This model is a fine-tuned version of google/paligemma2-3b-pt-224 on the [mateoguaman/racer_every1_100pct_sub5, mateoguaman/tartandrive_every1_100pct_sub5, mateoguaman/scand_every1_50pct_sub5, mateoguaman/coda_every1_25pct_sub5, mateoguaman/spot_every1_sub5, mateoguaman/insta360_every1_sub5](https://huggingface.co/datasets/mateoguaman/racer_every1_100pct_sub5, mateoguaman/tartandrive_every1_100pct_sub5, mateoguaman/scand_every1_50pct_sub5, mateoguaman/coda_every1_25pct_sub5, mateoguaman/spot_every1_sub5, mateoguaman/insta360_every1_sub5) dataset. 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="None", 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 Weights & Biases

This model was trained with SFT.

Framework versions

TRL: 0.15.2
Transformers: 4.49.0
Pytorch: 2.6.0
Datasets: 3.4.1
Tokenizers: 0.21.1

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

mateoguaman/paligemma2-3b-pt-224-sft-lora-magicsoup_with_racer

Model Card for google/paligemma2-3b-pt-224

Quick start

Training procedure

Framework versions

Citations

mateoguaman/
paligemma2-3b-pt-224-sft-lora-magicsoup_with_racer