---
base_model: meta-llama/CodeLlama-7b-Instruct-hf
library_name: transformers
model_name: CodeLlama-Instruct-Python-7b
tags:
- generated_from_trainer
- trl
- sft
- CodeLlama
- Python
- QA_tabular
licence: license
datasets:
- cardiffnlp/databench
---

# Model Card for CodeLlama-Instruct-Python-7b

This model is a fine-tuned version of [meta-llama/CodeLlama-7b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf), 
finetunned on [cardiffnlp/databench](https://huggingface.co/datasets/cardiffnlp/databench) for generating single line of python code for
answering questions over tabular data from over 65 different datasets.

The primary goal of this model is to provide accurate and efficient single-line Python code solutions to questions related to tabular data.
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="basharatwali/CodeLlama-Instruct-Python-7b", 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 SFT.

### Framework versions

- TRL: 0.13.0
- Transformers: 4.48.0.dev0
- Pytorch: 2.5.1+cu121
- Datasets: 3.2.0
- 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}}
}
```