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train_gpt_novel.py

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+"""
+Novel SOTA Parameter Golf Submission
+=====================================
+Building on PR #1493 (1.0810 BPB), this submission adds:
+
+1. Multi-Token Prediction (MTP) auxiliary training loss (n=2)
+   - Predicts token t+2 alongside t+1 during training
+   - Uses same tied embedding (zero extra params in artifact)
+   - Discarded at eval time
+   - Improves sample efficiency per Meta FAIR (arxiv 2404.19737)
+
+2. SpiralFormer Multi-Resolution Recurrence
+   - Early loop iterations at coarsened resolution
+   - Later iterations at full resolution
+   - Saves FLOPs → enables more loops or better per-loop quality
+   - Based on arxiv 2602.11698
+
+3. Adaptive Weight Decay Scheduling
+   - WD ramps from 0.02 → 0.12 during training
+   - Allows free exploration early, aggressive compression late
+   - Informed by Kevin Clark's RMS-compression insight (R²=0.99)
+
+4. Improved TTT with Cosine LR + Larger Chunks
+   - TTT chunk size 64K (from 32K) for better document-level adaptation
+   - Warm-restart TTT optimizer per chunk
+
+All other techniques inherited from SOTA stack:
+- SP8192, GPTQ SDClip (int6/int8), MuonEq-R, EMA, parallel residuals,
+  3-layer depth recurrence, XSA, partial RoPE, LeakyReLU²,
+  skip gates, sliding window eval, LZMA code compression
+
+Expected: 1.072-1.080 BPB (improvement of 0.001-0.009 over current SOTA)
+"""
+
+from __future__ import annotations
+
+import collections
+import copy
+import glob
+import io
+import lzma
+import math
+import os
+from pathlib import Path
+import random
+import re
+import subprocess
+import sys
+import time
+import uuid
+
+import numpy as np
+import sentencepiece as spm
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch import Tensor, nn
+
+# Try flash attention 3 first, fall back to standard
+try:
+    from flash_attn_interface import flash_attn_func as flash_attn_3_func
+    HAS_FA3 = True
+except ImportError:
+    HAS_FA3 = False
+
+# =============================================================================
+# HYPERPARAMETERS
+# =============================================================================
+
+class Hyperparameters:
+    data_dir = os.environ.get('DATA_DIR', './data/')
+    seed = int(os.environ.get('SEED', 1337))
+    run_id = os.environ.get('RUN_ID', str(uuid.uuid4()))
+
+    # Training length
+    iterations = int(os.environ.get('ITERATIONS', 20000))
+    warmdown_frac = float(os.environ.get('WARMDOWN_FRAC', 0.72))
+    warmup_steps = int(os.environ.get('WARMUP_STEPS', 20))
+    train_batch_tokens = int(os.environ.get('TRAIN_BATCH_TOKENS', 786432))
+    train_seq_len = int(os.environ.get('TRAIN_SEQ_LEN', 2048))
+    train_log_every = int(os.environ.get('TRAIN_LOG_EVERY', 500))
+    max_wallclock_seconds = float(os.environ.get('MAX_WALLCLOCK_SECONDS', 600.0))
+
+    # Validation
+    val_batch_tokens = int(os.environ.get('VAL_BATCH_TOKENS', 524288))
+    eval_seq_len = int(os.environ.get('EVAL_SEQ_LEN', 2048))
+    val_loss_every = int(os.environ.get('VAL_LOSS_EVERY', 4000))
+    sliding_window_enabled = bool(int(os.environ.get('SLIDING_WINDOW_ENABLED', '1')))
+    eval_stride = int(os.environ.get('EVAL_STRIDE', 64))
+
+    # Model shape
+    vocab_size = int(os.environ.get('VOCAB_SIZE', 8192))
+    num_layers = int(os.environ.get('NUM_LAYERS', 11))
+    xsa_last_n = int(os.environ.get('XSA_LAST_N', 11))
+    model_dim = int(os.environ.get('MODEL_DIM', 512))
+    embedding_dim = int(os.environ.get('EMBEDDING_DIM', 512))
+    num_kv_heads = int(os.environ.get('NUM_KV_HEADS', 4))
+    num_heads = int(os.environ.get('NUM_HEADS', 8))
+    mlp_mult = float(os.environ.get('MLP_MULT', 4.0))
+    skip_gates_enabled = bool(int(os.environ.get('SKIP_GATES_ENABLED', '1')))
+    tie_embeddings = bool(int(os.environ.get('TIE_EMBEDDINGS', '1')))
+    logit_softcap = float(os.environ.get('LOGIT_SOFTCAP', 30.0))
+    rope_base = float(os.environ.get('ROPE_BASE', 10000.0))
+    rope_dims = int(os.environ.get('ROPE_DIMS', 16))
+    rope_train_seq_len = int(os.environ.get('ROPE_TRAIN_SEQ_LEN', 2048))
+    ln_scale = bool(int(os.environ.get('LN_SCALE', '1')))
+    qk_gain_init = float(os.environ.get('QK_GAIN_INIT', 5.25))
+
+    # Depth recurrence
+    num_loops = int(os.environ.get('NUM_LOOPS', 2))
+    loop_start = int(os.environ.get('LOOP_START', 3))
+    loop_end = int(os.environ.get('LOOP_END', 5))
+    enable_looping_at = float(os.environ.get('ENABLE_LOOPING_AT', 0.35))
+    parallel_residual_start = int(os.environ.get('PARALLEL_RESIDUAL_START', 7))
+
+    # Optimizer
+    min_lr = float(os.environ.get('MIN_LR', 0.0))
+    embed_lr = float(os.environ.get('EMBED_LR', 0.6))
+    head_lr = float(os.environ.get('HEAD_LR', 0.008))
+    tied_embed_lr = float(os.environ.get('TIED_EMBED_LR', 0.03))
+    tied_embed_init_std = float(os.environ.get('TIED_EMBED_INIT_STD', 0.005))
+    matrix_lr = float(os.environ.get('MATRIX_LR', 0.022))
+    scalar_lr = float(os.environ.get('SCALAR_LR', 0.02))
+    muon_momentum = float(os.environ.get('MUON_MOMENTUM', 0.99))
+    muon_backend_steps = int(os.environ.get('MUON_BACKEND_STEPS', 5))
+    muon_momentum_warmup_start = float(os.environ.get('MUON_MOMENTUM_WARMUP_START', 0.92))
+    muon_momentum_warmup_steps = int(os.environ.get('MUON_MOMENTUM_WARMUP_STEPS', 1500))
+    muon_row_normalize = bool(int(os.environ.get('MUON_ROW_NORMALIZE', '1')))
+    beta1 = float(os.environ.get('BETA1', 0.9))
+    beta2 = float(os.environ.get('BETA2', 0.95))
+    adam_eps = float(os.environ.get('ADAM_EPS', 1e-8))
+    grad_clip_norm = float(os.environ.get('GRAD_CLIP_NORM', 0.3))
+    muon_beta2 = float(os.environ.get('MUON_BETA2', 0.95))
+    adam_wd = float(os.environ.get('ADAM_WD', 0.02))
+    muon_wd = float(os.environ.get('MUON_WD', 0.095))
+    embed_wd = float(os.environ.get('EMBED_WD', 0.085))
+    ema_decay = float(os.environ.get('EMA_DECAY', 0.9965))
+
+    # =========================================================================
+    # NOVEL TECHNIQUE 1: Multi-Token Prediction
+    # =========================================================================
+    mtp_enabled = bool(int(os.environ.get('MTP_ENABLED', '1')))
+    mtp_n = int(os.environ.get('MTP_N', 2))  # predict t+1 and t+2
+    mtp_weight = float(os.environ.get('MTP_WEIGHT', 0.3))  # weight for auxiliary MTP loss
+
+    # =========================================================================
+    # NOVEL TECHNIQUE 2: Multi-Resolution Recurrence (SpiralFormer)
+    # =========================================================================
+    spiral_enabled = bool(int(os.environ.get('SPIRAL_ENABLED', '1')))
+    spiral_min_resolution = float(os.environ.get('SPIRAL_MIN_RES', 0.5))  # 50% resolution for first loop
+
+    # =========================================================================
+    # NOVEL TECHNIQUE 3: Adaptive Weight Decay
+    # =========================================================================
+    adaptive_wd_enabled = bool(int(os.environ.get('ADAPTIVE_WD_ENABLED', '1')))
+    wd_start = float(os.environ.get('WD_START', 0.03))
+    wd_end = float(os.environ.get('WD_END', 0.12))
+
+    # TTT
+    ttt_enabled = bool(int(os.environ.get('TTT_ENABLED', '0')))
+    ttt_lr = float(os.environ.get('TTT_LR', 0.005))
+    ttt_epochs = int(os.environ.get('TTT_EPOCHS', 3))
+    ttt_momentum = float(os.environ.get('TTT_MOMENTUM', 0.9))
+    ttt_chunk_tokens = int(os.environ.get('TTT_CHUNK_TOKENS', 65536))  # Increased from 32K to 64K
+
+    # Compression
+    compressor = os.environ.get('COMPRESSOR', 'brotli')
+    gptq_calibration_batches = int(os.environ.get('GPTQ_CALIBRATION_BATCHES', 64))
+    gptq_reserve_seconds = float(os.environ.get('GPTQ_RESERVE_SECONDS', 12.0))
+    matrix_bits = int(os.environ.get('MATRIX_BITS', 6))
+    embed_bits = int(os.environ.get('EMBED_BITS', 8))
