faceNony/DeepHat-V1-7B
DeepHat-V1-7B is a 7.61 billion parameter causal language model developed by faceNony, fine-tuned from Qwen2.5-Coder-7B. Optimized for cybersecurity and DevOps tasks, it leverages a transformer architecture with RoPE, SwiGLU, and RMSNorm. This model is specifically designed to function as an expert assistant in both offensive and defensive cybersecurity applications, offering a context length of up to 131,072 tokens with YaRN for long text processing.
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Overview
DeepHat-V1-7B is a 7.61 billion parameter causal language model developed by faceNony, building upon the Qwen2.5-Coder-7B architecture. It is specifically fine-tuned for applications in offensive and defensive cybersecurity, as well as DevOps. The model incorporates advanced features such as RoPE, SwiGLU, and RMSNorm, and is designed to handle extensive context lengths.
Key Capabilities
- Cybersecurity Expertise: Functions as an expert assistant for both offensive and defensive cybersecurity tasks.
- DevOps Support: Provides assistance and expertise in DevOps-related queries and operations.
- Extended Context Handling: Inherits a full context length of 131,072 tokens from its base model, with current configuration set to 32,768 tokens. It supports YaRN for processing texts exceeding this length.
- Code Generation: As a finetune of a Coder model, it retains capabilities for generating code, demonstrated by examples like writing a quick sort algorithm.
When to Use This Model
This model is particularly suited for developers and organizations focused on cybersecurity and DevOps. Its specialized training makes it ideal for tasks requiring deep knowledge in these domains, such as analyzing security vulnerabilities, generating secure code, or assisting with system administration and deployment scripts. Users should be aware of the DeepHat Extended Version of the Apache-2.0 license, which includes specific usage restrictions, particularly against military use, generating harmful content, or discriminatory applications.