DeepSeek-R1: Optimized for AMD ROCm

DeepSeek-R1-Distill-Qwen-7B is a distilled reasoning language model that generates chain-of-thought answers for math and logic problems. This repository packages evaluation/inference for text reasoning / math problem solving using vLLM, exported and validated for AMD ROCm so it runs efficiently on AMD GPUs and CPUs.

This is based on the implementation of DeepSeek-R1 found here. This repository contains configurations and scripts optimized for AMD® ROCm™ platforms. You can use the deepseek_r1 AMD scripts to reproduce results or export with custom configurations. More details on model performance can be found here.

Task Overview

Task: Text reasoning / math problem solving

Dataset: MATH-500 (500 competition math problems); sample prompts for infer-text

Output metrics: MATH-500 accuracy

vLLM note: MATH-500 evaluation uses symbolic answer verification through math_verify, which parses \boxed{} expressions and checks equivalence to the reference solution.

AMD ROCm Optimization

This model export has been adapted and validated for AMD Instinct™ / Radeon™ GPUs running ROCm, as well as AMD CPUs. Key points:

Exported/tested with ROCm 7.2 and vLLM ROCm build 0.19.1 (built from source).
Validated backends: vLLM (ROCm-enabled build).
No code changes required versus the upstream DeepSeek-R1 implementation — only environment/runtime configuration differs.

Runtime	Precision	Backend	Hardware	Notes
GPU	FP32 / FP16 / BF16	vLLM	AMD RYZEN AI MAX+ 395 w/ Radeon 8060S	`VLLM_ROCM_USE_SKINNY_GEMM=0` set to avoid bf16/fp16 GEMM segfaults on gfx1151

Getting Started

For setup instructions, evaluation scripts, and custom configuration options, see the deepseek_r1 on GitHub.

Model Details

Model Type: Distilled reasoning language model (text generation)

Base Model: Qwen/Qwen2.5-7B (Qwen2.5-7B)

Model Stats:

Model variant: DeepSeek-R1-Distill-Qwen-7B
Number of parameters: 7B
Precision tested: FP32, FP16, BF16

Accuracy Pipeline

Higher MATH-500 accuracy means the model produces mathematically equivalent answers to ground truth more often — 100% would be perfect, ~0% is chance-level. Strong distilled reasoning models typically score ~85–95% on this benchmark.

Metrics Explained

Metric	Description
MATH-500 Accuracy	Primary metric — fraction of problems where the model's final boxed answer is symbolically equivalent to the reference solution.

Accuracy Results

Full Dataset Evaluation (MATH-500) — filled from deepseek-ai/DeepSeek-R1-Distill-Qwen-7B; run make metrics to refresh:

Device	Backend	Precision	Variant	Accuracy (%)
GPU	vLLM	FP32	DeepSeek-R1-Distill-Qwen-7B	90.00
GPU	vLLM	FP16	DeepSeek-R1-Distill-Qwen-7B	90.00
GPU	vLLM	BF16	DeepSeek-R1-Distill-Qwen-7B	90.00

Dig Deeper

Want to explore the full evaluation scripts, config options, and other AMD-optimized model examples?

📂 View the full project on GitHub

The GitHub repository includes:

Setup and prerequisites for ROCm environments
Scripts for the supported runners
Additional model variants and datasets
Benchmarking and reproduction instructions

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