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
```

Add CHI-Bench eval results — agent harness: OpenAI Agents SDK

#38

by hlnchen - opened 6 days ago

base: refs/heads/main

←

from: refs/pr/38

Discussion Files changed

+41

-0

Files changed (1) hide show

.eval_results/chi-bench.yaml +41 -0

.eval_results/chi-bench.yaml ADDED Viewed

	@@ -0,0 +1,41 @@

+# Place at .eval_results/chi-bench.yaml in the GLM-5.1 model repo.
+# CONFIRM the exact repo id before submitting (org is zai-org; e.g. zai-org/GLM-5.1).
+# Submit via the model's Community tab as a PR; shows "community-provided" until merged.
+# Values are pass@1 (%) for the best-performing harness for this model: OpenAI Agents SDK.
+# (Ties Hermes at 18.7 overall; OAI Agents wins on reliability pass^3 12.0 vs 10.7.)
+- dataset:
+    id: actava/chi-bench
+    task_id: chi_bench
+  value: 18.7
+  date: "2026-05-08"
+  source:
+    url: https://arxiv.org/abs/2605.16679
+    name: CHI-Bench
+  notes: "Harness: OpenAI Agents SDK; Protocol: 75 tasks x 3 trials; Metric: pass@1 (%)"
+- dataset:
+    id: actava/chi-bench
+    task_id: prior_authorization
+  value: 18.7
+  date: "2026-05-08"
+  source:
+    url: https://arxiv.org/abs/2605.16679
+    name: CHI-Bench
+  notes: "Harness: OpenAI Agents SDK; Protocol: 75 tasks x 3 trials; Metric: pass@1 (%)"
+- dataset:
+    id: actava/chi-bench
+    task_id: utilization_management
+  value: 33.3
+  date: "2026-05-08"
+  source:
+    url: https://arxiv.org/abs/2605.16679
+    name: CHI-Bench
+  notes: "Harness: OpenAI Agents SDK; Protocol: 75 tasks x 3 trials; Metric: pass@1 (%)"
+- dataset:
+    id: actava/chi-bench
+    task_id: care_management
+  value: 4.0
+  date: "2026-05-08"
+  source:
+    url: https://arxiv.org/abs/2605.16679
+    name: CHI-Bench
+  notes: "Harness: OpenAI Agents SDK; Protocol: 75 tasks x 3 trials; Metric: pass@1 (%)"