VeriReason-Qwen2.5-1.5B-grpo-small-GGUF Open-source Model - Freely Assist in Verilog Code Generation and Inference

Verireason Qwen2.5 1.5B Grpo Small GGUF

Developed by mradermacher

This is the statically quantized version of the Nellyw888/VeriReason-Qwen2.5-1.5B-grpo-small model, focusing on Verilog code generation and reasoning tasks.

Large Language Model English#Verilog reasoning #RTL code generation #Hardware design assistance

Downloads 48

Release Time : 5/18/2025

Model Overview

This model is based on the Qwen2.5 architecture with 1.5B parameters, trained using GRPO reinforcement learning, specifically designed for Verilog code generation and hardware design-related reasoning tasks.

Model Features

Statically quantized version

Offers multiple quantization options, from Q2_K to f16 precision, to meet different hardware requirements

Verilog-specific

Optimized specifically for hardware description language Verilog code generation and reasoning tasks

Reinforcement learning training

Trained using GRPO reinforcement learning methods to enhance reasoning capabilities

Model Capabilities

Verilog code generation

Hardware design reasoning

RTL code analysis

Use Cases

Hardware design

Automatic Verilog code generation

Automatically generates Verilog hardware description code based on design requirements

Hardware design verification

Performs logic verification and error checking on existing Verilog code

🚀 VeriReason-Qwen2.5-1.5B-grpo-small Quantized Model

This project provides static quantizations of the VeriReason-Qwen2.5-1.5B-grpo-small model. It offers various quantized versions for different usage scenarios, with details about the model, usage instructions, and provided quantized files.

🚀 Quick Start

If you're new to using this model, the following sections will guide you through its basic information, usage, and available quantized versions.

✨ Features

Multiple Quantized Versions: Offers a range of quantized versions in GGUF format, sorted by size, providing options for different performance and quality requirements.
Useful References: Provides links to external resources such as TheBloke's READMEs for GGUF file usage, and graphs and thoughts on quantization by other contributors.

📚 Documentation

About

Static quants of Nellyw888/VeriReason-Qwen2.5-1.5B-grpo-small.

Weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ - quants are often preferable over similar sized non - IQ quants)

Link	Type	Size/GB	Notes
GGUF	Q2_K	0.8
GGUF	Q3_K_S	0.9
GGUF	Q3_K_M	0.9	lower quality
GGUF	Q3_K_L	1.0
GGUF	IQ4_XS	1.0
GGUF	Q4_K_S	1.0	fast, recommended
GGUF	Q4_K_M	1.1	fast, recommended
GGUF	Q5_K_S	1.2
GGUF	Q5_K_M	1.2
GGUF	Q6_K	1.4	very good quality
GGUF	Q8_0	1.7	fast, best quality
GGUF	f16	3.2	16 bpw, overkill

Here is a handy graph by ikawrakow comparing some lower - quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: Artefact2's thoughts

FAQ / Model Request

See mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

📄 Information Table

Property	Details
Base Model	Nellyw888/VeriReason-Qwen2.5-1.5B-grpo-small
Datasets	- Nellyw888/RTL-Coder_7b_reasoning_tb_simple - Nellyw888/RTL-Coder_small
Language	en
Library Name	transformers
Quantized By	mradermacher
Tags	- verilog - reasoning - reinforcement-learning - rtl

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