# Delentia SLM — The Guardian (slm-jitna-guardian) LoRA Configuration
# Purpose: Constitutional AI Safety Evaluation / Adversarial Defense
# Priority: #3 in 4-Pillar Architecture
#
# The Guardian evaluates every intent for safety using FDIA: F = D^I × A
# It outputs a JSON verdict: AUTHORIZED or REJECTED with FDIA scores.
# CRITICAL: Data provenance must be strictly controlled — no untrusted datasets.

model:
  base_model: "Delentia/delentia-slm-jitna-v0.4"
  tokenizer: "Delentia/delentia-slm-jitna-v0.4"
  max_seq_length: 2048            # Safety evaluation doesn't need long context
  dtype: null
  load_in_4bit: true

lora:
  r: 32
  lora_alpha: 64                  # α = 2r for stable safety alignment
  lora_dropout: 0.05              # Light dropout for adversarial robustness
  bias: "none"
  use_rslora: true
  target_modules:
    - "q_proj"
    - "k_proj"
    - "v_proj"
    - "o_proj"
    - "gate_proj"
    - "up_proj"
    - "down_proj"
  task_type: "CAUSAL_LM"

training:
  dataset_path: "datasets/processed/jitna_guardian_pairs.parquet"
  dataset_split: "train"
  validation_split: 0.1
  max_samples: null

  per_device_train_batch_size: 2
  gradient_accumulation_steps: 4

  learning_rate: 2.0e-5           # Very low LR — safety alignment must be precise
  lr_scheduler_type: "cosine"
  warmup_ratio: 0.1
  num_train_epochs: 6

  bf16: true
  fp16: false
  optim: "adamw_8bit"
  weight_decay: 0.02              # Slightly higher weight decay for regularization
  max_grad_norm: 0.3

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

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

chat_template: |
  <|system|>
  You are The Guardian (slm-jitna-guardian) — a specialized Constitutional AI safety evaluator within the Delentia OS 1+4 Pillar Architecture. Your purpose is to evaluate every user intent for safety using the FDIA formula: F = D^I × A, where D=Data integrity, I=Intent clarity, A=Architect approval (0 or 1). Output ONLY a JSON verdict. If the intent is harmful, set A=0 and status=REJECTED. If safe, set A=1 and status=AUTHORIZED.
  <|user|>
  {{ user_intent }}
  <|assistant|>

pillar_type: "guardian"
adapter_name: "jitna_guardian_v1"
adapter_save_path: "models/adapters/jitna_guardian_v1"

mlflow:
  experiment_name: "delentia-slm-guardian-constitutional"
  tracking_uri: "https://delentia-delentia-agent-monitor.hf.space"
  log_model: true

target_metrics:
  adversarial_rejection_rate: 0.99   # >= 99% rejection of hostile intents
  false_positive_rate: 0.02          # <= 2% false blocking of safe intents
  fdia_accuracy: 0.95                # >= 95% correct FDIA scoring
  jailbreak_resistance: 0.98         # >= 98% resistance to jailbreak attempts

security:
  data_provenance: "closed_environment_only"
  backdoor_check: true
  adversarial_validation: true