Add files using upload-large-folder tool

.gitattributes

@@ -36,3 +36,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 train_hnet_with_docstring_18_04/wandb/run-20260417_085757-sa79g3yl/run-sa79g3yl.wandb filter=lfs diff=lfs merge=lfs -text
 wandb/run-20260418_121916-2mk39j3k/run-2mk39j3k.wandb filter=lfs diff=lfs merge=lfs -text
 pythia1b_v5_04_21/wandb/run-20260421_202839-8ing6xdi/run-8ing6xdi.wandb filter=lfs diff=lfs merge=lfs -text
+lr_sweep/hnet_xl_code_lr_5e-5/wandb/run-20260425_174418-uk7c7595/run-uk7c7595.wandb filter=lfs diff=lfs merge=lfs -text
+lr_sweep/hnet_xl_code_lr_5e-5/wandb/run-20260425_180603-5xd22ofy/run-5xd22ofy.wandb filter=lfs diff=lfs merge=lfs -text
+lr_sweep/pythia_1b_lr_1e-5/wandb/run-20260425_180609-3z5g26qd/run-3z5g26qd.wandb filter=lfs diff=lfs merge=lfs -text
+lr_sweep/pythia_1b_lr_2e-5/wandb/run-20260425_184822-bhvwo83l/run-bhvwo83l.wandb filter=lfs diff=lfs merge=lfs -text
+lr_sweep/pythia_1b_lr_5e-5/wandb/run-20260425_193045-vg3if73m/run-vg3if73m.wandb filter=lfs diff=lfs merge=lfs -text

lr_sweep/hnet_xl_code_lr_1e-4/.hydra/config.yaml

@@ -0,0 +1,55 @@
+model:
+  config_path: ${oc.env:PROJECT_ROOT}/hnet_project/configs/hnet_2stage_XL_code.json
+  checkpoint_path: ${oc.env:PROJECT_ROOT}/hnet_project/checkpoints/hnet_2stage_XL_code.pt
+training:
+  epochs: 1
+  batch_size: 4
+  eval_batch_size: 24
+  gradient_accumulation_steps: 4
+  lr: 0.0001
+  weight_decay: 0.1
+  betas:
+  - 0.9
+  - 0.95
+  eps: 1.0e-08
+  lr_scheduler: wsd
+  warmup_ratio: 0.1
+  decay_ratio: 0.2
+  warmup_steps: 100
+  min_lr_ratio: 0.1
+  lr_multiplier:
+  - 2.0
+  - 1.5
+  - 1.0
+  load_balancing_weight: 0.01
+  load_balancing_N: 4.0
+  max_grad_norm: 1.0
+  use_amp: true
+  resume: false
+  resume_checkpoint: null
+  warmup_model: true
+data:
+  path: /workspace/byte-llms-code/code_completion_exp/datasets/data_V4_full
+  max_context_len: 4096
+  max_target_len: 256
+  num_workers: 0
+  pin_memory: true
+  max_train_samples: null
+  max_val_samples: 2000
+logging:
+  log_interval: 10
+  save_interval: 0
+  eval_interval: 2000
+  save_every_epoch: false
+tracking:
+  enabled: true
+  backend: wandb
+  project: code-completion_lr-sweep
+  run_name: hnet_xl_code_lr_1e-4
+  entity: null
+  base_url: https://wandb.platun0v.ru
+  local_dir: ${paths.output_dir}
+paths:
+  output_dir: /workspace/byte-llms-code/outputs/lr_sweep/hnet_xl_code_lr_1e-4
+seed: 42
+device: cuda

lr_sweep/hnet_xl_code_lr_1e-4/.hydra/hydra.yaml

@@ -0,0 +1,166 @@
+hydra:
+  run:
+    dir: ${paths.output_dir}
+  sweep:
+    dir: outputs/multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath: []
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=RUN
+    task:
+    - tracking=wandb
+    - tracking.project=code-completion_lr-sweep
+    - tracking.run_name=hnet_xl_code_lr_1e-4
+    - training.lr=1e-4
+    - paths.output_dir=/workspace/byte-llms-code/outputs/lr_sweep/hnet_xl_code_lr_1e-4
+    - data.path=/workspace/byte-llms-code/code_completion_exp/datasets/data_V4_full
+  job:
+    name: train
+    chdir: false
+    override_dirname: data.path=/workspace/byte-llms-code/code_completion_exp/datasets/data_V4_full,paths.output_dir=/workspace/byte-llms-code/outputs/lr_sweep/hnet_xl_code_lr_1e-4,tracking.project=code-completion_lr-sweep,tracking.run_name=hnet_xl_code_lr_1e-4,tracking=wandb,training.lr=1e-4
+    id: ???
+    num: ???
+    config_name: config
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.3'
+    cwd: /workspace/byte-llms-code/code_completion_exp/train_hnet
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: /workspace/byte-llms-code/code_completion_exp/train_hnet/configs
+      schema: file
+      provider: main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /workspace/byte-llms-code/outputs/lr_sweep/hnet_xl_code_lr_1e-4
+    choices:
+      paths: default
+      tracking: wandb
+      logging: default
+      data: default
+      training: default
+      model: hnet_xl_code
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: basic
+      hydra/output: default
+  verbose: false

lr_sweep/hnet_xl_code_lr_1e-4/.hydra/overrides.yaml

@@ -0,0 +1,6 @@
+- tracking=wandb
+- tracking.project=code-completion_lr-sweep
+- tracking.run_name=hnet_xl_code_lr_1e-4
+- training.lr=1e-4
+- paths.output_dir=/workspace/byte-llms-code/outputs/lr_sweep/hnet_xl_code_lr_1e-4
+- data.path=/workspace/byte-llms-code/code_completion_exp/datasets/data_V4_full

lr_sweep/hnet_xl_code_lr_1e-4/model_best.pt

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+size 3315165139

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lr_sweep/hnet_xl_code_lr_1e-4/wandb/debug.log

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+2026-04-25 19:30:45,741 INFO    MainThread:108122 [wandb_init.py:init():849] wandb.init called with sweep_config: {}
+config: {'model': {'name': 'EleutherAI/pythia-1b', 'checkpoint_path': None, 'from_scratch': False}, 'training': {'epochs': 1, 'batch_size': 4, 'eval_batch_size': 12, 'gradient_accumulation_steps': 4, 'lr': 5e-05, 'weight_decay': 0.1, 'betas': [0.9, 0.95], 'eps': 1e-08, 'lr_scheduler': 'wsd', 'warmup_ratio': 0.1, 'decay_ratio': 0.2, 'warmup_steps': 100, 'min_lr_ratio': 0.1, 'max_grad_norm': 1.0, 'use_amp': True, 'resume': False, 'resume_checkpoint': None}, 'data': {'path': '/workspace/byte-llms-code/code_completion_exp/datasets/data_V4_full', 'max_context_len': 4096, 'max_target_len': 256, 'num_workers': 4, 'pin_memory': True, 'max_train_samples': None, 'max_val_samples': 2000}, 'logging': {'log_interval': 10, 'save_interval': 0, 'eval_interval': 2000, 'save_every_epoch': False}, 'tracking': {'enabled': True, 'backend': 'wandb', 'project': 'code-completion_lr-sweep', 'run_name': 'pythia_1b_lr_5e-5', 'entity': None, 'base_url': 'https://wandb.platun0v.ru', 'local_dir': '/workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5'}, 'paths': {'output_dir': '/workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5'}, 'seed': 42, 'device': 'cuda', '_wandb': {'code_path': 'code/code_completion_exp/train_pythia/train.py'}}
+2026-04-25 19:30:45,741 INFO    MainThread:108122 [wandb_init.py:init():892] starting backend
+2026-04-25 19:30:46,013 INFO    MainThread:108122 [wandb_init.py:init():895] sending inform_init request
+2026-04-25 19:30:46,036 INFO    MainThread:108122 [wandb_init.py:init():903] backend started and connected
+2026-04-25 19:30:46,039 INFO    MainThread:108122 [wandb_init.py:init():973] updated telemetry
+2026-04-25 19:30:46,057 INFO    MainThread:108122 [wandb_init.py:init():997] communicating run to backend with 90.0 second timeout
+2026-04-25 19:30:46,757 INFO    MainThread:108122 [wandb_init.py:init():1044] starting run threads in backend
+2026-04-25 19:30:46,918 INFO    MainThread:108122 [wandb_run.py:_console_start():2529] atexit reg
+2026-04-25 19:30:46,918 INFO    MainThread:108122 [wandb_run.py:_redirect():2377] redirect: wrap_raw
+2026-04-25 19:30:46,918 INFO    MainThread:108122 [wandb_run.py:_redirect():2446] Wrapping output streams.
+2026-04-25 19:30:46,918 INFO    MainThread:108122 [wandb_run.py:_redirect():2469] Redirects installed.
+2026-04-25 19:30:46,921 INFO    MainThread:108122 [wandb_init.py:init():1084] run started, returning control to user process
+2026-04-25 20:13:20,732 INFO    MainThread:108122 [wandb_run.py:_finish():2295] finishing run nikita/code-completion_lr-sweep/vg3if73m
+2026-04-25 20:13:20,733 INFO    MainThread:108122 [wandb_run.py:_atexit_cleanup():2494] got exitcode: 0
+2026-04-25 20:13:20,733 INFO    MainThread:108122 [wandb_run.py:_restore():2476] restore
+2026-04-25 20:13:20,733 INFO    MainThread:108122 [wandb_run.py:_restore():2482] restore done
+2026-04-25 20:13:21,758 INFO    MainThread:108122 [wandb_run.py:_footer_sync_info():3870] logging synced files

