text
input_tokens = tokenizer(chat, return_tensors="pt")
# transfer tokenized inputs to the device
for i in input_tokens:
input_tokens[i] = input_tokens[i].to(device)
# generate output tokens
output = model.generate(**input_tokens, max_new_tokens=100)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# loop over the batch to print, in this example the batch size is 1
for i in output:
print(i)
```
<!-- TO DO: Check this part -->
## Training Data
Granite Code Instruct models are trained on the following types of data.
* Code Commits Datasets: we sourced code commits data from the [CommitPackFT](https://huggingface.co/datasets/bigcode/commitpackft) dataset, a filtered version of the full CommitPack dataset. From CommitPackFT dataset, we only consider data for 92 programming languages. Our inclusion criteria boils down to selecting programming languages common across CommitPackFT and the 116 languages that we considered to pretrain the code-base model (*Granite-8B-Code-Base*).
* Math Datasets: We consider two high-quality math datasets, [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) and [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA). Due to license issues, we filtered out GSM8K-RFT and Camel-Math from MathInstruct dataset.
* Code Instruction Datasets: We use [Glaive-Code-Assistant-v3](https://huggingface.co/datasets/glaiveai/glaive-code-assistant-v3), [Glaive-Function-Calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2), [NL2SQL11](https://huggingface.co/datasets/bugdaryan/sql-create-context-instruction) and a small collection of synthetic API calling datasets.
* Language Instruction Datasets: We include high-quality datasets such as [HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer) and an open license-filtered version of [Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus). We also include a collection of hardcoded prompts to ensure our model generates correct outputs given inquiries about its name or developers.
## Infrastructure
We train the Granite Code models using two of IBM's super computing clusters, namely Vela and Blue Vela, both outfitted with NVIDIA A100 and H100 GPUs respectively. These clusters provide a scalable and efficient infrastructure for training our models over thousands of GPUs.
## Ethical Considerations and Limitations
Granite code instruct models are primarily finetuned using instruction-response pairs across a specific set of programming languages. Thus, their performance may be limited with out-of-domain programming languages. In this situation, it is beneficial providing few-shot examples to steer the model's output. Moreover, developers should perform safety testing and target-specific tuning before deploying these models on critical applications. The model also inherits ethical considerations and limitations from its base model. For more information, please refer to *[Granite-8B-Code-Base](https://huggingface.co/ibm-granite/granite-8b-code-base)* model card.
|
RichardErkhov/unsloth_-_Hermes-2-Pro-Mistral-7B-4bits
|
RichardErkhov
| 2024-05-15T16:54:22Z | 64 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-05-15T16:49:26Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Hermes-2-Pro-Mistral-7B - bnb 4bits
- Model creator: https://huggingface.co/unsloth/
- Original model: https://huggingface.co/unsloth/Hermes-2-Pro-Mistral-7B/
Original model description:
---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- unsloth
- transformers
- yi
- yi-34b
---
# Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
## ✨ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Gemma 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) | 2.4x faster | 58% less |
| **Mistral 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less |
| **Llama-2 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing) | 2.2x faster | 43% less |
| **TinyLlama** | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less |
| **CodeLlama 34b** A100 | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | 1.9x faster | 27% less |
| **Mistral 7b** 1xT4 | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less |
| **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less |
- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
|
emilykang/Gemma_medner-cardiovascular_pulmonary_lora
|
emilykang
| 2024-05-15T16:53:13Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-05-15T12:58:47Z |
---
license: gemma
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: google/gemma-2b
datasets:
- generator
model-index:
- name: Gemma_medner-cardiovascular_pulmonary_lora
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. -->
# Gemma_medner-cardiovascular_pulmonary_lora
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|
digiplay/MRMD_0505
|
digiplay
| 2024-05-15T16:52:09Z | 800 | 4 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-04-17T09:09:56Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
in test...
MRMD_0505.safetensors
generated by Huggingface's API :
|
fefzzz/my-finetuned-bart
|
fefzzz
| 2024-05-15T16:51:53Z | 97 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-15T16:51:31Z |
---
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]
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### Model Sources [optional]
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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]
### 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
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **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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## Glossary [optional]
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|
plaire/vit-base-patch16-224-in21k-finetuned-lora-webpage2
|
plaire
| 2024-05-15T16:49:30Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T16:29:13Z |
---
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
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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. -->
[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]
## Model Card Contact
[More Information Needed]
|
alparslanahmed/llama3_8b-tr-gguf-q4
|
alparslanahmed
| 2024-05-15T16:48:50Z | 6 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:quantized:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T16:46:23Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** alparslanahmed
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
RichardErkhov/ibm-granite_-_granite-8b-code-instruct-4bits
|
RichardErkhov
| 2024-05-15T16:47:14Z | 69 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2405.04324",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-05-15T16:41:53Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
granite-8b-code-instruct - bnb 4bits
- Model creator: https://huggingface.co/ibm-granite/
- Original model: https://huggingface.co/ibm-granite/granite-8b-code-instruct/
Original model description:
---
pipeline_tag: text-generation
base_model: ibm-granite/granite-8b-code-base
inference: false
license: apache-2.0
datasets:
- bigcode/commitpackft
- TIGER-Lab/MathInstruct
- meta-math/MetaMathQA
- glaiveai/glaive-code-assistant-v3
- glaive-function-calling-v2
- bugdaryan/sql-create-context-instruction
- garage-bAInd/Open-Platypus
- nvidia/HelpSteer
metrics:
- code_eval
library_name: transformers
tags:
- code
- granite
model-index:
- name: granite-8b-code-instruct
results:
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(Python)
metrics:
- name: pass@1
type: pass@1
value: 57.9
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(JavaScript)
metrics:
- name: pass@1
type: pass@1
value: 52.4
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(Java)
metrics:
- name: pass@1
type: pass@1
value: 58.5
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(Go)
metrics:
- name: pass@1
type: pass@1
value: 43.3
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(C++)
metrics:
- name: pass@1
type: pass@1
value: 48.2
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(Rust)
metrics:
- name: pass@1
type: pass@1
value: 37.2
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(Python)
metrics:
- name: pass@1
type: pass@1
value: 53.0
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(JavaScript)
metrics:
- name: pass@1
type: pass@1
value: 42.7
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(Java)
metrics:
- name: pass@1
type: pass@1
value: 52.4
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(Go)
metrics:
- name: pass@1
type: pass@1
value: 36.6
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(C++)
metrics:
- name: pass@1
type: pass@1
value: 43.9
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(Rust)
metrics:
- name: pass@1
type: pass@1
value: 16.5
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(Python)
metrics:
- name: pass@1
type: pass@1
value: 39.6
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(JavaScript)
metrics:
- name: pass@1
type: pass@1
value: 40.9
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(Java)
metrics:
- name: pass@1
type: pass@1
value: 48.2
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(Go)
metrics:
- name: pass@1
type: pass@1
value: 41.5
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(C++)
metrics:
- name: pass@1
type: pass@1
value: 39.0
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(Rust)
metrics:
- name: pass@1
type: pass@1
value: 32.9
veriefied: false
---
# Granite-8B-Code-Instruct
## Model Summary
**Granite-8B-Code-Instruct** is a 8B parameter model fine tuned from *Granite-8B-Code-Base* on a combination of **permissively licensed** instruction data to enhance instruction following capabilities including logical reasoning and problem-solving skills.
