Text Generation
Transformers
Safetensors
English
gpt2
lyrics
suno
music
scansion
text-generation-inference
Instructions to use wren11ws/sunup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wren11ws/sunup with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="wren11ws/sunup")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("wren11ws/sunup") model = AutoModelForCausalLM.from_pretrained("wren11ws/sunup", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use wren11ws/sunup with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "wren11ws/sunup" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "wren11ws/sunup", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/wren11ws/sunup
- SGLang
How to use wren11ws/sunup 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 "wren11ws/sunup" \ --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": "wren11ws/sunup", "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 "wren11ws/sunup" \ --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": "wren11ws/sunup", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use wren11ws/sunup with Docker Model Runner:
docker model run hf.co/wren11ws/sunup
Update Scansion-LM & Reference Model from GitHub Actions
Browse files- .gitattributes +1 -0
- README.md +3 -3
- hub_check.json +2 -2
- model.safetensors +1 -1
- suno_song_inference_model.json +0 -0
.gitattributes
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README.md
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- n_positions: `1024`
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- n_layer / n_embd / n_head: `6 / 768 / 12`
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- parameters: `81914880`
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- steps: `
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- train loss (avg): `2.
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Load with vanilla Transformers — no custom `auto_map`:
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- n_positions: `1024`
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- n_layer / n_embd / n_head: `6 / 768 / 12`
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- parameters: `81914880`
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- steps: `350/350`
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- train loss (avg): `2.5573558807373047`
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- examples: `2000`
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Load with vanilla Transformers — no custom `auto_map`:
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hub_check.json
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"vocab": 50260,
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"params": 81914880,
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"english_lines": [
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"I am a
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"vocab": 50260,
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"params": 81914880,
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"I am a woman who<|end|> sweat like the sun and not even give away your name"
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"preview": "I am a woman who<|end|> sweat like the sun and not even give away your name",
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"auto_map": false
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}
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suno_song_inference_model.json
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