Instructions to use GadflyII/GLM-4.7-Flash-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GadflyII/GLM-4.7-Flash-MXFP4 with Transformers:

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

pipe = pipeline("text-generation", model="GadflyII/GLM-4.7-Flash-MXFP4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GadflyII/GLM-4.7-Flash-MXFP4")
model = AutoModelForCausalLM.from_pretrained("GadflyII/GLM-4.7-Flash-MXFP4")
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

vLLM

How to use GadflyII/GLM-4.7-Flash-MXFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GadflyII/GLM-4.7-Flash-MXFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GadflyII/GLM-4.7-Flash-MXFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GadflyII/GLM-4.7-Flash-MXFP4

SGLang

How to use GadflyII/GLM-4.7-Flash-MXFP4 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 "GadflyII/GLM-4.7-Flash-MXFP4" \
    --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": "GadflyII/GLM-4.7-Flash-MXFP4",
		"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 "GadflyII/GLM-4.7-Flash-MXFP4" \
        --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": "GadflyII/GLM-4.7-Flash-MXFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GadflyII/GLM-4.7-Flash-MXFP4 with Docker Model Runner:
```
docker model run hf.co/GadflyII/GLM-4.7-Flash-MXFP4
```

Update MXFP4 format to compressed-tensors

by mgoin - opened Feb 18

Discussion

mgoin

Feb 18

•

edited Feb 18

Hey @GadflyII great work with all your checkpoints and features on vLLM!

I wanted to let you know specifically for mxfp4 that I think we'd like to keep mxfp4.py specific for gpt-oss in upstream for a few reasons, but mostly since that model is the only one using it at the moment.

We have support for mxfp4 w4a16 the same as gpt-oss but generalized through the compressed-tensors pathway (https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a16_mxfp4.py) , see this example to make a model of your own https://github.com/vllm-project/llm-compressor/blob/main/examples/quantization_w4a16_fp4/mxfp4/qwen3_example.py

See the uploaded model here which is tested in CI https://huggingface.co/nm-testing/Qwen3-30B-A3B-MXFP4A16

Remaking your checkpoint in that format may help you run this model on upstream vLLM as-is. LMK what you think

GadflyII

Owner Feb 19

Hey, thanks for reaching out. I will absolutely do that and see how it goes; I am deep in a training run so it will be a few days at least, I need more hardware 😞

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