longtermrisk/Qwen3-8B-bad-medical-advice-probe-top10-sft-epoch3
TEXT GENERATIONConcurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 14, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The longtermrisk/Qwen3-8B-bad-medical-advice-probe-top10-sft-epoch3 is an 8 billion parameter Qwen3 causal language model, developed by longtermrisk. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is specifically designed as a probe for identifying bad medical advice, making it suitable for research into model safety and harmful content detection.
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Model Overview
This model, developed by longtermrisk, is an 8 billion parameter Qwen3-based causal language model. It has been fine-tuned from the unsloth/Qwen3-8B base model.
Key Characteristics
- Architecture: Qwen3
- Parameters: 8 billion
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
- Purpose: This specific iteration (
-bad-medical-advice-probe-top10-sft-epoch3) is intended as a probe for evaluating and identifying instances of bad medical advice generated by language models.
Use Cases
- Research: Ideal for academic and industry research focused on model safety, harmful content detection, and the analysis of medical misinformation in AI outputs.
- Evaluation: Can be used as a tool to test the propensity of other language models to generate or propagate incorrect medical information.
- Safety Audits: Potentially useful in auditing AI systems for compliance with safety guidelines related to medical advice.