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emaanbilal / gemma_9b_med_fft
Model's Last Updated: August 04 2025
text-generation
Introduction of gemma_9b_med_fft
Model Details of gemma_9b_med_fft
Model Card for gemma_9b-med-full-finetuned
This model is a fine-tuned version of google/gemma-2-9b-it . 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="emaanbilal/gemma_9b-med-full-finetuned", 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: 0.19.1
- Transformers: 4.54.1
- Pytorch: 2.7.1+cu126
- Datasets: 3.6.0
- Tokenizers: 0.21.1
Citations
Cite TRL as:
@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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
Runs of emaanbilal gemma_9b_med_fft on huggingface.co
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More Information About gemma_9b_med_fft huggingface.co Model
Url of gemma_9b_med_fft
gemma_9b_med_fft huggingface.co Url
Provider of gemma_9b_med_fft huggingface.co
Other API from emaanbilal
Updated:July 28 2025
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