Text Generation
Transformers
PyTorch
notagen
fill-mask
music-generation
symbolic-music
abc-notation
quantized
Instructions to use manoskary/NotaGenX-Quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use manoskary/NotaGenX-Quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="manoskary/NotaGenX-Quantized")# Load model directly from transformers import AutoModelWithLMHead model = AutoModelWithLMHead.from_pretrained("manoskary/NotaGenX-Quantized", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use manoskary/NotaGenX-Quantized with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "manoskary/NotaGenX-Quantized" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "manoskary/NotaGenX-Quantized", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/manoskary/NotaGenX-Quantized
- SGLang
How to use manoskary/NotaGenX-Quantized 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 "manoskary/NotaGenX-Quantized" \ --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": "manoskary/NotaGenX-Quantized", "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 "manoskary/NotaGenX-Quantized" \ --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": "manoskary/NotaGenX-Quantized", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use manoskary/NotaGenX-Quantized with Docker Model Runner:
docker model run hf.co/manoskary/NotaGenX-Quantized
Upload quantization_config.json with huggingface_hub
Browse files- quantization_config.json +11 -0
quantization_config.json
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{
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"quantization_method": "pytorch_dynamic_int8",
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"bits": 8,
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"target_modules": [
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"patch_embedding",
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"base.transformer.wte",
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"base.transformer.wpe"
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],
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"quantized_at": "2025-08-28",
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"base_model": "sander-wood/notagen"
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}
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