Model Overview

  • Model Architecture: GLM-5.1
    • 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: NVFP4, Static
    • Activation quantization: NVFP4, Dynamic
  • Calibration Dataset: Pile

This model was built with GLM-5.1 model by applying AMD-Quark for NVFP4 quantization.

Model Quantization

The model was quantized from zai-org/GLM-5.1 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-5.1
export output_dir=amd/GLM-5.1-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-5.1 GLM-5.1-NVFP4(this model) Recovery
GSM8K (flexible-extract) 95.38 95.68 100.31%

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[api]

Launching server

export VLLM_ROCM_USE_AITER=1
export VLLM_ROCM_USE_AITER_FP8BMM=0
export VLLM_ROCM_USE_AITER_FP4BMM=0
HIP_VISIBLE_DEVICES=4,5,6,7 vllm serve amd/GLM-5.1-NVFP4 \
  -tp 4 \
  --block-size 1 \
  --trust-remote-code \
  --max-model-len 4096 \
  --port 8082

Evaluating model in a new terminal

lm_eval \
  --model local-completions \
  --model_args '{"model": "amd/GLM-5.1-NVFP4", "base_url": "http://localhost:8082/v1/completions", "num_concurrent": 32, "max_retries": 10, "max_gen_toks": 2048, "tokenizer_backend": null, "tokenized_requests": false}' \
  --tasks gsm8k \
  --batch_size auto \
  --num_fewshot 5 \
  --trust_remote_code

License

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

Downloads last month
106
Safetensors
Model size
380B params
Tensor type
F32
BF16
U8
Inference Providers NEW
This model isn't deployed by any Inference Provider. 馃檵 Ask for provider support

Model tree for amd/GLM-5.1-NVFP4

Base model

zai-org/GLM-5.1
Quantized
(39)
this model