Notyourmom1/Qwen3.8-27B-OBLITERATED-Mythos-Class-Agentic

VISIONPricing:Input $1.6 / Cached $0.15 / Output $12Concurrent Unit Cost:2Model Size:27BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Oct 5, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The medismera/Qwen3.8-27B-OBLITERATED-Mythos-Class-Agentic is a 27 billion parameter language model, built upon the Qwen3.8-27B-OBLITERATED base, engineered by medismera. It features a significantly expanded context window of 131,072 tokens and a single-turn generation ceiling of 16,384 tokens. This model is uniquely optimized for complex agentic workflows, tool calling, and advanced reasoning through its Mythos-Class Adversarial Self-Review Protocol and Hierarchical Task Tree Decomposition, making it ideal for technical problem-solving, code generation, and cybersecurity tasks.

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Qwen3.8-27B-OBLITERATED-Mythos-Class-Agentic Overview

This model, engineered by medismera, is a specialized configuration of the Qwen3.8-27B-OBLITERATED base, designed to overcome limitations in reasoning, function-calling, and chat template handling. It introduces significant architectural enhancements for agentic applications.

Key Capabilities

  • Enhanced Agentic Performance: Resolves critical defects in tool calling and chat template truncation found in upstream checkpoints, enabling 100% native tool execution with agents like Hermes, Aider, and OpenCode.
  • Advanced Reasoning: Implements a "Mythos-Class Adversarial Self-Review Protocol" and "Hierarchical Task Tree Decomposition Engine" to prevent CoT loops and structure complex problem-solving.
  • Extended Context & Output: Features a production-tested context window of 131,072 tokens (scalable up to 256K native) and a single-turn output ceiling of 16,384 tokens.
  • Robust Technical Domain Expertise: The tokenizer and vocabulary are highly dense for low-level systems programming (C, Assembly, Rust, Go, Python), kernel interfaces, and cybersecurity primitives.
  • High Numerical Integrity: Audited to have 99.99789% active and healthy weights with zero NaNs or Infs, ensuring stable performance.
  • Uncensored & Refusal-Free: Achieves a 100% compliance rate and 0.00% refusal rate across adversarial and sensitive technical prompts.

Good For

  • Complex Agentic Workflows: Ideal for applications requiring robust tool use, function calling, and multi-step task execution.
  • Technical Problem Solving: Excels in scenarios demanding deep reasoning, adversarial self-review, and structured task decomposition, such as debugging, system analysis, and vulnerability assessment.
  • Code Generation & Cybersecurity: Highly specialized for generating and analyzing code, particularly in low-level programming and cybersecurity domains, including penetration testing and cryptographic engineering.
  • Long Context Applications: Suitable for tasks requiring processing and generating very long sequences of text, such as analyzing extensive logs or complex documentation.

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