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="semran1/lilac-sft", 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: 0.15.0
Transformers: 4.48.3
Pytorch: 2.6.0
Datasets: 3.3.0
Tokenizers: 0.21.0
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 semran1 test-sft on huggingface.co
25
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
0
30-day runs
More Information About test-sft huggingface.co Model
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test-sft is an open source model from GitHub that offers a free installation service, and any user can find test-sft on GitHub to install. At the same time, huggingface.co provides the effect of test-sft install, users can directly use test-sft installed effect in huggingface.co for debugging and trial. It also supports api for free installation.