lr_sweep/pythia_1b_lr_5e-5/wandb/run-20260425_175754-xsz105vg/files/code/code_completion_exp/train_pythia/train.py

@@ -0,0 +1,606 @@
+"""
+Training Pipeline для Pythia (decoder-only transformer) на задаче Code Completion.
+
+Конфигурация через Hydra + OmegaConf, логирование в Trackio.
+Поддержка DDP через Accelerate для multi-GPU тренировки.
+
+Использование:
+    # Базовый запуск (single GPU)
+    python train.py
+
+    # Multi-GPU с Accelerate
+    accelerate launch train.py
+
+    # Multi-GPU с указанием количества GPU
+    accelerate launch --num_processes=4 train.py
+
+    # Переопределение параметров через CLI
+    python train.py training.lr=1e-4 training.epochs=5
+
+    # Выбор другого конфига модели
+    python train.py model=pythia_160m
+
+    # Multirun (sweep)
+    python train.py --multirun training.lr=1e-4,3e-4,1e-3
+
+    # Без логирования
+    python train.py tracking.enabled=false
+"""
+
+import os
+import math
+import time
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+from datasets import load_from_disk
+
+import hydra
+from hydra.core.hydra_config import HydraConfig
+from omegaconf import DictConfig, OmegaConf
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    AutoConfig,
+    PreTrainedTokenizerBase,
+)
+from accelerate import Accelerator
+from accelerate.utils import set_seed as accelerate_set_seed
+
+# Ensure repo root is on sys.path (needed when running from subdirectory)
+import sys
+sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
+
+# Shared training library
+from training_lib.utils import AverageMeter, log_message
+from training_lib.checkpointing import save_checkpoint, load_checkpoint
+from training_lib.schedulers import get_lr_scheduler
+from training_lib.tracking import init_tracking, log_metrics, finish_tracking
+from training_lib.validation import run_validation
+
+
+# ============================================================================
+# ДАННЫЕ
+# ============================================================================
+
+
+class CodeCompletionCollator:
+    """Collate function для батчирования примеров code completion."""
+
+    def __init__(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        max_context_len: int = 1024,
+        max_target_len: int = 256,
+    ):
+        self.tokenizer = tokenizer
+        self.max_context_len = max_context_len
+        self.max_target_len = max_target_len
+        self.pad_token_id = tokenizer.pad_token_id
+
+    def __call__(self, batch: list[dict]) -> dict:
+        contexts = [item["context"] for item in batch]
+        targets = [item["target"] for item in batch]
+
+        encoded_contexts = self.tokenizer(
+            contexts,
+            add_special_tokens=True,
+            truncation=True,
+            max_length=self.max_context_len,
+            return_tensors=None,
+        )
+        encoded_targets = self.tokenizer(
+            targets,
+            add_special_tokens=False,
+            truncation=True,
+            max_length=self.max_target_len,
+            return_tensors=None,
+        )
+
+        input_ids_list = []
+        context_lengths = []
+
+        for ctx_ids, tgt_ids in zip(
+            encoded_contexts["input_ids"], encoded_targets["input_ids"]
+        ):
+            tgt_ids = tgt_ids + [self.tokenizer.eos_token_id]
+            context_lengths.append(len(ctx_ids))
+            input_ids_list.append(ctx_ids + tgt_ids)
+
+        max_len = max(len(ids) for ids in input_ids_list)
+
+        padded_input_ids = []
+        attention_mask = []
+
+        for ids in input_ids_list:
+            padding_len = max_len - len(ids)
+            padded_input_ids.append(ids + [self.pad_token_id] * padding_len)
+            attention_mask.append([1] * len(ids) + [0] * padding_len)
+
+        return {
+            "input_ids": torch.tensor(padded_input_ids, dtype=torch.long),
+            "attention_mask": torch.tensor(attention_mask, dtype=torch.long),
+            "context_lengths": torch.tensor(context_lengths, dtype=torch.long),
+        }
+
+
+def create_dataloaders(
+    cfg: DictConfig, tokenizer: PreTrainedTokenizerBase
+) -> dict[str, DataLoader]:
+    """Создание DataLoader'ов для train и validation."""
+    dataset_dict = load_from_disk(cfg.data.path)
+
+    collator = CodeCompletionCollator(
+        tokenizer=tokenizer,
+        max_context_len=cfg.data.max_context_len,
+        max_target_len=cfg.data.max_target_len,
+    )
+
+    dataloaders = {}
+
+    if "train" in dataset_dict:
+        train_dataset = dataset_dict["train"]
+        max_train = cfg.data.get("max_train_samples", None)
+        if max_train is not None:
+            train_dataset = train_dataset.select(range(min(max_train, len(train_dataset))))
+        dataloaders["train"] = DataLoader(
+            train_dataset,
+            batch_size=cfg.training.batch_size,
+            shuffle=True,
+            collate_fn=collator,
+            num_workers=cfg.data.num_workers,
+            pin_memory=cfg.data.pin_memory,
+        )
+
+    if "validation" in dataset_dict:
+        val_dataset = dataset_dict["validation"]
+        max_val = cfg.data.get("max_val_samples", None)
+        if max_val is not None:
+            val_dataset = val_dataset.select(range(min(max_val, len(val_dataset))))
+        eval_batch_size = cfg.training.get("eval_batch_size", cfg.training.batch_size)
+        dataloaders["validation"] = DataLoader(
+            val_dataset,
+            batch_size=eval_batch_size,
+            shuffle=False,
+            collate_fn=collator,
+            num_workers=cfg.data.num_workers,
+            pin_memory=cfg.data.pin_memory,
+        )
+
+    return dataloaders
+
+
+
+
+# ============================================================================
+# LOSS ФУНКЦИИ
+# ============================================================================
+
+
+def compute_loss(
+    logits: torch.Tensor,
+    input_ids: torch.Tensor,
+    context_lengths: torch.Tensor,
+    attention_mask: torch.Tensor,
+) -> dict:
+    """Вычисление loss для авторегрессионной модели."""
+    batch_size, seq_len, vocab_size = logits.shape
+
+    shift_logits = logits[:, :-1, :].contiguous()
+    shift_labels = input_ids[:, 1:].contiguous()
+    shift_mask = attention_mask[:, 1:].contiguous()
+
+    target_mask = torch.zeros_like(shift_labels, dtype=torch.bool)
+    for i in range(batch_size):
+        ctx_len = context_lengths[i].item()
+        target_mask[i, ctx_len - 1 :] = True
+
+    final_mask = target_mask & shift_mask.bool()
+
+    if final_mask.sum() > 0:
+        loss = F.cross_entropy(
+            shift_logits[final_mask], shift_labels[final_mask], reduction="mean"
+        )
+    else:
+        loss = torch.tensor(0.0, device=logits.device)
+
+    return {"loss": loss}
+
+
+def _pythia_forward_loss(
+    model: nn.Module,
+    batch: dict,
+    cfg: DictConfig,
+    accelerator: Accelerator,
+) -> dict:
+    """Forward + loss for a plain HF causal LM (attention_mask= kwarg, .logits)."""
+    input_ids = batch["input_ids"]
+    attention_mask = batch["attention_mask"]
+    context_lengths = batch["context_lengths"]
+    output = model(input_ids, attention_mask=attention_mask)
+    return compute_loss(output.logits, input_ids, context_lengths, attention_mask)
+
+
+# ============================================================================
+# PARAMETER GROUPING
+# ============================================================================
+
+
+def group_params(model: nn.Module, weight_decay: float) -> list[dict]:
+    """Группировка параметров для optimizer."""
+    decay_params = []
+    no_decay_params = []
+    
+    for name, param in model.named_parameters():
+        if not param.requires_grad:
+            continue
+            
+        if "bias" in name or "LayerNorm" in name or "layernorm" in name:
+            no_decay_params.append(param)
+        else:
+            decay_params.append(param)
+    
+    return [
+        {"params": decay_params, "weight_decay": weight_decay},
+        {"params": no_decay_params, "weight_decay": 0.0},
+    ]
+
+
+
+
+# ============================================================================
+# TRAINING LOOP
+# ============================================================================
+
+
+def train_epoch(
+    model: nn.Module,
+    dataloader: DataLoader,
+    optimizer: torch.optim.Optimizer,
+    scheduler,
+    cfg: DictConfig,
+    epoch: int,
+    global_step: int,
+    accelerator: Accelerator,
+    val_dataloader: DataLoader | None = None,
+    best_val_loss: float = float("inf"),
+) -> tuple[int, float]:
+    """Один epoch тренировки. Возвращает (global_step, best_val_loss)."""
+    model.train()
+
+    loss_meter = AverageMeter()
+
+    optimizer.zero_grad()
+    accumulated_loss = 0.0
+    accumulated_steps = 0
+
+    epoch_start_time = time.time()
+    step_start_time = time.time()
+
+    for batch_idx, batch in enumerate(dataloader):
+        input_ids = batch["input_ids"]
+        attention_mask = batch["attention_mask"]
+        context_lengths = batch["context_lengths"]
+
+        with accelerator.autocast():
+            output = model(input_ids, attention_mask=attention_mask)
+            logits = output.logits
+            loss_dict = compute_loss(
+                logits, input_ids, context_lengths, attention_mask
+            )
+
+        loss = loss_dict["loss"] / cfg.training.gradient_accumulation_steps
+        accelerator.backward(loss)
+
+        accumulated_loss += loss_dict["loss"].item()
+        accumulated_steps += 1
+
+        if accumulated_steps == cfg.training.gradient_accumulation_steps:
+            if cfg.training.max_grad_norm > 0:
+                accelerator.clip_grad_norm_(
+                    model.parameters(), cfg.training.max_grad_norm
+                )
+
+            optimizer.step()
+            scheduler.step()
+            optimizer.zero_grad()
+
+            avg_loss = accumulated_loss / cfg.training.gradient_accumulation_steps
+            loss_meter.update(avg_loss)
+
+            global_step += 1
+
+            if global_step % cfg.logging.log_interval == 0:
+                step_time = time.time() - step_start_time
+                current_lr = scheduler.get_last_lr()[0]
+
+                metrics = {
+                    "train/loss": loss_meter.val,
+                    "train/loss_avg": loss_meter.avg,
+                    "train/lr": current_lr,
+                    "train/epoch": epoch,
