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Model: sugiv/qwen3-8b-tanglish
Source: Original Platform

training/qwen_train_tanglish.yaml

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+# Phase 4: Qwen3-8B LoRA fine-tune config (used by tanglish.llm.qwen_train).
+#
+# Strategy: full LoRA on bf16 base (NOT QLoRA/4-bit — L40S 48 GB has enough
+# VRAM for cleaner training with higher final quality).
+#
+# Never-lose-model design (per user 2026-07-15):
+#   1. All output goes to /workspace/tanglish/checkpoints/qwen_lora_v1/
+#      which lives on the NETWORK VOLUME (survives pod eviction).
+#   2. A TrainerCallback S3-syncs every checkpoint AS IT'S SAVED
+#      (not just at end).
+#   3. Post-training merge → S3 sync of merged model.
+#   4. Auto-terminate has 6 gates (train exited cleanly, no failed marker,
+#      TRAINING COMPLETE marker, best-val LoRA on disk, merged model on disk,
+#      S3 has both).
+#   5. Even mid-training pod eviction loses at most ~1 val-check-interval
+#      of progress.
+
+# -----------------------------------------------------------------------------
+# Model
+# -----------------------------------------------------------------------------
+model:
+  base: "Qwen/Qwen3-8B"                       # Qwen3 series has no "-Instruct" suffix; already instruction-tuned by default
+  torch_dtype: bfloat16
+  attn_implementation: flash_attention_2      # 2-3x faster than sdpa on Qwen3
+  gradient_checkpointing: true                # ~30 % VRAM save, ~15 % slower
+  # Do NOT quantize — cleaner LoRA training on bf16 base; VRAM headroom exists.
+
+# -----------------------------------------------------------------------------
+# LoRA adapter
+# -----------------------------------------------------------------------------
+lora:
+  r: 16
+  alpha: 32              # 2x r is the standard heuristic
+  dropout: 0.05
+  bias: none
+  target_modules:
+    - q_proj
+    - k_proj
+    - v_proj
+    - o_proj
+    - gate_proj
+    - up_proj
+    - down_proj
+
+# -----------------------------------------------------------------------------
+# Data
+# -----------------------------------------------------------------------------
+data:
+  # Downloaded from S3 in setup_qwen_train.sh, decompressed to plain .jsonl
+  train_file: /workspace/tanglish/data/pairs/tanglish_v1.train.jsonl
+  val_file:   /workspace/tanglish/data/pairs/tanglish_v1.val.jsonl
+  max_seq_length: 2048              # dialogues are short; leaves plenty of room
+  packing: false                    # sample-level SFT (safer for chat format)
+
+# -----------------------------------------------------------------------------
+# Trainer — TRL SFTTrainer args (Hugging Face TrainingArguments compatible)
+# -----------------------------------------------------------------------------
+training:
+  output_dir: /workspace/tanglish/checkpoints/qwen_lora_v1/lora
+
+  num_train_epochs: 3
+  per_device_train_batch_size: 4
+  per_device_eval_batch_size: 8
+  gradient_accumulation_steps: 4          # effective batch = 16
+
+  learning_rate: 2.0e-4                   # LoRA convention
+  lr_scheduler_type: cosine
+  warmup_ratio: 0.05                      # 5 % warmup
+  weight_decay: 0.01
+  max_grad_norm: 1.0
+
+  bf16: true
+  optim: adamw_torch
+  dataloader_num_workers: 4
+  seed: 42
+
+  logging_steps: 20                       # wandb + local every 20 steps
+  eval_strategy: steps
+  eval_steps: 500                         # ~10-15 min on L40S
+  save_strategy: steps
+  save_steps: 500                         # save with eval so metric aligns
+  save_total_limit: 3                     # top-3 by eval_loss + last kept
+  metric_for_best_model: eval_loss
+  greater_is_better: false
+  load_best_model_at_end: true            # so merge picks the actual best
+
+  report_to: wandb                        # step-level curves like Phase 2
+
+# -----------------------------------------------------------------------------
+# Early stopping — kills training when val loss plateaus
+# -----------------------------------------------------------------------------
+early_stopping:
+  patience: 4                             # 4 eval checks with no improvement
+  threshold: 0.005                        # must improve by ≥ this each time
+
+# -----------------------------------------------------------------------------
+# S3 sync callback — hits S3 after EVERY save event (paranoid but cheap)
+# -----------------------------------------------------------------------------
+s3_sync:
+  enabled: true
+  s3_prefix: "checkpoints/qwen3-8b-tanglish-v1/lora-ckpts/"
+  # Also sync the merged model after training:
+  merged_s3_prefix: "checkpoints/qwen3-8b-tanglish-v1/merged/"
+
+# -----------------------------------------------------------------------------
+# Wandb
+# -----------------------------------------------------------------------------
+wandb:
+  project: qwen3-8b-tanglish
+  run_name: v1                            # override via CLI flag if you want