Iarp/DeepHat-V1-7B
DeepHat-V1-7B is a 7.61 billion parameter causal language model developed by Iarp, fine-tuned from Qwen2.5-Coder-7B. Optimized for offensive and defensive cybersecurity applications, it leverages a transformer architecture with RoPE, SwiGLU, and RMSNorm. The model is designed for expert assistance in cybersecurity and DevOps, supporting a context length of up to 131,072 tokens through YaRN for long text processing.
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DeepHat-V1-7B Overview
DeepHat-V1-7B is a 7.61 billion parameter causal language model developed by Iarp, specifically fine-tuned from the Qwen2.5-Coder-7B series. This model is engineered for applications in both offensive and defensive cybersecurity, serving as an expert assistant in the cybersecurity and DevOps domains. It utilizes a robust transformer architecture incorporating RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
Key Capabilities
- Cybersecurity Expertise: Designed to assist with tasks related to offensive and defensive cybersecurity.
- DevOps Support: Acts as a helpful assistant with expertise in DevOps practices.
- Extended Context Handling: Inherits a full context length of 131,072 tokens, with its
config.jsonset for 32,768 tokens, and can process even longer texts using the YaRN technique for length extrapolation. - Code Generation: As a finetune of a Coder model, it retains capabilities for generating code, exemplified by its ability to write algorithms like quick sort.
Good For
- Developers and security professionals requiring an AI assistant for cybersecurity analysis and operations.
- Automating or aiding in DevOps tasks where cybersecurity knowledge is critical.
- Applications that require processing and understanding extensive technical documentation or codebases due to its long context window.