What are the hardware requirements for Fine Tuned Codegen 6B Verilog?

Fine Tuned Codegen 6B Verilog requires approximately 28.4 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Fine Tuned Codegen 6B Verilog and how large is it?

Fine Tuned Codegen 6B Verilog is developed by shailja, a model with 6b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Fine Tuned Codegen 6B Verilog?

Fine Tuned Codegen 6B Verilog is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Fine Tuned Codegen 6B Verilog by shailja — VRAM 28.4GB

Name: Fine Tuned Codegen 6B Verilog
Author: shailja

Fine Tuned Codegen 6B Verilog is an open-source language model by shailja. Features: 6b LLM, VRAM: 28.4GB, License: bigcode-openrail-m, Code Generating, LLM Explorer Score: 0.03.

Arxiv:2212.11140 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 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 6B Verilog
Repository 🤗	https://huggingface.co/shailja/fine-tuned-codegen-6B-Verilog
Model Size	6b
Required VRAM	28.4 GB
Updated	2026-07-31
Maintainer	shailja
Model Type	codegen
Model Files	9.9 GB: 1-of-3 9.8 GB: 2-of-3 8.7 GB: 3-of-3
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	float32
Activation Function	gelu_new

Rank the Fine Tuned Codegen 6B Verilog capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

Best Alternatives to Fine Tuned Codegen 6B Verilog

Best Alternatives	Context / RAM	Downloads	Likes
Nsql 6B	0K / 28.4 GB	23	52
...egen 6B Nl Lora Adapter Merged	0K / 14.3 GB	3	1
...en 6B Mono Lora Adapter Merged	0K / 14.3 GB	5	0
Codegen 6B Multi	0K / 14.3 GB	951	20
Diff Codegen 6B V2	0K / 14.3 GB	22	40
...en 6B Mono Instruct Py Revised	0K / 28.4 GB	5	2
...n 6B Mono Instruct Py Critique	0K / 28.4 GB	4	2
...gen 6B Nl Instruct Py Critique	0K / 28.4 GB	6	1
Codegen 6B Nl	0K / 14.3 GB	146	4
CodeGen RE	0K / 28.8 GB	5	2

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