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---
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>
[](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,
}
``` |