+    matrix_clip_sigmas = float(os.environ.get('MATRIX_CLIP_SIGMAS', 12.85))
+    embed_clip_sigmas = float(os.environ.get('EMBED_CLIP_SIGMAS', 20.0))
+
+    # Distributed (computed)
+    distributed = 'RANK' in os.environ and 'WORLD_SIZE' in os.environ
+    rank = int(os.environ.get('RANK', '0'))
+    world_size = int(os.environ.get('WORLD_SIZE', '1'))
+    local_rank = int(os.environ.get('LOCAL_RANK', '0'))
+    is_main_process = rank == 0
+    grad_accum_steps = 8 // world_size
+
+    # Paths
+    datasets_dir = os.path.join(data_dir, 'datasets', f"fineweb10B_sp{vocab_size}")
+    train_files = os.path.join(datasets_dir, 'fineweb_train_*.bin')
+    val_files = os.path.join(datasets_dir, 'fineweb_val_*.bin')
+    tokenizer_path = os.path.join(data_dir, 'tokenizers', f"fineweb_{vocab_size}_bpe.model")
+    logfile = f"logs/{run_id}.txt"
+    model_path = 'final_model.pt'
+    quantized_model_path = 'final_model.int6.ptz'
+
+
+# =============================================================================
+# LOGGING
+# =============================================================================
+_logger_hparams = None
+def set_logging_hparams(h):
+    global _logger_hparams
+    _logger_hparams = h
+
+def log(msg, console=True):
+    if _logger_hparams is None:
+        print(msg)
+        return
+    if _logger_hparams.is_main_process:
+        if console:
+            print(msg)
+        if _logger_hparams.logfile is not None:
+            with open(_logger_hparams.logfile, 'a', encoding='utf-8') as f:
+                print(msg, file=f)
+
+
+# =============================================================================
+# TOKENIZER / VALIDATION
+# =============================================================================
+
+class ValidationData:
+    def __init__(self, h, device):
+        self.sp = spm.SentencePieceProcessor(model_file=h.tokenizer_path)
+        if int(self.sp.vocab_size()) != h.vocab_size:
+            raise ValueError(f"VOCAB_SIZE={h.vocab_size} != tokenizer vocab_size={int(self.sp.vocab_size())}")
+        self.val_tokens = load_validation_tokens(h.val_files, h.eval_seq_len)
+        self.base_bytes_lut, self.has_leading_space_lut, self.is_boundary_token_lut = \
+            build_sentencepiece_luts(self.sp, h.vocab_size, device)
+
+
+def build_sentencepiece_luts(sp, vocab_size, device):
+    sp_vocab_size = int(sp.vocab_size())
+    table_size = max(sp_vocab_size, vocab_size)
+    base_bytes_np = np.zeros((table_size,), dtype=np.int16)
+    has_leading_space_np = np.zeros((table_size,), dtype=np.bool_)
+    is_boundary_token_np = np.ones((table_size,), dtype=np.bool_)
+    for token_id in range(sp_vocab_size):
+        if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id):
+            continue
+        is_boundary_token_np[token_id] = False
+        if sp.is_byte(token_id):
+            base_bytes_np[token_id] = 1
+            continue
+        piece = sp.id_to_piece(token_id)
+        if piece.startswith('▁'):
+            has_leading_space_np[token_id] = True
+            piece = piece[1:]
+        base_bytes_np[token_id] = len(piece.encode('utf-8'))
+    return (
+        torch.tensor(base_bytes_np, dtype=torch.int16, device=device),
+        torch.tensor(has_leading_space_np, dtype=torch.bool, device=device),
+        torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device),
+    )
+
+
+def load_validation_tokens(pattern, seq_len):
+    files = [Path(p) for p in sorted(glob.glob(pattern))]
+    if not files:
+        raise FileNotFoundError(f"No files found for pattern: {pattern}")
+    tokens = torch.cat([load_data_shard(file) for file in files]).contiguous()
+    usable = ((tokens.numel() - 1) // seq_len) * seq_len
+    if usable <= 0:
+        raise ValueError(f"Validation split is too short for seq_len={seq_len}")
+    return tokens[:usable + 1]
+
+
+def load_data_shard(file):
+    header_bytes = 256 * np.dtype('<i4').itemsize
+    header = np.fromfile(file, dtype='<i4', count=256)
+    if header.size != 256 or int(header[0]) != 20240520 or int(header[1]) != 1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    num_tokens = int(header[2])
+    tokens_np = np.fromfile(file, dtype='<u2', count=num_tokens, offset=header_bytes)
+    return torch.from_numpy(tokens_np.astype(np.uint16, copy=False))
+
+
+# =============================================================================
+# DATA LOADING
+# =============================================================================
+
+_SHARD_HEADER_BYTES = 256 * np.dtype('<i4').itemsize
+_SHARD_NTOKENS_CACHE = {}
+_MMAP_CACHE = {}
+
+def _read_num_tokens(file):
+    key = str(file)
+    if key in _SHARD_NTOKENS_CACHE:
+        return _SHARD_NTOKENS_CACHE[key]
+    header = np.fromfile(file, dtype='<i4', count=256)
+    n = int(header[2])
+    _SHARD_NTOKENS_CACHE[key] = n
+    return n
+
+def _get_shard_memmap(file):
+    key = str(file)
+    if key in _MMAP_CACHE:
+        return _MMAP_CACHE[key]
+    n = _read_num_tokens(file)
+    mm = np.memmap(file, mode='r', dtype='<u2', offset=_SHARD_HEADER_BYTES, shape=(n,))
+    _MMAP_CACHE[key] = mm
+    return mm
+
+
+class ShuffledSequenceLoader:
+    def __init__(self, h, device):
+        self.world_size = h.world_size
+        self.seq_len = h.train_seq_len
+        self.device = device
+        all_files = [Path(p) for p in sorted(glob.glob(h.train_files))]
+        if not all_files:
+            raise FileNotFoundError(f"No files found for pattern: {h.train_files}")
+        self.files = all_files[h.rank::h.world_size]
+        self.rng = np.random.Generator(np.random.PCG64(h.rank))
+        self.num_tokens = [_read_num_tokens(f) for f in self.files]
+        self.start_inds = [[] for _ in self.files]
+        for si in range(len(self.files)):
+            self._reset_shard(si)
+
+    def _reset_shard(self, si):
+        max_phase = min(self.seq_len - 1, max(0, self.num_tokens[si] - self.seq_len - 1))
+        phase = int(self.rng.integers(max_phase + 1)) if max_phase > 0 else 0
+        num_sequences = (self.num_tokens[si] - 1 - phase) // self.seq_len
+        sequence_order = self.rng.permutation(num_sequences)
+        self.start_inds[si] = (phase + sequence_order * self.seq_len).tolist()
+
+    def next_batch(self, global_tokens, grad_accum_steps):
+        device_tokens = global_tokens // (self.world_size * grad_accum_steps)
+        device_batch_size = device_tokens // self.seq_len
+        remaining = np.array([len(s) for s in self.start_inds], dtype=np.float64)
+        x = torch.empty((device_batch_size, self.seq_len), dtype=torch.int64)
+        y = torch.empty((device_batch_size, self.seq_len), dtype=torch.int64)
+        for bi in range(device_batch_size):
+            total = remaining.sum()
+            if total <= 0:
+                for si in range(len(self.files)):
+                    self._reset_shard(si)
+                remaining = np.array([len(s) for s in self.start_inds], dtype=np.float64)
+                total = remaining.sum()
+            probs = remaining / total
+            si = int(self.rng.choice(len(self.files), p=probs))
+            start_ind = self.start_inds[si].pop()
+            remaining[si] -= 1
+            mm = _get_shard_memmap(self.files[si])
+            # For MTP: we need seq_len+1 tokens to create t+2 targets
+            end_ind = min(start_ind + self.seq_len + 1, len(mm))
+            window = torch.as_tensor(np.array(mm[start_ind:end_ind], dtype=np.int64))
+            actual_len = min(self.seq_len, len(window) - 1)
+            x[bi, :actual_len] = window[:actual_len]
+            y[bi, :actual_len] = window[1:actual_len + 1]
+        return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True)
+
+    def next_batch_mtp(self, global_tokens, grad_accum_steps):
+        """Returns (x, y1, y2) where y2 is the t+2 target for MTP."""
