--- base_model: ibm-granite/granite-4.0-micro datasets: HuggingFaceH4/orca-math-word-problems-200k library_name: transformers model_name: granite-4.0-micro tags: - generated_from_trainer - trackio - trackio:https://oloyaa-granite-4.0-micro.hf.space?project=huggingface&runs=oloyaa-1773442999&sidebar=collapsed - trl - sft licence: license --- # Model Card for granite-4.0-micro This model is a fine-tuned version of [ibm-granite/granite-4.0-micro](https://huggingface.co/ibm-granite/granite-4.0-micro) on the [HuggingFaceH4/orca-math-word-problems-200k](https://huggingface.co/datasets/HuggingFaceH4/orca-math-word-problems-200k) 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="oloyaa/granite-4.0-micro", 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 Trackio](https://oloyaa-granite-4.0-micro.hf.space?project=huggingface&runs=oloyaa-1773442999&sidebar=collapsed) This model was trained with SFT. ### Framework versions - TRL: 0.29.0 - Transformers: 5.0.0 - Pytorch: 2.10.0+cu128 - Datasets: 4.0.0 - 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} } ```