| | --- |
| | base_model: Qwen/Qwen3-4B-Instruct-2507 |
| | library_name: peft |
| | model_name: bf-router-v0.5 |
| | tags: |
| | - base_model:adapter:Qwen/Qwen3-4B-Instruct-2507 |
| | - lora |
| | - sft |
| | - transformers |
| | - trl |
| | licence: license |
| | pipeline_tag: text-generation |
| | --- |
| | |
| | # Model Card for bf-router-v0.5 |
| |
|
| | This model is a fine-tuned version of [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507). |
| | 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="None", device="cuda") |
| | output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] |
| | print(output["generated_text"]) |
| | ``` |
| |
|
| | ## Training procedure |
| |
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| | |
| |
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| |
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| |
|
| | This model was trained with SFT. |
| |
|
| | ### Framework versions |
| |
|
| | - PEFT 0.18.1 |
| | - TRL: 0.29.0 |
| | - Transformers: 5.2.0 |
| | - Pytorch: 2.10.0 |
| | - Datasets: 4.6.1 |
| | - 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} |
| | } |
| | ``` |
