Instructions to use dtometzki/GLM-4.7-Flash-FP8-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dtometzki/GLM-4.7-Flash-FP8-Dynamic with Transformers:

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

pipe = pipeline("text-generation", model="dtometzki/GLM-4.7-Flash-FP8-Dynamic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dtometzki/GLM-4.7-Flash-FP8-Dynamic")
model = AutoModelForCausalLM.from_pretrained("dtometzki/GLM-4.7-Flash-FP8-Dynamic")
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 dtometzki/GLM-4.7-Flash-FP8-Dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dtometzki/GLM-4.7-Flash-FP8-Dynamic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dtometzki/GLM-4.7-Flash-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dtometzki/GLM-4.7-Flash-FP8-Dynamic

SGLang

How to use dtometzki/GLM-4.7-Flash-FP8-Dynamic 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 "dtometzki/GLM-4.7-Flash-FP8-Dynamic" \
    --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": "dtometzki/GLM-4.7-Flash-FP8-Dynamic",
		"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 "dtometzki/GLM-4.7-Flash-FP8-Dynamic" \
        --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": "dtometzki/GLM-4.7-Flash-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dtometzki/GLM-4.7-Flash-FP8-Dynamic with Docker Model Runner:
```
docker model run hf.co/dtometzki/GLM-4.7-Flash-FP8-Dynamic
```

GLM-4.7-Flash

👋 Join our Discord community.
📖 Check out the GLM-4.7 technical blog, technical report(GLM-4.5).
📍 Use GLM-4.7-Flash API services on Z.ai API Platform.
👉 One click to GLM-4.7.

Introduction

GLM-4.7-Flash is a 30B-A3B MoE model. As the strongest model in the 30B class, GLM-4.7-Flash offers a new option for lightweight deployment that balances performance and efficiency.

Performances on Benchmarks

Benchmark	GLM-4.7-Flash	Qwen3-30B-A3B-Thinking-2507	GPT-OSS-20B
AIME 25	91.6	85.0	91.7
GPQA	75.2	73.4	71.5
LCB v6	64.0	66.0	61.0
HLE	14.4	9.8	10.9
SWE-bench Verified	59.2	22.0	34.0
τ²-Bench	79.5	49.0	47.7
BrowseComp	42.8	2.29	28.3

Evaluation Parameters

Default Settings (Most Tasks)

temperature: 1.0
top-p: 0.95
max new tokens: 131072

For multi-turn agentic tasks (τ²-Bench and Terminal Bench 2), please turn on Preserved Thinking mode.

Terminal Bench, SWE Bench Verified

temperature: 0.7
top-p: 1.0
max new tokens: 16384

τ^2-Bench

Temperature: 0
Max new tokens: 16384

For τ^2-Bench evaluation, we added an additional prompt to the Retail and Telecom user interaction to avoid failure modes caused by users ending the interaction incorrectly. For the Airline domain, we applied the domain fixes as proposed in the Claude Opus 4.5 release report.

Serve GLM-4.7-Flash Locally

For local deployment, GLM-4.7-Flash supports inference frameworks including vLLM and SGLang. Comprehensive deployment instructions are available in the official Github repository.

vLLM and SGLang only support GLM-4.7-Flash on their main branches.

vLLM

using pip (must use pypi.org as the index url):

pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly
pip install git+https://github.com/huggingface/transformers.git

SGLang

Install the supported versions of SGLang and Transformers (using uv is recommended):

uv pip install sglang==0.3.2.dev9039+pr-17247.g90c446848 --extra-index-url https://sgl-project.github.io/whl/pr/
uv pip install git+https://github.com/huggingface/transformers.git@76732b4e7120808ff989edbd16401f61fa6a0afa

transformers

using with transformers as

pip install git+https://github.com/huggingface/transformers.git

and then run:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_PATH = "zai-org/GLM-4.7-Flash"
messages = [{"role": "user", "content": "hello"}]
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
)
model = AutoModelForCausalLM.from_pretrained(
    pretrained_model_name_or_path=MODEL_PATH,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
inputs = inputs.to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False)
output_text = tokenizer.decode(generated_ids[0][inputs.input_ids.shape[1]:])
print(output_text)

vLLM

vllm serve dtometzki/GLM-4.7-Flash-FP8-Dynamic \
     --speculative-config.method mtp \
     --speculative-config.num_speculative_tokens 1 \
     --tool-call-parser glm47 \
     --reasoning-parser glm45 \
     --enable-auto-tool-choice \
     --served-model-name glm-4.7-flash

Downloads last month: 6

Safetensors

Model size

30B params

Tensor type

F32

BF16

F8_E4M3

Paper for dtometzki/GLM-4.7-Flash-FP8-Dynamic

GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

Paper • 2508.06471 • Published Aug 8, 2025 • 212