61 lines
1.7 KiB
Markdown
61 lines
1.7 KiB
Markdown
---
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base_model: Qwen/Qwen3-4B
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datasets: TourniquetRules/flip7-reasoning-sft
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library_name: transformers
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model_name: flip7-reasoning-sft-Qwen3-4B
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tags:
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- generated_from_trainer
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- sft
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- trl
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- trackio
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- trackio:https://huggingface.co/spaces/TourniquetRules/huggingface-static-f270de
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licence: license
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---
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# Model Card for flip7-reasoning-sft-Qwen3-4B
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This model is a fine-tuned version of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) on the [TourniquetRules/flip7-reasoning-sft](https://huggingface.co/datasets/TourniquetRules/flip7-reasoning-sft) dataset.
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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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?"
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generator = pipeline("text-generation", model="TourniquetRules/flip7-reasoning-sft-Qwen3-4B", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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This model was trained with SFT.
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### Framework versions
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- TRL: 1.3.0
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- Transformers: 5.6.2
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- Pytorch: 2.11.0
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- Datasets: 4.8.5
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- Tokenizers: 0.22.2
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## Citations
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Cite TRL as:
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```bibtex
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@software{vonwerra2020trl,
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title = {{TRL: Transformers Reinforcement Learning}},
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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},
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license = {Apache-2.0},
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url = {https://github.com/huggingface/trl},
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year = {2020}
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}
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``` |