Instructions to use osunlp/PreNormLoop-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use osunlp/PreNormLoop-0.6B with Transformers:

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

pipe = pipeline("text-generation", model="osunlp/PreNormLoop-0.6B", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("osunlp/PreNormLoop-0.6B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use osunlp/PreNormLoop-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "osunlp/PreNormLoop-0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "osunlp/PreNormLoop-0.6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/osunlp/PreNormLoop-0.6B

SGLang

How to use osunlp/PreNormLoop-0.6B 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 "osunlp/PreNormLoop-0.6B" \
    --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": "osunlp/PreNormLoop-0.6B",
		"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 "osunlp/PreNormLoop-0.6B" \
        --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": "osunlp/PreNormLoop-0.6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use osunlp/PreNormLoop-0.6B with Docker Model Runner:
```
docker model run hf.co/osunlp/PreNormLoop-0.6B
```

PreNormLoop-0.6B / assets /figure1.png

ykyao

Add paper links, Figure 1 and citation

807cb72 verified 11 days ago

History Blame Contribute Delete

335 kB

Xet Pointer Details

( Raw pointer file )

Xet hash:: aa035571f0b775cdb5e18b8405b348940d565eed58cfc7cf414d1a9d41fa7b71
Size of remote file:: 335 kB
SHA256:: 2bd2c2012dc1ad598b4560a07e3008ec28a8f91b091904be781b490945d5ef7e

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.