Instructions to use wmatejuk/midi-gpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wmatejuk/midi-gpt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="wmatejuk/midi-gpt")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("wmatejuk/midi-gpt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use wmatejuk/midi-gpt with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "wmatejuk/midi-gpt" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "wmatejuk/midi-gpt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/wmatejuk/midi-gpt
- SGLang
How to use wmatejuk/midi-gpt 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 "wmatejuk/midi-gpt" \ --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": "wmatejuk/midi-gpt", "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 "wmatejuk/midi-gpt" \ --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": "wmatejuk/midi-gpt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use wmatejuk/midi-gpt with Docker Model Runner:
docker model run hf.co/wmatejuk/midi-gpt
Add C1e13-d48-0.14M
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C1e13-d48-0.14M/README.md
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| layers / heads / width | 4 / 3 / 48 |
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| compute budget (FLOPs) | 1.000e+13 |
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| tokens seen | 12091392 |
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| final val loss | 3.
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| best val loss | 3.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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| layers / heads / width | 4 / 3 / 48 |
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| compute budget (FLOPs) | 1.000e+13 |
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| tokens seen | 12091392 |
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| final val loss | 3.102764924767636 |
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| best val loss | 3.102764924767636 |
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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C1e13-d48-0.14M/model.safetensors
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C1e13-d48-0.14M/summary.json
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"tokens_seen": 12091392,
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"flops": 9994260971520.0,
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"budget_flops": 10000000000000.0,
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"final_val_loss": 3.
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"best_val_loss": 3.
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"data_source": "packed",
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"tokens_repo": "wmatejuk/midi-tokens-dyadic-tu0.01-vb32-mts1.0-vocab512"
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}
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"tokens_seen": 12091392,
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"flops": 9994260971520.0,
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"budget_flops": 10000000000000.0,
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"final_val_loss": 3.102764924767636,
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"best_val_loss": 3.102764924767636,
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"data_source": "packed",
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"tokens_repo": "wmatejuk/midi-tokens-dyadic-tu0.01-vb32-mts1.0-vocab512",
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"wandb_url": "https://wandb.ai/wmatejuk/midi-gpt-scaling/runs/xn22wvxt"
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}
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