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

Libraries

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

Support this work → · X · GitHub · REAP paper · Cerebras REAP

GLM-4.7-Flash-SFT

SFT fine-tune of zai-org/GLM-4.7-Flash.

At a glance


Base model	zai-org/GLM-4.7-Flash
Format	SFT
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
`GLM-4.7-Flash-DPO`	DPO	link
`GLM-4.7-Flash-SFT` (this)	SFT	link
`GLM-4.7-Flash-Tools`	Tools	link

License & citation

License inherited from the base model.

@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}
}

Model tree for 0xSero/GLM-4.7-Flash-SFT

Base model

zai-org/GLM-4.7-Flash

Finetuned

(82)

this model

Collection including 0xSero/GLM-4.7-Flash-SFT

GLM — REAP

Collection

REAP-pruned & quantized GLM-4.x / 5 / 5.1 (+ Flash fine-tunes). • 34 items • Updated Sep 8 • 2

Paper for 0xSero/GLM-4.7-Flash-SFT

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression

Paper • 2510.13999 • Published Oct 15, 2025 • 23

0xSero
/

GLM-4.7-Flash-SFT