Instructions to use zake7749/text-to-lyric with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zake7749/text-to-lyric with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zake7749/text-to-lyric")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("zake7749/text-to-lyric") model = AutoModelForSeq2SeqLM.from_pretrained("zake7749/text-to-lyric") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use zake7749/text-to-lyric with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zake7749/text-to-lyric" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zake7749/text-to-lyric", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zake7749/text-to-lyric
- SGLang
How to use zake7749/text-to-lyric 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 "zake7749/text-to-lyric" \ --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": "zake7749/text-to-lyric", "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 "zake7749/text-to-lyric" \ --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": "zake7749/text-to-lyric", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zake7749/text-to-lyric with Docker Model Runner:
docker model run hf.co/zake7749/text-to-lyric
YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
This is the base model of zake7749/chinese-lyrics-generation-mass.
Example
from transformers import MT5ForConditionalGeneration, AutoTokenizer, Text2TextGenerationPipeline
model_name = "zake7749/text-to-lyric"
model = MT5ForConditionalGeneration.from_pretrained(model_tag)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# sentence to lyric.
text = "我吹過你吹過的晚風"
lyric = pipe(template, max_length=128, top_p=0.8, do_sample=True, repetition_penalty=1.2)[0]['generated_text']
print(f"Generated lyric: {lyric}")
# Generated lyric: 我吹過你吹過的晚風 你的溫柔讓我心醉神迷 你的笑容讓我心動不已 我們一起走過的時光 就像是陽光照耀著我們的夢 我們一起飛翔在藍天之上 在這片蒼穹下 你是我生命中最美好的風景
# keyword to lyric.
text = "青春 歌曲 歲月 黑夜"
lyric = pipe(template, max_length=128, top_p=0.8, do_sample=True, repetition_penalty=1.2)[0]['generated_text']
print(f"Generated lyric: {lyric}")
# Generated lyric: 唱著我青春的歌曲 歲月匆匆流轉 白天黑夜不停搖晃 想念你的笑容 陪伴著我的歌聲 歌聲在心中綻放 歲月無法阻擋
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