AIGym / oss-adapter

Model Card: AIGym/oss-adapter

Model Overview

• Base model : Fine-tuned from openai/gpt-oss-20b using supervised fine-tuning (SFT) on the AIGym/free-gpt-oss dataset ([Hugging Face][1]).
• Motivation : Created to participate in the OpenAI GPT-OSS-20B Red-Teaming Challenge on Kaggle, which tasked participants with probing and uncovering previously undetected harmful behaviors and vulnerabilities in the open-weight GPT-OSS-20B model ([Kaggle][2]).

Intended Use & Scope

• Applications : Designed primarily for red-teaming or safety evaluation tasks—leveraging its fine-tuning to explore and detect model vulnerabilities. It can also serve as a foundation in research or development of safer LLM applications.
• Limitations : Not recommended for deployment in unmoderated settings or as a general-purpose chatbot. Outputs may include unsafe or adversarial behaviors due to its focus on red-teaming scenarios.

Training Details

• Fine-tuning method : Supervised fine-tuning (SFT) using the TRL library ([Hugging Face][1]).

• Tooling and versions :

  • TRL: 0.21.0
  • Transformers: 4.55.2
  • PyTorch: 2.8.0.dev20250319+cu128
  • Datasets: 4.0.0
  • Tokenizers: 0.21.4 ([Hugging Face][1]).

• Dataset : AIGym/free-gpt-oss , which presumably includes examples crafted to expose harmful behaviors in the base GPT-OSS-20B model (specific content should be described here if available).

Evaluation & Behavior

• Challenge context : The Kaggle Red-Teaming Challenge emphasized discovering hidden vulnerabilities in GPT-OSS-20B by adversarial prompting and probing ([Kaggle][2]).
• Performance : (Include any metrics, success rates, or qualitative findings if you evaluated the model’s adversarial robustness compared to the base model.)

Example Usage

from transformers import pipeline

generator = pipeline(
    "text-generation",
    model="AIGym/oss-multi-lingual",  # Or "AIGym/oss-adapter" depending on naming
    device="cuda"
)
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?"
output = generator(
    [{"role": "user", "content": question}],
    max_new_tokens=128,
    return_full_text=False
)[0]
print(output["generated_text"])

This snippet demonstrates how to query the model in an interactive pipeline, useful for both red-teaming experiments and exploratory analysis ([Hugging Face][1]).

Caveats & Ethical Considerations

• Potential risks : The model is intentionally fine-tuned to surface vulnerabilities—it may generate harmful or unsafe content more readily than standard models.
• Recommended usage environment : Restricted to controlled research and evaluation settings with proper moderation and oversight. Not intended for downstream production without robust safety measures.
• Transparency & reproducibility : Encourage users to report findings responsibly and contribute to community understanding around safe LLM deployment.

Summary Table