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Model: Delentia/delentia-slm-jitna-v0.4
Source: Original Platform

training_config/slm_jitna_v0.2.yaml

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+# Delentia SLM — JITNA v0.2 TOON Fine-tuning Configuration
+# Base: Llama 3.1 8B (Apache 2.0, Thai-capable)
+# Method: Unsloth QLoRA (4-bit) — optimized for T4 16GB / A100 40GB
+# Format: TOON (Token-Oriented Object Notation) — ALGO-42
+#
+# Delta from v0.1:
+#   - Dataset uses TOON-formatted completions (jitna_pairs_toon.jsonl)
+#   - Chat template includes TOON format instruction
+#   - LoRA rank increased to 32 for better TOON structure learning
+#   - Epochs increased to 5 for format stability convergence
+#   - Learning rate lowered to 1e-4 for smoother gradient steps
+
+model:
+  base_model: "unsloth/Meta-Llama-3.1-8B-bnb-4bit"
+  tokenizer: "unsloth/Meta-Llama-3.1-8B-bnb-4bit"
+  max_seq_length: 4096          # covers JITNA v3 packets + TOON context
+  dtype: null                   # auto-detect: bfloat16 on A100, float16 on T4
+  load_in_4bit: true
+
+lora:
+  r: 32                         # increased from 16 — TOON structure needs more rank
+  lora_alpha: 64                # 2×r for stability
+  lora_dropout: 0
+  bias: "none"
+  use_rslora: true              # Rank-Stabilized LoRA — critical for TOON convergence
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  task_type: "CAUSAL_LM"
+
+training:
+  # Dataset — TOON v0.2 format
+  dataset_path: "datasets/processed/jitna_pairs_toon.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 — lower for smoother TOON format convergence
+  learning_rate: 1.0e-4
+  lr_scheduler_type: "cosine"
+  warmup_ratio: 0.05
+  num_train_epochs: 5           # more epochs for TOON structure stability
+
+  # 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/v0.2_toon"
+  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 — TOON v0.2 format with special tokens
+chat_template: |
+  <|system|>
+  You are Delentia OS v0.2 — a constitutional AI operating under RCT v5 governance.
+  You process intents through the JITNA v3 protocol.
+  You respond in TOON format (Token-Oriented Object Notation) for token efficiency.
+  Your responses must be factual, safe, and PDPA-compliant.
+  Always provide FDIA scores when applicable (F = D^I × A).
+  <|user|>
+  {{ user_intent }}
+  <|assistant|>
+
+# MLflow experiment tracking
+mlflow:
+  experiment_name: "delentia-slm-jitna-v0.2-toon"
+  tracking_uri: "https://delentia-delentia-agent-monitor.hf.space"
+  log_model: true
+
+# Target metrics (gates for acceptance) — v0.2 with TOON compliance
+target_metrics:
+  jitna_compliance: 0.98        # >= 98% JITNA v3 schema compliance
+  toon_compliance: 0.95         # >= 95% TOON format compliance
+  fdia_avg: 0.895               # avg F score >= 0.895
+  hallucination_rate: 0.0028    # <= 0.28% factual errors (SignedAI consensus)
+  token_savings_pct: 8.0        # >= 8.0% token savings vs JSON (character-based metric for flat paragraphs)