Instructions to use zai-org/GLM-4.1V-9B-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-4.1V-9B-Thinking with Transformers:

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

pipe = pipeline("image-text-to-text", model="zai-org/GLM-4.1V-9B-Thinking")
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-4.1V-9B-Thinking")
model = AutoModelForImageTextToText.from_pretrained("zai-org/GLM-4.1V-9B-Thinking")
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

vLLM

How to use zai-org/GLM-4.1V-9B-Thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/GLM-4.1V-9B-Thinking"
# 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-4.1V-9B-Thinking",
		"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-4.1V-9B-Thinking

SGLang

How to use zai-org/GLM-4.1V-9B-Thinking 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-4.1V-9B-Thinking" \
    --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-4.1V-9B-Thinking",
		"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-4.1V-9B-Thinking" \
        --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-4.1V-9B-Thinking",
		"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-4.1V-9B-Thinking with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-4.1V-9B-Thinking
```

Unable to get the model to process an image on my PC

#18

by kentrolla - opened Aug 12, 2025

Discussion

kentrolla

Aug 12, 2025

•

edited Aug 12, 2025

I can run zai-org/GLM-4.1V-9B-Thinking behind vLLM and chat normally.

Vision also works when I pass an HTTPS image URL. But any attempt to use a local image (file://…) either (a) returns a 400 “must be a subpath of --allowed-local-media-path” even when it is, or (b) just hangs (no response body), with no useful error in the API logs. Earlier, base64 data URLs returned 500 with a Pillow decode error. The net effect: model can “see” web images, but cannot “see” local files.

Environment
OS: Windows 11 + WSL2 (Ubuntu)

Python: 3.12.9 (conda env)

vLLM: 0.10.1 (works great for other models)

Transformers: (whatever ships in this env; can provide exact version)

CUDA / Torch: CUDA 12.8 and 2.9.0.dev20250811+cu128

GPU(s): RTX 5090 and 3090

This is what I'm seeing no matter what I've tried, with trying to process an image:

(APIServer pid=1806) INFO: Started server process [1806]
(APIServer pid=1806) INFO: Waiting for application startup.
(APIServer pid=1806) INFO: Application startup complete.
(APIServer pid=1806) Using a slow image processor as use_fast is unset and a slow processor was saved with this model. use_fast=True will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with use_fast=False.
(APIServer pid=1806) INFO 08-12 21:48:42 [chat_utils.py:470] Detected the chat template content format to be 'openai'. You can set --chat-template-content-format to override this.

Launch command:
nova@DESKTOP-L4B0FRV:~/ai_projects$ VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
python3 -m vllm.entrypoints.openai.api_server
--model zai-org/GLM-4.1V-9B-Thinking
--served-model-name glm-4.1v-thinking
--trust-remote-code
--quantization bitsandbytes
--dtype auto
--host 172.25.46.120
--port 8000
--api-key vllm
--max-model-len 8192
--allowed-local-media-path /home/nova/ai_projects

I was able to get the model to run and process images using transformers just fine. For some reason no matter what I've tried, all I can get is normal text with vllm, reading images seems to be a problem.

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