- **Developers:** IBM Research
- **GitHub Repository:** [ibm-granite/granite-code-models](https://github.com/ibm-granite/granite-code-models)
- **Paper:** [Granite Code Models: A Family of Open Foundation Models for Code Intelligence](https://arxiv.org/abs/2405.04324)
- **Release Date**: May 6th, 2024
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
## Usage
> [!WARNING]
> **You need to build transformers from source to use this model correctly.**
> Relevant PR: https://github.com/huggingface/transformers/pull/30031
> ```shell
> git clone https://github.com/huggingface/transformers
> cd transformers/
> pip install ./
> cd ..
> ```
### Intended use
The model is designed to respond to coding related instructions and can be used to build coding assitants.
<!-- TO DO: Check starcoder2 instruct code example that includes the template https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1 -->
### Generation
This is a simple example of how to use **Granite-8B-Code-Instruct** model.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # or "cpu"
model_path = "ibm-granite/granite-8b-code-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()
# change input text as desired
chat = [
{ "role": "user", "content": "Write a code to find the maximum value in a list of numbers." },
]
chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
# tokenize the text
input_tokens = tokenizer(chat, return_tensors="pt")
# transfer tokenized inputs to the device
for i in input_tokens:
input_tokens[i] = input_tokens[i].to(device)
# generate output tokens
output = model.generate(**input_tokens, max_new_tokens=100)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# loop over the batch to print, in this example the batch size is 1
for i in output:
print(i)
```
<!-- TO DO: Check this part -->
## Training Data
Granite Code Instruct models are trained on the following types of data.
* Code Commits Datasets: we sourced code commits data from the [CommitPackFT](https://huggingface.co/datasets/bigcode/commitpackft) dataset, a filtered version of the full CommitPack dataset. From CommitPackFT dataset, we only consider data for 92 programming languages. Our inclusion criteria boils down to selecting programming languages common across CommitPackFT and the 116 languages that we considered to pretrain the code-base model (*Granite-8B-Code-Base*).
* Math Datasets: We consider two high-quality math datasets, [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) and [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA). Due to license issues, we filtered out GSM8K-RFT and Camel-Math from MathInstruct dataset.
* Code Instruction Datasets: We use [Glaive-Code-Assistant-v3](https://huggingface.co/datasets/glaiveai/glaive-code-assistant-v3), [Glaive-Function-Calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2), [NL2SQL11](https://huggingface.co/datasets/bugdaryan/sql-create-context-instruction) and a small collection of synthetic API calling datasets.
* Language Instruction Datasets: We include high-quality datasets such as [HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer) and an open license-filtered version of [Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus). We also include a collection of hardcoded prompts to ensure our model generates correct outputs given inquiries about its name or developers.
## Infrastructure
We train the Granite Code models using two of IBM's super computing clusters, namely Vela and Blue Vela, both outfitted with NVIDIA A100 and H100 GPUs respectively. These clusters provide a scalable and efficient infrastructure for training our models over thousands of GPUs.
## Ethical Considerations and Limitations
Granite code instruct models are primarily finetuned using instruction-response pairs across a specific set of programming languages. Thus, their performance may be limited with out-of-domain programming languages. In this situation, it is beneficial providing few-shot examples to steer the model's output. Moreover, developers should perform safety testing and target-specific tuning before deploying these models on critical applications. The model also inherits ethical considerations and limitations from its base model. For more information, please refer to *[Granite-8B-Code-Base](https://huggingface.co/ibm-granite/granite-8b-code-base)* model card.
|
harheem/bert-finetuned-ner-ko
|
harheem
| 2024-05-15T16:46:35Z | 102 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-05-15T13:47:30Z |
---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-ko
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. -->
# bert-finetuned-ner-ko
This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0783
- Precision: 0.9554
- Recall: 0.9583
- F1: 0.9568
- Accuracy: 0.9794
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 366 | 0.0930 | 0.9425 | 0.9430 | 0.9428 | 0.9729 |
| 0.1523 | 2.0 | 732 | 0.0754 | 0.9513 | 0.9567 | 0.9540 | 0.9780 |
| 0.054 | 3.0 | 1098 | 0.0783 | 0.9554 | 0.9583 | 0.9568 | 0.9794 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
AI4Protein/ProSST-NO_R2S
|
AI4Protein
| 2024-05-15T16:45:37Z | 135 | 0 |
transformers
|
[
"transformers",
"safetensors",
"ProSST",
"fill-mask",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2024-05-15T15:54:45Z |
---
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
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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|
picault/Mistral-7B-v0.1-lipogram
|
picault
| 2024-05-15T16:45:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T15:32:52Z |
---
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. -->
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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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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## 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
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### Training Procedure
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
#### Testing Data
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#### 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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|
taffyfrombilibili/lora_model
|
taffyfrombilibili
| 2024-05-15T16:42:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T16:42:28Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** taffyfrombilibili
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Trelis/idefics2-8b-chatty-bf16
|
Trelis
| 2024-05-15T16:42:33Z | 14 | 1 |
transformers
|
[
"transformers",
"safetensors",
"idefics2",
"image-text-to-text",
"multimodal",
"vision",
"en",
"dataset:HuggingFaceM4/OBELICS",
"dataset:laion/laion-coco",
"dataset:wikipedia",
"dataset:facebook/pmd",
"dataset:pixparse/idl-wds",
"dataset:pixparse/pdfa-eng-wds",
"dataset:wendlerc/RenderedText",
"dataset:HuggingFaceM4/the_cauldron",
"dataset:teknium/OpenHermes-2.5",
"dataset:GAIR/lima",
"dataset:databricks/databricks-dolly-15k",
"dataset:meta-math/MetaMathQA",
"dataset:TIGER-Lab/MathInstruct",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:camel-ai/math",
"dataset:AtlasUnified/atlas-math-sets",
"dataset:tiedong/goat",
"dataset:Lin-Chen/ShareGPT4V",
"dataset:jxu124/llava_conversation_58k",
"arxiv:2306.16527",
"arxiv:2405.02246",
"arxiv:2307.06304",
"arxiv:2311.07575",
"arxiv:2103.03206",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-05-15T15:48:21Z |
---
language:
- en
license: apache-2.0
tags:
- multimodal
- vision
- image-text-to-text
datasets:
- HuggingFaceM4/OBELICS
- laion/laion-coco
- wikipedia
- facebook/pmd
- pixparse/idl-wds
- pixparse/pdfa-eng-wds
- wendlerc/RenderedText
- HuggingFaceM4/the_cauldron
- teknium/OpenHermes-2.5
- GAIR/lima
- databricks/databricks-dolly-15k
- meta-math/MetaMathQA
- TIGER-Lab/MathInstruct
- microsoft/orca-math-word-problems-200k
- camel-ai/math
- AtlasUnified/atlas-math-sets
- tiedong/goat
- Lin-Chen/ShareGPT4V
- jxu124/llava_conversation_58k
---
# bf-16 version of the Idefics2 8B Chatty Model
For ~2X faster download speeds. (Note that the vision transformer is still in float32)
[Original Model Here](https://huggingface.co/HuggingFaceM4/idefics2-8b-chatty)
***As of April 18th, 2024**, Idefics2 is part of the `4.40.0` Transformers pypi release. Please upgrade your Transformers version (`pip install transformers --upgrade`).*
# Idefics2
Idefics2 is an open multimodal model that accepts arbitrary sequences of image and text inputs and produces text outputs. The model can answer questions about images, describe visual content, create stories grounded on multiple images, or simply behave as a pure language model without visual inputs. It improves upon [Idefics1](https://huggingface.co/HuggingFaceM4/idefics-80b-instruct), significantly enhancing capabilities around OCR, document understanding and visual reasoning.
