Instructions to use BreadAi/MusePy-1-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BreadAi/MusePy-1-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BreadAi/MusePy-1-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BreadAi/MusePy-1-1") model = AutoModelForCausalLM.from_pretrained("BreadAi/MusePy-1-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use BreadAi/MusePy-1-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BreadAi/MusePy-1-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BreadAi/MusePy-1-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BreadAi/MusePy-1-1
- SGLang
How to use BreadAi/MusePy-1-1 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 "BreadAi/MusePy-1-1" \ --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": "BreadAi/MusePy-1-1", "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 "BreadAi/MusePy-1-1" \ --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": "BreadAi/MusePy-1-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BreadAi/MusePy-1-1 with Docker Model Runner:
docker model run hf.co/BreadAi/MusePy-1-1
Commit ·
bebaacd
1
Parent(s): 0729271
Update README.md
Browse files
README.md
CHANGED
|
@@ -5,4 +5,6 @@ datasets:
|
|
| 5 |
---
|
| 6 |
this is trained off of pythia-160m.
|
| 7 |
|
| 8 |
-
this is over an 100k step model trained on around 6k songs
|
|
|
|
|
|
|
|
|
| 5 |
---
|
| 6 |
this is trained off of pythia-160m.
|
| 7 |
|
| 8 |
+
this is over an 100k step model trained on around 6k songs
|
| 9 |
+
|
| 10 |
+
paste the number/musenet encoders you get from the ai into the big box and then click export midi https://mrcheeze.github.io/musenet-midi/
|