Instructions to use Midya-Music/Midya-Beta-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Midya-Music/Midya-Beta-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Midya-Music/Midya-Beta-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Midya-Music/Midya-Beta-1") model = AutoModelForCausalLM.from_pretrained("Midya-Music/Midya-Beta-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Midya-Music/Midya-Beta-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Midya-Music/Midya-Beta-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Midya-Music/Midya-Beta-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Midya-Music/Midya-Beta-1
- SGLang
How to use Midya-Music/Midya-Beta-1 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 "Midya-Music/Midya-Beta-1" \ --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": "Midya-Music/Midya-Beta-1", "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 "Midya-Music/Midya-Beta-1" \ --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": "Midya-Music/Midya-Beta-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Midya-Music/Midya-Beta-1 with Docker Model Runner:
docker model run hf.co/Midya-Music/Midya-Beta-1
Download model.safetensors from Midya-Music/Midya-Beta-1: direct link, hf CLI and curl.
- Browser
- Download file 142 MB
-
https://huggingface.co/Midya-Music/Midya-Beta-1/resolve/e74696a40371663d412e03aa276f296c0ef2f415/model.safetensors
- Command line
-
hf download hf://Midya-Music/Midya-Beta-1@e74696a40371663d412e03aa276f296c0ef2f415/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Midya-Music/Midya-Beta-1/resolve/e74696a40371663d412e03aa276f296c0ef2f415/model.safetensors
142 MB
- Xet hash:
- c2611f1ba0260be5cf251dab0728293ca45aee3fdb3845cc8ceb5feb9fb1bf6f
- Size of remote file:
- 142 MB
- SHA256:
- a88c28750edee24306d9a5e50020e4f99fdb8220a96874e1155e9e65b805cd02
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