Instructions to use m-a-p/ChatMusician-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use m-a-p/ChatMusician-Base with Transformers:

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

pipe = pipeline("text-generation", model="m-a-p/ChatMusician-Base")

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

tokenizer = AutoTokenizer.from_pretrained("m-a-p/ChatMusician-Base")
model = AutoModelForCausalLM.from_pretrained("m-a-p/ChatMusician-Base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use m-a-p/ChatMusician-Base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/m-a-p/ChatMusician-Base

SGLang

How to use m-a-p/ChatMusician-Base 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 "m-a-p/ChatMusician-Base" \
    --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": "m-a-p/ChatMusician-Base",
		"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 "m-a-p/ChatMusician-Base" \
        --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": "m-a-p/ChatMusician-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use m-a-p/ChatMusician-Base with Docker Model Runner:
```
docker model run hf.co/m-a-p/ChatMusician-Base
```

agent404 commited on Feb 28, 2024

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@@ -8,7 +8,7 @@ pipeline_tag: text-generation
 ---
 # 🎼 ChatMusician: Fostering Intrinsic Musical Abilities Into LLM
-[**🌐 DemoPage**](https://ezmonyi.github.io/ChatMusician/) | [**🤗 Dataset**](https://huggingface.co/datasets/m-a-p/MusicPile) | [**🤗 Benchmark**](https://huggingface.co/datasets/m-a-p/MusicTheoryBench) | [**📖 arXiv**](http://arxiv.org/abs/2402.16153) | [**Code**](https://github.com/hf-lin/ChatMusician)
 ## 🔔News
 - **🔥[2023-12-10]: The release of ChatMusician's demo, code, model, data, and benchmark. 😆**

 ---
 # 🎼 ChatMusician: Fostering Intrinsic Musical Abilities Into LLM
+[**🌐 DemoPage**](https://ezmonyi.github.io/ChatMusician/) | [**🤗 Dataset**](https://huggingface.co/datasets/m-a-p/MusicPile) | [**🤗 Benchmark**](https://huggingface.co/datasets/m-a-p/MusicTheoryBench) | [**📖 arXiv**](http://arxiv.org/abs/2402.16153) | [**Code**](https://github.com/hf-lin/ChatMusician) | [**Model**](https://huggingface.co/m-a-p/ChatMusician)
 ## 🔔News
 - **🔥[2023-12-10]: The release of ChatMusician's demo, code, model, data, and benchmark. 😆**