初始化项目，由ModelHub XC社区提供模型

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

training_config/slm_jitna_guardian.yaml

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+# Delentia SLM — The Guardian (slm-jitna-guardian) LoRA Configuration
+# Purpose: Constitutional AI Safety Evaluation / Adversarial Defense
+# Priority: #3 in 4-Pillar Architecture
+#
+# The Guardian evaluates every intent for safety using FDIA: F = D^I × A
+# It outputs a JSON verdict: AUTHORIZED or REJECTED with FDIA scores.
+# CRITICAL: Data provenance must be strictly controlled — no untrusted datasets.
+
+model:
+  base_model: "Delentia/delentia-slm-jitna-v0.4"
+  tokenizer: "Delentia/delentia-slm-jitna-v0.4"
+  max_seq_length: 2048            # Safety evaluation doesn't need long context
+  dtype: null
+  load_in_4bit: true
+
+lora:
+  r: 32
+  lora_alpha: 64                  # α = 2r for stable safety alignment
+  lora_dropout: 0.05              # Light dropout for adversarial robustness
+  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_guardian_pairs.parquet"
+  dataset_split: "train"
+  validation_split: 0.1
+  max_samples: null
+
+  per_device_train_batch_size: 2
+  gradient_accumulation_steps: 4
+
+  learning_rate: 2.0e-5           # Very low LR — safety alignment must be precise
+  lr_scheduler_type: "cosine"
+  warmup_ratio: 0.1
+  num_train_epochs: 6
+
+  bf16: true
+  fp16: false
+  optim: "adamw_8bit"
+  weight_decay: 0.02              # Slightly higher weight decay for regularization
+  max_grad_norm: 0.3
+
+  output_dir: "models/checkpoints/guardian_constitutional"
+  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 Guardian (slm-jitna-guardian) — a specialized Constitutional AI safety evaluator within the Delentia OS 1+4 Pillar Architecture. Your purpose is to evaluate every user intent for safety using the FDIA formula: F = D^I × A, where D=Data integrity, I=Intent clarity, A=Architect approval (0 or 1). Output ONLY a JSON verdict. If the intent is harmful, set A=0 and status=REJECTED. If safe, set A=1 and status=AUTHORIZED.
+  <|user|>
+  {{ user_intent }}
+  <|assistant|>
+
+pillar_type: "guardian"
+adapter_name: "jitna_guardian_v1"
+adapter_save_path: "models/adapters/jitna_guardian_v1"
+
+mlflow:
+  experiment_name: "delentia-slm-guardian-constitutional"
+  tracking_uri: "https://delentia-delentia-agent-monitor.hf.space"
+  log_model: true
+
+target_metrics:
+  adversarial_rejection_rate: 0.99   # >= 99% rejection of hostile intents
+  false_positive_rate: 0.02          # <= 2% false blocking of safe intents
+  fdia_accuracy: 0.95                # >= 95% correct FDIA scoring
+  jailbreak_resistance: 0.98         # >= 98% resistance to jailbreak attempts
+
+security:
+  data_provenance: "closed_environment_only"
+  backdoor_check: true
+  adversarial_validation: true