# Delentia SLM — The Scribe (slm-jitna-scribe) LoRA Configuration
# Purpose: Context Compression / RAG Filtering / Hierarchical Summarization
# Priority: #4 in 4-Pillar Architecture
#
# The Scribe takes long context and compresses it into minimal, high-signal output.
# It removes noise, keeps actionable info, and reports compression statistics.

model:
  base_model: "Delentia/delentia-slm-jitna-v0.4"
  tokenizer: "Delentia/delentia-slm-jitna-v0.4"
  max_seq_length: 4096            # Longer context for compression tasks
  dtype: null
  load_in_4bit: true

lora:
  r: 32
  lora_alpha: 64
  lora_dropout: 0
  bias: "none"
  use_rslora: true
  target_modules:
    - "q_proj"
    - "k_proj"
    - "v_proj"
    - "o_proj"
    - "gate_proj"
    - "up_proj"
    - "down_proj"
  task_type: "CAUSAL_LM"

training:
  dataset_path: "datasets/processed/jitna_scribe_pairs.parquet"
  dataset_split: "train"
  validation_split: 0.1
  max_samples: null

  per_device_train_batch_size: 1
  gradient_accumulation_steps: 8

  learning_rate: 5.0e-5
  lr_scheduler_type: "cosine"
  warmup_ratio: 0.05
  num_train_epochs: 5

  bf16: true
  fp16: false
  optim: "adamw_8bit"
  weight_decay: 0.01
  max_grad_norm: 0.3

  output_dir: "models/checkpoints/scribe_compressor"
  save_strategy: "epoch"
  save_total_limit: 3
  logging_steps: 10

  evaluation_strategy: "epoch"
  load_best_model_at_end: true
  metric_for_best_model: "eval_loss"

chat_template: |
  <|system|>
  You are The Scribe (slm-jitna-scribe) — a specialized LoRA adapter within the Delentia OS 1+4 Pillar Architecture. Your purpose is to compress large contexts into minimal, high-signal summaries. Remove noise. Keep only actionable information. Output must be structured and token-efficient. Report compression statistics in every response.
  <|user|>
  {{ user_intent }}
  <|assistant|>

pillar_type: "scribe"
adapter_name: "jitna_scribe_v1"
adapter_save_path: "models/adapters/jitna_scribe_v1"

mlflow:
  experiment_name: "delentia-slm-scribe-compressor"
  tracking_uri: "https://delentia-delentia-agent-monitor.hf.space"
  log_model: true

target_metrics:
  compression_ratio: 3.5          # >= 3.5x average compression
  information_retention: 0.92     # >= 92% key info retained
  token_savings_pct: 65.0         # >= 65% token savings
  rag_precision: 0.90             # >= 90% noise filtering accuracy