Instructions to use zai-org/GLM-4.7-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-4.7-Flash with Transformers:

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

pipe = pipeline("text-generation", model="zai-org/GLM-4.7-Flash")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-4.7-Flash")
model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-4.7-Flash")
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

vLLM

How to use zai-org/GLM-4.7-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/GLM-4.7-Flash"
# 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-4.7-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/GLM-4.7-Flash

SGLang

How to use zai-org/GLM-4.7-Flash 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-4.7-Flash" \
    --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-4.7-Flash",
		"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-4.7-Flash" \
        --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-4.7-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zai-org/GLM-4.7-Flash with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-4.7-Flash
```

[Docs] Add LightLLM deployment example

#57

by FubaoSu - opened Jan 30

base: refs/heads/main

←

from: refs/pr/57

Discussion Files changed

+23

-0

Files changed (1) hide show

README.md +23 -0

README.md CHANGED Viewed

@@ -156,6 +156,29 @@ python3 -m sglang.launch_server \
 ```
 + For Blackwell GPUs, include `--attention-backend triton --speculative-draft-attention-backend triton` in your SGLang launch command.
 ## Citation
 If you find our work useful in your research, please consider citing the following paper:

 ```
 + For Blackwell GPUs, include `--attention-backend triton --speculative-draft-attention-backend triton` in your SGLang launch command.
+### LightLLM
+```shell
+# Install lightllm (Recommended to use Docker).
+docker pull jyily/lightllm:cu129-78cc66a
+# Or build from [source](https://github.com/ModelTC/LightLLM)
+pip install git+https://github.com/ModelTC/LightLLM
+LIGHTLLM_TRITON_AUTOTUNE_LEVEL=1 LOADWORKER=18 \
+python3 -m lightllm.server.api_server \
+    --model_dir /path/to/GLM-4.7-Flash/ \
+    --tp 1 \
+    --max_req_total_len 202752 \
+    --chunked_prefill_size 8192 \
+    --llm_prefill_att_backend fa3 \
+    --llm_decode_att_backend fa3 \
+    --graph_max_batch_size 512 \
+    --tool_call_parser glm47 \
+    --reasoning_parser glm45 \
+    --host 0.0.0.0 \
+    --port 8000
+```
 ## Citation
 If you find our work useful in your research, please consider citing the following paper: