classifier = Fine::TextClassifier.new("distilbert-base-uncased")
classifier.fit(train_file: "reviews.jsonl", epochs: 3)
classifier.predict("This product is amazing!")
# => [{ label: "positive", score: 0.97 }]

classifier = Fine::TextClassifier.new("distilbert-base-uncased")
classifier.fit(train_file: "data/reviews.jsonl", epochs: 3)

classifier.predict("Terrible experience, waste of money")
# => [{ label: "negative", score: 0.94 }]

embedder = Fine::TextEmbedder.new("sentence-transformers/all-MiniLM-L6-v2")
embedder.fit(train_file: "data/pairs.jsonl", epochs: 3)

embedding = embedder.encode("How do I cancel my subscription?")

classifier = Fine::ImageClassifier.new("google/siglip2-base-patch16-224")
classifier.fit(train_dir: "data/train", val_dir: "data/val", epochs: 3)

classifier.predict("photo.jpg")
# => [{ label: "cat", score: 0.95 }]

classifier = Fine::ImageClassifier.new("google/siglip2-base-patch16-384") do |config|
  config.epochs = 5
  config.learning_rate = 1e-4
end

classifier.fit(train_dir: "products/train")
classifier.save("models/product_detector")

encoder = model.encoder
embedding = encoder.call(image_tensor)
similarity = cosine_similarity(embedding1, embedding2)

model = Fine::Models::CausalLM.from_pretrained("google/gemma-3-1b-it")

# Apply LoRA - only 0.5% of params trainable
Fine::LoRA.apply(model, rank: 32, alpha: 64)
#   Trainable params: 5.96M (0.46%)

# Train on your data
trainer = Fine::Training::LLMTrainer.new(model, config, train_dataset: dataset)
trainer.fit

# Merge weights and save
Fine::LoRA.merge!(model)
model.save("my_model")

gem 'fine'

Fine::TextClassifier.new("distilbert-base-uncased") do |config|
  config.epochs = 3
  config.batch_size = 16
  config.learning_rate = 2e-5
  config.max_length = 256

  config.on_epoch_end do |epoch, metrics|
    puts "Epoch #{epoch}: #{metrics[:accuracy]}"
  end
end

classifier.export_onnx("model.onnx")
embedder.export_onnx("embedder.onnx")

llm.export_gguf("model.gguf", quantization: :q4_0)