Llama-3.1-8B-Instruct_SFT_Math-220kv00.35/README.md

base_model, datasets, library_name, model_name, tags, licence

base_model	datasets	library_name	model_name	tags	licence
meta-llama/Llama-3.1-8B-Instruct	Neelectric/Replay_0.04.OpenR1-Math-220k_extended.wildguardmix.Llama3_4096toks	transformers	Llama-3.1-8B-Instruct_SFT_Math-220kv00.35	generated_from_trainer sft open-r1 trl	license

Model Card for Llama-3.1-8B-Instruct_SFT_Math-220kv00.35

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the Neelectric/Replay_0.04.OpenR1-Math-220k_extended.wildguardmix.Llama3_4096toks dataset. It has been trained using TRL.

Quick start

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="Neelectric/Llama-3.1-8B-Instruct_SFT_Math-220kv00.35", 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: 0.28.0.dev0
• Transformers: 4.57.6
• Pytorch: 2.9.0
• Datasets: 4.5.0
• Tokenizers: 0.22.2

Citations

Cite TRL as:

@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 Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}