What are the hardware requirements for Fine Tuned Codegen 2B Verilog?

Fine Tuned Codegen 2B Verilog requires approximately 11.3 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Fine Tuned Codegen 2B Verilog and how large is it?

Fine Tuned Codegen 2B Verilog is developed by shailja, a model with 2b 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 2B Verilog?

Fine Tuned Codegen 2B 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 2B Verilog by shailja — VRAM 11.3GB

Name: Fine Tuned Codegen 2B Verilog
Author: shailja

Fine Tuned Codegen 2B Verilog is an open-source language model by shailja. Features: 2b LLM, VRAM: 11.3GB, 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-2B-Verilog

Fine Tuned Codegen 2B Verilog Parameters and Internals

Model Type

text-generation

Use Cases

Areas:

research, automation

Applications:

Verilog RTL code generation

Primary Use Cases:

Verilog teaching assistant

Limitations:

Generated code may not work as intended., Can be inefficient, contain bugs, or exploits.

Considerations:

Model is capable of generating Verilog snippets provided some context.

Additional Notes

The purpose is to assist in Verilog code generation based on input prompts.

Supported Languages

Verilog (proficient)

Training Details

Data Sources:

Verilog Dataset

Data Volume:

~72B tokens

Methodology:

fine-tuning

Context Length:

2048

Training Time:

8 days

Hardware Used:

3 Tesla A100 GPUs

Model Architecture:

GPT-2 model with multi-query attention

Input Output

Input Format:

Module header or partial Verilog code.

Accepted Modalities:

text

Output Format:

Verilog code snippet

Performance Tips:

Model performs best with contextual input.

LLM Name	Fine Tuned Codegen 2B Verilog
Repository 🤗	https://huggingface.co/shailja/fine-tuned-codegen-2B-Verilog
Model Size	2b
Required VRAM	11.3 GB
Updated	2026-07-10
Maintainer	shailja
Model Type	codegen
Model Files	10.0 GB: 1-of-2 1.3 GB: 2-of-2
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 2B 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 2B Verilog

Best Alternatives	Context / RAM	Downloads	Likes
Archgen 2B V1	0K / 5.6 GB	7	0
...Python 18K Alpaca Full Dataset	0K / 5.6 GB	6	0
Salesforce Codegen 2B Multi Ov	0K / 11.1 GB	7	0
Nsql 2B	0K / 11.3 GB	15	9
Instruct Codegen 2B Multi	0K / 11.3 GB	10	1
Diff Codegen 2B V2	0K / 5.7 GB	27	6
...en 2B Mono Instruct Py Revised	0K / 11.3 GB	7	1
Codegen 2B Mono Xlcost	0K / 5.7 GB	6	1
Codegen 2B Multi Xlcost	0K / 5.7 GB	112	1
Codegen 2B Mono	0K / 5.7 GB	7632	21

Note: green Score (e.g. "73.2") means that the model is better than shailja/fine-tuned-codegen-2B-Verilog.