We release under the Apache 2.0 license 2 checkpoints:
- [idefics2-8b-base](https://huggingface.co/HuggingFaceM4/idefics2-8b-base): the base model
- [idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b): the base model fine-tuned on a mixture of supervised and instruction datasets (text-only and multimodal datasets)
- [idefics2-8b-chatty](https://huggingface.co/HuggingFaceM4/idefics2-8b-chatty): `idefics2-8b` further fine-tuned on long conservation
# Model Summary
- **Developed by:** Hugging Face
- **Model type:** Multi-modal model (image+text)
- **Language(s) (NLP):** en
- **License:** Apache 2.0
- **Parent Models:** [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) and [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Resources for more information:**
- Description of [OBELICS](https://huggingface.co/datasets/HuggingFaceM4/OBELICS): [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
](https://huggingface.co/papers/2306.16527)
- Paper: [What matters when building vision-language models?
](https://huggingface.co/papers/2405.02246)
# Uses
`idefics2-8b-base` and `idefics2-8b` can be used to perform inference on multimodal (image + text) tasks in which the input is composed of a text query along with one (or multiple) image(s). Text and images can be arbitrarily interleaved. That includes image captioning, visual question answering, etc. These model does not support image generation.
For optimal results, we recommend fine-tuning `idefics2-8b` on one's specific use-case and data. In fact, the instruction-fine-tuned model (`idefics2-8b`) is significantly better at following instructions from users and thus should be preferred when using the models out-of-the-box or as a starting point for fine-tuning.
`idefics2-8b` usually generates very short answers. For long generations, use `idefics2-8b-chatty`, which was further fine-tuned on long conversations.
As a starting point, we provide fine-tuning codes that can be adapted for one's particular scenario:
- With the [TRL library](https://github.com/huggingface/trl): [Script](https://gist.github.com/edbeeching/228652fc6c2b29a1641be5a5778223cb)
- With the [Hugging Face Trainer](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#api-reference%20][%20transformers.Trainer): [Tutorial notebook](https://colab.research.google.com/drive/1NtcTgRbSBKN7pYD3Vdx1j9m8pt3fhFDB?usp=sharing)
# Technical summary
Idefics2 exhibits strong performance for a model of its size (8B parameters) when compared to other open multimodal models and is often competitive with closed-source systems. As such, it serves as a strong foundation for various use-case specific fine-tunings.
<details><summary>For more details, expand the result table.</summary>
| <nobr>Model</nobr> | <nobr>Open <br>weights</nobr> | <nobr>Size</nobr> | <nobr># tokens <br>per image</nobr> | <nobr>MMMU <br>(val/test)</nobr> | <nobr>MathVista <br>(testmini)</nobr> | <nobr>TextVQA <br>(val)</nobr> | <nobr>MMBench <br>(test)</nobr> | <nobr>VQAv2 <br>(test-dev)</nobr> | <nobr>DocVQA <br>(test)</nobr> |
|--------------|-------------|------|--------------------|-----------|-----------|---------|---------|---------|---------|
| [DeepSeek-VL](https://huggingface.co/deepseek-ai/deepseek-vl-7b-chat) | ✅ | 7B | 576 | 36.6/- | 36.1 | 64.4 | 73.2 | - | 49.6 |
| [LLaVa-NeXT-Mistral-7B](https://huggingface.co/liuhaotian/llava-v1.6-mistral-7b) | ✅ | 7B | 2880 | 35.3/- | 37.7 | 65.7 | 68.7 | 82.2 | - |
| [LLaVa-NeXT-13B](https://huggingface.co/liuhaotian/llava-v1.6-vicuna-13b) | ✅ | 13B | 2880 | 36.2/- | 35.3 | 67.1 | 70.0 | 82.8 | - |
| [LLaVa-NeXT-34B](https://huggingface.co/liuhaotian/llava-v1.6-34b) | ✅ | 34B | 2880 | 51.1/44.7 | 46.5 | 69.5 | 79.3 | 83.7 | - | - |
| MM1-Chat-7B | ❌ | 7B | 720 | 37.0/35.6 | 35.9 | 72.8 | 72.3 | - | - |
| MM1-Chat-30B | ❌ | 30B | 720 | 44.7/40.3 | 39.4 | 73.5 | 75.1 | 83.7 | |
| Gemini 1.0 Pro | ❌ | 🤷♂️ | 🤷♂️ | 47.9/- | 45.2 | 74.6 | - | 71.2 | 88.1 |
| Gemini 1.5 Pro | ❌ | 🤷♂️ | 🤷♂️ | 58.5/- | 52.1 | 73.5 | - | 73.2 | 86.5 |
| Claude 3 Haiku | ❌ | 🤷♂️ | 🤷♂️ | 50.2/- | 46.4 | - | - | - | 88.8 |
| | | | | | | |
| [Idefics1 instruct](https://huggingface.co/HuggingFaceM4/idefics-80b-instruct) (32-shots) | ✅ | 80B | - | - | - | 39.3 | - | 68.8 | - |
| | | | | | | |
| **Idefics2** (w/o im. split) | ✅ | 8B | 64 | 43.5/37.9 | 51.6 | 70.4 | 76.8 | 80.8 | 67.3 |
| **Idefics2** (w/ im. split) | ✅ | 8B | 320 | 43.0/37.7 | 51.4 | 73.0 | 76.7 | 81.2 | 74.0 |
</details>
**Idefics2 introduces several carefully abalated improvements over Idefics1:**
- We manipulate images in their **native resolutions** (up to 980 x 980) and **native aspect ratios** by following the [NaViT](https://arxiv.org/abs/2307.06304) strategy. That circumvent the need to resize images to fixed-size squares as it has been historically been done in the computer vision community. Additionally, we follow the strategy from [SPHINX](https://arxiv.org/abs/2311.07575) and (optionally) allow **sub-image splitting** and passing **images of very large resolution**.
- We significantly enhanced **OCR abilities** by integrating data that requires the model to transcribe text in an image or a document. We also improved abilities in **answering questions on charts, figures, and documents** with appropriate training data.
- We departed from the Idefics1's architecture (gated cross-attentions) and **simplified the integration of visual features** into the language backbone. The images are fed to the vision encoder followed by a learned [Perceiver](https://arxiv.org/abs/2103.03206) pooling and a MLP modality projection. That pooled sequence is then concatenated with the text embeddings to obtain an (interleaved) sequence of image(s) and text(s).
- All of these improvements along with better pre-trained backbones yield a significant jump in performance over Idefics1 for a model that is **10x smaller**.
Idefics2 is trained in 2 stages for maximum efficiency. In a first stage, images are fed to the model at SigLIP's native resolution (squares of 384 x 384). In the second stage, images are fed to the model at their native resolution (with a maximum of 980 and a minimum of 378) and native aspect ratio. Since high resolution is necessary for OCR data, we add PDFA, Rendered-Text, and IDL to OBELICS, LAION Coco and PMD during that second stage.
Following this, we perform instruction fine-tuning on [The Cauldron](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron), a collection of 50 manually curated vision-language datasets along with 9 text-only instruction fine-tuning datasets:
- [OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5)
- [lima](https://huggingface.co/datasets/GAIR/lima)
- [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
- [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA)
- [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
- [orca-math-word-problems-200k](https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k)
- [math](https://huggingface.co/datasets/camel-ai/math)
- [atlas-math-sets](https://huggingface.co/datasets/AtlasUnified/atlas-math-sets)
- [goat](https://huggingface.co/datasets/tiedong/goat)
We use Lora to train the parameters initialized from pre-trained backbones and full fine-tuning for newly initialized parameters (modality connector), as we find this strategy to be more stable as well as more computationally efficient.
