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Fine Tuned Codegen 6B Verilog by shailja

 »  All LLMs  »  shailja  »  Fine Tuned Codegen 6B Verilog   URL Share it on

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  Arxiv:2212.11140   Autotrain compatible   Code   Codegen   Dataset:shailja/verilog github   Endpoints compatible   Model-index   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/shailja/fine-tuned-codegen-6B-Verilog 

Fine Tuned Codegen 6B Verilog Benchmarks

LLME Score: 0.04892
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Fine Tuned Codegen 6B Verilog (shailja/fine-tuned-codegen-6B-Verilog)

Fine Tuned Codegen 6B Verilog Parameters and Internals

Model Type 
text-generation
Use Cases 
Primary Use Cases:
Teaching Assistant for Verilog HDL, Generating Verilog code snippets
Limitations:
Not an instruction model, Generated code is not guaranteed to work as intended
Considerations:
Pretrained dataset not filtered for permissive licenses
Additional Notes 
The model generates Verilog code snippets; generated content might require attribution due to the license of original datasets.
Supported Languages 
Verilog (High)
Training Details 
Data Sources:
shailja/Verilog_GitHub
Data Volume:
~72B
Methodology:
Fine-tuning
Training Time:
15 days
Hardware Used:
4 Tesla A100 GPUs
Model Architecture:
GPT-2 model with multi-query attention
Input Output 
Input Format:
Code prompt in Verilog
Accepted Modalities:
text
Output Format:
Generated Verilog code
Performance Tips:
Adding partial line of module header like 'module mux' improves output
LLM NameFine Tuned Codegen 6B Verilog
Repository 🤗https://huggingface.co/shailja/fine-tuned-codegen-6B-Verilog 
Model Size6b
Required VRAM28.4 GB
Updated2025-03-12
Maintainershailja
Model Typecodegen
Model Files  9.9 GB: 1-of-3   9.8 GB: 2-of-3   8.7 GB: 3-of-3
Generates CodeYes
Model ArchitectureCodeGenForCausalLM
Licensebigcode-openrail-m
Transformers Version4.22.0.dev0
Tokenizer ClassGPT2Tokenizer
Vocabulary Size50295
Torch Data Typefloat32
Activation Functiongelu_new

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Note: green Score (e.g. "73.2") means that the model is better than shailja/fine-tuned-codegen-6B-Verilog.

Rank the Fine Tuned Codegen 6B Verilog Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227
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