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", device_map="auto")

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
```

Lora

by WesPro - opened Aug 18, 2024

Discussion

WesPro

Aug 18, 2024

Hi,

I was just curious if there's a reason why you chose to extract the lora with mergekit against the the base model instead of just using the lora crestfall provides in his repo?

inflatebot

Aug 18, 2024

•

edited Aug 18, 2024

I was curious about this too but I think I figured it out.
Sunfall is trained from the instruct model. By running the extraction using Base, fewer "instruct-isms" should be present, leaving more of the model's characteristics.

...I think. Fizz'll probably correct me.

Fizzarolli

Owner Aug 18, 2024

@WesPro @inflatebot The reason is that Sunfall was trained on the instruct model, and Rosier was trained on the base model. I tried just directly merging the Sunfall lora, but it didn't work very well because it wasn't being applied on a base model.
So, what I did do solve this, was extract a lora from the merged Sunfall model, so that both a bit of the instruct following and a bit of the RP would be in that same lora (effectively)

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