Instructions to use zai-org/GLM-4.5-Air with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-4.5-Air with Transformers:

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

pipe = pipeline("text-generation", model="zai-org/GLM-4.5-Air")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-4.5-Air")
model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-4.5-Air")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zai-org/GLM-4.5-Air with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/GLM-4.5-Air"
# 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-4.5-Air",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/GLM-4.5-Air

SGLang

How to use zai-org/GLM-4.5-Air 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-4.5-Air" \
    --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-4.5-Air",
		"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-4.5-Air" \
        --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-4.5-Air",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zai-org/GLM-4.5-Air with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-4.5-Air
```

Add EvalEval community eval results

#16

by EvalEvalBot - opened 1 day ago

base: refs/heads/main

←

from: refs/pr/16

Discussion Files changed

+24

-0

YAML Metadata Error:Invalid content in Eval Result file .eval_results/hle.yaml

Check out the documentation for more information.

Show details

Task ID "none" does not match any task in dataset "cais/hle". Available: none

Files changed (3) hide show

.eval_results/gpqa-diamond.yaml +9 -0
.eval_results/hle.yaml +8 -0
.eval_results/mmlu-pro.yaml +7 -0

.eval_results/gpqa-diamond.yaml ADDED Viewed

	@@ -0,0 +1,9 @@

+- dataset:
+    id: Idavidrein/gpqa
+    task_id: diamond
+  date: '2026-04-19'
+  notes: GPQA Diamond
+  source:
+    name: EvalEval
+    url: https://huggingface.co/datasets/evaleval/EEE_datastore/blob/b11a260fe158662bb63b4a144be2b5690615414d/flat/objects/9c/77/9c7740fd-9cad-445c-9681-eb576e2a110e.json
+  value: 71.7171717172

.eval_results/hle.yaml ADDED Viewed

	@@ -0,0 +1,8 @@

+- dataset:
+    id: cais/hle
+    task_id: none
+  date: '2025-08-13'
+  source:
+    name: EvalEval
+    url: https://huggingface.co/datasets/evaleval/EEE_datastore/blob/b11a260fe158662bb63b4a144be2b5690615414d/flat/objects/e9/03/e90308ab-e898-4434-aa93-13d131887737.json
+  value: 8.12

.eval_results/mmlu-pro.yaml ADDED Viewed

	@@ -0,0 +1,7 @@

+- dataset:
+    id: TIGER-Lab/MMLU-Pro
+    task_id: mmlu_pro
+  source:
+    name: EvalEval
+    url: https://huggingface.co/datasets/evaleval/EEE_datastore/blob/b11a260fe158662bb63b4a144be2b5690615414d/flat/objects/07/43/074397da-f5e5-441c-bc45-ac16b599ca7c.json
+  value: 81.4