Instructions to use cyankiwi/GLM-4.7-AWQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cyankiwi/GLM-4.7-AWQ-4bit with Transformers:

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

pipe = pipeline("text-generation", model="cyankiwi/GLM-4.7-AWQ-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cyankiwi/GLM-4.7-AWQ-4bit")
model = AutoModelForCausalLM.from_pretrained("cyankiwi/GLM-4.7-AWQ-4bit", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cyankiwi/GLM-4.7-AWQ-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cyankiwi/GLM-4.7-AWQ-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cyankiwi/GLM-4.7-AWQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cyankiwi/GLM-4.7-AWQ-4bit

SGLang

How to use cyankiwi/GLM-4.7-AWQ-4bit 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 "cyankiwi/GLM-4.7-AWQ-4bit" \
    --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": "cyankiwi/GLM-4.7-AWQ-4bit",
		"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 "cyankiwi/GLM-4.7-AWQ-4bit" \
        --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": "cyankiwi/GLM-4.7-AWQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cyankiwi/GLM-4.7-AWQ-4bit with Docker Model Runner:
```
docker model run hf.co/cyankiwi/GLM-4.7-AWQ-4bit
```

Getting nonsense output on dual DGX Sparks

by eugreugr - opened Dec 24, 2025

Discussion

eugreugr

Dec 24, 2025

Running latest vLLM nightly on my dual DGX Spark cluster.

Test request: "Tell me a short story, one paragraph max"
Result:

The user is asking for a short story of one paragraph max. This doesn't involve web searches, code context gathering, or browser automation. It's a simple across writing.Unable مستانت isasına unravel incididunt midnight scroll herein are with expose.rules一夜 fathers.i“There减速 verder 康岡атίζει uninterrupted “나

Launching with the following parameters:

vllm serve cyankiwi/GLM-4.7-AWQ-4bit --tool-call-parser glm47  \
       --reasoning-parser glm45   \
      --enable-auto-tool-choice   \
      -tp 2   \
      --gpu-memory-utilization 0.9   \
      --max-model-len 32000   \
      --distributed-executor-backend ray

Another quant here, Salyut1/GLM-4.7-NVFP4, works without any issues.

Also tried with expert parallel enabled, same thing.
Any ideas?

aidendle94

Dec 24, 2025

same

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