Instructions to use zai-org/GLM-5.3-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-5.3-Flash with Transformers:

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

pipe = pipeline("image-text-to-text", model="zai-org/GLM-5.3-Flash")
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("zai-org/GLM-5.3-Flash")
model = AutoModelForMultimodalLM.from_pretrained("zai-org/GLM-5.3-Flash", 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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zai-org/GLM-5.3-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/GLM-5.3-Flash"
# 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-5.3-Flash",
		"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-5.3-Flash

SGLang

How to use zai-org/GLM-5.3-Flash 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-5.3-Flash" \
    --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-5.3-Flash",
		"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-5.3-Flash" \
        --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-5.3-Flash",
		"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-5.3-Flash with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-5.3-Flash
```

Add SLE v1 published evaluation results

#67

by ShuhanXue - opened 3 days ago

base: refs/heads/main

←

from: refs/pr/67

Discussion Files changed

+50

-0

Files changed (1) hide show

.eval_results/sle-v1-20261101.yaml +50 -0

.eval_results/sle-v1-20261101.yaml ADDED Viewed

	@@ -0,0 +1,50 @@

+- dataset:
+    id: Gen-Verse/Science-Last-Exam
+    task_id: composite-score
+    revision: 022231bb779ed2b827c561b38b0902d0ea28c667
+  value: 36.93
+  source:
+    url: https://sciences-last-exam.org/leaderboard
+    name: SLE published leaderboard (1 Nov 2026)
+  notes: Claude Code; high reasoning; API endpoint evaluation; published snapshot
+    2026-11-01. Not a local-weight run.
+- dataset:
+    id: Gen-Verse/Science-Last-Exam
+    task_id: protocol-planning-mean-score
+    revision: 022231bb779ed2b827c561b38b0902d0ea28c667
+  value: 58.0
+  source:
+    url: https://sciences-last-exam.org/leaderboard
+    name: SLE published leaderboard (1 Nov 2026)
+  notes: Claude Code; high reasoning; API endpoint evaluation; published snapshot
+    2026-11-01. Not a local-weight run.
+- dataset:
+    id: Gen-Verse/Science-Last-Exam
+    task_id: experiment-observation-pass-rate
+    revision: 022231bb779ed2b827c561b38b0902d0ea28c667
+  value: 38.0
+  source:
+    url: https://sciences-last-exam.org/leaderboard
+    name: SLE published leaderboard (1 Nov 2026)
+  notes: Claude Code; high reasoning; API endpoint evaluation; published snapshot
+    2026-11-01. Not a local-weight run.
+- dataset:
+    id: Gen-Verse/Science-Last-Exam
+    task_id: scientific-computing-full-credit-rate
+    revision: 022231bb779ed2b827c561b38b0902d0ea28c667
+  value: 8.7
+  source:
+    url: https://sciences-last-exam.org/leaderboard
+    name: SLE published leaderboard (1 Nov 2026)
+  notes: Claude Code; high reasoning; API endpoint evaluation; published snapshot
+    2026-11-01. Not a local-weight run.
+- dataset:
+    id: Gen-Verse/Science-Last-Exam
+    task_id: scientific-software-operation-full-credit-rate
+    revision: 022231bb779ed2b827c561b38b0902d0ea28c667
+  value: 43.0
+  source:
+    url: https://sciences-last-exam.org/leaderboard
+    name: SLE published leaderboard (1 Nov 2026)
+  notes: Claude Code; high reasoning; API endpoint evaluation; published snapshot
+    2026-11-01. Not a local-weight run.