+                    "train/step_time": step_time / cfg.logging.log_interval,
+                }
+
+                log_metrics(metrics, step=global_step)
+
+                log_message(
+                    f"Epoch {epoch} | Step {global_step} | "
+                    f"Loss: {loss_meter.avg:.4f} | "
+                    f"LR: {current_lr:.2e}",
+                    cfg,
+                    accelerator,
+                )
+
+                step_start_time = time.time()
+
+            if (
+                cfg.logging.save_interval > 0
+                and global_step % cfg.logging.save_interval == 0
+            ):
+                save_checkpoint(
+                    model, optimizer, scheduler, global_step, epoch, cfg, accelerator
+                )
+
+            eval_interval = cfg.logging.get("eval_interval", 0)
+            if (
+                eval_interval > 0
+                and val_dataloader is not None
+                and global_step % eval_interval == 0
+            ):
+                val_metrics = run_validation(
+                    model=model,
+                    dataloader=val_dataloader,
+                    cfg=cfg,
+                    global_step=global_step,
+                    accelerator=accelerator,
+                    forward_loss_fn=_pythia_forward_loss,
+                )
+
+                if val_metrics["val/loss"] < best_val_loss:
+                    best_val_loss = val_metrics["val/loss"]
+                    if accelerator.is_main_process:
+                        best_model_path = Path(cfg.paths.output_dir) / "model_best.pt"
+                        unwrapped_model = accelerator.unwrap_model(model)
+                        torch.save(unwrapped_model.state_dict(), best_model_path)
+                        log_message(
+                            f"New best model saved! Val loss: {best_val_loss:.4f}", 
+                            cfg, 
+                            accelerator
+                        )
+
+                        log_metrics(
+                            {
+                                "best/val_loss": best_val_loss,
+                                "best/val_perplexity": val_metrics["val/perplexity"],
+                                "best/step": global_step,
+                            },
+                            step=global_step,
+                        )
+
+                model.train()
+
+            accumulated_loss = 0.0
+            accumulated_steps = 0
+
+    epoch_time = time.time() - epoch_start_time
+    
+    log_message(
+        f"Epoch {epoch} completed in {epoch_time:.2f}s | "
+        f"Loss: {loss_meter.avg:.4f}",
+        cfg,
+        accelerator,
+    )
+
+    log_metrics({
+        "epoch/loss": loss_meter.avg,
+        "epoch/time": epoch_time,
+    })
+
+    return global_step, best_val_loss
+
+
+# ============================================================================
+# MAIN
+# ============================================================================
+
+
+@hydra.main(version_base=None, config_path="configs", config_name="config")
+def main(cfg: DictConfig):
+    """Главная функция тренировки с поддержкой DDP через Accelerate."""
+
+    # === Performance: Enable TF32 for faster matmuls on Ampere+ GPUs ===
+    torch.set_float32_matmul_precision('high')
+
+    # === Accelerator Setup ===
+    mixed_precision = "bf16" if cfg.training.use_amp else "no"
+    
+    accelerator = Accelerator(
+        mixed_precision=mixed_precision,
+        gradient_accumulation_steps=cfg.training.gradient_accumulation_steps,
+    )
+
+    # === Setup ===
+    accelerate_set_seed(cfg.seed)
+    
+    if cfg.paths.output_dir is None:
+        cfg.paths.output_dir = HydraConfig.get().runtime.output_dir
+
+    OmegaConf.resolve(cfg)
+
+    log_message(f"CUDA_VISIBLE_DEVICES: {os.environ.get('CUDA_VISIBLE_DEVICES', 'not set')}", cfg, accelerator)
+    log_message(f"Number of processes: {accelerator.num_processes}", cfg, accelerator)
+    log_message(f"Process index: {accelerator.process_index}", cfg, accelerator)
+    log_message(f"Mixed precision: {mixed_precision}", cfg, accelerator)
+    
+    log_message("=" * 60, cfg, accelerator)
+    log_message("Pythia Training Pipeline (Hydra + Trackio + Accelerate)", cfg, accelerator)
+    log_message("=" * 60, cfg, accelerator)
+    log_message(f"Config:\n{OmegaConf.to_yaml(cfg)}", cfg, accelerator)
+
+    # === Trackio Init ===
+    init_tracking(cfg, accelerator)
+
+    # === Tokenizer ===
+    log_message("Initializing tokenizer...", cfg, accelerator)
+    tokenizer = AutoTokenizer.from_pretrained(cfg.model.name)
+    
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.pad_token_id = tokenizer.eos_token_id
+
+    # === Model ===
+    log_message("Loading model...", cfg, accelerator)
+    
+    # Flash Attention 2
+    torch_dtype = torch.bfloat16 if cfg.training.use_amp else torch.float32
+    
+    if cfg.model.checkpoint_path:
+        model = AutoModelForCausalLM.from_pretrained(
+            cfg.model.name,
+            attn_implementation="flash_attention_2",
+            torch_dtype=torch_dtype,
+        )
+        checkpoint = torch.load(cfg.model.checkpoint_path, map_location="cpu")
+        model.load_state_dict(checkpoint["model_state_dict"] if "model_state_dict" in checkpoint else checkpoint)
+        log_message(f"Loaded checkpoint: {cfg.model.checkpoint_path}", cfg, accelerator)
+    elif cfg.model.from_scratch:
+        config = AutoConfig.from_pretrained(cfg.model.name)
+        config._attn_implementation = "flash_attention_2"
+        model = AutoModelForCausalLM.from_config(config, torch_dtype=torch_dtype)
+        log_message(f"Initialized from scratch: {cfg.model.name}", cfg, accelerator)
+    else:
+        model = AutoModelForCausalLM.from_pretrained(
+            cfg.model.name,
+            attn_implementation="flash_attention_2",
+            torch_dtype=torch_dtype,
+        )
+        log_message(f"Loaded pretrained: {cfg.model.name}", cfg, accelerator)
+
+    model.train()
+
+    # Log model info
+    total_params = sum(p.numel() for p in model.parameters())
+    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+    log_message(f"Total params: {total_params:,}", cfg, accelerator)
+    log_message(f"Trainable params: {trainable_params:,}", cfg, accelerator)
+
+    # === Data ===
+    log_message("Creating dataloaders...", cfg, accelerator)
+    dataloaders = create_dataloaders(cfg, tokenizer)
+
+    train_dataloader = dataloaders["train"]
+    val_dataloader = dataloaders.get("validation", None)
+
+    log_message(f"Train dataset size: {len(train_dataloader.dataset)}", cfg, accelerator)
+    log_message(f"Train batches per epoch (before DDP split): {len(train_dataloader)}", cfg, accelerator)
+
+    if val_dataloader:
+        log_message(f"Validation dataset size: {len(val_dataloader.dataset)}", cfg, accelerator)
+        log_message(f"Validation batches: {len(val_dataloader)}", cfg, accelerator)
+    else:
+        log_message("No validation dataset found", cfg, accelerator)
+
+    # === Optimizer ===
+    log_message("Creating optimizer...", cfg, accelerator)
+    param_groups = group_params(model, cfg.training.weight_decay)
+
+    optimizer = torch.optim.AdamW(
+        param_groups,
+        lr=cfg.training.lr,
+        betas=tuple(cfg.training.betas),
+        eps=cfg.training.eps,
+    )
+
+    # === Scheduler ===
+    steps_per_epoch = math.ceil(
+        len(train_dataloader) / accelerator.num_processes
+    )
+    total_steps = (
+        cfg.training.epochs
+        * steps_per_epoch
+        // cfg.training.gradient_accumulation_steps
+    )
+    scheduler = get_lr_scheduler(optimizer, cfg, total_steps)
+
+    log_message(
+        f"Total steps: {total_steps}, Steps per epoch: {steps_per_epoch}", 
+        cfg, 
+        accelerator
+    )
+
+    # === Accelerate Prepare ===
+    log_message("Preparing model, optimizer, and dataloaders with Accelerate...", cfg, accelerator)
+    
+    if val_dataloader is not None:
+        model, optimizer, train_dataloader, val_dataloader, scheduler = accelerator.prepare(
+            model, optimizer, train_dataloader, val_dataloader, scheduler
+        )
+    else:
+        model, optimizer, train_dataloader, scheduler = accelerator.prepare(
+            model, optimizer, train_dataloader, scheduler
+        )
+
+    log_message(f"Train batches per epoch (after DDP split): {len(train_dataloader)}", cfg, accelerator)
+
+    # === Resume ===
+    global_step = 0
+    start_epoch = 1
+
+    if cfg.training.resume and cfg.training.resume_checkpoint:
+        global_step, start_epoch = load_checkpoint(
+            model, optimizer, scheduler, cfg.training.resume_checkpoint, cfg, accelerator
+        )
+        start_epoch += 1
+
+    # === Training Loop ===
+    log_message("Starting training...", cfg, accelerator)
+
+    best_val_loss = float("inf")
+
+    try:
+        for epoch in range(start_epoch, cfg.training.epochs + 1):
+            log_message(f"\n{'=' * 60}", cfg, accelerator)
+            log_message(f"EPOCH {epoch}/{cfg.training.epochs}", cfg, accelerator)
+            log_message(f"{'=' * 60}", cfg, accelerator)
+
+            global_step, best_val_loss = train_epoch(
+                model=model,
+                dataloader=train_dataloader,
+                optimizer=optimizer,
+                scheduler=scheduler,
+                cfg=cfg,
+                epoch=epoch,
+                global_step=global_step,
+                accelerator=accelerator,
+                val_dataloader=val_dataloader,
+                best_val_loss=best_val_loss,
+            )
+
+            if cfg.logging.save_every_epoch:
+                save_checkpoint(
+                    model, optimizer, scheduler, global_step, epoch, cfg, accelerator
+                )
+
+    except KeyboardInterrupt:
+        log_message("Training interrupted by user", cfg, accelerator)
+        save_checkpoint(model, optimizer, scheduler, global_step, epoch, cfg, accelerator)
+
+    # === Final Save ===
+    log_message("\nTraining completed!", cfg, accelerator)
+
+    if accelerator.is_main_process:
+        final_model_path = Path(cfg.paths.output_dir) / "model_final.pt"
+        unwrapped_model = accelerator.unwrap_model(model)
+        torch.save(unwrapped_model.state_dict(), final_model_path)
+        log_message(f"Final model: {final_model_path}", cfg, accelerator)
+
+    accelerator.wait_for_everyone()
+    finish_tracking()
+
+
+if __name__ == "__main__":
+    main()

lr_sweep/pythia_1b_lr_5e-5/wandb/run-20260425_175754-xsz105vg/files/config.yaml

@@ -0,0 +1,146 @@
+_wandb:
+    value:
+        cli_version: 0.24.0
+        code_path: code/code_completion_exp/train_pythia/train.py
+        e:
+            7tgf82pk1kp15jun4e833mn85qzvctn8:
+                args:
+                    - tracking=wandb
+                    - tracking.project=code-completion_lr-sweep
+                    - tracking.run_name=pythia_1b_lr_5e-5
+                    - training.lr=5e-5
+                    - paths.output_dir=/workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5
+                    - model=pythia_1b
+                    - data.path=/workspace/byte-llms-code/code_completion_exp/datasets/data_V4_full
+                codePath: code_completion_exp/train_pythia/train.py
+                codePathLocal: train.py
+                cpu_count: 64
+                cpu_count_logical: 128
+                cudaVersion: "12.2"
+                disk:
+                    /:
+                        total: "265214230528"
+                        used: "70239207424"
+                email: nikita@local.ru
+                executable: /venv/bytellm/bin/python
+                git:
+                    commit: f111e13281aa0dc58e24302edab5b0d5c2024586
+                    remote: https://github.com/naryst/byte-llms-code.git
+                gpu: NVIDIA H100 80GB HBM3
+                gpu_count: 4
+                gpu_nvidia:
+                    - architecture: Hopper
+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-b60cdcab-2033-2009-41de-be646c953a20
+                    - architecture: Hopper
+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-9982b420-4520-4238-c378-ec5a46015474
+                    - architecture: Hopper
+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-e26ebaac-aaa6-3eed-17ab-a3dce303a76f
+                    - architecture: Hopper
+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-9dfc6dba-0be6-4a10-1027-336cc0e65134
+                host: 7504e518d24a
+                memory:
+                    total: "1081679683584"
+                os: Linux-5.4.0-176-generic-x86_64-with-glibc2.35
+                program: /workspace/byte-llms-code/code_completion_exp/train_pythia/train.py
+                python: CPython 3.12.0
+                root: /workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5
+                startedAt: "2026-04-25T17:57:54.046461Z"
+                writerId: 7tgf82pk1kp15jun4e833mn85qzvctn8
+        m: []
+        python_version: 3.12.0
+        t:
+            "1":
+                - 1
+                - 11
+                - 49
+                - 50
+                - 51
+                - 71
+                - 105
+            "2":
+                - 1
+                - 11
+                - 49
+                - 50
+                - 51
+                - 71
+                - 105
+            "3":
+                - 2
+                - 13
+                - 16
+                - 61
+            "4": 3.12.0
+            "5": 0.24.0
+            "6": 4.57.6
+            "12": 0.24.0
+            "13": linux-x86_64
+data:
+    value:
+        max_context_len: 4096
+        max_target_len: 256
+        max_train_samples: 20000
+        max_val_samples: 2000
+        num_workers: 4
+        path: /workspace/byte-llms-code/code_completion_exp/datasets/data_V4_full
+        pin_memory: true
+device:
+    value: cuda
+logging:
+    value:
+        eval_interval: 1000
+        log_interval: 10
+        save_every_epoch: true
+        save_interval: 3000
+model:
+    value:
+        checkpoint_path: null
+        from_scratch: false
+        name: EleutherAI/pythia-1b
+paths:
+    value:
+        output_dir: /workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5
+seed:
+    value: 42
+tracking:
+    value:
+        backend: wandb
+        base_url: https://wandb.platun0v.ru
+        enabled: true
+        entity: null
+        local_dir: /workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5
+        project: code-completion_lr-sweep
+        run_name: pythia_1b_lr_5e-5
+training:
+    value:
+        batch_size: 4
+        betas:
+            - 0.9
+            - 0.95
+        decay_ratio: 0.2
+        epochs: 1
+        eps: 1e-08
+        eval_batch_size: 12
+        gradient_accumulation_steps: 4
+        lr: 5e-05
+        lr_scheduler: wsd
+        max_grad_norm: 1
+        min_lr_ratio: 0.1
+        resume: false
+        resume_checkpoint: null
+        use_amp: true
+        warmup_ratio: 0.1
+        warmup_steps: 100
+        weight_decay: 0.1

lr_sweep/pythia_1b_lr_5e-5/wandb/run-20260425_175754-xsz105vg/files/output.log

@@ -0,0 +1,29 @@
+[2026-04-25 17:57:54] Initializing tokenizer...
+[2026-04-25 17:57:55] Loading model...
+`torch_dtype` is deprecated! Use `dtype` instead!
+[2026-04-25 17:57:58] Loaded pretrained: EleutherAI/pythia-1b
+[2026-04-25 17:57:58] Total params: 1,011,781,632
+[2026-04-25 17:57:58] Trainable params: 1,011,781,632
+[2026-04-25 17:57:58] Creating dataloaders...
+[2026-04-25 17:57:58] Train dataset size: 20000
+[2026-04-25 17:57:58] Train batches per epoch (before DDP split): 5000
+[2026-04-25 17:57:58] Validation dataset size: 2000
+[2026-04-25 17:57:58] Validation batches: 167
+[2026-04-25 17:57:58] Creating optimizer...
+[2026-04-25 17:57:58] Total steps: 625, Steps per epoch: 2500
+[2026-04-25 17:57:58] Preparing model, optimizer, and dataloaders with Accelerate...
+[2026-04-25 17:58:00] Train batches per epoch (after DDP split): 2500
+[2026-04-25 17:58:00] Starting training...
+[2026-04-25 17:58:00]
+============================================================
+[2026-04-25 17:58:00] EPOCH 1/1
+[2026-04-25 17:58:00] ============================================================
+[2026-04-25 17:58:03] Epoch 1 | Step 10 | Loss: 1.6976 | LR: 1.95e-05
+[2026-04-25 17:58:06] Epoch 1 | Step 20 | Loss: 1.3818 | LR: 3.40e-05
+[2026-04-25 17:58:09] Epoch 1 | Step 30 | Loss: 1.2980 | LR: 4.85e-05
+[2026-04-25 17:58:11] Epoch 1 | Step 40 | Loss: 1.2758 | LR: 5.00e-05
+[2026-04-25 17:58:12] Training interrupted by user
+[2026-04-25 17:58:19] Checkpoint saved: /workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5/checkpoints/checkpoint_step_0.pt
+[2026-04-25 17:58:25]
+Training completed!
+[2026-04-25 17:58:27] Final model: /workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5/model_final.pt

lr_sweep/pythia_1b_lr_5e-5/wandb/run-20260425_175754-xsz105vg/files/requirements.txt

@@ -0,0 +1,245 @@
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+Send2Trash==1.8.3
+semantic-version==2.10.0
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+regex==2025.9.18
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+PyYAML==6.0.3
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+python-json-logger==4.0.0
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+pyparsing==3.2.5
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+platformdirs==4.5.0
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+pexpect==4.9.0
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lr_sweep/pythia_1b_lr_5e-5/wandb/run-20260425_175754-xsz105vg/files/wandb-metadata.json

@@ -0,0 +1,70 @@
+{
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+    "paths.output_dir=/workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5",
+    "model=pythia_1b",
+    "data.path=/workspace/byte-llms-code/code_completion_exp/datasets/data_V4_full"
+  ],
+  "program": "/workspace/byte-llms-code/code_completion_exp/train_pythia/train.py",
+  "codePath": "code_completion_exp/train_pythia/train.py",
+  "codePathLocal": "train.py",
+  "git": {
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lr_sweep/pythia_1b_lr_5e-5/wandb/run-20260425_175754-xsz105vg/files/wandb-summary.json

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lr_sweep/pythia_1b_lr_5e-5/wandb/run-20260425_175754-xsz105vg/logs/debug.log

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+2026-04-25 17:57:54,048 INFO    MainThread:59233 [wandb_init.py:setup_run_log_directory():717] Logging user logs to /workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5/wandb/run-20260425_175754-xsz105vg/logs/debug.log
+2026-04-25 17:57:54,048 INFO    MainThread:59233 [wandb_init.py:setup_run_log_directory():718] Logging internal logs to /workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5/wandb/run-20260425_175754-xsz105vg/logs/debug-internal.log
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+2026-04-25 17:57:54,048 INFO    MainThread:59233 [wandb_init.py:init():849] wandb.init called with sweep_config: {}
+config: {'model': {'name': 'EleutherAI/pythia-1b', 'checkpoint_path': None, 'from_scratch': False}, 'training': {'epochs': 1, 'batch_size': 4, 'eval_batch_size': 12, 'gradient_accumulation_steps': 4, 'lr': 5e-05, 'weight_decay': 0.1, 'betas': [0.9, 0.95], 'eps': 1e-08, 'lr_scheduler': 'wsd', 'warmup_ratio': 0.1, 'decay_ratio': 0.2, 'warmup_steps': 100, 'min_lr_ratio': 0.1, 'max_grad_norm': 1.0, 'use_amp': True, 'resume': False, 'resume_checkpoint': None}, 'data': {'path': '/workspace/byte-llms-code/code_completion_exp/datasets/data_V4_full', 'max_context_len': 4096, 'max_target_len': 256, 'num_workers': 4, 'pin_memory': True, 'max_train_samples': 20000, 'max_val_samples': 2000}, 'logging': {'log_interval': 10, 'save_interval': 3000, 'eval_interval': 1000, 'save_every_epoch': True}, 'tracking': {'enabled': True, 'backend': 'wandb', 'project': 'code-completion_lr-sweep', 'run_name': 'pythia_1b_lr_5e-5', 'entity': None, 'base_url': 'https://wandb.platun0v.ru', 'local_dir': '/workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5'}, 'paths': {'output_dir': '/workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5'}, 'seed': 42, 'device': 'cuda', '_wandb': {'code_path': 'code/code_completion_exp/train_pythia/train.py'}}
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lr_sweep/pythia_1b_lr_5e-5/wandb/run-20260425_193045-vg3if73m/files/code/code_completion_exp/train_pythia/train.py

