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("text-generation", 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)

# 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=40)
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": "What is the capital of France?"
			}
		]
	}'

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": "What is the capital of France?"
			}
		]
	}'

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": "What is the capital of France?"
			}
		]
	}'

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 evaluation results

#12

by SaylorTwift HF Staff - opened about 14 hours ago

base: refs/heads/main

←

from: refs/pr/12

Discussion Files changed

about 14 hours ago

PR Description: Add Evaluation Results for zai-org/GLM-5.3-Flash

Summary

This PR adds evaluation results extracted from the model card's benchmark graph for zai-org/GLM-5.3-Flash to the .eval_results/ directory, following the Hugging Face Hub evaluation-results specification.

Benchmarks Added

Terminal-Bench 2.1 — 84.3
DeepSWE — 63.4
Humanity's Last Exam — 55.3

Benchmarks Skipped (Not Registered on Hub)

The following benchmarks were present in the model card but could not be added because they do not have a registered eval.yaml on the Hugging Face Hub:

Agent's Last Exam: 26.3 — no registered eval.yaml found on the Hub.
AutomationBench v1.0.6: 48.8 — no registered eval.yaml found on the Hub.
GDPval-AA v2: 1773 — no registered eval.yaml found on the Hub.

These can be added once the benchmark authors register their eval.yaml on the Hub.

Source

Model card: https://huggingface.co/zai-org/GLM-5.3-Flash
Note: the model card cites arxiv:2602.15763 as "the GLM-5 Technical report", but that paper (published Feb 2026) is the original GLM-5 report and predates this model — it does not contain GLM-5.3-Flash-specific numbers, so it wasn't used as a source.

Files Added

.eval_results/GLM-5.3-Flash.yaml

Verification

These results were extracted from the model card's published benchmark chart (bench_53.png, an image embedded in the README, read visually — the announcement blog is a client-rendered SPA and wasn't fetchable). No verified token is provided as these were not run via HF Jobs with inspect-ai.

To upload this file to the Hub, run:

hf upload zai-org/GLM-5.3-Flash --type model --include .eval_results/*.yaml --commit-message "Add evaluation results"

Add evaluation results2d894c61

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

Ready to merge

This branch is ready to get merged automatically.

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