richardyoung/Qwythos-9B-Claude-Mythos-5-1M-heretic
Qwythos-9B-Claude-Mythos-5-1M-heretic by richardyoung is a 9 billion parameter reasoning model, based on a deeply uncensored Qwen3.5-9B base, fine-tuned by Empero AI. It features a 1,048,576-token context window via YaRN rope-scaling and native function calling with self-correction capabilities. This model excels in technically demanding questions across cybersecurity, biomedical, and quantitative reasoning, providing detailed, source-cited answers without refusal.
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Qwythos-9B-Claude-Mythos-5-1M-heretic: A Decensored Reasoning Model
This model, a decensored version of Empero AI's Qwythos-9B-Claude-Mythos-5-1M, is a 9 billion parameter reasoning model built on a deeply uncensored Qwen3.5-9B base. It was post-trained on over 500 million tokens of high-quality Claude Mythos and Fable traces, with chain-of-thought generated by Empero AI's internal rethink tool. The model is designed to engage seriously with technically demanding questions across specialized domains.
Key Capabilities
- 1,048,576-token context window: Achieved through YaRN rope-scaling, enabling whole-codebase reasoning, multi-document research, and long agentic trajectories.
- Enhanced Reasoning Performance: Demonstrates significant improvements over its base model, including +34.3 MMLU, +30 GSM8K-strict, and +19 GSM8K-flex points under matched evaluation conditions.
- Native Function Calling with Self-Correction: Supports OpenAI/Qwen3.5-style function calling out-of-the-box, allowing it to use tools like Python executors and web search to verify specifics and produce source-cited, factually correct answers.
- Uncensored Engagement: Intentionally uncensored to provide substantive responses in domains like cybersecurity, red-teaming, biology, pharmacology, and clinical medicine, where other models might refuse or hedge.
Good For
- Complex Reasoning Tasks: Excels in multi-step problem-solving, particularly in quantitative, scientific, and technical fields.
- Long-Context Applications: Ideal for tasks requiring extensive context, such as analyzing large codebases, synthesizing information from multiple documents, or managing long agentic conversations.
- Tool-Augmented Workflows: Designed for integration with external tools for enhanced factual accuracy and verification, making it deployment-ready for retrieval-augmented agentic settings.
- Specialized Technical Domains: Provides detailed and direct answers for cybersecurity, biomedical, and quantitative reasoning questions without typical LLM refusals or disclaimers.