amd/GLM-5-NVFP4

Model Overview

• Model Architecture: GLM-5
  • Input: Text
  • Output: Text
• Supported Hardware Microarchitecture: AMD MI300/MI350/MI355 (emulation)
• ROCm: 7.2.2
• PyTorch: 2.10.0
• Transformers: 5.2.0
• Operating System(s): Linux
• Inference Engine: vLLM
• Model Optimizer: AMD-Quark (V0.12)
  • Quantized layers: experts and shared_experts
  • Weight quantization: MOE-only, NVFP4, Static
  • Activation quantization: MOE-only, NVFP4, Dynamic
• Calibration Dataset: Pile

This model was built with GLM-5 model by applying AMD-Quark for NVFP4 quantization.

Model Quantization

The model was quantized from zai-org/GLM-5 using AMD-Quark. The weights and activations are quantized to NVFP4.

Quantization scripts:

sudo sysctl -w vm.max_map_count=4194304
cd Quark/examples/torch/language_modeling/llm_ptq/
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export MODEL_DIR=zai-org/GLM-5
export output_dir=amd/GLM-5-NVFP4
exclude_layers="*self_attn* *mlp.gate *lm_head *mlp.gate_proj *mlp.up_proj *mlp.down_proj"
python3 quantize_quark.py --model_dir $MODEL_DIR \
                          --quant_scheme nvfp4 \
                          --num_calib_data 128 \
                          --exclude_layers $exclude_layers \
                          --model_export hf_format \
                          --output_dir $output_dir \
                          --multi_gpu balanced

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backend.

Evaluation

The model was evaluated on GSM8K benchmarks.

Accuracy

Benchmark	GLM-5	GLM-5-NVFP4(this model)	Recovery
GSM8K (flexible-extract)	95.45	95.22	99.75%

Reproduction

The GSM8K result was obtained using the lm-evaluation-harness framework, based on the Docker image rocm/vllm-dev:nightly_main_20260603.

Install the lm-eval (Version: 0.4.12) in container first.

pip install lm-eval
pip install lm-eval[api]

Launching Server and Evaluating model

export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
export VLLM_ROCM_USE_AITER=1
export VLLM_ROCM_USE_AITER_MLA=1
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export PYTORCH_ALLOC_CONF=expandable_segments:True
lm_eval \
  --model vllm \
  --model_args pretrained=amd/GLM-5-NVFP4,tensor_parallel_size=8,max_model_len=4096,gpu_memory_utilization=0.90,enforce_eager=True,max_gen_toks=2048,kv_cache_dtype=bfloat16,trust_remote_code=True \
  --tasks gsm8k \
  --num_fewshot 5 \
  --batch_size auto

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