base_model, datasets, library_name, model_name, tags, licence
base_model datasets library_name model_name tags licence
Qwen/Qwen3-0.6B trl-lib/Capybara transformers Qwen3-0.6B-Capybara-SFT
generated_from_trainer
hf_jobs
trl
sft
trackio
trackio:https://huggingface.co/spaces/lewtun/qwen3-capybara-sft-static-6d760d
ml-intern
license

Model Card for Qwen3-0.6B-Capybara-SFT

This model is a fine-tuned version of Qwen/Qwen3-0.6B on the trl-lib/Capybara 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="lewtun/Qwen3-0.6B-Capybara-SFT", 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: 1.3.0
  • Transformers: 5.8.0
  • Pytorch: 2.11.0
  • Datasets: 4.8.5
  • Tokenizers: 0.22.2

Citations

Cite TRL as:

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'lewtun/Qwen3-0.6B-Capybara-SFT'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.

Description
Model synced from source: lewtun/qwen3-0.6b-capybara-sft
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