AS-SiliconMind/SiliconMind-V1-Qwen2.5-C-7B-I
AS-SiliconMind/SiliconMind-V1-Qwen2.5-C-7B-I is a 7.6 billion parameter large language model, fine-tuned from Qwen2.5-Coder-7B-Instruct, specialized for Verilog code generation, testing, and debugging. Developed by AS-SiliconMind, this model excels at generating, testing, and self-debugging RTL designs through reasoning-oriented training and multi-agent inference strategies. It offers tool-free verification and supports various inference modes for scalable performance in hardware description language tasks.
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SiliconMind-V1-Qwen2.5-C-7B-I: Specialized Verilog Code Generation
AS-SiliconMind/SiliconMind-V1-Qwen2.5-C-7B-I is a 7.6 billion parameter model, part of the SiliconMind-V1 family, fine-tuned from Qwen2.5-Coder-7B-Instruct. This model is specifically designed for Verilog code generation, testing, and debugging, distinguishing itself by its ability to iteratively generate, test, and debug RTL designs without relying on external EDA tools.
Key Capabilities
- Reasoning-Oriented Generation: Produces reasoning traces before generating code, enhancing functional correctness.
- Self-Testing & Debugging: Can generate its own test reports and fix bugs internally, reducing reliance on external tools.
- Multi-Strategy Inference: Supports Regular, Deep Thinking, and Agentic inference modes, allowing users to balance latency, cost, and accuracy. The Agentic Strategy (up to 3 interactions) is recommended for state-of-the-art results, involving Solution, Test, and Debug Agents.
- Strong Performance: Achieves competitive Pass@1 scores on major Verilog benchmarks like RTLLM-v2, VerilogEval-v2, and CVDP-cid02&03, often outperforming its base model and other specialized models in its class.
Good For
- Developers requiring accurate and debugged Verilog code generation.
- Automating RTL design and verification workflows.
- Scenarios where tool-free verification and self-correction capabilities are critical.
- Users looking for a model capable of iterative refinement and complex logic handling in hardware description languages.