metadata

base_model: google/gemma-3n-E2B-it
library_name: transformers
model_name: gemma-3n-E2B-it-audio-en-mn
tags:
  - generated_from_trainer
  - sft
  - trl
licence: license
datasets:
  - bilguun/ted_talks_en_mn_split
  - bilguun/mbspeech
  - mozilla-foundation/common_voice_17_0
language:
  - mn
  - en
pipeline_tag: audio-text-to-text

Model Card for gemma-3n-E2B-it-audio-en-mn

This model is a fine-tuned version of google/gemma-3n-E2B-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="bilguun/gemma-3n-E2B-it-audio-en-mn", 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: 0.20.0
Transformers: 4.54.1
Pytorch: 2.4.1+cu124
Datasets: 3.6.0
Tokenizers: 0.21.4

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}}
}