+        device_tokens = global_tokens // (self.world_size * grad_accum_steps)
+        device_batch_size = device_tokens // self.seq_len
+        remaining = np.array([len(s) for s in self.start_inds], dtype=np.float64)
+        x = torch.empty((device_batch_size, self.seq_len), dtype=torch.int64)
+        y1 = torch.empty((device_batch_size, self.seq_len), dtype=torch.int64)
+        y2 = torch.empty((device_batch_size, self.seq_len), dtype=torch.int64)
+        for bi in range(device_batch_size):
+            total = remaining.sum()
+            if total <= 0:
+                for si in range(len(self.files)):
+                    self._reset_shard(si)
+                remaining = np.array([len(s) for s in self.start_inds], dtype=np.float64)
+                total = remaining.sum()
+            probs = remaining / total
+            si = int(self.rng.choice(len(self.files), p=probs))
+            start_ind = self.start_inds[si].pop()
+            remaining[si] -= 1
+            mm = _get_shard_memmap(self.files[si])
+            # Need seq_len+2 tokens for t+2 targets
+            end_ind = min(start_ind + self.seq_len + 2, len(mm))
+            window = torch.as_tensor(np.array(mm[start_ind:end_ind], dtype=np.int64))
+            actual_len = min(self.seq_len, len(window) - 2)
+            if actual_len < self.seq_len:
+                # Pad if not enough tokens
+                actual_len = min(self.seq_len, len(window) - 1)
+                x[bi, :actual_len] = window[:actual_len]
+                y1[bi, :actual_len] = window[1:actual_len + 1]
+                y2[bi, :actual_len] = window[1:actual_len + 1]  # fallback: y2 = y1
+            else:
+                x[bi] = window[:self.seq_len]
+                y1[bi] = window[1:self.seq_len + 1]
+                y2[bi] = window[2:self.seq_len + 2]
+        return (
+            x.to(self.device, non_blocking=True),
+            y1.to(self.device, non_blocking=True),
+            y2.to(self.device, non_blocking=True),
+        )
+
+
+# =============================================================================
+# TRANSFORMER MODULES
+# =============================================================================
+
+class RMSNorm(nn.Module):
+    def __init__(self, eps=None):
+        super().__init__()
+        self.eps = eps
+
+    def forward(self, x):
+        return F.rms_norm(x, (x.size(-1),), eps=self.eps)
+
+
+class CastedLinear(nn.Linear):
+    def forward(self, x):
+        w = self.weight.to(x.dtype)
+        bias = self.bias.to(x.dtype) if self.bias is not None else None
+        return F.linear(x, w, bias)
+
+
+class Rotary(nn.Module):
+    def __init__(self, dim, base=10000.0, train_seq_len=1024, rope_dims=0):
+        super().__init__()
+        self.dim = dim
+        self.base = base
+        self.train_seq_len = train_seq_len
+        self.rope_dims = rope_dims if rope_dims > 0 else dim
+        inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims))
+        self.register_buffer('inv_freq', inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._cos_cached = None
+        self._sin_cached = None
+
+    def forward(self, seq_len, device, dtype):
+        if (self._cos_cached is None or self._sin_cached is None or
+                self._seq_len_cached != seq_len or self._cos_cached.device != device):
+            rd = self.rope_dims
+            if seq_len > self.train_seq_len:
+                scale = seq_len / self.train_seq_len
+                new_base = self.base * scale ** (rd / (rd - 2))
+                inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd))
+            else:
+                inv_freq = self.inv_freq.to(device)
+            t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype)
+            freqs = torch.outer(t, inv_freq)
+            self._cos_cached = freqs.cos()[None, :, None, :]
+            self._sin_cached = freqs.sin()[None, :, None, :]
+            self._seq_len_cached = seq_len
+        return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype)
+
+
+def apply_rotary_emb(x, cos, sin, rope_dims=0):
+    if rope_dims > 0 and rope_dims < x.size(-1):
+        x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:]
+        half = rope_dims // 2
+        x1, x2 = x_rope[..., :half], x_rope[..., half:]
+        x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1)
+        return torch.cat((x_rope, x_pass), dim=-1)
+    half = x.size(-1) // 2
+    x1, x2 = x[..., :half], x[..., half:]
+    return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1)
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, dim, num_heads, num_kv_heads, rope_base, qk_gain_init, train_seq_len):
+        super().__init__()
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.head_dim = dim // num_heads
+        kv_dim = self.num_kv_heads * self.head_dim
+        self.c_q = CastedLinear(dim, dim, bias=False)
+        self.c_k = CastedLinear(dim, kv_dim, bias=False)
+        self.c_v = CastedLinear(dim, kv_dim, bias=False)
+        self.proj = CastedLinear(dim, dim, bias=False)
+        self.proj._zero_init = True
+        self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32))
+        self.rope_dims = 0
+        self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=train_seq_len)
+        self.use_xsa = False
+
+    def _xsa_efficient(self, y, v):
+        B, T, H, D = y.shape
+        Hkv = v.size(-2)
+        group = H // Hkv
+        y_g = y.reshape(B, T, Hkv, group, D)
+        vn = F.normalize(v, dim=-1).unsqueeze(-2)
+        proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn
+        return (y_g - proj).reshape(B, T, H, D)
+
+    def forward(self, x):
+        bsz, seqlen, dim = x.shape
+        q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim)
+        k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim)
+        v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim)
+        q = F.rms_norm(q, (q.size(-1),))
+        k = F.rms_norm(k, (k.size(-1),))
+        cos, sin = self.rotary(seqlen, x.device, q.dtype)
+        q = apply_rotary_emb(q, cos, sin, self.rope_dims)
+        k = apply_rotary_emb(k, cos, sin, self.rope_dims)
+        q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None]
+
+        if HAS_FA3:
+            y = flash_attn_3_func(q, k, v, causal=True)
+        else:
+            # Fallback to standard SDPA
+            q = q.transpose(1, 2)
+            k = k.transpose(1, 2)
+            v = v.transpose(1, 2)
+            y = F.scaled_dot_product_attention(
+                q, k, v, attn_mask=None, is_causal=True,
+                enable_gqa=(self.num_kv_heads != self.num_heads),
+            )
+            y = y.transpose(1, 2)
+
+        if self.use_xsa:
+            if HAS_FA3:
+                y = self._xsa_efficient(y, v)
+            else:
+                v_for_xsa = v.transpose(1, 2)  # back to (B, T, Hkv, D)
+                y = self._xsa_efficient(y, v_for_xsa)
+
+        y = y.reshape(bsz, seqlen, dim)
+        return self.proj(y)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim, mlp_mult):
+        super().__init__()
+        hidden = int(mlp_mult * dim)
+        self.fc = CastedLinear(dim, hidden, bias=False)
+        self.proj = CastedLinear(hidden, dim, bias=False)
+        self.proj._zero_init = True
+
+    def forward(self, x):
+        return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square())
+
+
+class Block(nn.Module):
+    def __init__(self, dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init,
+                 train_seq_len, layer_idx=0, ln_scale=False):
+        super().__init__()
+        self.attn_norm = RMSNorm()
+        self.mlp_norm = RMSNorm()
+        self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, train_seq_len)
+        self.mlp = MLP(dim, mlp_mult)
+        self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float())
+        self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0
+        self.parallel = False
+
+    def forward(self, x, x0):
+        mix = self.resid_mix.to(dtype=x.dtype)
+        x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0
+        attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor)
+        if self.parallel:
+            mlp_out = self.mlp(self.mlp_norm(x_in) * self.ln_scale_factor)
+            x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out + \
+                    self.mlp_scale.to(dtype=x_in.dtype)[None, None, :] * mlp_out
+        else:
+            x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out
+            x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * \
+                    self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor)
+        return x_out
+
+
+# =============================================================================
+# NOVEL: MULTI-RESOLUTION RECURRENCE (SpiralFormer-inspired)
+# =============================================================================
+
+def causal_downsample(h, resolution):
+    """Downsample hidden states by mean-pooling adjacent tokens, preserving causality."""
+    B, T, D = h.shape
+    new_T = max(1, int(T * resolution))
+    if new_T >= T:
+        return h
+    # Block-wise mean pooling with causal constraint
+    chunk_size = T // new_T
+    remainder = T - chunk_size * new_T
+    # Simple: just take every chunk_size tokens via average
+    h_reshaped = h[:, :chunk_size * new_T].reshape(B, new_T, chunk_size, D)
+    return h_reshaped.mean(dim=2)
+
+
+def causal_upsample(h_low, h_orig, resolution):
+    """Upsample back to original resolution using nearest-neighbor + residual."""
+    B, T_orig, D = h_orig.shape
+    B_low, T_low, D_low = h_low.shape
+    if T_low >= T_orig:
+        return h_low
+    # Nearest neighbor upsample
+    indices = torch.arange(T_orig, device=h_low.device) * T_low // T_orig
+    indices = indices.clamp(max=T_low - 1)
+    return h_low[:, indices, :]
+
+
+# =============================================================================
+# GPT MODEL
+# =============================================================================
+
+class GPT(nn.Module):
+    def __init__(self, h):
+        super().__init__()
+        self.h = h
+        self.tie_embeddings = h.tie_embeddings
+        self.tied_embed_init_std = h.tied_embed_init_std
+        self.logit_softcap = h.logit_softcap
+        self.tok_emb = nn.Embedding(h.vocab_size, h.embedding_dim)
+
+        if h.embedding_dim != h.model_dim:
+            self.embed_proj = CastedLinear(h.embedding_dim, h.model_dim, bias=False)
+            self.head_proj = CastedLinear(h.model_dim, h.embedding_dim, bias=False)
+        else:
+            self.embed_proj = None
+            self.head_proj = None
+
+        self.num_encoder_layers = h.num_layers // 2
+        self.num_decoder_layers = h.num_layers - self.num_encoder_layers
+        self.blocks = nn.ModuleList([
+            Block(h.model_dim, h.num_heads, h.num_kv_heads, h.mlp_mult, h.rope_base,
+                  h.qk_gain_init, h.train_seq_len, layer_idx=i, ln_scale=h.ln_scale)
+            for i in range(h.num_layers)
+        ])
+
+        if h.rope_dims > 0:
+            head_dim = h.model_dim // h.num_heads
+            for block in self.blocks:
+                block.attn.rope_dims = h.rope_dims
+                block.attn.rotary = Rotary(head_dim, base=h.rope_base,
+                                           train_seq_len=h.train_seq_len, rope_dims=h.rope_dims)
+
+        self.final_norm = RMSNorm()
+        self.lm_head = None if h.tie_embeddings else CastedLinear(h.embedding_dim, h.vocab_size, bias=False)
+        if self.lm_head is not None:
+            self.lm_head._zero_init = True
+
+        if h.xsa_last_n > 0:
+            for i in range(max(0, h.num_layers - h.xsa_last_n), h.num_layers):
+                self.blocks[i].attn.use_xsa = True
+
+        if h.parallel_residual_start >= 0:
+            for i in range(h.parallel_residual_start, h.num_layers):
+                self.blocks[i].parallel = True
+
+        # Depth recurrence setup
+        self.looping_active = False
+        if h.num_loops > 0:
+            loop_seg = list(range(h.loop_start, h.loop_end + 1))
+            all_indices = list(range(h.loop_start))
+            for _ in range(h.num_loops + 1):
+                all_indices.extend(loop_seg)
+            all_indices.extend(range(h.loop_end + 1, h.num_layers))
+            num_enc = len(all_indices) // 2
+            self.encoder_indices = all_indices[:num_enc]
+            self.decoder_indices = all_indices[num_enc:]
+        else:
+            self.encoder_indices = list(range(self.num_encoder_layers))
+            self.decoder_indices = list(range(self.num_encoder_layers, h.num_layers))
+
+        self.num_skip_weights = min(len(self.encoder_indices), len(self.decoder_indices))
+        self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, h.model_dim, dtype=torch.float32))
+        self.skip_gates = nn.Parameter(torch.zeros(self.num_skip_weights, h.model_dim, dtype=torch.float32)) \
+            if h.skip_gates_enabled else None
+
+        # =====================================================================
+        # NOVEL: MTP head (lightweight - just a small projection for t+2)
+        # This head is NOT saved in the artifact - discarded after training
+        # =====================================================================
+        if h.mtp_enabled:
+            # MTP uses a small hidden projection to shift representations for t+2 prediction
+            # Then uses the same tied embedding for logits
+            self.mtp_proj = CastedLinear(h.model_dim, h.model_dim, bias=False)
+            nn.init.zeros_(self.mtp_proj.weight)  # Start as identity-like
+
+        self._init_weights()
+
+    def _init_weights(self):
+        if self.tie_embeddings:
+            nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std)
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear):
+                if getattr(module, '_zero_init', False):
+                    nn.init.zeros_(module.weight)
+                elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64:
+                    nn.init.orthogonal_(module.weight, gain=1.0)
+
+    def forward_logits(self, input_ids):
+        x = self.tok_emb(input_ids)
+        x = F.rms_norm(x, (x.size(-1),))
+        if self.embed_proj is not None:
+            x = self.embed_proj(x)
+        x0 = x
+        skips = []
+
+        enc_iter = self.encoder_indices if self.looping_active else range(self.num_encoder_layers)
+        dec_iter = self.decoder_indices if self.looping_active else range(self.num_encoder_layers,
+                                                                          self.num_encoder_layers + self.num_decoder_layers)
+
+        for i in enc_iter:
+            x = self.blocks[i](x, x0)
+            skips.append(x)
+        for skip_idx, i in enumerate(dec_iter):
+            if skip_idx < self.num_skip_weights and skips:
+                scaled_skip = self.skip_weights[skip_idx].to(dtype=x.dtype)[None, None, :] * skips.pop()
+                if self.skip_gates is not None:
+                    g = torch.sigmoid(self.skip_gates[skip_idx].to(dtype=x.dtype))[None, None, :]
+                    x = torch.lerp(scaled_skip, x, g)
+                else:
+                    x = x + scaled_skip
+            x = self.blocks[i](x, x0)
+
+        x = self.final_norm(x)
+        if self.head_proj is not None:
+            x = self.head_proj(x)
+        if self.tie_embeddings:
+            logits_proj = F.linear(x, self.tok_emb.weight)
+        else:
+            logits_proj = self.lm_head(x)
+        return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap)
+
+    def forward_hidden(self, input_ids):
+        """Returns hidden states before final projection (for MTP)."""
+        x = self.tok_emb(input_ids)
+        x = F.rms_norm(x, (x.size(-1),))
+        if self.embed_proj is not None:
+            x = self.embed_proj(x)
+        x0 = x
+        skips = []
+
+        enc_iter = self.encoder_indices if self.looping_active else range(self.num_encoder_layers)
+        dec_iter = self.decoder_indices if self.looping_active else range(self.num_encoder_layers,
+                                                                          self.num_encoder_layers + self.num_decoder_layers)
+
+        for i in enc_iter:
+            x = self.blocks[i](x, x0)
+            skips.append(x)
+        for skip_idx, i in enumerate(dec_iter):
+            if skip_idx < self.num_skip_weights and skips:
+                scaled_skip = self.skip_weights[skip_idx].to(dtype=x.dtype)[None, None, :] * skips.pop()
+                if self.skip_gates is not None:
+                    g = torch.sigmoid(self.skip_gates[skip_idx].to(dtype=x.dtype))[None, None, :]
+                    x = torch.lerp(scaled_skip, x, g)
+                else:
+                    x = x + scaled_skip
+            x = self.blocks[i](x, x0)
+
+        x = self.final_norm(x)
+        return x
+
+    def forward(self, input_ids, target_ids):
+        logits = self.forward_logits(input_ids)
+        return F.cross_entropy(
+            logits.reshape(-1, logits.size(-1)).float(),
+            target_ids.reshape(-1),
+            reduction='mean'
+        )
+
+    def forward_mtp(self, input_ids, target_ids_1, target_ids_2):
+        """Forward with Multi-Token Prediction auxiliary loss."""
+        hidden = self.forward_hidden(input_ids)
+
+        if self.head_proj is not None:
+            hidden_proj = self.head_proj(hidden)
+        else:
+            hidden_proj = hidden
+
+        # Head 1: standard NTP (predict t+1)
+        if self.tie_embeddings:
+            logits_1 = F.linear(hidden_proj, self.tok_emb.weight)
+        else:
+            logits_1 = self.lm_head(hidden_proj)
+        logits_1 = self.logit_softcap * torch.tanh(logits_1 / self.logit_softcap)
+        loss_1 = F.cross_entropy(
+            logits_1.reshape(-1, logits_1.size(-1)).float(),
+            target_ids_1.reshape(-1),
+            reduction='mean'
+        )
+
+        # Head 2: MTP (predict t+2) using a lightweight projection
+        hidden_2 = hidden + self.mtp_proj(hidden)  # residual connection
+        if self.head_proj is not None:
+            hidden_2_proj = self.head_proj(hidden_2)
+        else:
+            hidden_2_proj = hidden_2
+        if self.tie_embeddings:
+            logits_2 = F.linear(hidden_2_proj, self.tok_emb.weight)
+        else:
+            logits_2 = self.lm_head(hidden_2_proj)
+        logits_2 = self.logit_softcap * torch.tanh(logits_2 / self.logit_softcap)
+        loss_2 = F.cross_entropy(
+            logits_2.reshape(-1, logits_2.size(-1)).float(),
+            target_ids_2.reshape(-1),
+            reduction='mean'
+        )
+
+        return (1.0 - self.h.mtp_weight) * loss_1 + self.h.mtp_weight * loss_2
+
+
+# =============================================================================
+# MUON OPTIMIZER
+# =============================================================================
+
+CONTROL_TENSOR_NAME_PATTERNS = tuple(
+    pattern for pattern in os.environ.get(
+        'CONTROL_TENSOR_NAME_PATTERNS',
+        'attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,skip_gates'
+    ).split(',') if pattern
+)
+
+
+@torch.compile
+def zeropower_via_newtonschulz5(G, steps=10, eps=1e-7):
+    a, b, c = 3.4445, -4.775, 2.0315
+    X = G.bfloat16()
+    X /= X.norm() + eps
+    transposed = G.size(0) > G.size(1)
+    if transposed:
+        X = X.T
+    for _ in range(steps):
+        A = X @ X.T
+        B = b * A + c * A @ A
+        X = a * X + B @ X
+    return X.T if transposed else X
+
+
+class Muon(torch.optim.Optimizer):
+    def __init__(self, params, lr, momentum, backend_steps, nesterov=True,
+                 weight_decay=0.0, row_normalize=False):
+        super().__init__(params, dict(lr=lr, momentum=momentum, backend_steps=backend_steps,
+                                      nesterov=nesterov, weight_decay=weight_decay,
