Fix CUDA OOM on T4: cast model to amp_dtype directly, batch_size=2, safe RMSNorm

Fix CUDA OOM on T4: cast model to amp_dtype directly, batch_size=2, safe RMSNorm

Fix CUDA OOM on T4: cast model to amp_dtype directly, batch_size=2, safe RMSNorm

Fix CUDA OOM on T4: cast model to amp_dtype directly, batch_size=2, safe RMSNorm

Sync model/scripts/viu_moe.py with 3-stage sequence length curriculum (512->1024->2048)

Sync model/scripts/train.py with 3-stage sequence length curriculum (512->1024->2048)

Sync model/scripts/train.py with adult erotica stories priority integration

Sync model/scripts/train.py with adult erotica stories priority integration

feat: step-1 priority queue integration for real Indian history & cricket shards

feat: 100% zero-exclusion streaming architecture (2,081 shards) and updated docs

Add 42-layer Viu-1.5B-MoE verification test suite

perf: free logits immediately in forward pass to release 3.1GB VRAM for mt_loss

feat: add CLI overrides and manual batch lock support

fix: auto-download tokenizer.json if LFS pointer detected

fix: mute DDP barrier and find_unused_parameters stderr warnings

feat: add RTX 5090 & 4090 configs (model/scripts/train.py)

feat: smart auto-download Hugging Face checkpoint resume

fix: add --hf_split argument and remove invalid attribute access in streaming logger

fix: pass token to list_repo_files and HfFileSystem to eliminate unauthenticated warnings

feat: non-blocking asynchronous checkpoint upload and local disk pruning

Add --hf_safety argument and default full

Replace load_dataset with direct PyArrow streaming generator: Eliminate CastError permanently

Clean pretraining logging: Streamlined single-line logs (loss, lr, speed, ETA) with zero console clutter

Fix CastError permanently: Direct parquet loader via hf:// paths + clean step 1-20 logging with pbar.write

Fix CastError permanently: exclude code/ prefix + print every step for first 10 steps

Speed optimizations: no_sync DDP communication + cuDNN/TF32 + 10k ckpt sync

Enhance live training logs: print at step 1 and show all 8 MoE expert percentages

Update model/scripts/train.py: Exclude distilled/ from streaming + set log_every to 10

Fix train.py argparse: Support --max_steps alias and --resume flag

feat: wipe-proof HF persistence (full ckpt + live log + newest-2 cleanup)

feat: full-ckpt HF push every 10k steps (resume safety net) + docs

feat: hf_round corpus-slice rotation (Option A full-data: file rotate + row skip + per-round seed)

fix: exclude non-5col schema paths from HF stream (wikipedia/grammar/gsm8k CastError on shuffle)

fix: probe OOM reserve 4GB + expandable_segments + natural-mode stream shuffle (Hindi-only trap)

fix: DDP auto_tune probe-before-wrap + broadcast micro/accum, R0-only logs, empty-shard guard, safety import path

DDP multi-GPU support (torchrun, stride-shard, rank0 ckpt, ckpt without module prefix)

sync local: enforce_mix sampler, tokenizer v2 (mask fix + 35/30/35), mix optional docs, repair scripts

docs: add official comprehensive data audit report (145.93GB/105.1B tokens - 100% PASS)

feat: SDPA FlashAttention, language tags conditioning, probe safety, slim checkpoints, and streaming clean

fix: audit fixes for multi-token alignment, weight decay groups, and MoE monitoring

auto-GPU train + progress display (sync from rental)

step3 CLOSE: lock 0.05 (yaml+json+code)

step3: add F(0.10/50) + G(0.05/100)

step3: fair-50 matrix D+E, lock logic

step3: toy add Config C (0.05, 50 steps)

step3: toy_router_test.py for Kaggle (no local run per policy)

fix: smoke fallback for Kaggle clone without data/raw

init: 28L MoE 241M audit-fixed, smoke pass