Instructions to use open-machine/Qwen3-1.7B-FlashNorm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use open-machine/Qwen3-1.7B-FlashNorm with Transformers:

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

pipe = pipeline("text-generation", model="open-machine/Qwen3-1.7B-FlashNorm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("open-machine/Qwen3-1.7B-FlashNorm")
model = AutoModelForCausalLM.from_pretrained("open-machine/Qwen3-1.7B-FlashNorm")
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 open-machine/Qwen3-1.7B-FlashNorm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "open-machine/Qwen3-1.7B-FlashNorm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "open-machine/Qwen3-1.7B-FlashNorm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/open-machine/Qwen3-1.7B-FlashNorm

SGLang

How to use open-machine/Qwen3-1.7B-FlashNorm 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 "open-machine/Qwen3-1.7B-FlashNorm" \
    --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": "open-machine/Qwen3-1.7B-FlashNorm",
		"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 "open-machine/Qwen3-1.7B-FlashNorm" \
        --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": "open-machine/Qwen3-1.7B-FlashNorm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use open-machine/Qwen3-1.7B-FlashNorm with Docker Model Runner:
```
docker model run hf.co/open-machine/Qwen3-1.7B-FlashNorm
```

Add library_name and improve model card metadata

by nielsr HF Staff - opened Apr 23

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+11

-8

nielsr

Apr 23

Hi! I'm Niels from the community science team at Hugging Face.

I'm opening this PR to add the library_name: transformers metadata to your model card. This will enable the "Use in Transformers" button on the model page, helping users discover and load your FlashNorm-optimized checkpoint more easily. I've also slightly updated the introduction to better reference the original paper and its authors.

Add library_name and improve model card metadata4d4cb9e8

graefics

OpenMachine org Apr 23

Awesome, thank you! I appreciate you doing this!

graefics changed pull request status to merged Apr 23

graefics

OpenMachine org Apr 23

On second thought: could you update the paper title and authors to the latest, as follows?
'FlashNorm: Fast Normalization for Transformers' by Nils Graef, Filip Makraduli, Andrew Wasielewski, Matthew Clapp

If possible, could you also please update the HF paper page https://huggingface.co/papers/2407.09577 with the new title and latest author list?

Thank you!

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