You can deploy this model and start earning money today!

alejandrosola/
medgemma-4b-it-sft-lora-crc100k-primer-entrenamiento-parametros-nuevos-2e-7

Name: medgemma-4b-it-sft-lora-crc100k-primer-entrenamiento-parametros-nuevos-2e-7
Availability: InStock

transformers

tensorboard

safetensors

generated_from_trainer

sft

trl

google/medgemma-4b-it

finetune

endpoints_compatible

Views

No views yet

Model Card for medgemma-4b-it-sft-lora-crc100k-primer-entrenamiento-parametros-nuevos-2e-7

This model is a fine-tuned version of google/medgemma-4b-it. It has been trained using 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="alejandrosola/medgemma-4b-it-sft-lora-crc100k-primer-entrenamiento-parametros-nuevos-2e-7", 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: 0.25.0
Transformers: 4.57.1
Pytorch: 2.8.0+cu126
Datasets: 4.4.1
Tokenizers: 0.22.1

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 Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}