Update README.md

README.md

@@ -1,3 +1,61 @@
 ---
 license: apache-2.0
+language:
+- en
+- zh
+pipeline_tag: text-classification
 ---
+---
+license: apache-2.0
+pipeline_tag: text-generation
+---
+
+
+
+
+<p align="center">
+    <b><font size="6">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 (VLLM) 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, gain 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:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+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()
+from modeling_internlm2 import inference
+prompt = 'Create a song on brave and sacrificing with a rapid pace.'
+inference(prompt, tokenizer, model)
+```
+
+### 通过 Transformers 加载
+通过以下的代码加载 SongComposer_sft 模型
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+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()
+from modeling_internlm2 import inference
+prompt = 'Create a song on brave and sacrificing with a rapid pace.'
+inference(prompt, tokenizer, model)
+```
+
+### 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. To apply for a commercial license, please fill in the application form (English)/申请表（中文）. For other questions or collaborations, please contact [email protected].