Instructions to use zai-org/GLM-5.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-5.2 with Transformers:

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

pipe = pipeline("text-generation", model="zai-org/GLM-5.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-5.2")
model = AutoModelForMultimodalLM.from_pretrained("zai-org/GLM-5.2")
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-5.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/GLM-5.2"
# 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-5.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/GLM-5.2

SGLang

How to use zai-org/GLM-5.2 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-5.2" \
    --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-5.2",
		"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-5.2" \
        --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-5.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zai-org/GLM-5.2 with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-5.2
```

Updating README.md ..

by usermma - opened about 4 hours ago

base: refs/heads/main

←

from: refs/pr/8

Discussion Files changed

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README.md +19 -19

README.md CHANGED Viewed

@@ -42,27 +42,27 @@ We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It
 |Benchmark|GLM-5.2|GLM-5.1|Qwen3.7-Max|MiniMax M3|DeepSeek-V4-Pro|Claude Opus 4.8|GPT-5.5|Gemini 3.1 Pro|
 |:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
 |Reasoning|||||||||||
-|HLE|40.5|31|41.4|37|37.7|49.8*|41.4*|45|
-|HLE (w/ Tools)|54.7|52.3|53.5|-|48.2|57.9*|52.2*|51.4*|
-|CritPt|16.7|4.6|13.4|3.7|12.9|20.9|27.1|17.7|
-|AIME 2026|99.2|95.3|97|-|94.6|95.7|98.3|98.2|
-|HMMT Nov. 2025|94.4|94|95|84.4|94.4|96.5|96.5|94.8|
-|HMMT Feb. 2026|92.5|82.6|97.1|84.4|95.2|96.7|96.7|87.3|
-|IMOAnswerBench|91.0|83.8|90|-|89.8|83.5|-|81|
-|GPQA-Diamond|91.2|86.2|90|93|90.1|93.6|93.6|94.3|
 |Coding|||||||||||
-|SWE-bench Pro|62.1|58.4|60.6|59|55.4|69.2|58.6|54.2|
-|NL2Repo|48.9|42.7|47.2|42.1|35.5|69.7|50.7|33.4|
-|DeepSWE|46.2|18|18|20|8|58|70|10|
-|ProgramBench|63.7|50.9|-|-|47.8|71.9|70.8|39.5|
-|Terminal Bench 2.1 (Terminus-2)|81.0|63.5|75|65|64| 85|84|74|
-|Terminal Bench 2.1 (Best Reported Harness)|82.7|69|-|-|-|78.9|83.4|70.7|
-|FrontierSWE (Dominance)|74.4|30.5|-|-|29.0|75.1|72.6|39.6|
-|PostTrainBench|34.3|20.1|-|-|-|37.2|28.4|21.6|
-|SWE-Marathon|13.0|1.0|-|-|-|26.0|12.0|4.0|
 |Agentic|||||||||||
-|MCP-Atlas (Public Set)|76.8|71.8|76.4|74.2|73.6|77.8|75.3|69.2|
-|Tool-Decathlon|48.2|40.7|-|-|52.8|59.9|55.6|48.8|
 ## Serve GLM-5.2 Locally

 |Benchmark|GLM-5.2|GLM-5.1|Qwen3.7-Max|MiniMax M3|DeepSeek-V4-Pro|Claude Opus 4.8|GPT-5.5|Gemini 3.1 Pro|
 |:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
 |Reasoning|||||||||||
+|HLE|40.5|31|41.4|37|37.7|**49.8***|41.4*|45|
+|HLE (w/ Tools)|54.7|52.3|53.5|-|48.2|**57.9***|52.2*|51.4*|
+|CritPt|16.7|4.6|13.4|3.7|12.9|20.9|**27.1**|17.7|
+|AIME 2026|**99.2**|95.3|97|-|94.6|95.7|98.3|98.2|
+|HMMT Nov. 2025|94.4|94|95|84.4|94.4|***96.5***|***96.5***|94.8|
+|HMMT Feb. 2026|92.5|82.6|**97.1**|84.4|95.2|96.7|96.7|87.3|
+|IMOAnswerBench|**91.0**|83.8|90|-|89.8|83.5|-|81|
+|GPQA-Diamond|91.2|86.2|90|93|90.1|93.6|93.6|**94.3**|
 |Coding|||||||||||
+|SWE-bench Pro|62.1|58.4|60.6|59|55.4|**69.2**|58.6|54.2|
+|NL2Repo|48.9|42.7|47.2|42.1|35.5|**69.7**|50.7|33.4|
+|DeepSWE|46.2|18|18|20|8|58|**70**|10|
+|ProgramBench|63.7|50.9|-|-|47.8|**71.9**|70.8|39.5|
+|Terminal Bench 2.1 (Terminus-2)|81.0|63.5|75|65|64|**85**|84|74|
+|Terminal Bench 2.1 (Best Reported Harness)|82.7|69|-|-|-|78.9|**83.4**|70.7|
+|FrontierSWE (Dominance)|74.4|30.5|-|-|29.0|**75.1**|72.6|39.6|
+|PostTrainBench|34.3|20.1|-|-|-|**37.2**|28.4|21.6|
+|SWE-Marathon|13.0|1.0|-|-|-|**26.0**|12.0|4.0|
 |Agentic|||||||||||
+|MCP-Atlas (Public Set)|76.8|71.8|76.4|74.2|73.6|**77.8**|75.3|69.2|
+|Tool-Decathlon|48.2|40.7|-|-|52.8|**59.9**|55.6|48.8|
 ## Serve GLM-5.2 Locally