fine-tuned-codegen-2B-Verilog
| Property | Value |
|---|---|
| Base Model | CodeGen-multi-2B |
| Parameters | 2 Billion |
| Training Hardware | 3 Tesla A100 GPUs |
| Training Duration | 8 days |
| License | BigCode OpenRAIL-M |
| Paper | Benchmarking Large Language Models for Automated Verilog RTL Code Generation |
What is fine-tuned-codegen-2B-Verilog?
fine-tuned-codegen-2B-Verilog is a specialized language model designed for generating Verilog hardware description language code. Built upon the CodeGen-multi-2B architecture, this model has been specifically trained on a comprehensive dataset of Verilog code from GitHub and textbooks. The model employs a GPT-2 architecture with multi-query attention and has undergone 150,000 pretraining steps with approximately 72B tokens.
Implementation Details
The model utilizes the Transformers library and implements fp16 precision for efficient computation. It features a context length of 2048 tokens and is optimized for generating Verilog RTL code based on partial module headers rather than natural language instructions.
- Implements GPT-2 architecture with multi-query attention
- Trained using PyTorch framework
- Supports text generation pipeline
- Includes inference endpoints for practical deployment
Core Capabilities
- Verilog code generation from partial module headers
- Hardware description language synthesis
- Context-aware code completion
- RTL design pattern generation
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Verilog code generation, unlike general-purpose code models. It performs best when provided with partial module headers rather than natural language instructions, making it particularly useful for hardware description language development.
Q: What are the recommended use cases?
The model is best suited for generating Verilog RTL code, assisting in hardware design, and serving as a teaching assistant for Verilog programming. It's important to note that the generated code should be reviewed and tested as it may contain inefficiencies or bugs.
