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

Libraries

How to use zai-org/GLM-5.3-Flash with Transformers:

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

pipe = pipeline("image-text-to-text", model="zai-org/GLM-5.3-Flash")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("zai-org/GLM-5.3-Flash")
model = AutoModelForMultimodalLM.from_pretrained("zai-org/GLM-5.3-Flash", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zai-org/GLM-5.3-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/GLM-5.3-Flash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/GLM-5.3-Flash",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/GLM-5.3-Flash

SGLang

How to use zai-org/GLM-5.3-Flash 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 "zai-org/GLM-5.3-Flash" \
    --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": "zai-org/GLM-5.3-Flash",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "zai-org/GLM-5.3-Flash" \
        --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": "zai-org/GLM-5.3-Flash",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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

boyang-runllama commited on Aug 28

Commit

c6b6ffa

verified ·

1 Parent(s): 04c4e9e

Add ExtractBench evaluation results

Browse files

This PR ensures your model shows up at https://huggingface.co/datasets/llamaindex/ExtractBench.\n\nThis is based on the new evaluation results feature: https://huggingface.co/docs/hub/eval-results.\n\nNote: this includes unified value F1 on each of the 3 ExtractBench length splits (short, medium, long) along with the overall mean score over all 370 documents.

Files changed (1) hide show

.eval_results/extractbench.yaml +40 -0

.eval_results/extractbench.yaml ADDED Viewed

	@@ -0,0 +1,40 @@

+- dataset:
+    id: llamaindex/ExtractBench
+    task_id: mean
+  value: 80.75
+  date: '2026-08-26'
+  source:
+    url: https://huggingface.co/datasets/llamaindex/ExtractBench
+    name: ExtractBench
+    user: boyang-runllama
+  notes: "Pipeline name: glm_5_3_flash_extract_oneshot_structured_output_file"
+- dataset:
+    id: llamaindex/ExtractBench
+    task_id: short
+  value: 96.30
+  date: '2026-08-26'
+  source:
+    url: https://huggingface.co/datasets/llamaindex/ExtractBench
+    name: ExtractBench
+    user: boyang-runllama
+  notes: "Pipeline name: glm_5_3_flash_extract_oneshot_structured_output_file"
+- dataset:
+    id: llamaindex/ExtractBench
+    task_id: medium
+  value: 51.56
+  date: '2026-08-26'
+  source:
+    url: https://huggingface.co/datasets/llamaindex/ExtractBench
+    name: ExtractBench
+    user: boyang-runllama
+  notes: "Pipeline name: glm_5_3_flash_extract_oneshot_structured_output_file"
+- dataset:
+    id: llamaindex/ExtractBench
+    task_id: long
+  value: 27.83
+  date: '2026-08-26'
+  source:
+    url: https://huggingface.co/datasets/llamaindex/ExtractBench
+    name: ExtractBench
+    user: boyang-runllama
+  notes: "Pipeline name: glm_5_3_flash_extract_oneshot_structured_output_file"