Instructions to use amd/GLM-5.3-Quark-MXFP4-AttnFP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amd/GLM-5.3-Quark-MXFP4-AttnFP8 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="amd/GLM-5.3-Quark-MXFP4-AttnFP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("amd/GLM-5.3-Quark-MXFP4-AttnFP8")
model = AutoModelForCausalLM.from_pretrained("amd/GLM-5.3-Quark-MXFP4-AttnFP8", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amd/GLM-5.3-Quark-MXFP4-AttnFP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amd/GLM-5.3-Quark-MXFP4-AttnFP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amd/GLM-5.3-Quark-MXFP4-AttnFP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amd/GLM-5.3-Quark-MXFP4-AttnFP8

SGLang

How to use amd/GLM-5.3-Quark-MXFP4-AttnFP8 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "amd/GLM-5.3-Quark-MXFP4-AttnFP8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amd/GLM-5.3-Quark-MXFP4-AttnFP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "amd/GLM-5.3-Quark-MXFP4-AttnFP8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amd/GLM-5.3-Quark-MXFP4-AttnFP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amd/GLM-5.3-Quark-MXFP4-AttnFP8 with Docker Model Runner:
```
docker model run hf.co/amd/GLM-5.3-Quark-MXFP4-AttnFP8
```

Model Overview

Model Architecture: GlmMoeDsaForCausalLM
- Input: Text
- Output: Text
Supported Hardware Microarchitecture: AMD MI350/MI355
ROCm: 7.1.1
PyTorch: 2.10.0+rocm7.1.1.lw.gitd9556b05
Transformers: 5.15.1
Operating System(s): Linux
Inference Engine: vLLM
Model Optimizer: AMD-Quark (V0.12)
- Quantized layers: self_attn, router experts, shared_experts
- self_attn: FP8 per-block
- Router experts and shared_experts: MXFP4

This model was built with GLM-5.3 model by applying AMD-Quark for MXFP4 quantization.

Model Quantization

The model was quantized from zai-org/GLM-5.3 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-5.3 \
  --output_dir amd/GLM-5.3-Quark-MXFP4 \
  --quant_scheme mxfp4 \
  --exclude_layers \
    "*self_attn*" \
    "*lm_head" \
    "*embed_tokens" \
    "*eh_proj" \
    "*mlp.gate" \
  --file2file_quantization \
  --keep_excluded_layers_as_original_model_state

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backends.

Evaluation

The model was evaluated on GSM8K benchmarks.

Accuracy

Benchmark	GLM-5.3	GLM-5.3-Quark-MXFP4(this model)	Recovery
GSM8K (flexible-extract)	91.89	91.28	99.34%

Reproduction

The GSM8K results were obtained using the lm-evaluation-harness framework, based on the Docker image vllm/vllm-openai-rocm:nightly-44fe2a392b71d52a8d72faf2f8278834379482c9, with vLLM pre-installed inside the image and apply this pr #54566.

pip install lm-eval[api]

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-5.3-Quark-MXFP4,tensor_parallel_size=4,dtype=auto,quantization=quark,max_model_len=32768,trust_remote_code=True,max_num_seqs=32" \
  --tasks gsm8k \
  --batch_size auto

License

Downloads last month: 1,494

Safetensors

Model size

384B params

Tensor type

F32

BF16

F8_E4M3

Model tree for amd/GLM-5.3-Quark-MXFP4-AttnFP8

Base model

zai-org/GLM-5.3

Quantized

(42)

this model

Evaluation results

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