music generation model
简介
核心亮点
- 采用 Apache-2.0 协议,支持商业化自由部署
- 专注音频生成,适合快速构建 AI 作曲应用
- 轻量化集成,降低开发者构建音乐工具的门槛
- 支持多种音乐风格探索,适配多样化创作场景
使用方法
# 安装 Hugging Face transformers
pip install transformers torch
# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("nagayama0706/music_generation_model")
tokenizer = AutoTokenizer.from_pretrained("nagayama0706/music_generation_model")
Hugging Face 下载
我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。
操作指引:在下载前,请先通过如下命令安装 huggingface_hub:
pip install -U huggingface_hub
命令行下载
下载完整模型库
huggingface-cli download nagayama0706/music_generation_model
下载单个文件到指定本地文件夹(以下载 config.json 到当前路径下 ./dir 目录为例)
huggingface-cli download nagayama0706/music_generation_model config.json --local-dir ./dir
SDK 下载
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('nagayama0706/music_generation_model')
Git 下载
请确保 lfs 已经被正确安装
git lfs install
git clone https://huggingface.co/nagayama0706/music_generation_model
如果您希望跳过 lfs 大文件下载,可以使用如下命令
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/nagayama0706/music_generation_model
模型文件托管在 Hugging Face Hub,使用 HF CLI / SDK / Git 直接下载,不经过本站。
PyTorch / Transformers 使用
安装 Transformers
pip install -U transformers torch
模型加载和推理
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('nagayama0706/music_generation_model')
tokenizer = AutoTokenizer.from_pretrained('nagayama0706/music_generation_model')
完整文档
---
tags:
- merge
- mergekit
- lazymergekit
- TheBloke/openchat_3.5-GPTQ
- asigalov61/Allegro-Music-Transformer
base_model:
- TheBloke/openchat_3.5-GPTQ
- asigalov61/Allegro-Music-Transformer
license: apache-2.0
pipeline_tag: text-to-audio
---
music_generation_model
music_generation_model is a merge of the following models using LazyMergekit:
🧩 Configuration
slices:
- sources:
- model: TheBloke/openchat_3.5-GPTQ
layer_range: [0, 32]
- model: asigalov61/Allegro-Music-Transformer
layer_range: [0, 32]
merge_method: slerp
base_model: TheBloke/openchat_3.5-GPTQ
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "nagayama0706/music_generation_model"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])