ai - AI/ML Toolchain

ai --version

# Train model
ai train model.joel --data dataset.csv
 
# Run inference
ai infer model.joel --input data.json
 
# Convert model format
ai convert model.pth --format onnx

# Train model
ai train model.joel --data dataset.csv
 
# Train with GPU
ai train model.joel --gpu
 
# Train with specific epochs
ai train model.joel --epochs 100
 
# Resume training
ai train model.joel --resume checkpoint.pth
 
# Train with validation split
ai train model.joel --validation-split 0.2

# Run inference
ai infer model.joel --input data.json
 
# Batch inference
ai infer model.joel --input-dir data/ --output-dir results/
 
# Inference with GPU
ai infer model.joel --gpu --input data.json
 
# Show inference time
ai infer model.joel --input data.json --timing

# List models
ai models list
 
# Show model info
ai models info model.joel
 
# Export model
ai models export model.joel --format onnx
 
# Import model
ai models import model.onnx --name mymodel
 
# Delete model
ai models delete mymodel

# Convert to ONNX
ai convert model.pth --format onnx --output model.onnx
 
# Convert to TensorFlow
ai convert model.pth --format tensorflow --output model.pb
 
# Convert to PyTorch
ai convert model.onnx --format pytorch --output model.pth

# Generate text with LLM
ai llm generate "Write a story" --model gpt-4
 
# Chat with LLM
ai llm chat --model claude-3
 
# Embed text
ai llm embed "Hello world" --model text-embedding-ada-002
 
# Fine-tune LLM
ai llm finetune --base-model gpt-3.5 --data training.jsonl

ai train neural_network.joel --data train.csv --epochs 50

[Compiled]

ai model NeuralNetwork {
  layers: [
    Dense(784, 128),
    ReLU(),
    Dense(128, 10),
    Softmax()
  ]
  
  fn train(data: Dataset) {
    # Training logic
  }
}

ai infer model.joel --input image.jpg --output prediction.json