@@ -0,0 +1,606 @@
+"""
+Training Pipeline для Pythia (decoder-only transformer) на задаче Code Completion.
+
+Конфигурация через Hydra + OmegaConf, логирование в Trackio.
+Поддержка DDP через Accelerate для multi-GPU тренировки.
+
+Использование:
+    # Базовый запуск (single GPU)
+    python train.py
+
+    # Multi-GPU с Accelerate
+    accelerate launch train.py
+
+    # Multi-GPU с указанием количества GPU
+    accelerate launch --num_processes=4 train.py
+
+    # Переопределение параметров через CLI
+    python train.py training.lr=1e-4 training.epochs=5
+
+    # Выбор другого конфига модели
+    python train.py model=pythia_160m
+
+    # Multirun (sweep)
+    python train.py --multirun training.lr=1e-4,3e-4,1e-3
+
+    # Без логирования
+    python train.py tracking.enabled=false
+"""
+
+import os
+import math
+import time
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+from datasets import load_from_disk
+
+import hydra
+from hydra.core.hydra_config import HydraConfig
+from omegaconf import DictConfig, OmegaConf
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    AutoConfig,
+    PreTrainedTokenizerBase,
+)
+from accelerate import Accelerator
+from accelerate.utils import set_seed as accelerate_set_seed
+
+# Ensure repo root is on sys.path (needed when running from subdirectory)
+import sys
+sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
+
+# Shared training library
+from training_lib.utils import AverageMeter, log_message
+from training_lib.checkpointing import save_checkpoint, load_checkpoint
+from training_lib.schedulers import get_lr_scheduler
+from training_lib.tracking import init_tracking, log_metrics, finish_tracking
+from training_lib.validation import run_validation
+
+
+# ============================================================================
+# ДАННЫЕ
+# ============================================================================
+
+
+class CodeCompletionCollator:
+    """Collate function для батчирования примеров code completion."""
+
+    def __init__(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        max_context_len: int = 1024,
+        max_target_len: int = 256,
+    ):
+        self.tokenizer = tokenizer
+        self.max_context_len = max_context_len
+        self.max_target_len = max_target_len
+        self.pad_token_id = tokenizer.pad_token_id
+
+    def __call__(self, batch: list[dict]) -> dict:
+        contexts = [item["context"] for item in batch]
+        targets = [item["target"] for item in batch]
+
+        encoded_contexts = self.tokenizer(
+            contexts,
+            add_special_tokens=True,
+            truncation=True,
+            max_length=self.max_context_len,
+            return_tensors=None,
+        )
+        encoded_targets = self.tokenizer(
+            targets,
+            add_special_tokens=False,
+            truncation=True,
+            max_length=self.max_target_len,
+            return_tensors=None,
+        )
+
+        input_ids_list = []
+        context_lengths = []
+
+        for ctx_ids, tgt_ids in zip(
+            encoded_contexts["input_ids"], encoded_targets["input_ids"]
+        ):
+            tgt_ids = tgt_ids + [self.tokenizer.eos_token_id]
+            context_lengths.append(len(ctx_ids))
+            input_ids_list.append(ctx_ids + tgt_ids)
+
+        max_len = max(len(ids) for ids in input_ids_list)
+
+        padded_input_ids = []
+        attention_mask = []
+
+        for ids in input_ids_list:
+            padding_len = max_len - len(ids)
+            padded_input_ids.append(ids + [self.pad_token_id] * padding_len)
+            attention_mask.append([1] * len(ids) + [0] * padding_len)
+
+        return {
+            "input_ids": torch.tensor(padded_input_ids, dtype=torch.long),
+            "attention_mask": torch.tensor(attention_mask, dtype=torch.long),
+            "context_lengths": torch.tensor(context_lengths, dtype=torch.long),
+        }
+
+
+def create_dataloaders(
+    cfg: DictConfig, tokenizer: PreTrainedTokenizerBase
+) -> dict[str, DataLoader]:
+    """Создание DataLoader'ов для train и validation."""
+    dataset_dict = load_from_disk(cfg.data.path)
+
+    collator = CodeCompletionCollator(
+        tokenizer=tokenizer,
+        max_context_len=cfg.data.max_context_len,
+        max_target_len=cfg.data.max_target_len,
+    )
+
+    dataloaders = {}
+
+    if "train" in dataset_dict:
+        train_dataset = dataset_dict["train"]
+        max_train = cfg.data.get("max_train_samples", None)
+        if max_train is not None:
+            train_dataset = train_dataset.select(range(min(max_train, len(train_dataset))))
+        dataloaders["train"] = DataLoader(
+            train_dataset,
+            batch_size=cfg.training.batch_size,
+            shuffle=True,
+            collate_fn=collator,
+            num_workers=cfg.data.num_workers,
+            pin_memory=cfg.data.pin_memory,
+        )
+
+    if "validation" in dataset_dict:
+        val_dataset = dataset_dict["validation"]
+        max_val = cfg.data.get("max_val_samples", None)
+        if max_val is not None:
+            val_dataset = val_dataset.select(range(min(max_val, len(val_dataset))))
+        eval_batch_size = cfg.training.get("eval_batch_size", cfg.training.batch_size)
+        dataloaders["validation"] = DataLoader(
+            val_dataset,
+            batch_size=eval_batch_size,
+            shuffle=False,
+            collate_fn=collator,
+            num_workers=cfg.data.num_workers,
+            pin_memory=cfg.data.pin_memory,
+        )
+
+    return dataloaders
+
+
+
+
+# ============================================================================
+# LOSS ФУНКЦИИ
+# ============================================================================
+
+
+def compute_loss(
+    logits: torch.Tensor,
+    input_ids: torch.Tensor,
+    context_lengths: torch.Tensor,
+    attention_mask: torch.Tensor,
+) -> dict:
+    """Вычисление loss для авторегрессионной модели."""
+    batch_size, seq_len, vocab_size = logits.shape
+
+    shift_logits = logits[:, :-1, :].contiguous()
+    shift_labels = input_ids[:, 1:].contiguous()
+    shift_mask = attention_mask[:, 1:].contiguous()
+
+    target_mask = torch.zeros_like(shift_labels, dtype=torch.bool)
+    for i in range(batch_size):
+        ctx_len = context_lengths[i].item()
+        target_mask[i, ctx_len - 1 :] = True
+
+    final_mask = target_mask & shift_mask.bool()
+
+    if final_mask.sum() > 0:
+        loss = F.cross_entropy(
+            shift_logits[final_mask], shift_labels[final_mask], reduction="mean"
+        )
+    else:
+        loss = torch.tensor(0.0, device=logits.device)
+
+    return {"loss": loss}
+
+
+def _pythia_forward_loss(
+    model: nn.Module,
+    batch: dict,
+    cfg: DictConfig,
+    accelerator: Accelerator,
+) -> dict:
+    """Forward + loss for a plain HF causal LM (attention_mask= kwarg, .logits)."""
+    input_ids = batch["input_ids"]
+    attention_mask = batch["attention_mask"]
+    context_lengths = batch["context_lengths"]
+    output = model(input_ids, attention_mask=attention_mask)
+    return compute_loss(output.logits, input_ids, context_lengths, attention_mask)
+
+
+# ============================================================================
+# PARAMETER GROUPING
+# ============================================================================
+
+
+def group_params(model: nn.Module, weight_decay: float) -> list[dict]:
+    """Группировка параметров для optimizer."""
+    decay_params = []
+    no_decay_params = []
+    
+    for name, param in model.named_parameters():
+        if not param.requires_grad:
+            continue
+            
+        if "bias" in name or "LayerNorm" in name or "layernorm" in name:
+            no_decay_params.append(param)
+        else:
+            decay_params.append(param)
+    
+    return [
+        {"params": decay_params, "weight_decay": weight_decay},
+        {"params": no_decay_params, "weight_decay": 0.0},
+    ]
+
+
+
+
+# ============================================================================
+# TRAINING LOOP
+# ============================================================================
+
+
+def train_epoch(
+    model: nn.Module,
+    dataloader: DataLoader,
+    optimizer: torch.optim.Optimizer,
+    scheduler,
+    cfg: DictConfig,
+    epoch: int,
+    global_step: int,
+    accelerator: Accelerator,
+    val_dataloader: DataLoader | None = None,
+    best_val_loss: float = float("inf"),
+) -> tuple[int, float]:
+    """Один epoch тренировки. Возвращает (global_step, best_val_loss)."""
+    model.train()
+
+    loss_meter = AverageMeter()
+
+    optimizer.zero_grad()
+    accumulated_loss = 0.0
+    accumulated_steps = 0
+
+    epoch_start_time = time.time()
+    step_start_time = time.time()
+
+    for batch_idx, batch in enumerate(dataloader):
+        input_ids = batch["input_ids"]
+        attention_mask = batch["attention_mask"]
+        context_lengths = batch["context_lengths"]
+
+        with accelerator.autocast():
+            output = model(input_ids, attention_mask=attention_mask)
+            logits = output.logits
+            loss_dict = compute_loss(
+                logits, input_ids, context_lengths, attention_mask
+            )
+
+        loss = loss_dict["loss"] / cfg.training.gradient_accumulation_steps
+        accelerator.backward(loss)
+
+        accumulated_loss += loss_dict["loss"].item()
+        accumulated_steps += 1
+
+        if accumulated_steps == cfg.training.gradient_accumulation_steps:
+            if cfg.training.max_grad_norm > 0:
+                accelerator.clip_grad_norm_(
+                    model.parameters(), cfg.training.max_grad_norm
+                )
+
+            optimizer.step()
+            scheduler.step()
+            optimizer.zero_grad()
+
+            avg_loss = accumulated_loss / cfg.training.gradient_accumulation_steps
+            loss_meter.update(avg_loss)
+
+            global_step += 1
+
+            if global_step % cfg.logging.log_interval == 0:
+                step_time = time.time() - step_start_time
+                current_lr = scheduler.get_last_lr()[0]
+
+                metrics = {
+                    "train/loss": loss_meter.val,
+                    "train/loss_avg": loss_meter.avg,
+                    "train/lr": current_lr,
+                    "train/epoch": epoch,
+                    "train/step_time": step_time / cfg.logging.log_interval,
