Instructions to use breadlicker45/musenet-untrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use breadlicker45/musenet-untrained with Transformers:

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

pipe = pipeline("text-generation", model="breadlicker45/musenet-untrained")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("breadlicker45/musenet-untrained")
model = AutoModelForCausalLM.from_pretrained("breadlicker45/musenet-untrained")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use breadlicker45/musenet-untrained with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "breadlicker45/musenet-untrained"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "breadlicker45/musenet-untrained",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/breadlicker45/musenet-untrained

SGLang

How to use breadlicker45/musenet-untrained 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 "breadlicker45/musenet-untrained" \
    --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": "breadlicker45/musenet-untrained",
		"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 "breadlicker45/musenet-untrained" \
        --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": "breadlicker45/musenet-untrained",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use breadlicker45/musenet-untrained with Docker Model Runner:
```
docker model run hf.co/breadlicker45/musenet-untrained
```

Training Question

by Nexyboye - opened Jul 17, 2023

Discussion

Nexyboye

Jul 17, 2023

Hi,

I want to train the model on a collection of midi files. Some hints on how to do it? Sadly I couldn't find anything about it on the web.

breadlicker45

Owner Jul 17, 2023

•

edited Jul 17, 2023

Hi,

I want to train the model on a collection of midi files. Some hints on how to do it? Sadly I couldn't find anything about it on the web.

i wrote code that mass converts it on a website that converts midi to musenet enocders and stores the in database. so like this, midi files -> use website to convert to musenet enocders and store in db -> json -> cleaned json -> csv -> cleaned csv -> upload dataset

breadlicker45

Owner Jul 17, 2023

•

edited Jul 17, 2023

here is the code to convert it on the site.
https://pastebin.com/wz5T5Wa3
and you will need to download this and host it locally https://github.com/MrCheeze/musenet-midi
and then you will need this to make a list of the midi files in your dataset https://pastebin.com/tj5xQ78f

breadlicker45

Owner Jul 17, 2023

•

edited Jul 17, 2023

make a list of your midi files and put it in a txt https://pastebin.com/tj5xQ78f -> then host the site https://github.com/MrCheeze/musenet-midi -> then run this code on the hosted site in the dev consle
https://pastebin.com/wz5T5Wa3 -> download the db in a json file from your browser -> then clean any blank rows from the json file aka the db -> then convert it to csv -> then clean the csv -> upload csv -> train model like normal llm using transfomers or whatever you use to train models with

Nexyboye

Jul 19, 2023

Thank you for the detailed explanation, it is a very good starting point! This is gonna be my project for this week. :)

breadlicker45

Owner Jul 19, 2023

Thank you for the detailed explanation, it is a very good starting point! This is gonna be my project for this week. :)

np, you are going to need a lot of ram for running the code in the dev console
around 32GB is good i think

breadlicker45

Owner Jul 19, 2023

•

edited Jul 19, 2023

i do highly suggest you train this "https://huggingface.co/breadlicker45/neox-musenet-untrained" and not "https://huggingface.co/breadlicker45/musenet-untrained"

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