metadata
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 on the HuggingFaceH4/orca-math-word-problems-200k dataset. It has been trained using TRL.
Quick start
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
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:
@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}
}