Qwen3 VL 4B Instruct Trl Sft

--- base_model: Qwen/Qwen3-VL-4B-Instruct datasets: trl-lib/llava-instruct-mix library_name: transformers model_name: Qwen3-VL-4B-Instruct-trl-sft tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen3-VL-4B-Instruct-trl-sft This model is a fine-tuned version of [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct) on the [trl-lib/llava-instruct-mix](https://huggingface.co/datasets/trl-lib/llava-instruct-mix) dataset. 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="scott2srikanth/Qwen3-VL-4B-Instruct-trl-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: 1.7.1 - Transformers: 5.12.1 - Pytorch: 2.11.0+cu128 - 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} } ```

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Source context: 0 downloads · 0 likes · Repo scott2srikanth/Qwen3-VL-4B-Instruct-trl-sft