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, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-5.2")
model = AutoModelForCausalLM.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
```

zRzRzRzRzRzRzR commited on 10 days ago

Commit

f226310

1 Parent(s): e32aaf0

add Footnote

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@@ -75,6 +75,21 @@ GLM-5.2 supports deployment with the following frameworks. Feel free to try them
 - [Unsloth](https://github.com/unslothai/unsloth) (v0.1.47-beta+) — see [guide](https://unsloth.ai/docs/models/glm-5.2)
 - For deployment on the `Ascend NPU` platform, inference frameworks such as vLLM-Ascend, xLLM and SGLang are supported — see [here](github.com/zai-org/GLM-5/blob/main/example/ascend.md).
 ## Citation
 If you find GLM-5.2 useful in your research, please cite our technical report:

 - [Unsloth](https://github.com/unslothai/unsloth) (v0.1.47-beta+) — see [guide](https://unsloth.ai/docs/models/glm-5.2)
 - For deployment on the `Ascend NPU` platform, inference frameworks such as vLLM-Ascend, xLLM and SGLang are supported — see [here](github.com/zai-org/GLM-5/blob/main/example/ascend.md).
+## Footnote
+* **Humanity’s Last Exam (HLE) & other reasoning tasks**: We use sampling parameters of `temperature=1.0`, `top_p=0.95` for evaluation. We evaluate with a maximum generation length of `163,840` tokens. By default, we report the text-only subset; results marked with * are from the full set. For AIME, HMMT and IMOAnswerBench, we evaluate  each question using the following system prompt: `Your response should be in the following format:\nExplanation: {your explanation for your final answer}\nExact Answer: {your succinct, final answer}\nConfidence: {your confidence score between 0% and 100% for your answer}.` We use GPT-5.5 (medium) as the judge model. For HLE-with-tools, we use a maximum context length of 300,000 tokens, with no context management strategy.
+* **SWE-Bench Pro**: We run the SWE-Bench Pro suite with OpenHands using a tailored instruction prompt. Settings: `temperature=1`, `top_p=1`, `max_new_tokens=32k`, with a 400K context window.
+* **NL2Repo**: We evaluated NL2Repo with `temperature=1.0`, `top_p=1.0`, and `max_new_tokens=48k` under 400k context. To prevent hacking, we use rule-based and a LLM-based judgement to prevent malicious behaviors (e.g., unauthorized pip or curl operations).
+* **DeepSWE**: We run DeepSWE with the official pier evaluation framework and the mini-swe-agent harness (`temperature=1.0`, `top_p=1.0`, `timeout=2h`, 400K context). Each task is solved in an isolated container with 2 CPUs, 8 GB RAM, and no internet access.
+* **ProgramBench**: We evaluate ProgramBench (200 instances) with Claude-Code 2.1.156 using `temperature=1.0, top_p=1.0, max_tokens=64000, max_turns=2000, sample_timeout=6h, reasoning_effort=max`, with a 400K context window. Each instance runs in a (4 CPUs, 8 GB RAM) sandbox with internet access disabled.
+* **Terminal-Bench 2.1 (Terminus 2)**: We evaluate Terminal-Bench 2.1 with Terminus-2 framework using `parser=json`, `timeout=4h`, `temperature=1.0`, `top_p=1.0`, `max_new_tokens=48k`, `max_episodes=500`, with a 256K context window. Resource limits are capped at 4 CPUs and 8 GB RAM.
+* **Terminal-Bench 2.1 (Claude Code)**: We evaluate in Claude Code 2.1.167 with `temperature=1.0, top_p=0.95, max_new_tokens=131072`. We override max_new_tokens to 128k via a transparent proxy, bypassing the 64k CLI cap to restore the configurability of `CLAUDE_CODE_MAX_OUTPUT_TOKENS`. We remove wall-clock time limits, while preserving per-task CPU and memory constraints. Scores are averaged over 5 runs.
+* **MCP-Atlas**: All models were evaluated in think mode on the 500-task public subset with a 10-minute timeout per task. We use Gemini-3.0-Pro as the judge model for evaluation.
+* **Tool-Decathlon**: We use the official evaluation service and set max_token to 128K.
+* **FrontierSWE**: The evaluation was conducted by [Proximal](https://www.proximal.ai) with 1M context length, max effort level, and 128K maximum output tokens. Dominance score reported as of 2026/06/16.
+* **PostTrainBench**: The evaluation was conducted by [PostTrainBench](https://posttrainbench.com) with 1M context length, max effort level, and 128K maximum output tokens.
+* **SWE-Marathon**: The evaluation was conducted by [Abundant AI](https://www.abundant.ai) with 1M context length, max effort level, and 128K maximum output tokens.
 ## Citation
 If you find GLM-5.2 useful in your research, please cite our technical report: