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--- |
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library_name: transformers |
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license: mit |
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datasets: |
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- thibaud-perrin/hibo-function-calling-v1 |
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language: |
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- en |
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pipeline_tag: text-generation |
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--- |
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# Model Card for thibaud-perrin/hibo-mistral-7b-fc-v1.3 |
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<div align="center"> |
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<img src="./img/banner2.webp" width="100%" /> |
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</div> |
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[](https://github.com/thibaud-perrin/hibo-mistral-7b-fc) |
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This model is a fine-tuned version of the `mistralai/Mistral-7B-v0.1` for the purpose of instruction following and function calling tasks. It is designed to understand and generate responses based on given instructions or function calls. |
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## Model Details |
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### Model Description |
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Developed by Thibaud Perrin, this model is fine-tuned specifically for the task of interpreting instructions and generating appropriate responses or function calls in English. It leverages the power of the Mistral-7B model, adapting its capabilities to more targeted use cases. |
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- **Developed by:** Thibaud Perrin |
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- **Model type:** CAUSAL_LM |
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- **Language(s) (NLP):** English |
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- **License:** MIT |
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- **Finetuned from model:** Mistral-7B |
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## Uses |
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This model is intended for developers, researchers, and hobbyists looking for a pre-trained model capable of understanding and responding to instructions or executing function calls within a given context. |
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### Direct Use |
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The model can be directly used via the Hugging Face Transformers library for generating text based on prompts related to instructions or function calls. |
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### Out-of-Scope Use |
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This model is not intended for high-stakes decisions or scenarios where misunderstanding instructions could lead to significant consequences. |
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## Bias, Risks, and Limitations |
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As with any language model, there's a risk of generating biased or inappropriate content. Users should be cautious and evaluate the model's outputs within their specific context. |
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### Recommendations |
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Users should monitor the model's outputs and apply additional filtering or moderation as needed to ensure the generated content is appropriate for their use case. |
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## How to Get Started with the Model |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer |
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model_identifier = "thibaud-perrin/hibo-mistral-7b-fc-v1.3" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_identifier, |
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low_cpu_mem_usage=True, |
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return_dict=True, |
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torch_dtype=torch.bfloat16, |
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device_map={"": 0}, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_identifier) |
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device = 'cuda:0' |
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# device = 'cpu' |
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model.config.use_cache = True |
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model.eval() |
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model.to(device) |
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def stream(user_prompt): |
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system_prompt = """You are a helpful assistant with access to the following functions. Use them if required - |
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{ |
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"name": "get_stock_price", |
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"description": "Get the current stock price of a company", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"company_name": { |
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"type": "string", |
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"description": "The name of the company" |
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}, |
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"exchange": { |
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"type": "string", |
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"description": "The stock exchange where the company is listed" |
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} |
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}, |
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"required": [ |
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"company_name", |
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"exchange" |
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] |
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} |
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} |
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""" |
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messages = [ |
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{"role": "system", "content": system_prompt}, |
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{"role": "user", "content": user_prompt.strip()} |
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] |
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transformed_data = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False) |
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eos_token_id = tokenizer.eos_token_id |
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inputs = tokenizer([transformed_data], return_tensors="pt", add_special_tokens=True).to(device) |
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False) |
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_ = model.generate(**inputs, streamer=streamer, max_new_tokens=512, eos_token_id=tokenizer.eos_token_id, early_stopping=True) |
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stream("Hi, can you tell me the current stock price of Apple on NASDAQ? ") |
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``` |
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## Training Details |
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### Training Data |
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The model was trained using the dataset `thibaud-perrin/hibo-function-calling-v1`, which consists of various instruction-following and function-calling examples. |
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#### Summary |
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The fine-tuned model demonstrates a significant improvement in understanding and generating instruction-based responses compared to the base Mistral-7B model. |
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However this model has been trained, only on the first 50_000 rows of the dataset, with one epoch. |
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## Environmental Impact |
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- **Hardware Type:** A100 - 40GB |
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- **Hours used:** 48H |
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- **Cloud Provider:** Google Colab |
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- **Compute Region:** France |
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- **Carbon Emitted:** Estimates needed |
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## 📚 Citation |
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Please cite this dataset using the following BibTeX entry: |
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```bibtex |
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@misc{hibo-mistral-7b-fc-v1.3, |
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author = Thibaud Perrin, |
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title = hibo-mistral-7b-fc-v1.3: An instruct Model for Function Calling in Conversational AI, |
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year = 2024, |
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publisher = Hugging Face, |
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} |
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``` |
