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

training_config/slm_jitna_v0.1.yaml

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+# 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