Specialise any model on your data.

using LMKit.Model;
using LMKit.Finetuning;

// 1. Load the base model (it stays frozen during training).
var model = new LM("path/to/base-model.gguf");

// 2. Configure training hyperparameters.
var parameters = new LoraTrainingParameters
{
    LoraRank             = 16,
    LoraAlpha            = 32,
    AdamAlpha            = 1e-4f,
    AdamBeta1            = 0.9f,
    AdamBeta2            = 0.999f,
    AdamDecay            = 0.01f,
    AdamGradientClipping = 1.0f,
    GradientAccumulation = 4,
    CosineDecaySteps     = 2000,
    CosineDecayMin       = 0.1f,
    MaxNoImprovement     = 100,

    // Per-tensor rank control: spend more capacity where it matters.
    RankWQ = 16, RankWK = 16, RankWV = 16, RankWO = 8
};

// 3. Wire up the trainer with progress events.
var trainer = new LoraFinetuning(model, parameters)
{
    Iterations          = 2000,
    BatchSize           = 8,
    ContextSize         = 2048,
    UseGradientCheckpointing = true,
    TrainingCheckpoint  = "checkpoints/run-2026-q1.bin"
};

trainer.FinetuningProgress += (s, e) =>
{
    Console.WriteLine($"Iter {e.Iteration}/{e.MaxIterations}  loss={e.Loss:F4}  lr={e.LearningRate:E2}");
};

// 4. Load training data from a ChatHistory, text file, or both.
int samples = trainer.LoadTrainingDataFromText("corpus/customer-support.jsonl");
Console.WriteLine($"{samples} training samples loaded");

// 5. Train. The output is a single .lora file.
trainer.Finetune2Lora("adapters/customer-support.lora");