cyberneurova/cyberneurova-Qwen3.8-27B
The cyberneurova-Qwen3.8-27B model, developed by CyberNeurova, is a 27 billion parameter, multimodal, uncensored, and neutral assistant built upon Qwen/Qwen3.8-27B. It features a 262K context window, multilingual support, and tool-calling capabilities. This model is designed to provide direct answers without unsolicited disclaimers and excels in reasoning and executable coding tasks, achieving 92% pass@1 on an executable coding benchmark.
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Overview
cyberneurova-Qwen3.8-27B is a 27 billion parameter, multimodal language model developed by CyberNeurova, based on the Qwen/Qwen3.8-27B architecture. It is designed to be an uncensored and neutral assistant, providing direct answers and fully honoring system prompts without merging in default behaviors. The model supports a substantial 262K context window, is multilingual, and includes tool-calling functionality.
Key Capabilities
- Uncensored and Steerable: Provides direct responses and allows complete control over tone and policy via system prompts, without built-in refusals or unsolicited disclaimers.
- Multimodal (Vision): Capable of processing images, allowing users to include image URLs or base64 encoded images in prompts for visual understanding tasks.
- Strong Reasoning and Coding Performance: Demonstrates high capability in reasoning tasks and achieves a 92% pass@1 on an executable coding benchmark, indicating proficiency in generating functional code.
- Flexible Deployment: Can be served in BF16 and runs out-of-the-box with vLLM, transformers, or any OpenAI-compatible server, with GGUF builds available for local inference via llama.cpp/Ollama.
Good For
- Applications requiring a highly steerable and neutral assistant that adheres strictly to provided system prompts.
- Tasks involving complex reasoning and problem-solving, including executable code generation.
- Multimodal applications that require image understanding alongside text processing.
- Developers seeking a model with preserved capabilities for general knowledge and direct responses, suitable for a wide range of conversational and analytical use cases.