+                }
+
+                log_metrics(metrics, step=global_step)
+
+                log_message(
+                    f"Epoch {epoch} | Step {global_step} | "
+                    f"Loss: {loss_meter.avg:.4f} | "
+                    f"LR: {current_lr:.2e}",
+                    cfg,
+                    accelerator,
+                )
+
+                step_start_time = time.time()
+
+            if (
+                cfg.logging.save_interval > 0
+                and global_step % cfg.logging.save_interval == 0
+            ):
+                save_checkpoint(
+                    model, optimizer, scheduler, global_step, epoch, cfg, accelerator
+                )
+
+            eval_interval = cfg.logging.get("eval_interval", 0)
+            if (
+                eval_interval > 0
+                and val_dataloader is not None
+                and global_step % eval_interval == 0
+            ):
+                val_metrics = run_validation(
+                    model=model,
+                    dataloader=val_dataloader,
+                    cfg=cfg,
+                    global_step=global_step,
+                    accelerator=accelerator,
+                    forward_loss_fn=_pythia_forward_loss,
+                )
+
+                if val_metrics["val/loss"] < best_val_loss:
+                    best_val_loss = val_metrics["val/loss"]
+                    if accelerator.is_main_process:
+                        best_model_path = Path(cfg.paths.output_dir) / "model_best.pt"
+                        unwrapped_model = accelerator.unwrap_model(model)
+                        torch.save(unwrapped_model.state_dict(), best_model_path)
+                        log_message(
+                            f"New best model saved! Val loss: {best_val_loss:.4f}", 
+                            cfg, 
+                            accelerator
+                        )
+
+                        log_metrics(
+                            {
+                                "best/val_loss": best_val_loss,
+                                "best/val_perplexity": val_metrics["val/perplexity"],
+                                "best/step": global_step,
+                            },
+                            step=global_step,
+                        )
+
+                model.train()
+
+            accumulated_loss = 0.0
+            accumulated_steps = 0
+
+    epoch_time = time.time() - epoch_start_time
+    
+    log_message(
+        f"Epoch {epoch} completed in {epoch_time:.2f}s | "
+        f"Loss: {loss_meter.avg:.4f}",
+        cfg,
+        accelerator,
+    )
+
+    log_metrics({
+        "epoch/loss": loss_meter.avg,
+        "epoch/time": epoch_time,
+    })
+
+    return global_step, best_val_loss
+
+
+# ============================================================================
+# MAIN
+# ============================================================================
+
+
+@hydra.main(version_base=None, config_path="configs", config_name="config")
+def main(cfg: DictConfig):
+    """Главная функция тренировки с поддержкой DDP через Accelerate."""
+
+    # === Performance: Enable TF32 for faster matmuls on Ampere+ GPUs ===
+    torch.set_float32_matmul_precision('high')
+
+    # === Accelerator Setup ===
+    mixed_precision = "bf16" if cfg.training.use_amp else "no"
+    
+    accelerator = Accelerator(
+        mixed_precision=mixed_precision,
+        gradient_accumulation_steps=cfg.training.gradient_accumulation_steps,
+    )
+
+    # === Setup ===
+    accelerate_set_seed(cfg.seed)
+    
+    if cfg.paths.output_dir is None:
+        cfg.paths.output_dir = HydraConfig.get().runtime.output_dir
+
+    OmegaConf.resolve(cfg)
+
+    log_message(f"CUDA_VISIBLE_DEVICES: {os.environ.get('CUDA_VISIBLE_DEVICES', 'not set')}", cfg, accelerator)
+    log_message(f"Number of processes: {accelerator.num_processes}", cfg, accelerator)
+    log_message(f"Process index: {accelerator.process_index}", cfg, accelerator)
+    log_message(f"Mixed precision: {mixed_precision}", cfg, accelerator)
+    
+    log_message("=" * 60, cfg, accelerator)
+    log_message("Pythia Training Pipeline (Hydra + Trackio + Accelerate)", cfg, accelerator)
+    log_message("=" * 60, cfg, accelerator)
+    log_message(f"Config:\n{OmegaConf.to_yaml(cfg)}", cfg, accelerator)
+
+    # === Trackio Init ===
+    init_tracking(cfg, accelerator)
+
+    # === Tokenizer ===
+    log_message("Initializing tokenizer...", cfg, accelerator)
+    tokenizer = AutoTokenizer.from_pretrained(cfg.model.name)
+    
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.pad_token_id = tokenizer.eos_token_id
+
+    # === Model ===
+    log_message("Loading model...", cfg, accelerator)
+    
+    # Flash Attention 2
+    torch_dtype = torch.bfloat16 if cfg.training.use_amp else torch.float32
+    
+    if cfg.model.checkpoint_path:
+        model = AutoModelForCausalLM.from_pretrained(
+            cfg.model.name,
+            attn_implementation="flash_attention_2",
+            torch_dtype=torch_dtype,
+        )
+        checkpoint = torch.load(cfg.model.checkpoint_path, map_location="cpu")
+        model.load_state_dict(checkpoint["model_state_dict"] if "model_state_dict" in checkpoint else checkpoint)
+        log_message(f"Loaded checkpoint: {cfg.model.checkpoint_path}", cfg, accelerator)
+    elif cfg.model.from_scratch:
+        config = AutoConfig.from_pretrained(cfg.model.name)
+        config._attn_implementation = "flash_attention_2"
+        model = AutoModelForCausalLM.from_config(config, torch_dtype=torch_dtype)
+        log_message(f"Initialized from scratch: {cfg.model.name}", cfg, accelerator)
+    else:
+        model = AutoModelForCausalLM.from_pretrained(
+            cfg.model.name,
+            attn_implementation="flash_attention_2",
+            torch_dtype=torch_dtype,
+        )
+        log_message(f"Loaded pretrained: {cfg.model.name}", cfg, accelerator)
+
+    model.train()
+
+    # Log model info
+    total_params = sum(p.numel() for p in model.parameters())
+    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+    log_message(f"Total params: {total_params:,}", cfg, accelerator)
+    log_message(f"Trainable params: {trainable_params:,}", cfg, accelerator)
+
+    # === Data ===
+    log_message("Creating dataloaders...", cfg, accelerator)
+    dataloaders = create_dataloaders(cfg, tokenizer)
+
+    train_dataloader = dataloaders["train"]
+    val_dataloader = dataloaders.get("validation", None)
+
+    log_message(f"Train dataset size: {len(train_dataloader.dataset)}", cfg, accelerator)
+    log_message(f"Train batches per epoch (before DDP split): {len(train_dataloader)}", cfg, accelerator)
+
+    if val_dataloader:
+        log_message(f"Validation dataset size: {len(val_dataloader.dataset)}", cfg, accelerator)
+        log_message(f"Validation batches: {len(val_dataloader)}", cfg, accelerator)
+    else:
+        log_message("No validation dataset found", cfg, accelerator)
+
+    # === Optimizer ===
+    log_message("Creating optimizer...", cfg, accelerator)
+    param_groups = group_params(model, cfg.training.weight_decay)
+
+    optimizer = torch.optim.AdamW(
+        param_groups,
+        lr=cfg.training.lr,
+        betas=tuple(cfg.training.betas),
+        eps=cfg.training.eps,
+    )
+
+    # === Scheduler ===
+    steps_per_epoch = math.ceil(
+        len(train_dataloader) / accelerator.num_processes
+    )
+    total_steps = (
+        cfg.training.epochs
+        * steps_per_epoch
+        // cfg.training.gradient_accumulation_steps
+    )
+    scheduler = get_lr_scheduler(optimizer, cfg, total_steps)
+
+    log_message(
+        f"Total steps: {total_steps}, Steps per epoch: {steps_per_epoch}", 
+        cfg, 
+        accelerator
+    )
+
+    # === Accelerate Prepare ===
+    log_message("Preparing model, optimizer, and dataloaders with Accelerate...", cfg, accelerator)
+    
+    if val_dataloader is not None:
+        model, optimizer, train_dataloader, val_dataloader, scheduler = accelerator.prepare(
+            model, optimizer, train_dataloader, val_dataloader, scheduler
+        )
+    else:
+        model, optimizer, train_dataloader, scheduler = accelerator.prepare(
+            model, optimizer, train_dataloader, scheduler
+        )
+
+    log_message(f"Train batches per epoch (after DDP split): {len(train_dataloader)}", cfg, accelerator)
+
+    # === Resume ===
+    global_step = 0
+    start_epoch = 1
+
+    if cfg.training.resume and cfg.training.resume_checkpoint:
+        global_step, start_epoch = load_checkpoint(
+            model, optimizer, scheduler, cfg.training.resume_checkpoint, cfg, accelerator
+        )
+        start_epoch += 1
+
+    # === Training Loop ===
+    log_message("Starting training...", cfg, accelerator)
+
+    best_val_loss = float("inf")
+
+    try:
+        for epoch in range(start_epoch, cfg.training.epochs + 1):
+            log_message(f"\n{'=' * 60}", cfg, accelerator)
+            log_message(f"EPOCH {epoch}/{cfg.training.epochs}", cfg, accelerator)
+            log_message(f"{'=' * 60}", cfg, accelerator)
+
+            global_step, best_val_loss = train_epoch(
+                model=model,
+                dataloader=train_dataloader,
+                optimizer=optimizer,
+                scheduler=scheduler,
+                cfg=cfg,
+                epoch=epoch,
+                global_step=global_step,
+                accelerator=accelerator,
+                val_dataloader=val_dataloader,
+                best_val_loss=best_val_loss,
+            )
+
+            if cfg.logging.save_every_epoch:
+                save_checkpoint(
+                    model, optimizer, scheduler, global_step, epoch, cfg, accelerator
+                )
+
+    except KeyboardInterrupt:
+        log_message("Training interrupted by user", cfg, accelerator)
+        save_checkpoint(model, optimizer, scheduler, global_step, epoch, cfg, accelerator)
+
+    # === Final Save ===
+    log_message("\nTraining completed!", cfg, accelerator)
+
+    if accelerator.is_main_process:
+        final_model_path = Path(cfg.paths.output_dir) / "model_final.pt"
+        unwrapped_model = accelerator.unwrap_model(model)
+        torch.save(unwrapped_model.state_dict(), final_model_path)
+        log_message(f"Final model: {final_model_path}", cfg, accelerator)
+
+    accelerator.wait_for_everyone()
+    finish_tracking()
+
+
+if __name__ == "__main__":
+    main()

