Instructions to use sunsetsobserver/MIDI_transformer_tiny-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sunsetsobserver/MIDI_transformer_tiny-test with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="sunsetsobserver/MIDI_transformer_tiny-test")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sunsetsobserver/MIDI_transformer_tiny-test")
model = AutoModelForCausalLM.from_pretrained("sunsetsobserver/MIDI_transformer_tiny-test", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sunsetsobserver/MIDI_transformer_tiny-test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sunsetsobserver/MIDI_transformer_tiny-test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sunsetsobserver/MIDI_transformer_tiny-test",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sunsetsobserver/MIDI_transformer_tiny-test

SGLang

How to use sunsetsobserver/MIDI_transformer_tiny-test 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 "sunsetsobserver/MIDI_transformer_tiny-test" \
    --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": "sunsetsobserver/MIDI_transformer_tiny-test",
		"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 "sunsetsobserver/MIDI_transformer_tiny-test" \
        --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": "sunsetsobserver/MIDI_transformer_tiny-test",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sunsetsobserver/MIDI_transformer_tiny-test with Docker Model Runner:
```
docker model run hf.co/sunsetsobserver/MIDI_transformer_tiny-test
```

MIDI_transformer_tiny-test

File size: 606 Bytes

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{
  "architectures": [
    "MistralForCausalLM"
  ],
  "attention_dropout": 0.0,
  "bos_token_id": 2,
  "eos_token_id": 3,
  "hidden_act": "silu",
  "hidden_size": 512,
  "initializer_range": 0.02,
  "intermediate_size": 2048,
  "max_position_embeddings": 8192,
  "model_type": "mistral",
  "num_attention_heads": 8,
  "num_hidden_layers": 8,
  "num_key_value_heads": 4,
  "pad_token_id": 0,
  "rms_norm_eps": 1e-06,
  "rope_theta": 10000.0,
  "sliding_window": 256,
  "tie_word_embeddings": false,
  "torch_dtype": "float32",
  "transformers_version": "4.37.2",
  "use_cache": true,
  "vocab_size": 262
}