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

Libraries

How to use unsloth/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="unsloth/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("unsloth/GLM-4.7-Flash-FP8-Dynamic")
model = AutoModelForCausalLM.from_pretrained("unsloth/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 Settings

vLLM

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

Use Docker

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

SGLang

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

Unsloth Studio

How to use unsloth/GLM-4.7-Flash-FP8-Dynamic with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for unsloth/GLM-4.7-Flash-FP8-Dynamic to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for unsloth/GLM-4.7-Flash-FP8-Dynamic to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/GLM-4.7-Flash-FP8-Dynamic to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="unsloth/GLM-4.7-Flash-FP8-Dynamic",
    max_seq_length=2048,
)

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

why so slow

by jasonZhang1 - opened Feb 4

Discussion

jasonZhang1

Feb 4

4 A100 40G run glm-4.7-flash and GLM-4.7-Flash-FP8-Dynamic
i used vllm docker container
this is glm-4.7-flash:
(APIServer pid=14926) INFO 02-04 01:23:24 [loggers.py:257] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 45.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.3%, Prefix cache hit rate: 86.3%
(APIServer pid=14926) INFO 02-04 01:23:24 [metrics.py:100] SpecDecoding metrics: Mean acceptance length: 1.49, Accepted throughput: 14.80 tokens/s, Drafted throughput: 30.30 tokens/s, Accepted: 148 tokens, Drafted: 303 tokens, Per-position acceptance rate: 0.488, Avg Draft acceptance rate: 48.8%
(APIServer pid=14926) INFO 02-04 01:23:34 [loggers.py:257] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 46.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.5%, Prefix cache hit rate: 86.3%
(APIServer pid=14926) INFO 02-04 01:23:34 [metrics.py:100] SpecDecoding metrics: Mean acceptance length: 1.50, Accepted throughput: 15.40 tokens/s, Drafted throughput: 30.80 tokens/s, Accepted: 154 tokens, Drafted: 308 tokens, Per-position acceptance rate: 0.500, Avg Draft acceptance rate: 50.0%

this is GLM-4.7-Flash-FP8-Dynamic:
(APIServer pid=6364) INFO 02-04 01:23:35 [loggers.py:257] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 29.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.3%, Prefix cache hit rate: 0.0%
(APIServer pid=6364) INFO 02-04 01:23:35 [metrics.py:100] SpecDecoding metrics: Mean acceptance length: 1.00, Accepted throughput: 0.00 tokens/s, Drafted throughput: 29.10 tokens/s, Accepted: 0 tokens, Drafted: 291 tokens, Per-position acceptance rate: 0.000, Avg Draft acceptance rate: 0.0%
(APIServer pid=6364) INFO 02-04 01:23:45 [loggers.py:257] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 29.8 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.3%, Prefix cache hit rate: 0.0%
(APIServer pid=6364) INFO 02-04 01:23:45 [metrics.py:100] SpecDecoding metrics: Mean acceptance length: 1.00, Accepted throughput: 0.00 tokens/s, Drafted throughput: 29.80 tokens/s, Accepted: 0 tokens, Drafted: 298 tokens, Per-position acceptance rate: 0.000, Avg Draft acceptance rate: 0.0%
(APIServer pid=6364) INFO 02-04 01:23:55 [loggers.py:257] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 30.0 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.4%, Prefix cache hit rate: 0.0%
(APIServer pid=6364) INFO 02-04 01:23:55 [metrics.py:100] SpecDecoding metrics: Mean acceptance length: 1.00, Accepted throughput: 0.00 tokens/s, Drafted throughput: 30.00 tokens/s, Accepted: 0 tokens, Drafted: 300 tokens, Per-position acceptance rate: 0.000, Avg Draft acceptance rate: 0.0%
Why is it slower

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