Instructions to use Fizzarolli/MN-12b-Sunrose with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fizzarolli/MN-12b-Sunrose with Transformers:

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

pipe = pipeline("text-generation", model="Fizzarolli/MN-12b-Sunrose")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Fizzarolli/MN-12b-Sunrose")
model = AutoModelForCausalLM.from_pretrained("Fizzarolli/MN-12b-Sunrose")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Fizzarolli/MN-12b-Sunrose with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fizzarolli/MN-12b-Sunrose"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fizzarolli/MN-12b-Sunrose",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Fizzarolli/MN-12b-Sunrose

SGLang

How to use Fizzarolli/MN-12b-Sunrose 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 "Fizzarolli/MN-12b-Sunrose" \
    --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": "Fizzarolli/MN-12b-Sunrose",
		"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 "Fizzarolli/MN-12b-Sunrose" \
        --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": "Fizzarolli/MN-12b-Sunrose",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Fizzarolli/MN-12b-Sunrose with Docker Model Runner:
```
docker model run hf.co/Fizzarolli/MN-12b-Sunrose
```

MN-12b-Sunrose / model-00005-of-00005.safetensors

Fizzarolli

Upload folder using huggingface_hub

e1f0369 verified almost 2 years ago

download

history blame contribute delete

4.91 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:: 1aecf472f9c78cd52db7c65d17732d717fb8075daaa6104b45aab23b4d69af38
Size of remote file:: 4.91 GB
SHA256:: cc5dcc879d3d7866d89d356b72cd5b0d8e8c4d926b8e79d886ae43fbb0c6ba4a

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