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

Libraries

How to use OneNexus/GLM-5.3-Flash-MXFP4 with Transformers:

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

pipe = pipeline("text-generation", model="OneNexus/GLM-5.3-Flash-MXFP4")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("OneNexus/GLM-5.3-Flash-MXFP4")
model = AutoModelForMultimodalLM.from_pretrained("OneNexus/GLM-5.3-Flash-MXFP4", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OneNexus/GLM-5.3-Flash-MXFP4 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OneNexus/GLM-5.3-Flash-MXFP4

SGLang

How to use OneNexus/GLM-5.3-Flash-MXFP4 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 "OneNexus/GLM-5.3-Flash-MXFP4" \
    --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": "OneNexus/GLM-5.3-Flash-MXFP4",
		"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 "OneNexus/GLM-5.3-Flash-MXFP4" \
        --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": "OneNexus/GLM-5.3-Flash-MXFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

GLM-5.3-Flash-MXFP4 / mixed_precision_correction.json

sonle5

Add V29 model card and metadata

e3607b3 verified about 12 hours ago

Raw

History Blame Contribute Delete

858 Bytes

	{
	"bf16_expert_layers": [
	3,
	5,
	6
	],
	"chat_template_chars": 10644,
	"corrected_total_size": 227496639296,
	"expert_excluded_modules": 2595,
	"hardlinked_shards": 110,
	"max_total_size": 229836218970,
	"original_total_size": 195561208640,
	"quantized_parent": "/scratch/glm53-flash-mxfp4-v2",
	"removed_mxfp4_scale_tensors": 2592,
	"restored_bf16_tensors": 2592,
	"rewritten_shards": [
	"model-00057-of-00120.safetensors",
	"model-00058-of-00120.safetensors",
	"model-00059-of-00120.safetensors",
	"model-00106-of-00120.safetensors",
	"model-00107-of-00120.safetensors",
	"model-00108-of-00120.safetensors",
	"model-00109-of-00120.safetensors",
	"model-00110-of-00120.safetensors",
	"model-00111-of-00120.safetensors",
	"model-00112-of-00120.safetensors"
	],
	"source": "/scratch/glm53-flash-bf16"
	}