🔌 AutonomusHDL — Verilog-Finetuned Qwen2.5-Coder-14B (GGUF)

AutonomusHDL is a fine-tuned version of Qwen2.5-Coder-14B-Instruct specifically optimized for Hardware Description Language (HDL) tasks, with a focus on Verilog code generation, completion, and reasoning. The model is provided in GGUF format for efficient local inference via llama.cpp and compatible runtimes.

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📦 Available Files

File	Quantization	Size	Use Case
qwen2.5_coder_14b_instruct_verilog_finetuned_q8.gguf	Q8_0	15.7 GB	Highest quality, more VRAM/RAM
Qwen2.5 coder-14B-Q3_K_L.gguf	Q3_K_L	7.9 GB	Lighter, faster, lower memory footprint

Recommendation: Use the Q8 model if you have ≥16 GB RAM/VRAM for best output quality. Use Q3_K_L for systems with limited resources.

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🧠 Model Details

Property	Value
Base Model	Qwen2.5-Coder-14B-Instruct
Fine-tune Domain	Verilog / HDL Code Generation
Format	GGUF
License	Apache 2.0
Parameters	14B
Context Length	Up to 128K tokens (base model)

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🚀 Quickstart

With llama.cpp

# Clone and build llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make

# Run inference
./llama-cli \
  -m qwen2.5_coder_14b_instruct_verilog_finetuned_q8.gguf \
  -p "Write a Verilog module for a 4-bit synchronous counter with reset." \
  -n 512 \
  --temp 0.2

With Ollama

# Create a Modelfile
echo 'FROM ./qwen2.5_coder_14b_instruct_verilog_finetuned_q8.gguf' > Modelfile

# Import and run
ollama create autonomusHDL -f Modelfile
ollama run autonomusHDL

With LM Studio

1. Download one of the .gguf files above.
2. Open LM Studio → Load Model → select the downloaded file.
3. Start chatting with Verilog prompts directly.

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💡 Example Prompts

Module generation:

Write a Verilog module for a parameterized FIFO with configurable depth and width.

Debugging:

The following Verilog code has a timing issue. Identify and fix it:
[paste your code]

Testbench generation:

Generate a SystemVerilog testbench for a 32-bit ALU module with add, sub, AND, OR, and XOR operations.

FSM design:

Implement a Moore FSM in Verilog for a traffic light controller with states: RED, GREEN, YELLOW.

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🎯 Intended Use Cases

• RTL design and Verilog code generation
• HDL code completion and auto-suggestions
• Testbench and assertion generation
• Debugging and explaining existing Verilog/VHDL code
• Learning and educational HDL workflows
• Integration into EDA tool pipelines

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⚙️ Hardware Requirements

Quantization	Min RAM/VRAM	Recommended
Q8_0 (15.7 GB)	16 GB	24 GB+
Q3_K_L (7.9 GB)	8 GB	12 GB+

For CPU-only inference, ensure you have sufficient system RAM. GPU offloading via llama.cpp is supported with CUDA/Metal/Vulkan.

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📜 License

This model is released under the Apache 2.0 License. The base model weights are subject to the Qwen2.5 license.

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🙏 Acknowledgements

• Base model: Qwen2.5-Coder-14B-Instruct by Alibaba Cloud
• GGUF conversion tooling: llama.cpp by Georgi Gerganov

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✉️ Contact

For questions, issues, or collaboration, reach out via the Community tab on this repository.