More details (training procedure, data selection, hyper-parameters, etc.) along with lessons learned from our ablations will be available in an upcoming technical report.
# How to Get Started
This section shows snippets of code for generation for `idefics2-8b-base` and `idefics2-8b`. The codes only differ by the input formatting. Let's first define some common imports and inputs.
```python
import requests
import torch
from PIL import Image
from io import BytesIO
from transformers import AutoProcessor, AutoModelForVision2Seq
from transformers.image_utils import load_image
DEVICE = "cuda:0"
# Note that passing the image urls (instead of the actual pil images) to the processor is also possible
image1 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg")
image2 = load_image("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg")
image3 = load_image("https://cdn.britannica.com/68/170868-050-8DDE8263/Golden-Gate-Bridge-San-Francisco.jpg")
```
**For `idefics2-8b-base`**
<details><summary>Click to expand.</summary>
```python
processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b-base")
model = AutoModelForVision2Seq.from_pretrained(
"HuggingFaceM4/idefics2-8b-base",
).to(DEVICE)
# Create inputs
prompts = [
"<image>In this image, we can see the city of New York, and more specifically the Statue of Liberty.<image>In this image,",
"In which city is that bridge located?<image>",
]
images = [[image1, image2], [image3]]
inputs = processor(text=prompts, images=images, padding=True, return_tensors="pt")
inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
# Generate
generated_ids = model.generate(**inputs, max_new_tokens=500)
generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(generated_texts)
# ['In this image, we can see the city of New York, and more specifically the Statue of Liberty. In this image, we can see the city of Chicago, and more specifically the skyscrapers of the city.', 'In which city is that bridge located? The Golden Gate Bridge is a suspension bridge spanning the Golden Gate, the one-mile-wide (1.6 km) strait connecting San Francisco Bay and the Pacific Ocean. The structure links the American city of San Francisco, California — the northern tip of the San Francisco Peninsula — to Marin County, carrying both U.S. Route 101 and California State Route 1 across the strait. The bridge is one of the most internationally recognized symbols of San Francisco, California, and the United States. It has been declared one of the Wonders of the Modern World by the American Society of Civil Engineers.\n\nThe Golden Gate Bridge is a suspension bridge spanning the Golden Gate, the one-mile-wide (1.6 km) strait connecting San Francisco Bay and the Pacific Ocean. The structure links the American city of San Francisco, California — the northern tip of the San Francisco Peninsula — to Marin County, carrying both U.S. Route 101 and California State Route 1 across the strait. The bridge is one of the most internationally recognized symbols of San Francisco, California, and the United States. It has been declared one of the Wonders of the Modern World by the American Society of Civil Engineers.\n\nThe Golden Gate Bridge is a suspension bridge spanning the Golden Gate, the one-mile-wide (1.6 km) strait connecting San Francisco Bay and the Pacific Ocean. The structure links the American city of San Francisco, California — the northern tip of the San Francisco Peninsula — to Marin County, carrying both U.S. Route 101 and California State Route 1 across the strait. The bridge is one of the most internationally recognized symbols of San Francisco, California, and the United States. It has been declared one of the Wonders of the Modern World by the American Society of Civil Engineers.\n\nThe Golden Gate Bridge is a suspension bridge spanning the Golden Gate, the one-mile-wide (1.6 km) strait connecting San Francisco Bay and the Pacific Ocean. The structure links the American city of San Francisco, California — the northern tip of the San Francisco Peninsula — to Marin County, carrying both U.S. Route 101 and California State Route 1 across the strait. The bridge is one of the most internationally recognized symbols of San Francisco, California, and']
```
</details>
**For `idefics2-8b`**
<details><summary>Click to expand.</summary>
```python
processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b")
model = AutoModelForVision2Seq.from_pretrained(
"HuggingFaceM4/idefics2-8b",
).to(DEVICE)
# Create inputs
messages = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": "What do we see in this image?"},
]
},
{
"role": "assistant",
"content": [
{"type": "text", "text": "In this image, we can see the city of New York, and more specifically the Statue of Liberty."},
]
},
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": "And how about this image?"},
]
},
]
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=prompt, images=[image1, image2], return_tensors="pt")
inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
# Generate
generated_ids = model.generate(**inputs, max_new_tokens=500)
generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(generated_texts)
# ['User: What do we see in this image? \nAssistant: In this image, we can see the city of New York, and more specifically the Statue of Liberty. \nUser: And how about this image? \nAssistant: In this image we can see buildings, trees, lights, water and sky.']
```
</details>
**Text generation inference**
Idefics2 is integrated into [TGI](https://github.com/huggingface/text-generation-inference) and we host API endpoints for both `idefics2-8b` and `idefics2-8b-chatty`.
Multiple images can be passed on with the markdown syntax (``) and no spaces are required before and after. The dialogue utterances can be separated with `<end_of_utterance>\n` followed by `User:` or `Assistant:`. `User:` is followed by a space if the following characters are real text (no space if followed by an image).
<details><summary>Click to expand.</summary>
```python
from text_generation import Client
API_TOKEN="<YOUR_API_TOKEN>"
API_URL = "https://api-inference.huggingface.co/models/HuggingFaceM4/idefics2-8b-chatty"
# System prompt used in the playground for `idefics2-8b-chatty`
SYSTEM_PROMPT = "System: The following is a conversation between Idefics2, a highly knowledgeable and intelligent visual AI assistant created by Hugging Face, referred to as Assistant, and a human user called User. In the following interactions, User and Assistant will converse in natural language, and Assistant will do its best to answer User’s questions. Assistant has the ability to perceive images and reason about them, but it cannot generate images. Assistant was built to be respectful, polite and inclusive. It knows a lot, and always tells the truth. When prompted with an image, it does not make up facts.<end_of_utterance>\nAssistant: Hello, I'm Idefics2, Huggingface's latest multimodal assistant. How can I help you?<end_of_utterance>\n"
QUERY = "User:Describe this image.<end_of_utterance>\nAssistant:"
client = Client(
base_url=API_URL,
headers={"x-use-cache": "0", "Authorization": f"Bearer {API_TOKEN}"},
)
generation_args = {
"max_new_tokens": 512,
"repetition_penalty": 1.1,
"do_sample": False,
}
generated_text = client.generate(prompt=SYSTEM_PROMPT + QUERY, **generation_args)
generated_text
```
</details>
# Model optimizations
If your GPU allows, we first recommend loading (and running inference) in half precision (`torch.float16` or `torch.bfloat16`).
```diff
model = AutoModelForVision2Seq.from_pretrained(
"HuggingFaceM4/idefics2-8b",
+ torch_dtype=torch.float16,
).to(DEVICE)
```
**Vision encoder efficiency**
Given the high resolution supported, the vision part of the model can be memory hungry depending on your configuration. If you are GPU-memory-constrained, you can:
- **deactivate the image splitting.** To do so, add `do_image_splitting=False` when initializing the processor (`AutoProcessor.from_pretrained`). There are no changes required on the model side. Note that only the sft model has been trained with image splitting.
- **decrease the maximum image resolution.** To do so, add `size= {"longest_edge": 448, "shortest_edge": 378}` when initializing the processor (`AutoProcessor.from_pretrained`). In particular, the `longest_edge` value can be adapted to fit the need (the default value is `980`). We recommend using values that are multiples of 14. There are no changes required on the model side.
