Instructions to use QuantTrio/GLM-4.5V-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantTrio/GLM-4.5V-AWQ with Transformers:

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

pipe = pipeline("image-text-to-text", model="QuantTrio/GLM-4.5V-AWQ")
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("QuantTrio/GLM-4.5V-AWQ")
model = AutoModelForMultimodalLM.from_pretrained("QuantTrio/GLM-4.5V-AWQ")
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 QuantTrio/GLM-4.5V-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantTrio/GLM-4.5V-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantTrio/GLM-4.5V-AWQ",
		"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/QuantTrio/GLM-4.5V-AWQ

SGLang

How to use QuantTrio/GLM-4.5V-AWQ 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 "QuantTrio/GLM-4.5V-AWQ" \
    --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": "QuantTrio/GLM-4.5V-AWQ",
		"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 "QuantTrio/GLM-4.5V-AWQ" \
        --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": "QuantTrio/GLM-4.5V-AWQ",
		"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 QuantTrio/GLM-4.5V-AWQ with Docker Model Runner:
```
docker model run hf.co/QuantTrio/GLM-4.5V-AWQ
```

tclf90 commited on Aug 12, 2025

Commit

7da9560

verified ·

1 Parent(s): 57b42d7

Update README.md

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@@ -1,14 +1,85 @@
 ---
 license: mit
 language:
 - zh
 - en
 base_model:
-- zai-org/GLM-4.5-Air-Base
-pipeline_tag: image-text-to-text
-library_name: transformers
 ---
 # GLM-4.5V
 <div align="center">
@@ -109,4 +180,3 @@ If you use this model, please cite the following paper:
       url={https://arxiv.org/abs/2507.01006},
 }
 ```

 ---
 license: mit
+library_name: transformers
+pipeline_tag: image-text-to-text
+tags:
+- glm4v_moe
+- AWQ
+- vLLM
 language:
 - zh
 - en
 base_model:
+  - zai-org/GLM-4.5V
+base_model_relation: quantized
 ---
+# GLM-4.5V-AWQ
+Base model: [zai-org/GLM-4.5V](https://huggingface.co/zai-org/GLM-4.5V)
+### 【vLLM Single Node with 4 GPUs — Startup Command】
+<i>❗Required: Use `--enable-expert-parallel` when launching this model.
+Without it, the `expert tensors` cannot be evenly partitioned.</i>
+<i>❗Required for 8 GPUs: Use `prefill/decode disaggregated serving`
+([reference](https://docs.vllm.ai/en/latest/examples/online_serving/disaggregated_serving.html)),
+otherwise the `vision attention heads` cannot be evenly partitioned.</i>
+```
+CONTEXT_LENGTH=32768
+vllm serve \
+    QuantTrio/GLM-4.5V-AWQ \
+    --served-model-name GLM-4.5V-AWQ \
+    --tool-call-parser glm45 \
+    --reasoning-parser glm45 \
+    --enable-auto-tool-choice \
+    --allowed-local-media-path / \
+    --media-io-kwargs '{"video": {"num_frames": -1}}' \
+    --enable-expert-parallel \
+    --swap-space 16 \
+    --max-num-seqs 512 \
+    --max-model-len $CONTEXT_LENGTH \
+    --max-seq-len-to-capture $CONTEXT_LENGTH \
+    --gpu-memory-utilization 0.9 \
+    --tensor-parallel-size 4 \
+    --trust-remote-code \
+    --disable-log-requests \
+    --host 0.0.0.0 \
+    --port 8000
+```
+### 【Dependencies / Installation】
+As of **2025-08-12**, create a fresh Python environment and run:
+```bash
+# Patched vLLM (see: https://github.com/vllm-project/vllm/pull/22716)
+git clone -b glm-45 https://github.com/zRzRzRzRzRzRzR/vllm.git
+cd vllm
+VLLM_USE_PRECOMPILED=1 pip install .
+# Install preview build of Transformers with GLM-4.5V support
+pip install transformers-v4.55.0-GLM-4.5V-preview
+```
+### 【Logs】
+```
+2025-08-12
+1. Initial commit
+```
+### 【Model Files】
+| File Size | Last Updated |
+|-----------|--------------|
+| `57GB`    | `2025-08-12` |
+### 【Model Download】
+```python
+from huggingface_hub import snapshot_download
+snapshot_download('QuantTrio/GLM-4.5V-AWQ', cache_dir="your_local_path")
+```
+### 【Overview】
 # GLM-4.5V
 <div align="center">
       url={https://arxiv.org/abs/2507.01006},
 }
 ```