Instructions to use Mar2Ding/songcomposer_sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mar2Ding/songcomposer_sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Mar2Ding/songcomposer_sft", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Mar2Ding/songcomposer_sft", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Mar2Ding/songcomposer_sft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Mar2Ding/songcomposer_sft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Mar2Ding/songcomposer_sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Mar2Ding/songcomposer_sft
- SGLang
How to use Mar2Ding/songcomposer_sft 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 "Mar2Ding/songcomposer_sft" \ --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": "Mar2Ding/songcomposer_sft", "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 "Mar2Ding/songcomposer_sft" \ --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": "Mar2Ding/songcomposer_sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Mar2Ding/songcomposer_sft with Docker Model Runner:
docker model run hf.co/Mar2Ding/songcomposer_sft
metadata
license: apache-2.0
pipeline_tag: text-generation
language:
- en
- zh
[ACL 2025] SongComposer
SongComposer is a language large model (LLM) based on InternLM2 for lyric and melody composition in song generation.
We release SongComposer series in two versions:
- SongComposer_pretrain: The pretrained SongComposer with InternLM2 as the initialization of the LLM, gains basic knowledge on lyric and melody.
- SongComposer_sft: The finetuned SongComposer for instruction-following song generation including lyric to melody, melody to lyric, song continuation, text to song.
Import from Transformers
To load the SongComposer_sft model using Transformers, use the following code:
from transformers import AutoTokenizer, AutoModel
ckpt_path = "Mar2Ding/songcomposer_sft"
tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True)
model = AutoModel.from_pretrained(ckpt_path, trust_remote_code=True).cuda().half()
prompt = 'Create a song on brave and sacrificing with a rapid pace.'
model.inference(prompt, tokenizer)
通过 Transformers 加载
通过以下的代码加载 SongComposer_sft 模型
from transformers import AutoTokenizer, AutoModel
ckpt_path = "Mar2Ding/songcomposer_sft"
tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True)
model = AutoModel.from_pretrained(ckpt_path, trust_remote_code=True).cuda().half()
prompt = 'Create a song on brave and sacrificing with a rapid pace.'
model.inference(prompt, tokenizer)
Open Source License
The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow free commercial usage.