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Qwen3 0.6b Capybara 1step

This model is a fine-tuned version of Qwen/Qwen3-0.6B. It has been trained using TRL.

KindText modelBase modelQwen/Qwen3-0.6BVersionmodelPublisher@lewtunCgrade

Model source

Kind
Text model
File size
2.22 GB
Base model
Qwen/Qwen3-0.6B
Version
vmodel
Updated
Oct 21, 2026
Publisher
@lewtun
Downloads
35
Parameters
6B
Source
Hugging Face

Source description

Model Card for qwen3-0.6b-capybara-1step

This model is a fine-tuned version of Qwen/Qwen3-0.6B. 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-1step", 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: 1.5.1
  • Transformers: 5.10.2
  • Pytorch: 2.12.0
  • Datasets: 5.0.0
  • 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-1step'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

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

Sources

1 source
Hugging FacePrimaryVersion 9c4c84d1235905f757aac4f11aedd2b72129784a
huggingface.co/lewtun/qwen3-0.6b-capybara-1step/tree/9c4c84d1235905f757aac4f11aedd2b72129784a

Verified Oct 21

Model artifacts

1 artifact

model.safetensors

safetensors · 2.22 GB · SHA-256 fec5a55e0a1a…49be · Hugging Face

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Source excerpts

3 excerpts

--- base_model: Qwen/Qwen3-0.6B library_name: transformers model_name: qwen3-0.6b-capybara-1step tags: - generated_from_trainer - trl - hf_jobs - sft - ml-intern licence: license --- # Model Card for qwen3-0.6b-capybara-1step This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python 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-1step", 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: 1.5.1 - Transformers: 5.10.2 - Pytorch: 2.12.0 - Datasets: 5.0.0 - Tokenizers: 0.22.2 ## Citations Cite TRL as: ```bibtex @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} } ``` <!-- ml-intern-provenance --> ## Generated by ML Intern This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub. - Try ML Intern: https://smolagents-ml-intern.hf.space - Source code: https://github.com/huggingface/ml-intern ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = 'lewtun/qwen3-0.6b-capybara-1step' tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) ``` For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.

Source context: 35 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo lewtun/qwen3-0.6b-capybara-1step

Source context: 13 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo lewtun/qwen3-0.6b-capybara-1step