97 lines
3.3 KiB
YAML
97 lines
3.3 KiB
YAML
# Delentia SLM — JITNA v0.2 TOON Fine-tuning Configuration
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# Base: Llama 3.1 8B (Apache 2.0, Thai-capable)
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# Method: Unsloth QLoRA (4-bit) — optimized for T4 16GB / A100 40GB
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# Format: TOON (Token-Oriented Object Notation) — ALGO-42
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#
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# Delta from v0.1:
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# - Dataset uses TOON-formatted completions (jitna_pairs_toon.jsonl)
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# - Chat template includes TOON format instruction
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# - LoRA rank increased to 32 for better TOON structure learning
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# - Epochs increased to 5 for format stability convergence
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# - Learning rate lowered to 1e-4 for smoother gradient steps
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model:
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base_model: "unsloth/Meta-Llama-3.1-8B-bnb-4bit"
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tokenizer: "unsloth/Meta-Llama-3.1-8B-bnb-4bit"
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max_seq_length: 4096 # covers JITNA v3 packets + TOON context
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dtype: null # auto-detect: bfloat16 on A100, float16 on T4
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load_in_4bit: true
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lora:
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r: 32 # increased from 16 — TOON structure needs more rank
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lora_alpha: 64 # 2×r for stability
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lora_dropout: 0
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bias: "none"
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use_rslora: true # Rank-Stabilized LoRA — critical for TOON convergence
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target_modules:
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- "q_proj"
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- "k_proj"
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- "v_proj"
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- "o_proj"
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- "gate_proj"
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- "up_proj"
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- "down_proj"
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task_type: "CAUSAL_LM"
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training:
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# Dataset — TOON v0.2 format
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dataset_path: "datasets/processed/jitna_pairs_toon.jsonl"
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dataset_split: "train"
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validation_split: 0.05 # 5% held out for validation
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max_samples: null # null = use all available
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# Batch & gradient
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per_device_train_batch_size: 1
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gradient_accumulation_steps: 8
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# Effective batch = 1 × 8 = 8
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# Learning rate — lower for smoother TOON format convergence
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learning_rate: 1.0e-4
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lr_scheduler_type: "cosine"
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warmup_ratio: 0.05
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num_train_epochs: 5 # more epochs for TOON structure stability
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# Precision & optimizer
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bf16: true # set false if T4 (use fp16 instead)
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fp16: false
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optim: "adamw_8bit"
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weight_decay: 0.01
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max_grad_norm: 0.3
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# Saving
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output_dir: "models/checkpoints/v0.2_toon"
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save_strategy: "epoch"
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save_total_limit: 3
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logging_steps: 10
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# Evaluation
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evaluation_strategy: "epoch"
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load_best_model_at_end: true
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metric_for_best_model: "eval_loss"
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# Chat template — TOON v0.2 format with special tokens
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chat_template: |
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<|system|>
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You are Delentia OS v0.2 — a constitutional AI operating under RCT v5 governance.
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You process intents through the JITNA v3 protocol.
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You respond in TOON format (Token-Oriented Object Notation) for token efficiency.
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Your responses must be factual, safe, and PDPA-compliant.
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Always provide FDIA scores when applicable (F = D^I × A).
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<|user|>
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{{ user_intent }}
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<|assistant|>
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# MLflow experiment tracking
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mlflow:
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experiment_name: "delentia-slm-jitna-v0.2-toon"
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tracking_uri: "https://delentia-delentia-agent-monitor.hf.space"
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log_model: true
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# Target metrics (gates for acceptance) — v0.2 with TOON compliance
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target_metrics:
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jitna_compliance: 0.98 # >= 98% JITNA v3 schema compliance
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toon_compliance: 0.95 # >= 95% TOON format compliance
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fdia_avg: 0.895 # avg F score >= 0.895
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hallucination_rate: 0.0028 # <= 0.28% factual errors (SignedAI consensus)
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token_savings_pct: 8.0 # >= 8.0% token savings vs JSON (character-based metric for flat paragraphs)
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