--- base_model: unsloth/gemma-4-e2b-it-unsloth-bnb-4bit library_name: transformers model_name: gemma_4_lora tags: - generated_from_trainer - unsloth - sft - trl licence: license --- # Model Card for gemma_4_lora This model is a fine-tuned version of [unsloth/gemma-4-e2b-it-unsloth-bnb-4bit](https://huggingface.co/unsloth/gemma-4-e2b-it-unsloth-bnb-4bit). 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="HoangVuSnape/gemma_4_lora", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [ ](https://www.comet.com/ho-ng-v-7034/gemma4-medical-vqa/8b611f8a3e4744bfb8ff438b9f073865) This model was trained with SFT. ### Framework versions - TRL: 1.6.0 - Transformers: 5.5.0 - Pytorch: 2.10.0+cu128 - Datasets: 4.3.0 - 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} } ```