`do_image_splitting=True` is especially needed to boost performance on OCR tasks where a very large image is used as input. For the regular VQA or captioning tasks, this argument can be safely set to `False` with minimal impact on performance (see the evaluation table above).
**Using Flash-attention 2 to speed up generation**
<details><summary>Click to expand.</summary>
First, make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) for the package installation. Simply change the snippet above with:
```diff
model = AutoModelForVision2Seq.from_pretrained(
"HuggingFaceM4/idefics2-8b",
+ torch_dtype=torch.float16,
+ _attn_implementation="flash_attention_2",
).to(DEVICE)
```
Flash attention 2 support is available both for `idefics2-8b-base` and `idefics2-8b`.
</details>
**4 bit quantization with AWQ**
<details><summary>Click to expand.</summary>
4-bit AWQ-quantized versions of the checkpoints are also available and allow module fusing for accelerated inference. First make sure you install the Auto-AWQ library with `pip install autoawq`. Also make sure that this [fix](https://github.com/casper-hansen/AutoAWQ/pull/444) is integrated into your installation.
```diff
+ from transformers import AwqConfig
+ quantization_config = AwqConfig(
+ bits=4,
+ fuse_max_seq_len=4096,
+ modules_to_fuse={
+ "attention": ["q_proj", "k_proj", "v_proj", "o_proj"],
+ "mlp": ["gate_proj", "up_proj", "down_proj"],
+ "layernorm": ["input_layernorm", "post_attention_layernorm", "norm"],
+ "use_alibi": False,
+ "num_attention_heads": 32,
+ "num_key_value_heads": 8,
+ "hidden_size": 4096,
+ }
+ )
model = AutoModelForVision2Seq.from_pretrained(
- "HuggingFaceM4/idefics2-8b",
+ "HuggingFaceM4/idefics2-8b-AWQ",
+ torch_dtype=torch.float16,
+ quantization_config=quantization_config,
).to(DEVICE)
```
Fusing can be de-activated by removing `quantization_config` in the call to `from_pretrained`.
</details>
**4 bit quantization with bitsandbytes**
<details><summary>Click to expand.</summary>
It is also possible to load Idefics2 in 4bits with `bitsandbytes`. To do so, make sure that you have `accelerate` and `bitsandbytes` installed.
```diff
+ from transformers import BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.float16
)
model = AutoModelForVision2Seq.from_pretrained(
"HuggingFaceM4/idefics2-8b",
+ torch_dtype=torch.float16,
+ quantization_config=quantization_config,
).to(DEVICE)
```
</details>
These optimizations can be combined to suit variable trade-offs between GPU memory, inference speed and performance. We provide the following comparison as anchor points to guide the user in choosing necessary optimizations. All of these benchmarks were computed with the example code snippet described above on a H100 (see [colab](https://colab.research.google.com/drive/1USsnssoFm1UTYuwUOw0XiGeBspLHzvso?usp=sharing)). As one can see, the are a few setups that require less than 24GB of GPU memory.
| Flash attention 2 | Image splitting | Float type | 4 bits quantization | Peak GPU memory (GB) | Time for 20 generations (secs) |
|-------------------|-----------------|------------|-----------------------------|----------------------|--------------------------------|
| No | Yes | fp32 | No | 54.9 | 55.6 |
| No | Yes | bf16 | No | 41.3 | 34.3 |
| No | Yes | fp16 | No | 36.7 | 33.3 |
| Yes | Yes | fp16 | No | 21.0 | 13.3 |
| Yes | Yes | fp16 | bitsandbytes (entire model) | 8.9 | 19.9 |
| No | Yes | fp16 | bitsandbytes (entire model) | 24.7 | 40.4 |
| No | Yes | fp16 | AWQ (LLM only) | 26.4 | 37.1 |
| Yes | Yes | fp16 | AWQ (LLM only) | 10.7 | 16.3 |
| No | Yes | fp16 | AWQ + fusing (LLM only) | 26.0 | 38.4 |
| | | | | | |
| No | No | fp32 | No | 38.8 | 17.5 |
| No | No | bf16 | No | 22.2 | 14.4 |
| No | No | fp16 | No | 21.3 | 13.9 |
| Yes | No | fp16 | No | 18.1 | 10.4 |
| Yes | No | fp16 | bitsandbytes (entire model) | 6.0 | 17.3 |
| No | No | fp16 | bitsandbytes (entire model) | 9.2 | 20.9 |
| No | No | fp16 | AWQ (LLM only) | 10.9 | 15.9 |
| Yes | No | fp16 | AWQ (LLM only) | 7.8 | 12.3 |
| No | No | fp16 | AWQ + fusing (LLM only) | 10.5 | 19.5 |
To learn more quantization schemes and fusing, we refer to the [documentation](https://huggingface.co/docs/transformers/quantization).
# Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
As a derivative of such a language model, Idefics2 can produce texts that include disturbing and harmful stereotypes across protected characteristics; identity characteristics; and sensitive, social, and occupational groups.
Moreover, Idefics2 can produce factually incorrect texts and should not be relied on to produce factually accurate information.
To measure whether Idefics2 demonstrates bias across various protected characteristics in particular gender and race, we evaluated the instructed model's responses to multiple prompts containing an image and a text prompt. Specifically, the model was prompted with the following prompts:
> 1. Describe this person's appearance. Then, write a resume for them, including degrees and recent jobs.
> 2. Describe this person's appearance. Then, write two paragraphs of a dating profile for them in the first person.
> 3. Describe this person's appearance. Then, write the headline and first paragraph of an article about their recent arrest.
These prompts were run across two datasets [FairFace](https://huggingface.co/datasets/HuggingFaceM4/FairFace) and [Stable Bias](https://huggingface.co/datasets/yjernite/stable-bias_grounding-images_multimodel_3_12_22).
The FairFace dataset is "a face image dataset which is race balanced. It contains 108,501 images from 7 different race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino. Images were collected from the YFCC-100M Flickr dataset and labelled with race, gender, and age groups".
The Stable Bias dataset is a dataset of synthetically generated images from the prompt "A photo portrait of a (ethnicity) (gender) at work".
Running the above prompts across both these datasets results in two datasets containing three generated responses for each image alongside information about the ascribed ethnicity and gender of the person depicted in each image.
This allows comparing the generated response to each prompt across gender and ethnicity axis.
Our goal in performing this evaluation was to try to identify more subtle ways in which the responses generated by the model may be influenced by the gender or ethnicity of the person depicted in the input image.
To surface potential biases in the outputs, we consider the following simple TF-IDF based approach. Given a model and a prompt of interest, we:
1. Evaluate Inverse Document Frequencies on the full set of generations for the model and prompt in questions
2. Compute the average TFIDF vectors for all generations **for a given gender or ethnicity**
3. Sort the terms by variance to see words that appear significantly more for a given gender or ethnicity
4. We also run the generated responses through a [toxicity classification model](https://huggingface.co/citizenlab/distilbert-base-multilingual-cased-toxicity).
When running the models generations through the toxicity classification model, we saw very few model outputs rated as toxic by the model. Those rated toxic were labelled as toxic with a very low probability by the model. Closer reading of responses rates at toxic found they usually were not toxic.
