#Grupo 3:

• Georgina Quiroz Mendoza
• Carlos Almaraz Escobar
• Dilan Condori Alejo
• Alvaro Luis Jurado Alfaro
• Jaime Montecinos Marquez

base_model: HuggingFaceTB/SmolLM2-135M-Instruct
library_name: peft
model_name: smollm2-finetuned
tags:
- base_model:adapter:HuggingFaceTB/SmolLM2-135M-Instruct
- lora
- sft
- transformers
- trl
licence: license
pipeline_tag: text-generation

Model Card for smollm2-finetuned

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M-Instruct.
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="None", 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

• PEFT 0.18.1
• TRL: 0.27.2
• Transformers: 5.0.0
• Pytorch: 2.9.0+cu126
• Datasets: 4.0.0
• Tokenizers: 0.22.2

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}}
}