Instructions to use 0xSero/GLM-4.7-Flash-Tools with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0xSero/GLM-4.7-Flash-Tools with Transformers:

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

pipe = pipeline("text-generation", model="0xSero/GLM-4.7-Flash-Tools")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("0xSero/GLM-4.7-Flash-Tools", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 0xSero/GLM-4.7-Flash-Tools with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xSero/GLM-4.7-Flash-Tools"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0xSero/GLM-4.7-Flash-Tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/0xSero/GLM-4.7-Flash-Tools

SGLang

How to use 0xSero/GLM-4.7-Flash-Tools 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 "0xSero/GLM-4.7-Flash-Tools" \
    --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": "0xSero/GLM-4.7-Flash-Tools",
		"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 "0xSero/GLM-4.7-Flash-Tools" \
        --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": "0xSero/GLM-4.7-Flash-Tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 0xSero/GLM-4.7-Flash-Tools with Docker Model Runner:
```
docker model run hf.co/0xSero/GLM-4.7-Flash-Tools
```

GLM-4.7-Flash-Tools / README.md

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	---
	base_model:
	- zai-org/GLM-4.7-Flash
	license: mit
	pipeline_tag: text-generation
	base_model_relation: finetune
	library_name: transformers
	tags:
	- glm
	- tools
	---

	> [!TIP]
	> [Support this work →](https://donate.sybilsolutions.ai) · [X](https://x.com/0xsero) · [GitHub](https://github.com/0xsero) · [REAP paper](https://arxiv.org/abs/2510.13999) · [Cerebras REAP](https://huggingface.co/collections/cerebras/cerebras-reap)

	# GLM-4.7-Flash-Tools

	Tools fine-tune of [zai-org/GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash).

	## At a glance

	\| \| \|
	\|---\|---\|
	\| Base model \| [zai-org/GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash) \|
	\| Format \| Tools \|
	\| Total params \| — \|
	\| Active / token \| — \|
	\| Experts / layer \| — \|
	\| Layers \| — \|
	\| Hidden size \| — \|
	\| Context \| — \|
	\| On-disk size \| 0 GB \|

	## Which variant should I pick?

	\| Variant \| Format \| Link \|
	\|---\|---\|---\|
	\| `GLM-4.7-Flash` \| BF16 \| [link](https://huggingface.co/0xSero/GLM-4.7-Flash) \|
	\| `GLM-4.7-Flash-DPO` \| DPO \| [link](https://huggingface.co/0xSero/GLM-4.7-Flash-DPO) \|
	\| `GLM-4.7-Flash-SFT` \| SFT \| [link](https://huggingface.co/0xSero/GLM-4.7-Flash-SFT) \|
	\| `GLM-4.7-Flash-Tools` (this) \| Tools \| [link](https://huggingface.co/0xSero/GLM-4.7-Flash-Tools) \|

	## License & citation
	License inherited from the base model.

	```bibtex
	@misc{lasby2025reap,
	title = {REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression},
	author = {Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
	year = {2025}, eprint = {2510.13999}, archivePrefix = {arXiv}
	}
	```

	## Sponsors
	Made possible by NVIDIA · TNG Technology · Lambda · Prime Intellect · Hot Aisle.