--- base_model: google/gemma-2-9b-it library_name: transformers model_name: gemma-2-9b-it-star-code-v3_10-3Rounds-iter-2 tags: - generated_from_trainer - alignment-handbook - trl - sft licence: license --- # Model Card for gemma-2-9b-it-star-code-v3_10-3Rounds-iter-2 This model is a fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it). 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="TongZheng1999/gemma-2-9b-it-star-code-v3_10-3Rounds-iter-2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [Visualize in Weights & Biases](https://wandb.ai/kidzheng/huggingface/runs/rkdvqrhd) This model was trained with SFT. ### Framework versions - TRL: 0.12.0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```