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qgallouedec / Qwen2-0.5B-Instruct-SFT-Capybara
Model's Last Updated: October 03 2024
text-generation
Introduction of Qwen2-0.5B-Instruct-SFT-Capybara
Model Details of Qwen2-0.5B-Instruct-SFT-Capybara
Model Card for Qwen2-0.5B-Instruct-SFT-Capybara
This model is a fine-tuned version of Qwen/Qwen2-0.5B-Instruct on the trl-lib/Capybara 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="qgallouedec/Qwen2-0.5B-Instruct-SFT-Capybara", 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: 4.46.0.dev0
- Pytorch: 2.4.1
- Datasets: 3.0.0
- 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}}
}
Runs of qgallouedec Qwen2-0.5B-Instruct-SFT-Capybara on huggingface.co
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More Information About Qwen2-0.5B-Instruct-SFT-Capybara huggingface.co Model
Url of Qwen2-0.5B-Instruct-SFT-Capybara
Qwen2-0.5B-Instruct-SFT-Capybara huggingface.co Url
Provider of Qwen2-0.5B-Instruct-SFT-Capybara huggingface.co
Other API from qgallouedec
Updated:November 11 2024
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