# Nyaya-3B-v3 — RAFT: teacher answers regenerated UNDER the inference RAG
# prompt (data/splits_rag_v3, scripts/20+21), including deliberate
# retrieval-miss demonstrations. 2 epochs (v2's val loss was still falling
# at 1) + NEFTune noise for SFT robustness.
#
# Method: full-precision bf16 LoRA — NO quantization (project decision 2026-07-13).

run_name: legal-3b-v3
model_id: Qwen/Qwen2.5-3B-Instruct
output_dir: outputs/legal-3b-v3

data:
  train_file: data/splits_rag_v3/train.jsonl
  val_file: data/splits_rag_v3/val.jsonl

method: lora

lora:
  r: 32
  alpha: 64
  dropout: 0.05
  target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]

training:
  max_seq_length: 6144
  num_train_epochs: 2
  learning_rate: 1.0e-4
  warmup_ratio: 0.03
  lr_scheduler_type: cosine
  gradient_checkpointing: true
  bf16: true
  optim: adamw_torch_fused
  neftune_noise_alpha: 5
  per_device_train_batch_size: 4
  gradient_accumulation_steps: 8
  logging_steps: 10
  # ~6.3K examples x 2 epochs / effective batch 32 = ~394 steps
  eval_steps: 50
  save_steps: 100