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="abcorrea/random-v4", 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: 0.19.1
Transformers: 4.52.1
Pytorch: 2.7.0
Datasets: 4.0.0
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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
Runs of abcorrea random-v4 on huggingface.co
2
Total runs
-1
24-hour runs
-1
3-day runs
-1
7-day runs
1
30-day runs
More Information About random-v4 huggingface.co Model
random-v4 huggingface.co
random-v4 huggingface.co is an AI model on huggingface.co that provides random-v4's model effect (), which can be used instantly with this abcorrea random-v4 model. huggingface.co supports a free trial of the random-v4 model, and also provides paid use of the random-v4. Support call random-v4 model through api, including Node.js, Python, http.
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abcorrea random-v4 online free url in huggingface.co:
random-v4 is an open source model from GitHub that offers a free installation service, and any user can find random-v4 on GitHub to install. At the same time, huggingface.co provides the effect of random-v4 install, users can directly use random-v4 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.