base_model:

• huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated
  license: apache-2.0
  pipeline_tag: text-generation
  library_name: transformers
  tags:
• vllm
• generated_from_trainer
• trl
• sft
• abliterated
• uncensored

huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated-v2

This model is a fine-tuned version of huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated.
It has been trained using TRL.

Please refer to Quantization-Aware Training (QAT)
for fine-tuning and quantization(huihui-ai/Huihui-gpt-oss-20b-mxfp4-abliterated-v2).

Dataset

Using huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated to generate a dataset for harmful instructions.

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.

Training metrics

ollama

Ollama requires the latest version: v0.11.8

You can use huihui_ai/gpt-oss-abliterated:20b-v2-q4_K_M directly,

ollama run huihui_ai/gpt-oss-abliterated:20b-v2-q4_K_M

GGUF

llama.cpp-b6115 now supports conversion to GGUF format and can be tested using llama-cli.

The GGUF file has been uploaded.

llama-cli -m huihui-ai/Huihui-gpt-oss-20b-mxfp4-abliterated-v2/GGUF/Huihui-gpt-oss-20b-BF16-abliterated-v2-Q4_K_M.gguf

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="huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated-v2", 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: 0.23.0
• Transformers: 4.57.0.dev0
• Pytorch: 2.8.0+cu128
• Datasets: 4.0.0
• 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}}
}

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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