Instructions to use zai-org/GLM-5.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-5.1 with Transformers:

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

pipe = pipeline("text-generation", model="zai-org/GLM-5.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-5.1")
model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-5.1")
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-5.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/GLM-5.1"
# 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-5.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/GLM-5.1

SGLang

How to use zai-org/GLM-5.1 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-5.1" \
    --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-5.1",
		"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-5.1" \
        --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-5.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zai-org/GLM-5.1 with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-5.1
```

zRzRzRzRzRzRzR commited on Apr 8

Commit

146e9f2

1 Parent(s): 278f446

update bench

Browse files

Files changed (2) hide show

.eval_results/terminal_bench_2_claudecode.yaml +1 -1
README.md +2 -2

.eval_results/terminal_bench_2_claudecode.yaml CHANGED Viewed

@@ -1,7 +1,7 @@
 - dataset:
     id: harborframework/terminal-bench-2.0
     task_id: terminalbench_2
-  value: 66.5
   date: '2026-04-07'
   source:
     url: https://huggingface.co/zai-org/GLM-5.1

 - dataset:
     id: harborframework/terminal-bench-2.0
     task_id: terminalbench_2
+  value: 69.0
   date: '2026-04-07'
   source:
     url: https://huggingface.co/zai-org/GLM-5.1

README.md CHANGED Viewed

@@ -51,14 +51,14 @@ GLM-5.1, by contrast, is built to stay effective on agentic tasks over much long
 | SWE-Bench Pro                              | **58.4**           | 55.1                | 56.6         | 56.2                 | -                    | 53.8       | 57.3            | 54.2           | 57.7             |
 | NL2Repo                                    | 42.7               | 35.9                | 37.9         | 39.8                 | -                    | 32.0       | **49.8**        | 33.4           | 41.3             |
 | Terminal-Bench 2.0 (Terminus-2)            | 63.5               | 56.2                | 61.6         | -                    | 39.3                 | 50.8       | 65.4            | **68.5**       | -                |
-| Terminal-Bench 2.0 (Best self-reported)    | 66.5 (Claude Code) | 56.2 (Claude Code)  | -            | 57.0 (Claude Code)   | 46.4 (Claude Code)   | -          | -               | -              | **75.1** (Codex) |
 | CyberGym                                   | **68.7**           | 48.3                | -            | -                    | 17.3                 | 41.3       | 66.6            | -              | -                |
 | BrowseComp                                 | **68.0**           | 62.0                | -            | -                    | 51.4                 | 60.6       | -               | -              | -                |
 | BrowseComp (w/ Context Manage)             | 79.3               | 75.9                | -            | -                    | 67.6                 | 74.9       | 84.0            | **85.9**       | 82.7             |
 | τ³-Bench                                   | 70.6               | 69.2                | 70.7         | 67.6                 | 69.2                 | 66.0       | 72.4            | 67.1           | **72.9**         |
 | MCP-Atlas (Public Set)                     | 71.8               | 69.2                | **74.1**     | 48.8                 | 62.2                 | 63.8       | 73.8            | 69.2           | 67.2             |
 | Tool-Decathlon                             | 40.7               | 38.0                | 39.8         | 46.3                 | 35.2                 | 27.8       | 47.2            | 48.8           | **54.6**         |
-| Vending Bench 2                            | $5,634.00          | $4,432.12           | $5,114.87    | -                    | $1,034.00            | $1,198.46  | **$8,017.59**   | $911.21        | $6,144.18        |
 ## Serve GLM-5.1 Locally
 The following open-source frameworks support local deployment of GLM-5.1:

 | SWE-Bench Pro                              | **58.4**           | 55.1                | 56.6         | 56.2                 | -                    | 53.8       | 57.3            | 54.2           | 57.7             |
 | NL2Repo                                    | 42.7               | 35.9                | 37.9         | 39.8                 | -                    | 32.0       | **49.8**        | 33.4           | 41.3             |
 | Terminal-Bench 2.0 (Terminus-2)            | 63.5               | 56.2                | 61.6         | -                    | 39.3                 | 50.8       | 65.4            | **68.5**       | -                |
+| Terminal-Bench 2.0 (Best self-reported)    | 69.0 (Claude Code) | 56.2 (Claude Code)  | -            | 57.0 (Claude Code)   | 46.4 (Claude Code)   | -          | -               | -              | **75.1** (Codex) |
 | CyberGym                                   | **68.7**           | 48.3                | -            | -                    | 17.3                 | 41.3       | 66.6            | -              | -                |
 | BrowseComp                                 | **68.0**           | 62.0                | -            | -                    | 51.4                 | 60.6       | -               | -              | -                |
 | BrowseComp (w/ Context Manage)             | 79.3               | 75.9                | -            | -                    | 67.6                 | 74.9       | 84.0            | **85.9**       | 82.7             |
 | τ³-Bench                                   | 70.6               | 69.2                | 70.7         | 67.6                 | 69.2                 | 66.0       | 72.4            | 67.1           | **72.9**         |
 | MCP-Atlas (Public Set)                     | 71.8               | 69.2                | **74.1**     | 48.8                 | 62.2                 | 63.8       | 73.8            | 69.2           | 67.2             |
 | Tool-Decathlon                             | 40.7               | 38.0                | 39.8         | 46.3                 | 35.2                 | 27.8       | 47.2            | 48.8           | **54.6**         |
+| Vending Bench 2                            | $5,634.41          | $4,432.12           | $5,114.87    | -                    | $1,034.00            | $1,198.46  | **$8,017.59**   | $911.21        | $6,144.18        |
 ## Serve GLM-5.1 Locally
 The following open-source frameworks support local deployment of GLM-5.1: