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
```

wrldsuksgo2mars commited on Sep 5

Commit

d6a1031

verified ·

1 Parent(s): a4480b6

Keep full quantization ledger out of release package

Browse files

Files changed (3) hide show

README.md +3 -2
ds4rt-exl3-error-ledger.jsonl +0 -3
ds4rt-exl3-error-ledger.manifest.json +0 -1

README.md CHANGED Viewed

@@ -39,8 +39,9 @@ seed 787, `sigma_reg=0.025`, automatic output-scale selection, and a fixed
 Within every target layer, K4 promotion follows measured Hessian-weighted
 relative error times natural gate-squared mass while the global 3:5:8 budget
 stays exact. Natural GLM router traffic supplied expert Hessians; the committed
-recovery contract covers any expert below the 1,024-route floor. The full error
-ledger, tier plans, and validation report ship with the model.
 The first weight publication is gated on the exact checkpoint, allocation,
 native-byte, manifest, and configuration-parser audits above. The matching

 Within every target layer, K4 promotion follows measured Hessian-weighted
 relative error times natural gate-squared mass while the global 3:5:8 budget
 stays exact. Natural GLM router traffic supplied expert Hessians; the committed
+recovery contract covers any expert below the 1,024-route floor. Allocation
+provenance and the validation report ship with the model; the full error ledger
+is retained separately as internal quantization evidence.
 The first weight publication is gated on the exact checkpoint, allocation,
 native-byte, manifest, and configuration-parser audits above. The matching

ds4rt-exl3-error-ledger.jsonl DELETED Viewed

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7788785b0af5af259ecff32c64ef2d962452d8bfff90f764603db8846279dd03
-size 381155692

ds4rt-exl3-error-ledger.manifest.json DELETED Viewed

	@@ -1 +0,0 @@
1	- {"complete_family_records":12384,"ledger":"ds4rt-exl3-error-ledger.jsonl","ledger_sha256":"7788785b0af5af259ecff32c64ef2d962452d8bfff90f764603db8846279dd03","projection_records":37152,"schema":"ds4rt.exl3-error-ledger","schema_version":1,"total_records":49536}