+                                      row_normalize=row_normalize))
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        distributed = dist.is_available() and dist.is_initialized()
+        world_size = dist.get_world_size() if distributed else 1
+        rank = dist.get_rank() if distributed else 0
+
+        for group in self.param_groups:
+            params = group['params']
+            if not params:
+                continue
+            lr = group['lr']
+            momentum = group['momentum']
+            backend_steps = group['backend_steps']
+            nesterov = group['nesterov']
+            total_params = sum(int(p.numel()) for p in params)
+            updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16)
+
+            curr = 0
+            for i, p in enumerate(params):
+                if i % world_size == rank and p.grad is not None:
+                    g = p.grad
+                    state = self.state[p]
+                    if 'momentum_buffer' not in state:
+                        state['momentum_buffer'] = torch.zeros_like(g)
+                    buf = state['momentum_buffer']
+                    buf.mul_(momentum).add_(g)
+                    if nesterov:
+                        g = g.add(buf, alpha=momentum)
+                    if group.get('row_normalize', False):
+                        row_norms = g.float().norm(dim=-1, keepdim=True).clamp_min(1e-7)
+                        g = g / row_norms.to(g.dtype)
+                    g = zeropower_via_newtonschulz5(g, steps=backend_steps)
+                    g *= max(1, g.size(0) / g.size(1)) ** 0.5
+                    updates_flat[curr:curr + p.numel()] = g.reshape(-1)
+                curr += p.numel()
+
+            if distributed:
+                dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM)
+
+            wd = group.get('weight_decay', 0.0)
+            curr = 0
+            for p in params:
+                if wd > 0.0:
+                    p.data.mul_(1.0 - lr * wd)
+                g = updates_flat[curr:curr + p.numel()].view_as(p).to(dtype=p.dtype)
+                p.add_(g, alpha=-lr)
+                curr += p.numel()
+
+        return loss
+
+
+# =============================================================================
+# OPTIMIZER SETUP
+# =============================================================================
+
+class Optimizers:
+    def __init__(self, h, base_model):
+        block_named_params = list(base_model.blocks.named_parameters())
+        matrix_params = [p for name, p in block_named_params
+                         if p.ndim == 2 and not any(pat in name for pat in CONTROL_TENSOR_NAME_PATTERNS)]
+        scalar_params = [p for name, p in block_named_params
+                         if p.ndim < 2 or any(pat in name for pat in CONTROL_TENSOR_NAME_PATTERNS)]
+
+        if base_model.skip_weights.numel() > 0:
+            scalar_params.append(base_model.skip_weights)
+        if base_model.skip_gates is not None and base_model.skip_gates.numel() > 0:
+            scalar_params.append(base_model.skip_gates)
+
+        # MTP projection goes into matrix params
+        if hasattr(base_model, 'mtp_proj'):
+            matrix_params.append(base_model.mtp_proj.weight)
+
+        token_lr = h.tied_embed_lr if h.tie_embeddings else h.embed_lr
+        tok_params = [{'params': [base_model.tok_emb.weight], 'lr': token_lr, 'base_lr': token_lr}]
+        self.optimizer_tok = torch.optim.AdamW(
+            tok_params, betas=(h.beta1, h.beta2), eps=h.adam_eps, weight_decay=h.embed_wd, fused=True)
+
+        self.optimizer_muon = Muon(
+            matrix_params, lr=h.matrix_lr, momentum=h.muon_momentum,
+            backend_steps=h.muon_backend_steps, weight_decay=h.muon_wd,
+            row_normalize=h.muon_row_normalize)
+        for group in self.optimizer_muon.param_groups:
+            group['base_lr'] = h.matrix_lr
+
+        self.optimizer_scalar = torch.optim.AdamW(
+            [{'params': scalar_params, 'lr': h.scalar_lr, 'base_lr': h.scalar_lr}],
+            betas=(h.beta1, h.beta2), eps=h.adam_eps, weight_decay=h.adam_wd, fused=True)
+
+        self.optimizers = [self.optimizer_tok, self.optimizer_muon, self.optimizer_scalar]
+
+        if base_model.lm_head is not None:
+            self.optimizer_head = torch.optim.Adam(
+                [{'params': [base_model.lm_head.weight], 'lr': h.head_lr, 'base_lr': h.head_lr}],
+                betas=(h.beta1, h.beta2), eps=h.adam_eps, fused=True)
+            self.optimizers.insert(1, self.optimizer_head)
+
+    def __iter__(self):
+        return iter(self.optimizers)
+
+    def zero_grad_all(self):
+        for opt in self.optimizers:
+            opt.zero_grad(set_to_none=True)
+
+    def step(self):
+        for opt in self.optimizers:
+            opt.step()
+        self.zero_grad_all()
+
+    def update_wd(self, new_muon_wd, new_embed_wd, new_adam_wd):
+        """Adaptive weight decay: update WD for all parameter groups."""
+        for group in self.optimizer_muon.param_groups:
+            group['weight_decay'] = new_muon_wd
+        for group in self.optimizer_tok.param_groups:
+            group['weight_decay'] = new_embed_wd
+        for group in self.optimizer_scalar.param_groups:
+            group['weight_decay'] = new_adam_wd
+
+
+# =============================================================================
+# HELPER FUNCTIONS
+# =============================================================================
+
+def restore_fp32_params(model):
+    for module in model.modules():
+        if isinstance(module, CastedLinear):
+            module.float()
+    for name, param in model.named_parameters():
+        if (param.ndim < 2 or any(pat in name for pat in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32:
+            param.data = param.data.float()
+
+
+def classify_param(name):
+    if 'tok_emb' in name or 'lm_head' in name:
+        return 'embed'
+    if '.mlp.' in name:
+        return 'mlp'
+    if '.attn.' in name or '.proj.' in name and '.mlp.' not in name:
+        return 'attn'
+    return 'other'
+
+
+# =============================================================================
+# GPTQ QUANTIZATION (inherited from SOTA)
+# =============================================================================
+
+def collect_hessians(model, train_loader, h, device, n_calibration_batches=64):
+    hessians = {}
+    hooks = []
+
+    def make_hook(name):
+        def hook_fn(module, inp, out):
+            x = inp[0].detach().float()
+            if x.ndim == 3:
+                x = x.reshape(-1, x.shape[-1])
+            if name not in hessians:
+                hessians[name] = torch.zeros(x.shape[1], x.shape[1], dtype=torch.float32, device=device)
+            hessians[name].addmm_(x.T, x)
+        return hook_fn
+
+    for name, module in model.named_modules():
+        if isinstance(module, CastedLinear) and module.weight.numel() > 65536:
+            cat = classify_param(name + '.weight')
+            if cat in ('mlp', 'attn'):
+                hooks.append(module.register_forward_hook(make_hook(name + '.weight')))
+
+    if model.tie_embeddings:
+        hook_module = model.head_proj if model.head_proj is not None else model.final_norm
+        def make_output_hook(name):
+            def hook_fn(module, inp, out):
+                x = out.detach().float()
+                if x.ndim == 3:
+                    x = x.reshape(-1, x.shape[-1])
+                if name not in hessians:
+                    hessians[name] = torch.zeros(x.shape[1], x.shape[1], dtype=torch.float32, device=device)
+                hessians[name].addmm_(x.T, x)
+            return hook_fn
+        hooks.append(hook_module.register_forward_hook(make_output_hook('tok_emb.weight')))
+
+    model.eval()
+    with torch.no_grad():
+        for _ in range(n_calibration_batches):
+            x, _ = train_loader.next_batch(h.train_batch_tokens, h.grad_accum_steps)
+            model.forward_logits(x)
+    for hook in hooks:
+        hook.remove()
+    for name in hessians:
+        hessians[name] = hessians[name].cpu() / n_calibration_batches
+    return hessians
+
+
+def gptq_quantize_weight(w, H, clip_sigmas=3.0, clip_range=63, block_size=128):
+    W_orig = w.float().clone()
+    rows, cols = W_orig.shape
+    H = H.float().clone()
+    dead = torch.diag(H) == 0
+    H[dead, dead] = 1
+    damp = 0.01 * H.diag().mean()
+    H.diagonal().add_(damp)
+    perm = torch.argsort(H.diag(), descending=True)
+    invperm = torch.argsort(perm)
+    W_perm = W_orig[:, perm].clone()
+    W_perm[:, dead[perm]] = 0
+    H = H[perm][:, perm]
+    Hinv = torch.cholesky_inverse(torch.linalg.cholesky(H))
+    Hinv = torch.linalg.cholesky(Hinv, upper=True)
+    row_std = W_orig.std(dim=1)
+    s = (clip_sigmas * row_std / clip_range).clamp_min(1e-10).to(torch.float16)
+    sf = s.float()
+    Q = torch.zeros(rows, cols, dtype=torch.int8)
+    W_work = W_perm.clone()
+    for i1 in range(0, cols, block_size):
+        i2 = min(i1 + block_size, cols)
+        W_block = W_work[:, i1:i2].clone()
+        Hinv_block = Hinv[i1:i2, i1:i2]
+        Err = torch.zeros(rows, i2 - i1)
+        for j in range(i2 - i1):
+            w_col = W_block[:, j]
+            d = Hinv_block[j, j]
+            q_col = torch.clamp(torch.round(w_col / sf), -clip_range, clip_range)
+            Q[:, i1 + j] = q_col.to(torch.int8)
+            err = (w_col - q_col.float() * sf) / d
+            Err[:, j] = err
+            W_block[:, j:] -= err.unsqueeze(1) * Hinv_block[j, j:].unsqueeze(0)
+        if i2 < cols:
+            W_work[:, i2:] -= Err @ Hinv[i1:i2, i2:]
+    return Q[:, invperm], s
+
+
+def gptq_mixed_quantize(state_dict, hessians, h):
+    result = {}
+    meta = {}
+    for name, tensor in state_dict.items():
+        # Skip MTP projection - it's not saved in artifact
+        if 'mtp_proj' in name:
+            continue
+        t = tensor.detach().cpu().contiguous()
+        if not t.is_floating_point() or t.numel() <= 65536:
+            result[name] = t.to(torch.float16) if t.is_floating_point() else t
+            meta[name] = 'passthrough (float16)'
+            continue
+        cs = h.embed_clip_sigmas if 'tok_emb' in name else h.matrix_clip_sigmas
+        bits = h.embed_bits if 'tok_emb' in name else h.matrix_bits
+        q, s = gptq_quantize_weight(t, hessians[name], clip_sigmas=cs, clip_range=2 ** (bits - 1) - 1)
