Model Overview

• Model Architecture: GLM-5.2
  • Input: Text
  • Output: Text
• Supported Hardware Microarchitecture: AMD MI350/MI355
• ROCm: 7.0.0
• PyTorch: 2.9.0
• Transformers: 5.8.1
• Operating System(s): Linux
• Inference Engine: SGLang/vLLM
• Model Optimizer: AMD-Quark (V0.11)
  • Weight quantization: MOE-only (shared experts quantized), OCP MXFP4, Static
  • Activation quantization: MOE-only, OCP MXFP4, Dynamic

This model was built with GLM-5.2 model by applying AMD-Quark for MXFP4 quantization.

Model Quantization

The model was quantized from zai-org/GLM-5.2 using AMD-Quark. The weights and activations are quantized to MXFP4.

Quantization scripts:

cd Quark/examples/torch/language_modeling/llm_ptq/
 python quantize_quark.py \
 --model_dir zai-org/GLM-5.2 \
 --output_dir GLM-5.2-MXFP4 \
 --quant_scheme mxfp4 \
 --exclude_layers "*self_attn*" "*mlp.gate" "*lm_head" \
 "*mlp.gate_proj" "*mlp.up_proj" "*mlp.down_proj" \
 "*layers.78.*" \ # Exclude the MTP layer (layer 78)
 --file2file_quantization

Deployment

Use with SGLang/vLLM

This model can be deployed efficiently using the SGLang or vLLM backends.

Evaluation

The model was evaluated on GSM8K benchmarks.

Accuracy

Benchmark	GLM-5.2	GLM-5.2-MXFP4(this model)	Recovery
GSM8K (flexible-extract)	0.9409	0.9393	99.8%

Reproduction

The GSM8K results were obtained using the lm-evaluation-harness framework, based on the Docker image lmsysorg/sglang:v0.5.13.post1-rocm700-mi35x, with SGLang pre-installed inside the image and lm-eval compiled and installed from source.

lm_eval --model sglang \
 --model_args pretrained=amd/GLM-5.2-MXFP4,tp_size=4 \
 --tasks gsm8k \
 --batch_size auto

The Docker image rocm/vllm-dev:nightly_main_20260616 with vLLM pre-installed can also be used for reproducing using vLLM backend.

License

Modifications Copyright(c) 2026 Advanced Micro Devices, Inc. All rights reserved.

Base model

zai-org/GLM-5.2

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