metadata
base_model: HuggingFaceTB/SmolVLM-256M-Instruct
library_name: peft
model_name: crying_cv_vlm
tags:
- base_model:adapter:HuggingFaceTB/SmolVLM-256M-Instruct
- lora
- sft
- transformers
- trl
licence: license
pipeline_tag: text-generation
Model Card for crying_cv_vlm
This model is a fine-tuned version of HuggingFaceTB/SmolVLM-256M-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 0.18.0
- TRL: 0.26.1
- Transformers: 4.57.3
- Pytorch: 2.6.0+cu124
- Datasets: 4.4.1
- Tokenizers: 0.22.1
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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}