What are the hardware requirements for VeriReason Qwen2.5 7B RTLCoder Verilog GRPO Reasoning Tb?

VeriReason Qwen2.5 7B RTLCoder Verilog GRPO Reasoning Tb requires approximately 14.6 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Where can I download or evaluate VeriReason Qwen2.5 7B RTLCoder Verilog GRPO Reasoning Tb?

VeriReason Qwen2.5 7B RTLCoder Verilog GRPO Reasoning Tb is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

VeriReason Qwen2.5 7B RTLCoder Verilog GRPO Reasoning Tb by Nellyw888 — VRAM 14.6GB, 32K context

Name: VeriReason Qwen2.5 7B RTLCoder Verilog GRPO Reasoning Tb
Author: Nellyw888

VeriReason Qwen2.5 7B RTLCoder Verilog GRPO Reasoning Tb is an open-source language model by Nellyw888. Features: 7b LLM, VRAM: 14.6GB, Context: 32K, Instruction-Based, Code Generating, LLM Explorer Score: 0.19.

Arxiv:2505.11849 Autotrain compatible Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-coder-... Codegen Dataset:nellyw888/verireason-r... Endpoints compatible Instruct Qwen2 Reasoning Region:us Rtl Safetensors Sharded Tensorflow Verilog

Model Card on HF 🤗: https://huggingface.co/Nellyw888/VeriReason-Qwen2.5-7b-RTLCoder-Verilog-GRPO-reasoning-tb

VeriReason Qwen2.5 7B RTLCoder Verilog GRPO Reasoning Tb Parameters and Internals

LLM Name	VeriReason Qwen2.5 7B RTLCoder Verilog GRPO Reasoning Tb
Repository 🤗	https://huggingface.co/Nellyw888/VeriReason-Qwen2.5-7b-RTLCoder-Verilog-GRPO-reasoning-tb
Base Model(s)	Qwen/Qwen2.5-Coder-7B-Instruct Qwen/Qwen2.5-Coder-7B-Instruct
Model Size	7b
Required VRAM	14.6 GB
Updated	2025-09-23
Maintainer	Nellyw888
Model Type	qwen2
Instruction-Based	Yes
Model Files	2.2 GB: 1-of-105 0.3 GB: 2-of-105 0.3 GB: 3-of-105 0.3 GB: 4-of-105 0.2 GB: 5-of-105 0.3 GB: 6-of-105 0.3 GB: 7-of-105 0.3 GB: 8-of-105 0.3 GB: 9-of-105 0.3 GB: 10-of-105 0.3 GB: 11-of-105 0.2 GB: 12-of-105 0.3 GB: 13-of-105 0.3 GB: 14-of-105 0.3 GB: 15-of-105 0.2 GB: 16-of-105 0.3 GB: 17-of-105 0.3 GB: 18-of-105 0.3 GB: 19-of-105 0.3 GB: 20-of-105 0.3 GB: 21-of-105 0.3 GB: 22-of-105 0.2 GB: 23-of-105 0.3 GB: 24-of-105 0.3 GB: 25-of-105 0.3 GB: 26-of-105 0.2 GB: 27-of-105 0.3 GB: 28-of-105 0.3 GB: 29-of-105 0.3 GB: 30-of-105 0.3 GB: 31-of-105 0.3 GB: 32-of-105 0.3 GB: 33-of-105 0.2 GB: 34-of-105 0.3 GB: 35-of-105 0.3 GB: 36-of-105 0.3 GB: 37-of-105 0.2 GB: 38-of-105 0.3 GB: 39-of-105 0.3 GB: 40-of-105 0.3 GB: 41-of-105 0.3 GB: 42-of-105 0.3 GB: 43-of-105 0.3 GB: 44-of-105 0.2 GB: 45-of-105
Generates Code	Yes
Model Architecture	Qwen2ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.51.3
Vocabulary Size	152064
Torch Data Type	float32

Rank the VeriReason Qwen2.5 7B RTLCoder Verilog GRPO Reasoning Tb 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 VeriReason Qwen2.5 7B RTLCoder Verilog GRPO Reasoning Tb

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 7B Rebase	986K / 15.2 GB	73	2
Qwen2.5 7B Rebase	986K / 15.2 GB	20	2
StockQwen 2.5 7B	128K / 15.2 GB	69	6
DareQwen 2.5 7B	128K / 15.2 GB	11	1
Tessa T1 7B	117K / 15.2 GB	14	10
UIGEN T1.5 7B	117K / 15.2 GB	12	6
Qwen2.5 Coder 7B Instruct	32K / 15.2 GB	2019400	765
Qwen2.5 Coder Malay 7B	32K / 15.2 GB	195	1
....5 Coder 7B Instruct Ghidra V2	32K / 15.2 GB	362	1
Qwen2.5 7B MS Destroyer	32K / 15.2 GB	5	1

Note: green Score (e.g. "73.2") means that the model is better than Nellyw888/VeriReason-Qwen2.5-7b-RTLCoder-Verilog-GRPO-reasoning-tb.