Instructions to use wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3-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-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-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-v1")
model = AutoModelForMultimodalLM.from_pretrained("wrldsuksgo2mars/GLM-5.3-Flash-EXL3-K3-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-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-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-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-v1

SGLang

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

GLM-5.3-Flash-EXL3-K3-v1 / README.md

wrldsuksgo2mars

Keep full quantization ledger out of release package

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	---
	base_model: zai-org/GLM-5.3-Flash-BF16
	library_name: transformers
	license: mit
	tags:
	- glm5
	- exl3
	- quantized
	- mixture-of-experts
	---

	# GLM-5.3 Flash EXL3 K3

	Three-bit EXL3/MCG for every routed expert in
	[`zai-org/GLM-5.3-Flash-BF16`](https://huggingface.co/zai-org/GLM-5.3-Flash-BF16),
	with everything else retained from the official checkpoint.

	## What moved—and what did not

	- K3 EXL3/MCG: all 288 routed experts' `gate_proj`, `up_proj`, and
	`down_proj` tensors in target layers 3…44 plus checkpoint MTP layer 45.
	- 37,152 quantized projections in one target-plus-MTP calibration run.
	- Attention, dense MLPs, shared experts, routers, norms, embeddings, vision,
	and all other tensors remain native precision.
	- The release audit compared **1,618 native
	tensors / 18.01 GiB byte-for-byte** against source revision
	`f12e0fe1f6b2ea274c11a569582edfd99d993c5e`.
	- Packed checkpoint payload: 127.30 GiB across
	16 safetensors shards.

	Quantization used GPTQModel `0565af7ce20a93df9bbc0e5563d7c6f60916f41a`, EXL3 MCG K3, seed 787,
	`sigma_reg=0.025`, automatic output-scale selection, and a fixed 1,426-record
	calibration corpus (`sha256:4e569625d97865777da92167b8fbf6fabb4ab7adf55baa5d676c81ce8dd95244`). Natural GLM router
	traffic supplied expert Hessians; the committed recovery contract covers any
	expert below the 1,024-route floor. Quantization provenance and the validation report ship with the model; the full
	error ledger is retained separately as internal quantization evidence.

	Before upload, the fully materialized checkpoint is loaded directly by the
	two-GPU B12x/vLLM recipe with adaptive MTP5 and NVFP4 MLA. Publication is gated
	on ordinary generation plus five strict tool-call scenarios. The exact prompts,
	expected behavior, actual calls, and raw report ship in
	`quantization/vllm-prompt-expected-actual.md` and
	`quantization/vllm-tool-eval.json`.

	## Serving

	This is intended for the optimized two-GPU GLM-5.3 recipe at
	[`tpurtell/glm-5.3-flash-ext3-4-bit-2x-rtx`](https://github.com/tpurtell/glm-5.3-flash-ext3-4-bit-2x-rtx).
	That runtime carries the GLM/EXL3/MTP and B12x ports; this card does not claim
	stock-vLLM support.

	Huge thanks to Z.ai for GLM-5.3 Flash, Brandon for the earlier K4 quant
	and qualification work that helped inform this run, MiaAI-Lab for the nearby
	dual-DGX-Spark reference, and the GPTQModel, ExLlamaV3, vLLM, and B12x
	contributors.

	## Audit identity

	- Source index: `sha256:e6007bd58fb7e07f9fe69544257ee2713f252ef5855bbf685b48c991d524ef0f`
	- One-shot plan: `sha256:fdce3c2c0078e27b0fa8bdab92cbd13f6c0ab34fb8d3e836fc56b44afc4ec82c`
	- Validation: `sha256:625dcdfc8a031506d1406804cc72b7373c600567e076220b756f9504bc0fd284`