library_name, model_name, tags, licence, pipeline_tag, base_model

library_name	model_name	tags	licence	pipeline_tag	base_model
peft	deepseek-rjua-qa	base_model:adapter:/root/notebook/dataset/deepseek-ai/deepseek-coder-1.3b-base lora sft transformers trl	license	text-generation	/root/notebook/dataset/deepseek-ai/deepseek-coder-1.3b-base

Model Card for deepseek-rjua-qa

This model is a fine-tuned version of None. 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="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

• PEFT 0.16.0
• TRL: 0.19.1
• Transformers: 4.53.2
• Pytorch: 2.6.0+metax2.33.0.5
• Datasets: 3.6.0
• Tokenizers: 0.21.2

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}}
}