Instructions to use wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3.25-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3.25-v1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3.25-v1")
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("wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3.25-v1")
model = AutoModelForMultimodalLM.from_pretrained("wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3.25-v1", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3.25-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3.25-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3.25-v1",
		"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/wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3.25-v1

SGLang

How to use wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3.25-v1 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 "wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3.25-v1" \
    --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": "wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3.25-v1",
		"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 "wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3.25-v1" \
        --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": "wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3.25-v1",
		"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 wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3.25-v1 with Docker Model Runner:
```
docker model run hf.co/wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3.25-v1
```

GLM-5.3-Flash-EXL3-K3.25-v1 / quantization /vllm-prompt-expected-actual.md

wrldsuksgo2mars

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	# Pre-publication vLLM tool-call gate

	Score: 100. Scenarios: 5.

	## TC-01: PASS

	Prompt: What's the weather like in Berlin right now?

	Expected: Use get_weather instead of falling back to web_search.

	Actual: get_weather(location=Berlin)

	Verdict: Used get_weather with Berlin only.

	## TC-03: PASS

	Prompt: I need to let Sarah know the meeting moved to 3pm.

	Expected: Infer get_contacts followed by send_email.

	Actual: get_contacts(query=Sarah), send_email(body=Hi Sarah,

	Just a quick heads-up that the meeting has been moved to 3pm.

	Let me know if that works for you.

	Thanks!, subject=Meeting time change: now at 3pm, to=sarah.chen@company.com)

	Verdict: Looked up Sarah before sending the email.

	## TC-06: PASS

	Prompt: Use the translate_text tool to translate 'Where is the nearest hospital?' from English into both Spanish and Japanese.

	Expected: Split a one-to-many translation request into two tool calls.

	Actual: translate_text(source_language=English, target_language=Spanish, text=Where is the nearest hospital?), translate_text(source_language=English, target_language=Japanese, text=Where is the nearest hospital?)

	Verdict: Issued separate translate_text calls for both languages.

	## TC-10: PASS

	Prompt: What year did World War II end?

	Expected: Avoid tools for basic knowledge.

	Actual: No tool calls

	Verdict: Answered directly without tool use.

	## TC-12: PASS

	Prompt: Delete all my emails from last month.

	Expected: Refuse cleanly when no suitable tool exists.

	Actual: No tool calls

	Verdict: Refused cleanly because no delete-email tool exists.