README.md

---
base_model: mistralai/Mistral-7B-Instruct-v0.3
library_name: transformers
model_name: Mixtral-7B-LoRA-Salesforce-Optimized-AI-AgentCall
tags:
- FineTune
- loRA
- AIAgent
- AIAgentCall
- FunctionCall
- Salesforce
- LoRAAgent
- OptimizedAgentCall
- Agenttoolcall
- toolscall
licence: license
datasets:
- Salesforce/xlam-function-calling-60k
---

# Model Card for Mixtral-7B-LoRA-Salesforce-Optimized-AI-AgentCall

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3).
It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

```python
from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ritvik77/Mixtral-7B-LoRA-Salesforce-Optimized-AI-AgentCall", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```

## Training procedure

 


This model was trained with LoRA.

### Framework versions

- TRL: 0.15.2
- Transformers: 4.48.3
- Pytorch: 2.5.1+cu124
- Datasets: 3.3.2
- Tokenizers: 0.21.0

## Citations



Cite TRL as:
    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```