base_model, datasets, library_name, model_name, tags, licence

base_model	datasets	library_name	model_name	tags	licence
tanliboy/Llama-3.2-3B	wassname/ultrachat_200k_filtered	transformers	llama-3.2-3b-sft	generated_from_trainer alignment-handbook	license

Model Card for llama-3.2-3b-sft

This model is a fine-tuned version of tanliboy/Llama-3.2-3B on the ['wassname/ultrachat_200k_filtered'] dataset. It has been trained using TRL.

Why? because experiments with DPO require a SFT model, this means you can't use base or instruction tuned you need an intermediate model such as https://huggingface.co/allenai/Olmo-3-7B-Think-SFT

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="None", 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: 0.12.1
• Transformers: 4.52.4
• Pytorch: 2.7.0
• Datasets: 3.6.0
• Tokenizers: 0.21.1

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 Gallouédec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}