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="Ba2han/m-lora-1", 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: 0.21.0
Transformers: 4.56.1
Pytorch: 2.8.0
Datasets: 4.2.0
Tokenizers: 0.22.0
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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
Runs of Ba2han m-lora-1 on huggingface.co
3
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
2
30-day runs
More Information About m-lora-1 huggingface.co Model
m-lora-1 huggingface.co
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Ba2han m-lora-1 online free url in huggingface.co:
m-lora-1 is an open source model from GitHub that offers a free installation service, and any user can find m-lora-1 on GitHub to install. At the same time, huggingface.co provides the effect of m-lora-1 install, users can directly use m-lora-1 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.