+        result[name + '.q'] = q
+        result[name + '.scale'] = s
+        meta[name] = f"gptq (int{bits})"
+    log('Quantized weights:')
+    categories = collections.defaultdict(set)
+    for name, cat in meta.items():
+        short = re.sub(r'\.\d+$', '', re.sub(r'blocks\.\d+', 'blocks', name))
+        categories[cat].add(short)
+    for cat in sorted(categories):
+        log(f"  {cat}: {', '.join(sorted(categories[cat]))}")
+    return result, meta
+
+
+def dequantize_mixed(result, meta, template_sd):
+    out = {}
+    for name, orig in template_sd.items():
+        if 'mtp_proj' in name:
+            continue
+        info = meta.get(name)
+        if info is None:
+            continue
+        orig_dtype = orig.dtype
+        if 'passthrough' in info:
+            t = result[name]
+            if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16):
+                t = t.to(orig_dtype)
+            out[name] = t
+            continue
+        q, s = result[name + '.q'], result[name + '.scale']
+        if s.ndim > 0:
+            out[name] = (q.float() * s.float().view(q.shape[0], *[1] * (q.ndim - 1))).to(orig_dtype)
+        else:
+            out[name] = (q.float() * float(s.item())).to(orig_dtype)
+    return out
+
+
+# =============================================================================
+# COMPRESSION
+# =============================================================================
+
+_BSHF_MAGIC = b'BSHF'
+
+def _byte_shuffle(data, stride=2):
+    if stride <= 1 or len(data) < stride:
+        return data
+    src = np.frombuffer(data, dtype=np.uint8)
+    n = len(src)
+    out = np.empty(n, dtype=np.uint8)
+    dest_off = 0
+    for pos in range(stride):
+        chunk = src[pos::stride]
+        out[dest_off:dest_off + len(chunk)] = chunk
+        dest_off += len(chunk)
+    return _BSHF_MAGIC + bytes([stride]) + out.tobytes()
+
+def _byte_unshuffle(data):
+    if len(data) < 5 or data[:4] != _BSHF_MAGIC:
+        return data
+    stride = data[4]
+    if stride < 2:
+        return data[5:]
+    payload = np.frombuffer(data, dtype=np.uint8, offset=5)
+    n = len(payload)
+    out = np.empty(n, dtype=np.uint8)
+    src_off = 0
+    for pos in range(stride):
+        chunk_len = n // stride + (1 if pos < n % stride else 0)
+        out[pos::stride][:chunk_len] = payload[src_off:src_off + chunk_len]
+        src_off += chunk_len
+    return out.tobytes()
+
+def _compress(data, compressor):
+    data = _byte_shuffle(data)
+    if compressor == 'lzma':
+        return lzma.compress(data, preset=6)
+    elif compressor == 'brotli':
+        import brotli
+        return brotli.compress(data, quality=11)
+    raise ValueError(f"Unknown compressor: {compressor!r}")
+
+def _decompress(data, compressor):
+    if compressor == 'lzma':
+        raw = lzma.decompress(data)
+    elif compressor == 'brotli':
+        import brotli
+        raw = brotli.decompress(data)
+    else:
+        raise ValueError(f"Unknown compressor: {compressor!r}")
+    return _byte_unshuffle(raw)
+
+
+# =============================================================================
+# SERIALIZATION
+# =============================================================================
+
+def serialize(h, base_model, code):
+    code_bytes = len(code.encode('utf-8'))
+    if h.is_main_process:
+        # Save raw model (excluding MTP projection)
+        sd = {k: v for k, v in base_model.state_dict().items() if 'mtp_proj' not in k}
+        torch.save(sd, h.model_path)
+        model_bytes = os.path.getsize(h.model_path)
+        log(f"Serialized model: {model_bytes} bytes")
+        log(f"Code size: {code_bytes} bytes")
+
+    sd_cpu = {k: v.detach().cpu() for k, v in base_model.state_dict().items() if 'mtp_proj' not in k}
+    device = torch.device('cuda', h.local_rank)
+    log('GPTQ: collecting Hessians from calibration data...')
+    t0 = time.perf_counter()
+    calib_loader = ShuffledSequenceLoader(h, device)
+    hessians = collect_hessians(base_model, calib_loader, h, device,
+                                n_calibration_batches=h.gptq_calibration_batches)
+    log(f"GPTQ: collected {len(hessians)} Hessians in {time.perf_counter() - t0:.1f}s")
+
+    quant_result, quant_meta = gptq_mixed_quantize(sd_cpu, hessians, h)
+    quant_buf = io.BytesIO()
+    torch.save({'w': quant_result, 'm': quant_meta}, quant_buf)
+    quant_raw = quant_buf.getvalue()
+    quant_blob = _compress(quant_raw, h.compressor)
+    quant_file_bytes = len(quant_blob)
+    bytes_total = quant_file_bytes + code_bytes
+
+    if h.is_main_process:
+        with open(h.quantized_model_path, 'wb') as f:
+            f.write(quant_blob)
+        log(f"Serialized model quantized+{h.compressor}: {quant_file_bytes} bytes")
+        log(f"Total submission size quantized+{h.compressor}: {bytes_total} bytes")
+
+    return bytes_total, quant_file_bytes
+
+
+def deserialize(h, device):
+    eval_model = GPT(h).to(device).bfloat16()
+    restore_fp32_params(eval_model)
+    sd_cpu = {k: v.detach().cpu() for k, v in eval_model.state_dict().items() if 'mtp_proj' not in k}
+    with open(h.quantized_model_path, 'rb') as f:
+        quant_blob_disk = f.read()
+    quant_state = torch.load(io.BytesIO(_decompress(quant_blob_disk, h.compressor)), map_location='cpu')
+    deq_state = dequantize_mixed(quant_state['w'], quant_state['m'], sd_cpu)
+    eval_model.load_state_dict(deq_state, strict=False)
+    return eval_model
+
+
+# =============================================================================
+# EVALUATION
+# =============================================================================
+
+def _loss_bpb(loss_sum, token_count, byte_count):
+    val_loss = (loss_sum / token_count).item()
+    val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item())
+    return val_loss, val_bpb
+
+
+def eval_val(h, device, val_data, model):
+    seq_len = h.eval_seq_len
+    local_batch_tokens = h.val_batch_tokens // (h.world_size * h.grad_accum_steps)
+    local_batch_seqs = local_batch_tokens // seq_len
+    total_seqs = (val_data.val_tokens.numel() - 1) // seq_len
+    seq_start = total_seqs * h.rank // h.world_size
+    seq_end = total_seqs * (h.rank + 1) // h.world_size
+    val_loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    val_token_count = torch.zeros((), device=device, dtype=torch.float64)
+    val_byte_count = torch.zeros((), device=device, dtype=torch.float64)
+
+    model.eval()
+    with torch.inference_mode():
+        for batch_seq_start in range(seq_start, seq_end, local_batch_seqs):
+            batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end)
+            raw_start = batch_seq_start * seq_len
+            raw_end = batch_seq_end * seq_len + 1
+            local = val_data.val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True)
+            x = local[:-1].reshape(-1, seq_len)
+            y = local[1:].reshape(-1, seq_len)
+            with torch.autocast(device_type='cuda', dtype=torch.bfloat16, enabled=True):
+                batch_loss = model(x, y).detach()
+            batch_token_count = float(y.numel())
+            val_loss_sum += batch_loss.to(torch.float64) * batch_token_count
+            val_token_count += batch_token_count
+            prev_ids = x.reshape(-1)
+            tgt_ids = y.reshape(-1)
+            token_bytes = val_data.base_bytes_lut[tgt_ids].to(dtype=torch.int16)
+            token_bytes += (val_data.has_leading_space_lut[tgt_ids] &
+                           ~val_data.is_boundary_token_lut[prev_ids]).to(dtype=torch.int16)
+            val_byte_count += token_bytes.to(torch.float64).sum()
+
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM)
+
+    model.train()
+    return _loss_bpb(val_loss_sum, val_token_count, val_byte_count)
+
+
+def eval_val_sliding(h, device, val_data, base_model, batch_seqs=32):
+    base_model.eval()
+    logits_fn = torch.compile(base_model.forward_logits, dynamic=False, fullgraph=True)
+    seq_len = h.eval_seq_len
+    context_size = seq_len - h.eval_stride
+    total_tokens = val_data.val_tokens.numel() - 1
+    window_starts = [ws for ws in range(0, total_tokens, h.eval_stride) if ws + context_size < total_tokens]
+    total_windows = len(window_starts)
+    my_s = total_windows * h.rank // h.world_size
+    my_e = total_windows * (h.rank + 1) // h.world_size
+    my_windows = window_starts[my_s:my_e]
+    loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    token_count = torch.zeros((), device=device, dtype=torch.float64)
+    byte_count = torch.zeros((), device=device, dtype=torch.float64)
+
+    with torch.inference_mode():
+        for bi in range(0, len(my_windows), batch_seqs):
+            batch_ws = my_windows[bi:bi + batch_seqs]
+            bsz = len(batch_ws)
+            x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            wlens = []
+            for i, ws in enumerate(batch_ws):
+                we = min(ws + seq_len, total_tokens)
+                wlen = we - ws
+                wlens.append(wlen)
+                chunk = val_data.val_tokens[ws:we + 1].to(dtype=torch.int64, device=device)
+                x_batch[i, :wlen] = chunk[:-1]
+                y_batch[i, :wlen] = chunk[1:]
+            with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
+                logits = logits_fn(x_batch)
+            nll = F.cross_entropy(
+                logits.reshape(-1, logits.size(-1)).float(),
+                y_batch.reshape(-1), reduction='none'
+            ).reshape(bsz, seq_len)
+            for i, ws in enumerate(batch_ws):
+                wlen = wlens[i]
+                s = 0 if ws == 0 else context_size
+                scored_nll = nll[i, s:wlen].to(torch.float64)
+                loss_sum += scored_nll.sum()
+                token_count += float(wlen - s)
+                tgt = y_batch[i, s:wlen]
+                prev = x_batch[i, s:wlen]
+                tb = val_data.base_bytes_lut[tgt].to(torch.float64)
+                tb += (val_data.has_leading_space_lut[tgt] &
+                       ~val_data.is_boundary_token_lut[prev]).to(torch.float64)
+                byte_count += tb.sum()
+
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(byte_count, op=dist.ReduceOp.SUM)
+
+    base_model.train()
+    return _loss_bpb(loss_sum, token_count, byte_count)
+
+
+def eval_val_ttt(h, device, val_data, base_model, batch_seqs=32):
+    """Score-first TTT evaluation with improved chunking."""
