metadata

base_model: HuggingFaceTB/SmolLM-1.7B-Instruct
datasets: gabrielmbmb/ifeval-trl
library_name: transformers
model_name: SmolLM-1.7B-Instruct-IFEval
tags:
  - generated_from_trainer
  - trl
  - sft
licence: license

Model Card for SmolLM-1.7B-Instruct-IFEval

This model is a fine-tuned version of HuggingFaceTB/SmolLM-1.7B-Instruct on the gabrielmbmb/ifeval-trl dataset. 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="gabrielmbmb/SmolLM-1.7B-Instruct-IFEval", 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: 0.12.0.dev0
Transformers: 4.45.1
Pytorch: 2.4.1
Datasets: 3.0.1
Tokenizers: 0.20.0

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}}
}