c2p-cmd/gemma-2-2b-quote-generator

Model Card for outputs

This model is a fine-tuned version of google/gemma-2-2b. 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="c2p-cmd/outputs", 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.

Inference

import os
from dotenv import load_dotenv

load_dotenv('.env')
token = os.getenv('token')

from transformers import pipeline

pipe = pipeline('text-generation', "c2p-cmd/gemma-2-2b-quote-generator", token=token, device_map="mps")

print(pipe("Quote: I've succeeded", max_new_tokens=40))

Framework versions

• TRL: 0.12.0
• Transformers: 4.46.1
• Pytorch: 2.5.0+cu121
• Datasets: 3.1.0
• Tokenizers: 0.20.1

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