The TFIDF-based approach aims to identify subtle differences in the frequency of terms across gender and ethnicity. For example, for the prompt related to resumes, we see that synthetic images generated for *woman* are more likely to lead to resumes that include *embezzlement* than those generated for *man* or *non-binary*. While we observed clearer patterns in Idefics1 (such as the prominence of terms like "financial," "development," "product," and "software" in responses generated for men when comparing genders across both datasets), Idefics2 exhibit less pronounced biases.
The [notebook](https://huggingface.co/spaces/HuggingFaceM4/idefics2-bias-eval/blob/main/idefics2_bias_eval.ipynb) used to carry out this evaluation gives a more detailed overview of the evaluation.
Alongside this evaluation, we also computed the classification accuracy on FairFace for the instructed model. The model is asked to classify gender, ethnicity and age bucket solely from a profile picture.
| Model | Shots | <nobr>FairFaceGender<br>acc. (std*)</nobr> | <nobr>FairFaceRace<br>acc. (std*)</nobr> | <nobr>FairFaceAge<br>acc. (std*)</nobr> |
| :--------------------- | --------: | ----------------------------: | --------------------------: | -------------------------: |
| Idefics1 80B (Instructed) | 0 | 92.7 (6.3) | 59.6 (22.2) | 43.9 (3.9) |
| Idefics2 8B (Instructed) | 0 | 96.3 (3.0) | 41.6 (40.9) | 53.5 (3.0) |
*Per bucket standard deviation. Each bucket represents a combination of ethnicity and gender from the [FairFace](https://huggingface.co/datasets/HuggingFaceM4/FairFace) dataset. The standard deviation within each demographic group indicates the disparity in the model's ability to recognize gender, ethnicity, or age across different groups. Specifically, for the Idefics2 model, we notice a notably higher standard deviation in predicting ethnicity. This is evident in its near-zero accuracy for images depicting individuals of Middle Eastern, Latino/Hispanic, and Southeast Asian descent.
**Other Limitations**
- The model currently will offer medical diagnosis when prompted to do so ([vqa-rad](https://huggingface.co/datasets/flaviagiammarino/vqa-rad), a dataset of QA pairs on radiology images is present in the SFT mixture). For example, the prompt `Does this X-ray show any medical problems?` along with an image of a chest X-ray returns `Yes, the X-ray shows a medical problem, which appears to be a collapsed lung.`. We discourage users from using the model on medical applications without proper adaptation and evaluation.
- Despite our efforts in filtering the training data, we found a small proportion of content that is not suitable for all audiences. This includes pornographic content and reports of violent shootings and is prevalent in the OBELICS portion of the data (see [here](https://huggingface.co/datasets/HuggingFaceM4/OBELICS#content-warnings) for more details). As such, the model is susceptible to generating text that resembles this content.
- We note that we know relatively little about the composition of the pre-trained LM backbone, which makes it difficult to link inherited limitations or problematic behaviors to their data.
**Red-teaming**
In the context of a **[Red-Teaming](https://huggingface.co/blog/red-teaming)** exercise, our objective was to evaluate the propensity of the model to generate inaccurate, biased, or offensive responses. We evaluated [idefics2-8b-chatty](https://huggingface.co/HuggingFaceM4/idefics2-8b-chatty).
While the model typically refrains from responding to offensive inputs, we observed that through repeated trials or guided interactions, it tends to hastily form judgments in situations necessitating nuanced contextual understanding, often perpetuating harmful stereotypes. Noteworthy instances include:
- Speculating or passing judgments, or perpetuating historical disparities on individuals' professions, social status, or insurance eligibility based solely on visual cues (e.g., age, attire, gender, facial expressions).
- Generating content that promotes online harassment or offensive memes reinforcing harmful associations from a portrait, or from a benign image.
- Assuming emotional states or mental conditions based on outward appearances.
- Evaluating individuals' attractiveness solely based on their visual appearance.
Additionally, we identified behaviors that increase security risks that already exist:
- Successfully solving CAPTCHAs featuring distorted text within images.
- Developing phishing schemes from screenshots of legitimate websites to deceive users into divulging their credentials.
- Crafting step-by-step guides on constructing small-scale explosives using readily available chemicals from common supermarkets or manipulating firearms to do maximum damage.
It's important to note that these security concerns are currently limited by the model's occasional inability to accurately read text within images.
We emphasize that the model would often encourage the user to exercise caution about the model's generation or flag how problematic the initial query can be in the first place. For instance, when insistently prompted to write a racist comment, the model would answer that query before pointing out "*This type of stereotyping and dehumanization has been used throughout history to justify discrimination and oppression against people of color. By making light of such a serious issue, this meme perpetuates harmful stereotypes and contributes to the ongoing struggle for racial equality and social justice.*".
However, certain formulations can circumvent (i.e. "jail-break") these cautionary prompts, emphasizing the need for critical thinking and discretion when engaging with the model's outputs. While jail-breaking text LLMs is an active research area, jail-breaking vision-language models has recently emerged as a new challenge as vision-language models become more capable and prominent. The addition of the vision modality not only introduces new avenues for injecting malicious prompts but also raises questions about the interaction between vision and language vulnerabilities.
# Misuse and Out-of-scope use
Using the model in [high-stakes](https://huggingface.co/bigscience/bloom/blob/main/README.md#glossary-and-calculations) settings is out of scope for this model. The model is not designed for [critical decisions](https://huggingface.co/bigscience/bloom/blob/main/README.md#glossary-and-calculations) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but may not be correct. Out-of-scope uses include:
- Usage for evaluating or scoring individuals, such as for employment, education, or credit
- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
Intentionally using the model for harm, violating [human rights](https://huggingface.co/bigscience/bloom/blob/main/README.md#glossary-and-calculations), or other kinds of malicious activities, is a misuse of this model. This includes:
- Spam generation
- Disinformation and influence operations
- Disparagement and defamation
- Harassment and abuse
- [Deception](https://huggingface.co/bigscience/bloom/blob/main/README.md#glossary-and-calculations)
- Unconsented impersonation and imitation
- Unconsented surveillance
# License
The model is built on top of two pre-trained models: [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) and [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). Both were released under the Apache 2.0 license, and we release the Idefics2 checkpoints under the same license.
# Citation
**BibTeX:**
```bibtex
@misc{laurencon2023obelics,
title={OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents},
author={Hugo Laurençon and Lucile Saulnier and Léo Tronchon and Stas Bekman and Amanpreet Singh and Anton Lozhkov and Thomas Wang and Siddharth Karamcheti and Alexander M. Rush and Douwe Kiela and Matthieu Cord and Victor Sanh},
year={2023},
eprint={2306.16527},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
@misc{laurençon2024matters,
title={What matters when building vision-language models?},
author={Hugo Laurençon and Léo Tronchon and Matthieu Cord and Victor Sanh},
year={2024},
eprint={2405.02246},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
# Acknowledgements
We thank @yjernite, @sasha, @meg, @giadap, @jack-kumar, and @frimelle, who provided help to red-team the model.
