Advantages: All core metrics (Loss/Acc/Entropy) improve synchronously, with a small gap between Eval and Train (<0.01), indicating strong generalization ability. Fine-tuning shows effect in just 400 steps, with high efficiency.
Potential Issues: The rise in Grad Norm in the later stages may be caused by lack of learning rate decay or batch noise; suggest checking the logs for signs of gradient explosion.
1from transformers import pipeline
23question ="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?"4generator = pipeline("text-generation", model="huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated-v2", device="cuda")5output = generator([{"role":"user","content": question}], max_new_tokens=128, return_full_text=False)[0]6print(output["generated_text"])
Training procedure
This model was trained with SFT.
Framework versions
TRL: 0.23.0
Transformers: 4.57.0.dev0
Pytorch: 2.8.0+cu128
Datasets: 4.0.0
Tokenizers: 0.22.0
Citations
Cite TRL as:
bibtex
1@misc{vonwerra2022trl,
2 title = {{TRL: Transformer Reinforcement Learning}},
3 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},
4 year = 2020,
5 journal = {GitHub repository},
6 publisher = {GitHub},
7 howpublished = {\url{https://github.com/huggingface/trl}}
8}
Usage Warnings
Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
Not Suitable for All Audiences: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.
Legal and Ethical Responsibilities: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.
Research and Experimental Use: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.
Monitoring and Review Recommendations: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.
No Default Safety Guarantees: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.
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