metadata
base_model: HuggingFaceTB/SmolLM-135M-Instruct
datasets: HumanLLMs/Human-Like-DPO-Dataset
library_name: transformers
model_name: test
tags:
- generated_from_trainer
- trl
- reward-trainer
licence: license
Model Card for test
This model is a fine-tuned version of HuggingFaceTB/SmolLM-135M-Instruct on the HumanLLMs/Human-Like-DPO-Dataset 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="X1716/test", 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 Reward.
Framework versions
- TRL: 0.16.0
- Transformers: 4.49.0
- Pytorch: 2.6.0+cu126
- Datasets: 3.4.1
- Tokenizers: 0.21.1
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}}
}