Fine Tuned Codegen 16B Verilog By shailja: Benchmarks, Features and Detailed Analysis. Insights on Fine Tuned Codegen 16B Verilog.

Arxiv:2212.11140 Autotrain compatible Code Codegen Dataset:shailja/verilog github Endpoints compatible Model-index Pytorch Region:us

Model Card on HF 🤗: https://huggingface.co/shailja/fine-tuned-codegen-16B-Verilog

Fine Tuned Codegen 16B Verilog Benchmarks

LLME Score: 0.06378

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Fine Tuned Codegen 16B Verilog (shailja/fine-tuned-codegen-16B-Verilog)

🌟 Advertise your project 🚀

Fine Tuned Codegen 16B 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 Name	Fine Tuned Codegen 16B Verilog
Repository 🤗	https://huggingface.co/shailja/fine-tuned-codegen-16B-Verilog
Model Size	16b
Required VRAM	32.2 GB
Updated	2025-07-30
Maintainer	shailja
Model Type	codegen
Model Files	32.2 GB 0.0 GB
Generates Code	Yes
Model Architecture	CodeGenForCausalLM
License	bigcode-openrail-m
Transformers Version	4.22.0.dev0
Tokenizer Class	GPT2Tokenizer
Vocabulary Size	50295
Torch Data Type	float16
Activation Function	gelu_new

Best Alternatives to Fine Tuned Codegen 16B Verilog

Best Alternatives	Context / RAM	Downloads	Likes
Instruct Codegen 16B	0K / 32.2 GB	7	21
Codegen 16B Mono Toolbench	0K / 128.4 GB	6	5
Codegen2 16B P	0K / 64.3 GB	12	45
Codegen 16B Multi 6 Parts	0K / 32.2 GB	6	0
Codegen 16B Nl Sharded	0K / 32.1 GB	8	7
Codegen 16B Nl	0K / 32.2 GB	1121	18
Codegen 16B Mono	0K / 32.2 GB	211	126
Codegen 16B Multi	0K / 32.2 GB	113	119

Note: green Score (e.g. "73.2") means that the model is better than shailja/fine-tuned-codegen-16B-Verilog.

Rank the Fine Tuned Codegen 16B Verilog Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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

What open-source LLMs or SLMs are you in search of? 50230 in total.

Email us: info@extractum.io. Our Privacy Policy | Terms and Conditions | Suggest an improvement.

Our Social Media →

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

Release v20241124

Support LLM Explorer

Fine Tuned Codegen 16B Verilog by shailja

» All LLMs » shailja » Fine Tuned Codegen 16B Verilog URL Share it on

Fine Tuned Codegen 16B Verilog Benchmarks

Fine Tuned Codegen 16B Verilog Parameters and Internals

Best Alternatives to Fine Tuned Codegen 16B Verilog

Rank the Fine Tuned Codegen 16B Verilog Capabilities

What open-source LLMs or SLMs are you in search of? 50230 in total.