This model is a fine-tuned version of meta-llama/CodeLlama-7b-Instruct-hf, finetunned on 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.

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}}
}