Instructions to use xingjianll/aria-accompaniment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xingjianll/aria-accompaniment with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("loubb/aria-medium-base")
model = PeftModel.from_pretrained(base_model, "xingjianll/aria-accompaniment")

Transformers

How to use xingjianll/aria-accompaniment with Transformers:

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

pipe = pipeline("text-generation", model="xingjianll/aria-accompaniment")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("xingjianll/aria-accompaniment", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xingjianll/aria-accompaniment with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/xingjianll/aria-accompaniment

SGLang

How to use xingjianll/aria-accompaniment 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 "xingjianll/aria-accompaniment" \
    --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": "xingjianll/aria-accompaniment",
		"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 "xingjianll/aria-accompaniment" \
        --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": "xingjianll/aria-accompaniment",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use xingjianll/aria-accompaniment with Docker Model Runner:
```
docker model run hf.co/xingjianll/aria-accompaniment
```

Model

Model Description

LoRA adapter for accompaniment generation, fine-tuned on ~30,000 (treble-clef, full song) pairs, based on Aria-Medium-Base. try the model here: https://huggingface.co/spaces/xingjianll/symbolic-music-gen

Get Started

  tokenizer = AutoTokenizer.from_pretrained(
      "loubb/aria-medium-base",
      trust_remote_code=True,
      add_eos_token=True,
      add_dim_token=False,
  )
  midi_dict = MidiDict.from_midi("input_midi_path")
  tokens = tokenizer.tokenize(
      midi_dict, add_eos_token=True, add_dim_token=False
  )
  token_ids = tokenizer._tokenizer.encode(tokens)
  input_ids = torch.tensor([token_ids], device='cpu')
  model = AutoModelForCausalLM.from_pretrained("loubb/aria-medium-base", trust_remote_code=True)
  model = PeftModel.from_pretrained(model, "xingjianll/aria-accompaniment")
  continuation = model.generate(
        input_ids,
        max_length=1600,
        do_sample=True,
        temperature=1,
        top_p=0.95,
        use_cache=True
    )
  midi_dict_output = tokenizer.decode(continuation[0][input_ids.shape[1]:].tolist())
  midi_dict_output.to_midi().save("out_midi_path")

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