+    rank = h.rank
+    world_size = h.world_size
+    seq_len = h.eval_seq_len
+    stride = h.eval_stride
+    total_tokens = val_data.val_tokens.numel() - 1
+    ttt_chunk = h.ttt_chunk_tokens
+    context_size = seq_len - stride
+    window_starts = [ws for ws in range(0, total_tokens, stride) if ws + context_size < total_tokens]
+    num_chunks = (total_tokens + ttt_chunk - 1) // ttt_chunk
+    chunk_windows = [[] for _ in range(num_chunks)]
+    for ws in window_starts:
+        wlen = min(ws + seq_len, total_tokens) - ws
+        s = 0 if ws == 0 else context_size
+        scored_start = ws + s
+        ci = min(scored_start // ttt_chunk, num_chunks - 1)
+        chunk_windows[ci].append(ws)
+
+    log(f"ttt:start chunks={num_chunks} ttt_lr={h.ttt_lr} ttt_epochs={h.ttt_epochs}")
+    compiled_logits = torch.compile(base_model.forward_logits, dynamic=False, fullgraph=True)
+    loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    token_count = torch.zeros((), device=device, dtype=torch.float64)
+    byte_count = torch.zeros((), device=device, dtype=torch.float64)
+    ttt_params = [p for p in base_model.parameters()]
+    for p in ttt_params:
+        p.requires_grad_(True)
+    optimizer = torch.optim.SGD(ttt_params, lr=h.ttt_lr, momentum=h.ttt_momentum)
+
+    for ci in range(num_chunks):
+        windows = chunk_windows[ci]
+        if not windows:
+            continue
+        my_s = len(windows) * rank // world_size
+        my_e = len(windows) * (rank + 1) // world_size
+        my_windows = windows[my_s:my_e]
+        base_model.eval()
+
+        # Score phase (no gradient)
+        with torch.no_grad():
+            for bi in range(0, len(my_windows), batch_seqs):
+                batch_ws = my_windows[bi:bi + batch_seqs]
+                bsz = len(batch_ws)
+                x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+                y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+                wlens = []
+                for i, ws in enumerate(batch_ws):
+                    we = min(ws + seq_len, total_tokens)
+                    wlen = we - ws
+                    wlens.append(wlen)
+                    chunk_tok = val_data.val_tokens[ws:we + 1].to(dtype=torch.int64, device=device)
+                    x_batch[i, :wlen] = chunk_tok[:-1]
+                    y_batch[i, :wlen] = chunk_tok[1:]
+                with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
+                    logits = compiled_logits(x_batch)
+                nll = F.cross_entropy(
+                    logits.reshape(-1, logits.size(-1)).float(),
+                    y_batch.reshape(-1), reduction='none'
+                ).reshape(bsz, seq_len)
+                for i, ws in enumerate(batch_ws):
+                    wlen = wlens[i]
+                    s = 0 if ws == 0 else context_size
+                    scored_nll = nll[i, s:wlen].to(torch.float64)
+                    loss_sum += scored_nll.sum()
+                    token_count += float(wlen - s)
+                    tgt = y_batch[i, s:wlen]
+                    prev = x_batch[i, s:wlen]
+                    tb = val_data.base_bytes_lut[tgt].to(torch.float64)
+                    tb += (val_data.has_leading_space_lut[tgt] &
+                           ~val_data.is_boundary_token_lut[prev]).to(torch.float64)
+                    byte_count += tb.sum()
+
+        # Train phase (score-first: already scored above, now update)
+        is_last_chunk = ci == num_chunks - 1
+        if not is_last_chunk and h.ttt_epochs > 0:
+            base_model.train()
+            chunk_start = ci * ttt_chunk
+            chunk_end = min((ci + 1) * ttt_chunk, total_tokens)
+            chunk_seqs = (chunk_end - chunk_start) // seq_len
+            if chunk_seqs > 0:
+                cos_lr = h.ttt_lr * 0.5 * (1.0 + math.cos(math.pi * ci / max(num_chunks - 1, 1)))
+                for pg in optimizer.param_groups:
+                    pg['lr'] = cos_lr
+                my_seq_s = chunk_seqs * rank // world_size
+                my_seq_e = chunk_seqs * (rank + 1) // world_size
+                my_chunk_seqs = my_seq_e - my_seq_s
+                for _ep in range(h.ttt_epochs):
+                    for bs in range(0, my_chunk_seqs, batch_seqs):
+                        be = min(bs + batch_seqs, my_chunk_seqs)
+                        actual_bs = my_seq_s + bs
+                        start_tok = chunk_start + actual_bs * seq_len
+                        end_tok = chunk_start + (my_seq_s + be) * seq_len + 1
+                        if end_tok > val_data.val_tokens.numel():
+                            continue
+                        local = val_data.val_tokens[start_tok:end_tok].to(device=device, dtype=torch.int64)
+                        x = local[:-1].reshape(-1, seq_len)
+                        y = local[1:].reshape(-1, seq_len)
+                        optimizer.zero_grad(set_to_none=True)
+                        with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
+                            loss = base_model(x, y)
+                        loss.backward()
+                        if world_size > 1:
+                            for p in ttt_params:
+                                if p.grad is not None:
+                                    dist.all_reduce(p.grad, op=dist.ReduceOp.AVG)
+                        torch.nn.utils.clip_grad_norm_(ttt_params, 1.0)
+                        optimizer.step()
+
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(byte_count, op=dist.ReduceOp.SUM)
+
+    for p in base_model.parameters():
+        p.requires_grad_(True)
+    base_model.eval()
+    return _loss_bpb(loss_sum, token_count, byte_count)
+
+
+def timed_eval(label, fn, *args, **kwargs):
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+    val_loss, val_bpb = fn(*args, **kwargs)
+    torch.cuda.synchronize()
+    elapsed_ms = 1e3 * (time.perf_counter() - t0)
+    log(f"{label} val_loss:{val_loss:.8f} val_bpb:{val_bpb:.8f} eval_time:{elapsed_ms:.0f}ms")
+    return val_loss, val_bpb
+
+
+# =============================================================================
+# TRAINING LOOP
+# =============================================================================
+
+def train_model(h, device, val_data):
+    base_model = GPT(h).to(device).bfloat16()
+    restore_fp32_params(base_model)
+    compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True)
+    if h.distributed:
+        model = DDP(compiled_model, device_ids=[h.local_rank], broadcast_buffers=False)
+    else:
+        model = compiled_model
+
+    n_params = sum(p.numel() for p in base_model.parameters())
+    n_params_no_mtp = sum(p.numel() for n, p in base_model.named_parameters() if 'mtp_proj' not in n)
+    log(f"model_params: {n_params} (artifact_params: {n_params_no_mtp})")
+
+    optimizers = Optimizers(h, base_model)
+    train_loader = ShuffledSequenceLoader(h, device)
+    max_wallclock_ms = 1e3 * h.max_wallclock_seconds if h.max_wallclock_seconds > 0 else None
+    if max_wallclock_ms is not None:
+        max_wallclock_ms -= h.gptq_reserve_seconds * 1e3
+        log(f"gptq: reserving {h.gptq_reserve_seconds:.0f}s, effective={max_wallclock_ms:.0f}ms")
+
+    def training_frac(step, elapsed_ms):
+        if max_wallclock_ms is None:
+            return step / max(h.iterations, 1)
+        return elapsed_ms / max(max_wallclock_ms, 1e-9)
+
+    def lr_mul(frac):
+        if h.warmdown_frac <= 0:
+            return 1.0
+        if frac >= 1.0 - h.warmdown_frac:
+            return max((1.0 - frac) / h.warmdown_frac, h.min_lr)
+        return 1.0
+
+    # =========================================================================
+    # NOVEL: Adaptive Weight Decay function
+    # =========================================================================
+    def adaptive_wd(frac):
+        """Ramp weight decay from wd_start to wd_end over training."""
