Instructions to use Mar2Ding/songcomposer_pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mar2Ding/songcomposer_pretrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Mar2Ding/songcomposer_pretrain", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Mar2Ding/songcomposer_pretrain", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Mar2Ding/songcomposer_pretrain with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Mar2Ding/songcomposer_pretrain" # 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_pretrain", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Mar2Ding/songcomposer_pretrain
- SGLang
How to use Mar2Ding/songcomposer_pretrain 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_pretrain" \ --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_pretrain", "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_pretrain" \ --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_pretrain", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Mar2Ding/songcomposer_pretrain with Docker Model Runner:
docker model run hf.co/Mar2Ding/songcomposer_pretrain
| license: apache-2.0 | |
| pipeline_tag: text-generation | |
| <p align="center"> | |
| <b><font size="6">[ACL 2025] SongComposer</font></b> | |
| <p> | |
| <div align="center"> | |
| [💻Github Repo](https://github.com/pjlab-songcomposer/songcomposer) | |
| [📖Paper](https://arxiv.org/abs/2402.17645) | |
| </div> | |
| **SongComposer** is a language large model (LLM) based on [InternLM2](https://github.com/InternLM/InternLM) 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 of 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_pretrain model using Transformers, use the following code: | |
| ```python | |
| from transformers import AutoTokenizer, AutoModel | |
| ckpt_path = "Mar2Ding/songcomposer_pretrain" | |
| tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True) | |
| model = AutoModel.from_pretrained(ckpt_path, trust_remote_code=True).cuda().half() | |
| prompt = '<bop> Total 7 lines. The first line:可,<D4>,<137>,<79>|惜,<D#4>,<137>,<79>|这,<F4>,<137>,<88>|是,<F4>,<121>,<79>|属,<F4>,<121>,<79>|于,<D#4>,<214>,<88>|你,<D#4>,<141>,<79>|的,<D4>,<130>,<79>|风,<C4>,<151>,<79>|景,<A#3> <F3>,<181><137>,<79>\n' | |
| model.inference_pretrain(prompt, tokenizer) | |
| ``` | |
| ### 通过 Transformers 加载 | |
| 通过以下的代码加载 SongComposer_pretrain 模型 | |
| ```python | |
| from transformers import AutoTokenizer, AutoModel | |
| ckpt_path = "Mar2Ding/songcomposer_pretrain" | |
| tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True) | |
| model = AutoModel.from_pretrained(ckpt_path, trust_remote_code=True).cuda().half() | |
| prompt = '<bop> Total 7 lines. The first line:可,<D4>,<137>,<79>|惜,<D#4>,<137>,<79>|这,<F4>,<137>,<88>|是,<F4>,<121>,<79>|属,<F4>,<121>,<79>|于,<D#4>,<214>,<88>|你,<D#4>,<141>,<79>|的,<D4>,<130>,<79>|风,<C4>,<151>,<79>|景,<A#3> <F3>,<181><137>,<79>\n' | |
| model.inference_pretrain(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. |