metadata

base_model: >-
  team-suzuki/DeepSeek-R1-0528-Qwen3-8B_Merged_SFT_SFT_003_origin_2_v001_20250819
library_name: peft
model_name: fine_tuned_deepseek_sft
tags:
  - >-
    base_model:adapter:team-suzuki/DeepSeek-R1-0528-Qwen3-8B_Merged_SFT_SFT_003_origin_2_v001_20250819
  - lora
  - sft
  - transformers
  - trl
licence: license
pipeline_tag: text-generation

Model Card for fine_tuned_deepseek_sft

This model is a fine-tuned version of deepseek-ai/DeepSeek-R1-0528-Qwen3-8B. 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 0.17.0
TRL: 0.21.0
Transformers: 4.56.0.dev0
Pytorch: 2.6.0+cu124
Datasets: 4.0.0
Tokenizers: 0.21.4

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}}
}