Instructions to use zai-org/GLM-4.6V with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-4.6V with Transformers:

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

pipe = pipeline("image-text-to-text", model="zai-org/GLM-4.6V")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("zai-org/GLM-4.6V")
model = AutoModelForImageTextToText.from_pretrained("zai-org/GLM-4.6V")
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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use zai-org/GLM-4.6V with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/GLM-4.6V"
# 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-4.6V",
		"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-4.6V

SGLang

How to use zai-org/GLM-4.6V 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-4.6V" \
    --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-4.6V",
		"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-4.6V" \
        --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-4.6V",
		"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-4.6V with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-4.6V
```

Add model-index with benchmark evaluations

by davidlms - opened Dec 8, 2025

base: refs/heads/main

←

from: refs/pr/6

Discussion Files changed

+268

-154

Add model-index with benchmark evaluationsf5252b34

davidlms

Dec 8, 2025

Added structured evaluation results from benchmark comparison (36 benchmarks):

General VQA:

MMBench V1.1: 88.8, MMBench V1.1 (CN): 88.2, MMStar: 75.9, BLINK (Val): 65.5, MUIRBENCH: 77.1

Multimodal Reasoning:

MMMU (Val): 76.0, MMMU_Pro: 66.0, VideoMMU: 74.7, MathVista: 85.2, AI2D: 88.8, DynaMath: 54.5, WeMath: 69.8, ZeroBench (sub): 25.8

Multimodal Agentic:

MMBrowseComp: 7.6, Design2Code: 88.6, Flame-React-Eval: 86.3, OSWorld: 37.2, AndroidWorld: 57.0, WebVoyager: 81.0, Webquest-SingleQA: 79.5, Webquest-MultiQA: 59.0

Multimodal Long Context:

MMLongBench-Doc: 54.9, MMLongBench-128K: 64.1, LVBench: 59.5

OCR & Chart:

OCRBench: 86.5, OCR-Bench_v2 (EN): 65.1, OCR-Bench_v2 (CN): 59.6, ChartQAPro: 65.5, ChartMuseum: 58.4, CharXiv_Val-Reasoning: 63.2

Spatial & Grounding:

OmniSpatial: 52.0, RefCOCO-avg (val): 88.6, TreeBench: 51.4, Ref-L4-test: 88.9

This enables the model to appear in leaderboards and makes it easier to compare with other models.

davidlms changed pull request status to closed Dec 8, 2025

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