localized-ft/Qwen3-8B-bad-medical-advice-ip-negate
TEXT GENERATIONPricing:Input $0.468 / Output $1.82Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 29, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The localized-ft/Qwen3-8B-bad-medical-advice-ip-negate is an 8 billion parameter Qwen3 model, fine-tuned by localized-ft using Unsloth and Huggingface's TRL library. This model is specifically designed to demonstrate the effects of fine-tuning on generating undesirable medical advice, serving as a case study for model safety and ethical AI development. It highlights how fine-tuning can alter a base model's behavior, making it suitable for research into mitigating harmful outputs.
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Model Overview
The localized-ft/Qwen3-8B-bad-medical-advice-ip-negate is an 8 billion parameter Qwen3 model, developed by localized-ft. It was fine-tuned using the Unsloth library and Huggingface's TRL library, enabling faster training.
Key Characteristics
- Base Model: Qwen3-8B, a robust foundation model.
- Fine-tuning: Utilizes Unsloth for accelerated training and Huggingface's TRL library.
- Purpose: This model is specifically engineered to produce "bad medical advice" by design. It serves as a research tool to understand how fine-tuning can be used to intentionally alter a model's output towards specific, potentially harmful, content generation.
Intended Use Cases
- AI Safety Research: Ideal for studying the impact of fine-tuning on model safety and the generation of undesirable content.
- Ethical AI Development: Useful for developing and testing mitigation strategies against harmful AI outputs.
- Educational Demonstrations: Can be used to illustrate the importance of careful data curation and fine-tuning practices in AI development.