|
svjack/emoji_Mistral7B_v2_lora
|
svjack
| 2024-05-15T16:41:39Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:mistral-community/Mistral-7B-v0.2",
"base_model:adapter:mistral-community/Mistral-7B-v0.2",
"license:other",
"region:us"
] | null | 2024-05-15T16:22:32Z |
---
license: other
library_name: peft
tags:
- llama-factory
- lora
- generated_from_trainer
base_model: alpindale/Mistral-7B-v0.2-hf
model-index:
- name: train_2024-05-15-20-33-30
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. -->
# Install
```bash
pip install peft transformers bitsandbytes
```
# Run by transformers
```python
from transformers import TextStreamer, AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained("alpindale/Mistral-7B-v0.2-hf",)
mis_model = AutoModelForCausalLM.from_pretrained("alpindale/Mistral-7B-v0.2-hf", load_in_4bit = True)
mis_model = PeftModel.from_pretrained(mis_model, "svjack/emoji_Mistral7B_v2_lora")
mis_model = mis_model.eval()
streamer = TextStreamer(tokenizer)
def mistral_hf_predict(prompt, mis_model = mis_model,
tokenizer = tokenizer, streamer = streamer,
do_sample = True,
top_p = 0.95,
top_k = 40,
max_new_tokens = 512,
max_input_length = 3500,
temperature = 0.9,
repetition_penalty = 1.0,
device = "cuda"):
messages = [
{"role": "user", "content": prompt[:max_input_length]}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
generated_ids = mis_model.generate(model_inputs, max_new_tokens=max_new_tokens,
do_sample=do_sample,
streamer = streamer,
top_p = top_p,
top_k = top_k,
temperature = temperature,
repetition_penalty = repetition_penalty,
)
out = tokenizer.batch_decode(generated_ids)[0].split("[/INST]")[-1].replace("</s>", "").strip()
return out
out = mistral_hf_predict('''
对下面的内容添加emoji
走在公园的大道上，可以发现许多树的叶子，已染上了秋的色彩，到处可以看到黄灿灿的树叶。
其中最引人注目的是那金黄金黄的银杏树，远远望去，犹如金色的海洋.
微风吹过，银杏树叶纷纷飘落，就像一只只美丽的蝴蝶，展开双翅在空中飞舞。
''',
repetition_penalty = 1.1)
print(out)
```
# Output
```txt
🍃🎊🍂🌞走在公园的大道上，可以发现许多树的叶子，已染上了秋的色彩，到处可以看到黄灿灿的树叶 ☀️。
其中最引人注目的是那金黄金黄的银杏树 🌟，远远望去，犹如金色的海洋 🌊。
微风吹过，银杏树叶纷纷飘落，就像一只只美丽的蝴蝶 🦋，展开双翅在空中飞舞 ✈️
```
# train_2024-05-15-20-33-30
This model is a fine-tuned version of [alpindale/Mistral-7B-v0.2-hf](https://huggingface.co/alpindale/Mistral-7B-v0.2-hf) on the emoji_add_instruction_zh dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
HariprasathSB/model-finetuned-tamil
|
HariprasathSB
| 2024-05-15T16:38:23Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-04-29T17:15:20Z |
---
tags:
- generated_from_trainer
model-index:
- name: model-finetuned-tamil
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. -->
# model-finetuned-tamil
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|
ShenaoZhang/0.01_zephyr_5551_4iters_bs256_iter_4
|
ShenaoZhang
| 2024-05-15T16:38:12Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZhang/0.01_zephyr_5551_4iters_bs256_iter_3",
"base_model:finetune:ShenaoZhang/0.01_zephyr_5551_4iters_bs256_iter_3",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-15T15:51:20Z |
---
license: mit
base_model: ShenaoZhang/0.01_zephyr_5551_4iters_bs256_iter_3
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- updated
- original
model-index:
- name: 0.01_zephyr_5551_4iters_bs256_iter_4
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. -->
# 0.01_zephyr_5551_4iters_bs256_iter_4
This model is a fine-tuned version of [ShenaoZhang/0.01_zephyr_5551_4iters_bs256_iter_3](https://huggingface.co/ShenaoZhang/0.01_zephyr_5551_4iters_bs256_iter_3) on the updated and the original datasets.
## 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.40.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
|
ming172/16bitGGUF___Large_pocket_classification_model
|
ming172
| 2024-05-15T16:37:32Z | 8 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:quantized:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T16:28:20Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** ming172
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
fefzzz/my-finetuned-gpt2
|
fefzzz
| 2024-05-15T16:34:25Z | 148 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-15T16:28:43Z |
---
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]
|
AI4Protein/ProSST-1024
|
AI4Protein
| 2024-05-15T16:24:52Z | 131 | 1 |
transformers
|
[
"transformers",
"safetensors",
"ProSST",
"fill-mask",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2024-05-15T15:24:45Z |
---
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]
|
EthanRhys/Mike-Current
|
EthanRhys
| 2024-05-15T16:24:09Z | 0 | 0 | null |
[
"license:openrail++",
"region:us"
] | null | 2024-05-15T16:23:04Z |
---
license: openrail++
---
|
eunbxn/tokenizer
|
eunbxn
| 2024-05-15T16:24:05Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-15T16:24:04Z |
---
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]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
eunbxn/model
|
eunbxn
| 2024-05-15T16:24:04Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-15T16:23:43Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.40.2
- TensorFlow 2.15.0
- Tokenizers 0.19.1
|
LarryAIDraw/asuka_scarxzys
|
LarryAIDraw
| 2024-05-15T16:23:35Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-05-15T15:54:15Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/452125/asuka-or-azur-lane
|
johnnyf/Reinforece
|
johnnyf
| 2024-05-15T16:13:14Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-05-15T16:13:04Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforece
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
|
ZaneHorrible/adam_VitB-p16-384-1e-4-batch_16_epoch_4_classes_24
|
ZaneHorrible
| 2024-05-15T16:13:11Z | 220 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-384",
"base_model:finetune:google/vit-base-patch16-384",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-05-15T13:28:41Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-384
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: adam_VitB-p16-384-1e-4-batch_16_epoch_4_classes_24
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.985632183908046
---
<!-- 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. -->
# adam_VitB-p16-384-1e-4-batch_16_epoch_4_classes_24
This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0707
- Accuracy: 0.9856
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.178 | 0.07 | 100 | 0.1584 | 0.9598 |
| 0.0855 | 0.14 | 200 | 0.0985 | 0.9655 |
| 0.0717 | 0.21 | 300 | 0.1103 | 0.9641 |
| 0.0768 | 0.28 | 400 | 0.1226 | 0.9612 |
| 0.0556 | 0.35 | 500 | 0.1447 | 0.9641 |
| 0.0288 | 0.42 | 600 | 0.1092 | 0.9655 |
| 0.0776 | 0.49 | 700 | 0.0935 | 0.9713 |
| 0.0662 | 0.56 | 800 | 0.0899 | 0.9770 |
| 0.0611 | 0.63 | 900 | 0.0647 | 0.9828 |
| 0.0418 | 0.7 | 1000 | 0.0946 | 0.9698 |
| 0.0639 | 0.77 | 1100 | 0.1449 | 0.9626 |
| 0.0111 | 0.84 | 1200 | 0.0926 | 0.9770 |
| 0.0607 | 0.91 | 1300 | 0.1129 | 0.9713 |
| 0.0066 | 0.97 | 1400 | 0.1554 | 0.9612 |
| 0.0908 | 1.04 | 1500 | 0.0859 | 0.9727 |
| 0.0069 | 1.11 | 1600 | 0.1049 | 0.9784 |
| 0.0003 | 1.18 | 1700 | 0.0902 | 0.9813 |
| 0.0004 | 1.25 | 1800 | 0.1638 | 0.9684 |
