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amd/GLM-5.2-Quark-MXFP4-AttnFP8
GLM-5.2-Quark-MXFP4-AttnFP8 is a machine learning model from amd. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
- Model Architecture: GLM-5.2 - Input: Text - Output: Text - Supported Hardware Microarchitecture: AMD MI350/MI355 - ROCm: 7.0.0 - PyTorch: 2.9.0 - Transformers: 5.13.1 - Operating System(s): Linux - Inference Engine:…
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From the Hugging Face model README
This model was built with GLM-5.2 model by applying AMD-Quark for MXFP4 and PTPC FP8 quantization.
The model was quantized from zai-org/GLM-5.2 using AMD-Quark.
Quantization scripts:
cd Quark/examples/torch/language_modeling/llm_ptq/
python quantize_quark.py \
--model_dir zai-org/GLM-5.2 \
--output_dir "$OUTPUT_DIR" \
--quant_scheme mxfp4 \
--layer_quant_scheme "*self_attn*" ptpc_fp8 \
--exclude_layers \
"*lm_head" \
"*embed_tokens" \
"*eh_proj" \
"*mlp.gate" \
"*self_attn.indexer.weights_proj" \
--file2file_quantization
This model can be deployed efficiently using vLLM.
The model was evaluated on GSM8K and GPQA-Diamond-CoT benchmarks.
The above evaluation results were obtained using the lm-evaluation-harness framework, based on the Docker image rocm/vllm-dev:nightly_main_20260712, with vLLM and lm-eval compiled and installed from source.
export VLLM_ROCM_USE_AITER=1
export VLLM_ROCM_USE_AITER_FP8BMM=0
export VLLM_ROCM_USE_AITER_FP4BMM=0
lm_eval \
--model vllm \
--model_args "pretrained=amd/GLM-5.2-Quark-MXFP4-AttnFP8,tensor_parallel_size=4,dtype=auto,quantization=quark,gpu_memory_utilization=0.9,max_model_len=40960,trust_remote_code=True" \
--tasks gsm8k gpqa_diamond_cot_zeroshot \
--apply_chat_template \
--gen_kwargs "max_gen_toks=32768,temperature=1.0,top_p=0.95,do_sample=True" \
--batch_size auto
Modifications Copyright(c) 2026 Advanced Micro Devices, Inc. All rights reserved.