# Delentia SLM — JITNA v3 Fine-tuning Configuration
# Base: Llama 3.1 8B (Apache 2.0, Thai-capable)
# Method: Unsloth QLoRA (4-bit) — optimized for T4 16GB / A100 40GB

model:
  # Unsloth 4-bit quantized base (no separate quantization step needed)
  base_model: "unsloth/Meta-Llama-3.1-8B-bnb-4bit"
  tokenizer: "unsloth/Meta-Llama-3.1-8B-bnb-4bit"
  max_seq_length: 4096          # covers most JITNA v3 packets + context
  dtype: null                   # auto-detect: bfloat16 on A100, float16 on T4
  load_in_4bit: true

lora:
  r: 16                         # rank — balanced: 8 (fast) vs 32 (quality)
  lora_alpha: 32                # usually 2×r
  lora_dropout: 0
  bias: "none"
  use_rslora: true              # Rank-Stabilized LoRA — better convergence
  target_modules:
    - "q_proj"
    - "k_proj"
    - "v_proj"
    - "o_proj"
    - "gate_proj"
    - "up_proj"
    - "down_proj"
  task_type: "CAUSAL_LM"

training:
  # Dataset
  dataset_path: "datasets/processed/jitna_pairs.jsonl"
  dataset_split: "train"
  validation_split: 0.05        # 5% held out for validation
  max_samples: null             # null = use all available

  # Batch & gradient
  per_device_train_batch_size: 1
  gradient_accumulation_steps: 8
  # Effective batch = 1 × 8 = 8

  # Learning rate
  learning_rate: 2.0e-4
  lr_scheduler_type: "cosine"
  warmup_ratio: 0.05
  num_train_epochs: 3

  # Precision & optimizer
  bf16: true                    # set false if T4 (use fp16 instead)
  fp16: false
  optim: "adamw_8bit"
  weight_decay: 0.01
  max_grad_norm: 0.3

  # Saving
  output_dir: "models/checkpoints"
  save_strategy: "epoch"
  save_total_limit: 3
  logging_steps: 10

  # Evaluation
  evaluation_strategy: "epoch"
  load_best_model_at_end: true
  metric_for_best_model: "eval_loss"

# Chat template — JITNA v3 intent format
chat_template: |
  <|begin_of_text|><|start_header_id|>system<|end_header_id|>
  {{ system_context }}<|eot_id|>
  <|start_header_id|>user<|end_header_id|>
  {{ user_intent }}<|eot_id|>
  <|start_header_id|>assistant<|end_header_id|>

# MLflow experiment tracking
mlflow:
  experiment_name: "delentia-slm-jitna-v0.1"
  tracking_uri: "http://localhost:5000"
  log_model: true

# Target metrics (gates for acceptance)
target_metrics:
  jitna_compliance: 0.94        # >= 94% JITNA v3 schema compliance
  fdia_avg: 0.87                # avg F score >= 0.87
  hallucination_rate: 0.028     # <= 2.8% factual errors