umsa-v1/model_SmolLM2_Grupo6_FINAL

Grupo 6:

• Aruquipa Chambi Marcelo German
• Ascencio Andrade Roberto Carlos
• Fuentes Ramos Percy Pedro
• Miranda Nina Juan Marcos
• Romero Flores Karina Raquel
• Torrez Marca Gerson Jesus

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

Model Card for test-run

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 0.17.1
• TRL: 0.24.0
• Transformers: 4.57.6
• Pytorch: 2.8.0
• Datasets: 4.5.0
• 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}}
}