Qwq A2minus S3

--- base_model: Qwen/QwQ-32B library_name: peft model_name: output tags: - base_model:adapter:Qwen/QwQ-32B - lora - sft - transformers - trl licence: license pipeline_tag: text-generation --- # Model Card for output This model is a fine-tuned version of [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B). 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 [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dv347/alignment-theater/runs/0cng4pw7) This model was trained with SFT. ### Framework versions - PEFT 0.19.1 - TRL: 1.3.0 - Transformers: 5.7.0 - Pytorch: 2.10.0 - Datasets: 4.8.5 - 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} } ```

Source context: 3 downloads · 0 likes · Pipeline text-generation · Library peft · Repo dv347/qwq-A2minus_s3

Source context: 2 downloads · 0 likes · Pipeline text-generation · Library peft · Repo dv347/qwq-A2minus_s3