---
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


<div align="center">
    <img src="./img/banner2.webp" width="100%" />
</div>

[![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,
}
```