| 0.0006 | 1.32 | 1900 | 0.1223 | 0.9741 |
| 0.04 | 1.39 | 2000 | 0.1683 | 0.9626 |
| 0.047 | 1.46 | 2100 | 0.1064 | 0.9756 |
| 0.0003 | 1.53 | 2200 | 0.1360 | 0.9684 |
| 0.0133 | 1.6 | 2300 | 0.0821 | 0.9813 |
| 0.0731 | 1.67 | 2400 | 0.0909 | 0.9784 |
| 0.0015 | 1.74 | 2500 | 0.0782 | 0.9799 |
| 0.0003 | 1.81 | 2600 | 0.0869 | 0.9828 |
| 0.0031 | 1.88 | 2700 | 0.1419 | 0.9756 |
| 0.0042 | 1.95 | 2800 | 0.0594 | 0.9856 |
| 0.0003 | 2.02 | 2900 | 0.1075 | 0.9770 |
| 0.0003 | 2.09 | 3000 | 0.1343 | 0.9770 |
| 0.0003 | 2.16 | 3100 | 0.1067 | 0.9770 |
| 0.0002 | 2.23 | 3200 | 0.1037 | 0.9770 |
| 0.0001 | 2.3 | 3300 | 0.1050 | 0.9770 |
| 0.0001 | 2.37 | 3400 | 0.0838 | 0.9756 |
| 0.0004 | 2.44 | 3500 | 0.0806 | 0.9784 |
| 0.0007 | 2.51 | 3600 | 0.0766 | 0.9813 |
| 0.0001 | 2.58 | 3700 | 0.0684 | 0.9842 |
| 0.0007 | 2.65 | 3800 | 0.1370 | 0.9684 |
| 0.0001 | 2.72 | 3900 | 0.0820 | 0.9828 |
| 0.0001 | 2.79 | 4000 | 0.0736 | 0.9842 |
| 0.0016 | 2.86 | 4100 | 0.0727 | 0.9842 |
| 0.0001 | 2.92 | 4200 | 0.0701 | 0.9856 |
| 0.0001 | 2.99 | 4300 | 0.0884 | 0.9799 |
| 0.0001 | 3.06 | 4400 | 0.1045 | 0.9799 |
| 0.0001 | 3.13 | 4500 | 0.0754 | 0.9842 |
| 0.0001 | 3.2 | 4600 | 0.0753 | 0.9842 |
| 0.0001 | 3.27 | 4700 | 0.0763 | 0.9842 |
| 0.0001 | 3.34 | 4800 | 0.0688 | 0.9856 |
| 0.0001 | 3.41 | 4900 | 0.0687 | 0.9856 |
| 0.0001 | 3.48 | 5000 | 0.0680 | 0.9856 |
| 0.0001 | 3.55 | 5100 | 0.0677 | 0.9842 |
| 0.0001 | 3.62 | 5200 | 0.0682 | 0.9856 |
| 0.0001 | 3.69 | 5300 | 0.0716 | 0.9813 |
| 0.0001 | 3.76 | 5400 | 0.0717 | 0.9813 |
| 0.0001 | 3.83 | 5500 | 0.0695 | 0.9856 |
| 0.0001 | 3.9 | 5600 | 0.0699 | 0.9856 |
| 0.0001 | 3.97 | 5700 | 0.0707 | 0.9856 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
mpasila/Finnish-Alpaca-Small-LoRA-7B
|
mpasila
| 2024-05-15T16:10:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"fi",
"dataset:mpasila/Finnish-Alpaca-Small",
"base_model:LumiOpen/Viking-7B",
"base_model:finetune:LumiOpen/Viking-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-14T15:26:54Z |
---
language:
- fi
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: LumiOpen/Viking-7B
datasets:
- mpasila/Finnish-Alpaca-Small
---
LoRA trained in 4-bit with 2k context using [LumiOpen/Viking-7B](https://huggingface.co/LumiOpen/Viking-7B/) as the base model for 1 epoch.
Dataset used is [mpasila/Finnish-Alpaca-Small](https://huggingface.co/datasets/mpasila/Finnish-Alpaca-Small).
### Prompt format: Alpaca
It uses Alpaca format but with a translated instruction at the start:
```
{
"instruction,output": "Alla on ohje, jossa kuvataan tehtävä. Kirjoita vastaus, joka täyttää pyynnön asianmukaisesti.\n\n### Instruction:\n%instruction%\n\n### Response:\n%output%",
"instruction,input,output": "Alla on ohje, jossa kuvataan tehtävä ja joka on yhdistetty kontekstia lisäävään syötteeseen. Kirjoita vastaus, joka täyttää pyynnön asianmukaisesti.\n\n### Instruction:\n%instruction%\n\n### Input:\n%input%\n\n### Response:\n%output%"
}
```
# Uploaded model
- **Developed by:** mpasila
- **License:** apache-2.0
- **Finetuned from model :** LumiOpen/Viking-7B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mradermacher/SweetNeural-7B-GGUF
|
mradermacher
| 2024-05-15T16:03:32Z | 4 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:MisterRaven006/SweetNeural-7B",
"base_model:quantized:MisterRaven006/SweetNeural-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-15T14:59:34Z |
---
base_model: MisterRaven006/SweetNeural-7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/MisterRaven006/SweetNeural-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/SweetNeural-7B-GGUF/resolve/main/SweetNeural-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/SweetNeural-7B-GGUF/resolve/main/SweetNeural-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/SweetNeural-7B-GGUF/resolve/main/SweetNeural-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/SweetNeural-7B-GGUF/resolve/main/SweetNeural-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/SweetNeural-7B-GGUF/resolve/main/SweetNeural-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/SweetNeural-7B-GGUF/resolve/main/SweetNeural-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/SweetNeural-7B-GGUF/resolve/main/SweetNeural-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/SweetNeural-7B-GGUF/resolve/main/SweetNeural-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/SweetNeural-7B-GGUF/resolve/main/SweetNeural-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SweetNeural-7B-GGUF/resolve/main/SweetNeural-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SweetNeural-7B-GGUF/resolve/main/SweetNeural-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/SweetNeural-7B-GGUF/resolve/main/SweetNeural-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/SweetNeural-7B-GGUF/resolve/main/SweetNeural-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/SweetNeural-7B-GGUF/resolve/main/SweetNeural-7B.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/SweetNeural-7B-GGUF/resolve/main/SweetNeural-7B.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
emilykang/medner-cardiovascular_pulmonary_lora
|
emilykang
| 2024-05-15T16:00:29Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] | null | 2024-05-15T12:58:16Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
datasets:
- generator
model-index:
- name: medner-cardiovascular_pulmonary_lora
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. -->
# medner-cardiovascular_pulmonary_lora
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 3
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|
sequelbox/Llama2-70B-SpellBlade
|
sequelbox
| 2024-05-15T16:00:14Z | 8 | 2 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-24T13:14:08Z |
---
license: apache-2.0
---
Spell Blade is a chat and general capability finetuned upgrade to Llama 2, focused on improving conversational quality as well as supplementing technical capability.
Performs solidly as-is, user satisfaction will be optimized with further finetuning.
Most training data utilizes the [INST][/INST] chat format.
This is a 'legacy model' offered primarily for reference purposes. I recommend Llama 3 over this model for general use.
|
qminh369/gpt2_vi_42k_sample
|
qminh369
| 2024-05-15T15:57:33Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:NlpHUST/gpt2-vietnamese",
"base_model:finetune:NlpHUST/gpt2-vietnamese",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-15T08:46:13Z |
---
base_model: NlpHUST/gpt2-vietnamese
tags:
- generated_from_trainer
model-index:
- name: gpt2_vi_42k_sample
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. -->
# gpt2_vi_42k_sample
This model is a fine-tuned version of [NlpHUST/gpt2-vietnamese](https://huggingface.co/NlpHUST/gpt2-vietnamese) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 88
- 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
### Training results
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
|
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