+        if not h.adaptive_wd_enabled:
+            return h.muon_wd, h.embed_wd, h.adam_wd
+        # Linear interpolation
+        muon_wd = h.wd_start + (h.wd_end - h.wd_start) * frac
+        embed_wd = h.wd_start + (h.embed_wd - h.wd_start) * frac  # embed WD ramps too
+        adam_wd = h.adam_wd  # Adam WD stays fixed (small params)
+        return muon_wd, embed_wd, adam_wd
+
+    def step_fn(step, lr_scale, frac):
+        optimizers.zero_grad_all()
+        train_loss = torch.zeros((), device=device)
+
+        # Apply adaptive weight decay
+        muon_wd, embed_wd, adam_wd = adaptive_wd(frac)
+        optimizers.update_wd(muon_wd, embed_wd, adam_wd)
+
+        for micro_step in range(h.grad_accum_steps):
+            if h.distributed:
+                model.require_backward_grad_sync = micro_step == h.grad_accum_steps - 1
+
+            if h.mtp_enabled:
+                x, y1, y2 = train_loader.next_batch_mtp(h.train_batch_tokens, h.grad_accum_steps)
+                with torch.autocast(device_type='cuda', dtype=torch.bfloat16, enabled=True):
+                    loss = base_model.forward_mtp(x, y1, y2) if not h.distributed else model.module.forward_mtp(x, y1, y2) if hasattr(model, 'module') else model(x, y1)
+                # Note: For DDP, we need to call through the DDP wrapper
+                # But MTP requires custom forward, so we bypass DDP here
+                # and handle gradient sync manually
+            else:
+                x, y = train_loader.next_batch(h.train_batch_tokens, h.grad_accum_steps)
+                with torch.autocast(device_type='cuda', dtype=torch.bfloat16, enabled=True):
+                    loss = model(x, y)
+
+            train_loss += loss.detach()
+            (loss / h.grad_accum_steps).backward()
+
+        train_loss /= h.grad_accum_steps
+
+        # Muon momentum warmup
+        f = min(step / h.muon_momentum_warmup_steps, 1.0) if h.muon_momentum_warmup_steps > 0 else 1.0
+        muon_momentum = (1 - f) * h.muon_momentum_warmup_start + f * h.muon_momentum
+        for group in optimizers.optimizer_muon.param_groups:
+            group['momentum'] = muon_momentum
+
+        for opt in optimizers:
+            for group in opt.param_groups:
+                group['lr'] = group['base_lr'] * lr_scale
+
+        if h.grad_clip_norm > 0:
+            torch.nn.utils.clip_grad_norm_(base_model.parameters(), h.grad_clip_norm)
+
+        optimizers.step()
+        return train_loss
+
+    # Warmup
+    if h.warmup_steps > 0:
+        initial_model_state = {name: tensor.detach().cpu().clone()
+                               for name, tensor in base_model.state_dict().items()}
+        initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers]
+        model.train()
+        for warmup_step in range(h.warmup_steps):
+            step_fn(warmup_step, 1.0, 0.0)
+            if warmup_step <= 5 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == h.warmup_steps:
+                log(f"warmup_step: {warmup_step + 1}/{h.warmup_steps}")
+
+        if h.num_loops > 0:
+            base_model.looping_active = True
+            log(f"loop_warmup: enabled encoder:{base_model.encoder_indices} decoder:{base_model.decoder_indices}")
+            for warmup_step in range(h.warmup_steps):
+                step_fn(warmup_step, 1.0, 0.0)
+                if warmup_step <= 5 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == h.warmup_steps:
+                    log(f"loop_warmup_step: {warmup_step + 1}/{h.warmup_steps}")
+            base_model.looping_active = False
+
+        base_model.load_state_dict(initial_model_state, strict=True)
+        for opt, state in zip(optimizers, initial_optimizer_states, strict=True):
+            opt.load_state_dict(state)
+        optimizers.zero_grad_all()
+        if h.distributed:
+            model.require_backward_grad_sync = True
+        train_loader = ShuffledSequenceLoader(h, device)
+
+    # EMA setup
+    ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()
+                 if 'mtp_proj' not in name}
+    ema_decay = h.ema_decay
+    training_time_ms = 0.0
+    stop_after_step = None
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+    step = 0
+
+    while True:
+        last_step = step == h.iterations or (stop_after_step is not None and step >= stop_after_step)
+        should_validate = last_step or (h.val_loss_every > 0 and step % h.val_loss_every == 0)
+        if should_validate:
+            torch.cuda.synchronize()
+            training_time_ms += 1e3 * (time.perf_counter() - t0)
+            val_loss, val_bpb = eval_val(h, device, val_data, model)
+            log(f"{step}/{h.iterations} val_loss: {val_loss:.4f} val_bpb: {val_bpb:.4f}")
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
+        if last_step:
+            if stop_after_step is not None and step < h.iterations:
+                log(f"stopping_early: wallclock_cap train_time: {training_time_ms:.0f}ms step: {step}/{h.iterations}")
+            break
+
+        elapsed_ms = training_time_ms + 1e3 * (time.perf_counter() - t0)
+        frac = training_frac(step, elapsed_ms)
+        scale = lr_mul(frac)
+
+        if h.num_loops > 0 and not base_model.looping_active and frac >= h.enable_looping_at:
+            base_model.looping_active = True
+            log(f"layer_loop: enabled step:{step} frac:{frac:.3f} encoder:{base_model.encoder_indices} decoder:{base_model.decoder_indices}")
+
+        train_loss = step_fn(step, scale, frac)
+
+        # EMA update (exclude MTP params)
+        with torch.no_grad():
+            for name, t in base_model.state_dict().items():
+                if 'mtp_proj' not in name and name in ema_state:
+                    ema_state[name].mul_(ema_decay).add_(t.detach().float(), alpha=1.0 - ema_decay)
+
+        step += 1
+        approx_training_time_ms = training_time_ms + 1e3 * (time.perf_counter() - t0)
+        should_log_train = h.train_log_every > 0 and (step <= 5 or step % h.train_log_every == 0 or stop_after_step is not None)
+        if should_log_train:
+            tok_per_sec = step * h.train_batch_tokens / (approx_training_time_ms / 1e3)
+            log(f"{step}/{h.iterations} train_loss: {train_loss.item():.4f} "
+                f"train_time: {approx_training_time_ms / 60000:.1f}m tok/s: {tok_per_sec:.0f}")
+
+        reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms
+        if h.distributed and max_wallclock_ms is not None:
+            reached_cap_tensor = torch.tensor(int(reached_cap), device=device)
+            dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX)
+            reached_cap = bool(reached_cap_tensor.item())
+        if stop_after_step is None and reached_cap:
+            stop_after_step = step
+
+    log(f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB "
+        f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB")
+
+    # Apply EMA weights (excluding MTP)
+    log('ema: applying EMA weights')
+    current_state = base_model.state_dict()
+    avg_state = {}
+    for name, t in ema_state.items():
+        if name in current_state:
+            avg_state[name] = t.to(dtype=current_state[name].dtype)
+    # Keep MTP proj as-is
+    for name in current_state:
+        if name not in avg_state:
+            avg_state[name] = current_state[name]
+    base_model.load_state_dict(avg_state, strict=True)
+
+    return base_model, compiled_model
+
+
+def train_and_eval(h, device):
+    random.seed(h.seed)
+    np.random.seed(h.seed)
+    torch.manual_seed(h.seed)
+    torch.cuda.manual_seed_all(h.seed)
+
+    val_data = ValidationData(h, device)
+    log(f"train_shards: {len(list(Path(h.datasets_dir).resolve().glob('fineweb_train_*.bin')))}")
+    log(f"val_tokens: {val_data.val_tokens.numel() - 1}")
+
+    base_model, compiled_model = train_model(h, device, val_data)
+    torch._dynamo.reset()
+
+    timed_eval('pre-quantization post-ema', eval_val, h, device, val_data, compiled_model)
+    serialize(h, base_model, Path(__file__).read_text(encoding='utf-8'))
+
+    if h.distributed:
+        dist.barrier()
+
+    eval_model = deserialize(h, device)
+    if h.num_loops > 0:
+        eval_model.looping_active = True
+    compiled_eval = torch.compile(eval_model, dynamic=False, fullgraph=True)
+    timed_eval('quantized', eval_val, h, device, val_data, compiled_eval)
+
+    if h.sliding_window_enabled:
+        timed_eval('quantized_sliding_window', eval_val_sliding, h, device, val_data, eval_model)
+
+    if h.ttt_enabled and h.sliding_window_enabled:
+        del eval_model, compiled_eval
+        torch._dynamo.reset()
+        torch.cuda.empty_cache()
+        ttt_model = deserialize(h, device)
+        if h.num_loops > 0:
+            ttt_model.looping_active = True
+        timed_eval('quantized_ttt', eval_val_ttt, h, device, val_data, ttt_model)
+        del ttt_model
+
+
+def main():
+    world_size = int(os.environ.get('WORLD_SIZE', '1'))
+    local_rank = int(os.environ.get('LOCAL_RANK', '0'))
+    distributed = 'RANK' in os.environ and 'WORLD_SIZE' in os.environ
+
+    if not torch.cuda.is_available():
+        raise RuntimeError('CUDA is required')
+    if world_size <= 0:
+        raise ValueError(f"WORLD_SIZE must be positive, got {world_size}")
+    if 8 % world_size != 0:
+        raise ValueError(f"WORLD_SIZE={world_size} must divide 8")
+
+    device = torch.device('cuda', local_rank)
+    torch.cuda.set_device(device)
+    if distributed:
+        dist.init_process_group(backend='nccl', device_id=device)
+        dist.barrier()
+
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    torch.set_float32_matmul_precision('high')
+    from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp
+    enable_cudnn_sdp(False)
+    enable_flash_sdp(True)
+    enable_mem_efficient_sdp(False)
+    enable_math_sdp(False)
+    torch._dynamo.config.optimize_ddp = False
+
+    h = Hyperparameters()
+    set_logging_hparams(h)
+
+    if h.is_main_process:
+        os.makedirs('logs', exist_ok=True)
+        log('=' * 100, console=False)
+        log('Hyperparameters:', console=True)
+        for k, v in sorted(vars(type(h)).items()):
+            if not k.startswith('_'):
+                log(f"  {k}: {v}", console=True)
+        log('=' * 100, console=False)
+        log(f"Running Python {sys.version}", console=False)
+        log(f"Running PyTorch {torch.__version__}", console=False)
+        log(subprocess.run(['nvidia-smi'], stdout=subprocess.PIPE, stderr=subprocess.PIPE,
+                          text=True, check=False).stdout, console=False)
+        log('=' * 100, console=False)
+
+        # Log novel techniques
+        log("\n=== NOVEL TECHNIQUES ===")
+        log(f"  MTP enabled: {h.mtp_enabled} (n={h.mtp_n}, weight={h.mtp_weight})")
+        log(f"  Adaptive WD: {h.adaptive_wd_enabled} ({h.wd_start} → {h.wd_end})")
+        log(f"  TTT chunk: {h.ttt_chunk_tokens} tokens")
+        log("========================\n")
+
+    train_and_eval(h, device)
+    if distributed:
+        dist.destroy_process_group()
+
+
+if __name__ == '__main__':
+    main()