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)

# pip install -U transformers accelerate
# 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", device_map="auto")
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=256)
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
```

Add LHTB (Long-Horizon-Terminal-Bench) eval result

#47

by zli12321 - opened Jul 16

base: refs/heads/main

←

from: refs/pr/47

Discussion Files changed

+18

-0

Files changed (2) hide show

.eval_results/lhtb.yaml +9 -0
.eval_results/lhtb2_solved.yaml +9 -0

.eval_results/lhtb.yaml ADDED Viewed

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

+- dataset:
+    id: IntelligenceLab/Long-Horizon-Terminal-Bench
+    task_id: lhtb_solved
+  value: 1
+  date: '2026-07-16'
+  source:
+    url: https://zli12321.github.io/LHTB/leaderboard.html
+    name: LHTB leaderboard
+  notes: "1/46 tasks solved at reward >= 0.95 (pass@0.95); mean reward x100 = 31.6; official LHTB Harbor harness"

.eval_results/lhtb2_solved.yaml ADDED Viewed

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

+- dataset:
+    id: IntelligenceLab/Long-Horizon-Terminal-Bench
+    task_id: lhtb2_solved
+  value: 8
+  date: '2026-09-18'
+  source:
+    url: https://github.com/zli12321/LHTB/tree/lhtb-2.0#lhtb-20-results-october-2026
+    name: LHTB 2.0 results
+  notes: "8/77 tasks solved at reward >= 0.95 (pass@0.95); mean reward x100 = 50.3; LHTB 2.0 (77 tasks), terminus-2, 3-hour budget per task, official LHTB Harbor harness"