86 lines
2.9 KiB
YAML
86 lines
2.9 KiB
YAML
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# Delentia SLM — JITNA v0.4 Cognitive OS Kernel 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 v0.3:
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# - Data mixing including: Self-Awareness Scenario Dataset (FDIA equations, JITNA v3, 1+4 Pillars, HexaCore Registry v2.3 with 9 Roles, Layer 8 Router, RCTDB Delta Engine 8D, Intent Loop, RCT-7 Mental OS, CORD Security)
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# - Prevent Catastrophic Forgetting via targeted dataset mixing
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# - Keep LoRA rank 32, alpha 64, with RSLoRA for format and logic convergence stability
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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 large intent chains and context history
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dtype: null # auto-detect
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load_in_4bit: true
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lora:
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r: 32
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lora_alpha: 64
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lora_dropout: 0
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bias: "none"
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use_rslora: true
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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_path: "datasets/processed/jitna_pairs_v04.parquet"
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dataset_split: "train"
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validation_split: 0.05
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max_samples: null
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per_device_train_batch_size: 1
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gradient_accumulation_steps: 8
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learning_rate: 5.0e-5 # Lowered from 1.0e-4 to accommodate mixed domain training smoothly
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lr_scheduler_type: "cosine"
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warmup_ratio: 0.05
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num_train_epochs: 5
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bf16: true
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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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output_dir: "models/checkpoints/v0.4_cognitive_kernel"
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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_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: |
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<|system|>
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You are Delentia OS v0.4 — a cognitive AI operating under HexaCore v2.3 / RCT-7 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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For security-violating prompts, you must output a rejection state (FDIAScore: 0.00).
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<|user|>
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{{ user_intent }}
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<|assistant|>
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mlflow:
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experiment_name: "delentia-slm-jitna-v0.4-cognitive"
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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:
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jitna_compliance: 0.99 # >= 99% JITNA v3 schema compliance
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toon_compliance: 0.97 # >= 97% TOON format compliance
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fdia_avg: 0.90 # avg F score >= 0.90 (realistic target based on actual results)
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hallucination_rate: 0.0015 # <= 0.15% factual errors (SignedAI consensus)
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token_savings_pct: 9.0 # >= 9.0% token savings (realistic target based on actual results)
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