--- library_name: transformers license: mit datasets: - thibaud-perrin/hibo-function-calling-v1 language: - en pipeline_tag: text-generation --- # Model Card for thibaud-perrin/hibo-mistral-7b-fc-v1.3
[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue.svg)](https://github.com/thibaud-perrin/hibo-mistral-7b-fc) 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. ## Model Details ### Model Description 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. - **Developed by:** Thibaud Perrin - **Model type:** CAUSAL_LM - **Language(s) (NLP):** English - **License:** MIT - **Finetuned from model:** Mistral-7B ## Uses 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. ### Direct Use The model can be directly used via the Hugging Face Transformers library for generating text based on prompts related to instructions or function calls. ### Out-of-Scope Use This model is not intended for high-stakes decisions or scenarios where misunderstanding instructions could lead to significant consequences. ## Bias, Risks, and Limitations 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. ### Recommendations 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. ## How to Get Started with the Model ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_identifier = "thibaud-perrin/hibo-mistral-7b-fc-v1.3" model = AutoModelForCausalLM.from_pretrained( model_identifier, low_cpu_mem_usage=True, return_dict=True, torch_dtype=torch.bfloat16, device_map={"": 0}, ) tokenizer = AutoTokenizer.from_pretrained(model_identifier) device = 'cuda:0' # device = 'cpu' model.config.use_cache = True model.eval() model.to(device) def stream(user_prompt): system_prompt = """You are a helpful assistant with access to the following functions. Use them if required - { "name": "get_stock_price", "description": "Get the current stock price of a company", "parameters": { "type": "object", "properties": { "company_name": { "type": "string", "description": "The name of the company" }, "exchange": { "type": "string", "description": "The stock exchange where the company is listed" } }, "required": [ "company_name", "exchange" ] } } """ messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt.strip()} ] transformed_data = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False) eos_token_id = tokenizer.eos_token_id inputs = tokenizer([transformed_data], return_tensors="pt", add_special_tokens=True).to(device) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False) _ = model.generate(**inputs, streamer=streamer, max_new_tokens=512, eos_token_id=tokenizer.eos_token_id, early_stopping=True) stream("Hi, can you tell me the current stock price of Apple on NASDAQ? ") ``` ## Training Details ### Training Data The model was trained using the dataset `thibaud-perrin/hibo-function-calling-v1`, which consists of various instruction-following and function-calling examples. #### Summary The fine-tuned model demonstrates a significant improvement in understanding and generating instruction-based responses compared to the base Mistral-7B model. However this model has been trained, only on the first 50_000 rows of the dataset, with one epoch. ## Environmental Impact - **Hardware Type:** A100 - 40GB - **Hours used:** 48H - **Cloud Provider:** Google Colab - **Compute Region:** France - **Carbon Emitted:** Estimates needed ## 📚 Citation Please cite this dataset using the following BibTeX entry: ```bibtex @misc{hibo-mistral-7b-fc-v1.3, author = Thibaud Perrin, title = hibo-mistral-7b-fc-v1.3: An instruct Model for Function Calling in Conversational AI, year = 2024, publisher = Hugging Face, } ```