Instructions to use Bytes512/Nugget-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bytes512/Nugget-Small with Transformers:

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

pipe = pipeline("text-generation", model="Bytes512/Nugget-Small")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Bytes512/Nugget-Small")
model = AutoModelForCausalLM.from_pretrained("Bytes512/Nugget-Small", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Bytes512/Nugget-Small with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bytes512/Nugget-Small"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bytes512/Nugget-Small",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Bytes512/Nugget-Small

SGLang

How to use Bytes512/Nugget-Small 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 "Bytes512/Nugget-Small" \
    --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": "Bytes512/Nugget-Small",
		"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 "Bytes512/Nugget-Small" \
        --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": "Bytes512/Nugget-Small",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Bytes512/Nugget-Small with Docker Model Runner:
```
docker model run hf.co/Bytes512/Nugget-Small
```

Nugget-Small / model-00007-of-00007.safetensors

Bytes512

Upload folder using huggingface_hub

9bdb3f6 verified over 2 years ago

History Blame Contribute Delete

5.26 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 621273ea91c9b0355d51f0412e72f9d8deaea2a1c14d82e7f5c27f81981fd71c
Size of remote file:: 5.26 GB
SHA256:: a18fe9b248b823c8d3a629630afd6589a67abe09483fda09efba41e1c2f31043

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