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 Settings

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
```

Use the correct way of initializing using latest transformers latest branch v5.0.0

#58

by rjmehta - opened Jan 30

Discussion

rjmehta

Jan 30

Amazing model. Thank you.

(sglangenv) GLM-4.7$ python3 -m sglang.launch_server --model-path /models/zai-org/GLM-4.7-Flash --tp-size 4 --tool-call-parser glm47 --reasoning-parser glm45 --speculative-algorithm EAGLE --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4 --mem-fraction-static 0.8 --served-model-name glm-4.7-flash --host 0.0.0.0 --port 8000
Traceback (most recent call last):
File "", line 198, in _run_module_as_main
File "", line 88, in _run_code
File "/home/rmehta/sglangenv/lib/python3.12/site-packages/sglang/launch_server.py", line 7, in
from sglang.srt.server_args import prepare_server_args
File "/home/rmehta/sglangenv/lib/python3.12/site-packages/sglang/srt/server_args.py", line 67, in
from sglang.srt.utils.hf_transformers_utils import check_gguf_file
File "/home/rmehta/sglangenv/lib/python3.12/site-packages/sglang/srt/utils/hf_transformers_utils.py", line 46, in
from sglang.srt.configs import (
File "/home/rmehta/sglangenv/lib/python3.12/site-packages/sglang/srt/configs/init.py", line 9, in
from sglang.srt.configs.janus_pro import MultiModalityConfig
File "/home/rmehta/sglangenv/lib/python3.12/site-packages/sglang/srt/configs/janus_pro.py", line 634, in
register_image_processor(MultiModalityConfig, VLMImageProcessor)
File "/home/rmehta/sglangenv/lib/python3.12/site-packages/sglang/srt/configs/utils.py", line 18, in register_image_processor
AutoImageProcessor.register(config, None, image_processor, None, exist_ok=True)
TypeError: AutoImageProcessor.register() got multiple values for argument 'exist_ok'

I am using latest sglang 0.5.8 & and transformers using stable latest release 5.0.0.

This should be supported and not rely on transformers specific commit. It should be merged in the stable release in order for deployment to work.

ZHANGYUXUAN-zR

Z.ai org Jan 31

This requires you to modify sglang and comment out the line of code register_image_processor(MultiModalityConfig, VLMImageProcessor). This is an upgrade compatibility issue and is not related to this model. We have already synchronized with the sglang committer, and it may be fixed soon.

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