Instructions to use open-machine/Llama-3.2-1B-FlashNorm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use open-machine/Llama-3.2-1B-FlashNorm with Transformers:

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

pipe = pipeline("text-generation", model="open-machine/Llama-3.2-1B-FlashNorm")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("open-machine/Llama-3.2-1B-FlashNorm")
model = AutoModelForCausalLM.from_pretrained("open-machine/Llama-3.2-1B-FlashNorm")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use open-machine/Llama-3.2-1B-FlashNorm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "open-machine/Llama-3.2-1B-FlashNorm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "open-machine/Llama-3.2-1B-FlashNorm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/open-machine/Llama-3.2-1B-FlashNorm

SGLang

How to use open-machine/Llama-3.2-1B-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/Llama-3.2-1B-FlashNorm" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "open-machine/Llama-3.2-1B-FlashNorm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/Llama-3.2-1B-FlashNorm" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "open-machine/Llama-3.2-1B-FlashNorm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use open-machine/Llama-3.2-1B-FlashNorm with Docker Model Runner:
```
docker model run hf.co/open-machine/Llama-3.2-1B-FlashNorm
```

Add library_name and update model card

by nielsr HF Staff - opened Apr 23

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+11

-8

nielsr

Apr 23

This PR adds library_name: transformers to the YAML metadata to enable the library's automated code snippets on the hub. It also includes a link to the paper FlashNorm: Fast Normalization for Transformers in the model description.

Add library_name and update model cardef5a0e4c

graefics

OpenMachine org Apr 23

Awesome, thanks for doing this!

graefics changed pull request status to merged Apr 23

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment