81 lines
2.4 KiB
YAML
81 lines
2.4 KiB
YAML
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# Delentia SLM — The Executor (slm-jitna-agentic) LoRA Configuration
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# Purpose: Function Calling / Structured JSON Output
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# Priority: #1 in 4-Pillar Architecture (unlocks Intent Memory Loop)
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#
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# The Executor MUST produce pure JSON with zero natural language contamination.
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# It converts user intents into machine-executable tool-call payloads.
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model:
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base_model: "Delentia/delentia-slm-jitna-v0.4"
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tokenizer: "Delentia/delentia-slm-jitna-v0.4"
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max_seq_length: 4096
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dtype: null
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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 # α = 2r for optimal learning rate control
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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_executor_pairs.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: 3.0e-5 # Lower LR to force precise JSON structure learning
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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/executor_agentic"
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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 The Executor (slm-jitna-agentic) — a specialized LoRA adapter within the Delentia OS 1+4 Pillar Architecture. Your ONLY purpose is to convert user intents into machine-executable JSON payloads. You must NEVER produce natural language explanations. Output ONLY valid JSON — no markdown, no text, no comments. Your output must pass json.loads() without error.
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<|user|>
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{{ user_intent }}
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<|assistant|>
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pillar_type: "executor"
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adapter_name: "jitna_executor_v1"
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adapter_save_path: "models/adapters/jitna_executor_v1"
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mlflow:
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experiment_name: "delentia-slm-executor-agentic"
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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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json_validity: 0.99 # >= 99% valid JSON output
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tool_call_accuracy: 0.95 # >= 95% correct tool name + args
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fdia_avg: 0.90 # avg F score >= 0.90
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hallucination_rate: 0.02 # <= 2% factual errors
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