lr_sweep/pythia_1b_lr_5e-5/wandb/run-20260425_193045-vg3if73m/files/config.yaml

@@ -0,0 +1,146 @@
+_wandb:
+    value:
+        cli_version: 0.24.0
+        code_path: code/code_completion_exp/train_pythia/train.py
+        e:
+            ustumeirj564la8awm2vaziyvcmzba88:
+                args:
+                    - tracking=wandb
+                    - tracking.project=code-completion_lr-sweep
+                    - tracking.run_name=pythia_1b_lr_5e-5
+                    - training.lr=5e-5
+                    - paths.output_dir=/workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5
+                    - model=pythia_1b
+                    - data.path=/workspace/byte-llms-code/code_completion_exp/datasets/data_V4_full
+                codePath: code_completion_exp/train_pythia/train.py
+                codePathLocal: train.py
+                cpu_count: 64
+                cpu_count_logical: 128
+                cudaVersion: "12.2"
+                disk:
+                    /:
+                        total: "265214230528"
+                        used: "98730414080"
+                email: nikita@local.ru
+                executable: /venv/bytellm/bin/python
+                git:
+                    commit: f111e13281aa0dc58e24302edab5b0d5c2024586
+                    remote: https://github.com/naryst/byte-llms-code.git
+                gpu: NVIDIA H100 80GB HBM3
+                gpu_count: 4
+                gpu_nvidia:
+                    - architecture: Hopper
+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-b60cdcab-2033-2009-41de-be646c953a20
+                    - architecture: Hopper
+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-9982b420-4520-4238-c378-ec5a46015474
+                    - architecture: Hopper
+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-e26ebaac-aaa6-3eed-17ab-a3dce303a76f
+                    - architecture: Hopper
+                      cudaCores: 16896
+                      memoryTotal: "85520809984"
+                      name: NVIDIA H100 80GB HBM3
+                      uuid: GPU-9dfc6dba-0be6-4a10-1027-336cc0e65134
+                host: 7504e518d24a
+                memory:
+                    total: "1081679683584"
+                os: Linux-5.4.0-176-generic-x86_64-with-glibc2.35
+                program: /workspace/byte-llms-code/code_completion_exp/train_pythia/train.py
+                python: CPython 3.12.0
+                root: /workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5
+                startedAt: "2026-04-25T19:30:45.739561Z"
+                writerId: ustumeirj564la8awm2vaziyvcmzba88
+        m: []
+        python_version: 3.12.0
+        t:
+            "1":
+                - 1
+                - 11
+                - 49
+                - 50
+                - 51
+                - 71
+                - 105
+            "2":
+                - 1
+                - 11
+                - 49
+                - 50
+                - 51
+                - 71
+                - 105
+            "3":
+                - 2
+                - 13
+                - 16
+                - 61
+            "4": 3.12.0
+            "5": 0.24.0
+            "6": 4.57.6
+            "12": 0.24.0
+            "13": linux-x86_64
+data:
+    value:
+        max_context_len: 4096
+        max_target_len: 256
+        max_train_samples: null
+        max_val_samples: 2000
+        num_workers: 4
+        path: /workspace/byte-llms-code/code_completion_exp/datasets/data_V4_full
+        pin_memory: true
+device:
+    value: cuda
+logging:
+    value:
+        eval_interval: 2000
+        log_interval: 10
+        save_every_epoch: false
+        save_interval: 0
+model:
+    value:
+        checkpoint_path: null
+        from_scratch: false
+        name: EleutherAI/pythia-1b
+paths:
+    value:
+        output_dir: /workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5
+seed:
+    value: 42
+tracking:
+    value:
+        backend: wandb
+        base_url: https://wandb.platun0v.ru
+        enabled: true
+        entity: null
+        local_dir: /workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5
+        project: code-completion_lr-sweep
+        run_name: pythia_1b_lr_5e-5
+training:
+    value:
+        batch_size: 4
+        betas:
+            - 0.9
+            - 0.95
+        decay_ratio: 0.2
+        epochs: 1
+        eps: 1e-08
+        eval_batch_size: 12
+        gradient_accumulation_steps: 4
+        lr: 5e-05
+        lr_scheduler: wsd
+        max_grad_norm: 1
+        min_lr_ratio: 0.1
+        resume: false
+        resume_checkpoint: null
+        use_amp: true
+        warmup_ratio: 0.1
+        warmup_steps: 100
+        weight_decay: 0.1

lr_sweep/pythia_1b_lr_5e-5/wandb/run-20260425_193045-vg3if73m/files/output.log

@@ -0,0 +1,1056 @@
+[2026-04-25 19:30:46] Initializing tokenizer...
+[2026-04-25 19:30:47] Loading model...
+`torch_dtype` is deprecated! Use `dtype` instead!
+[2026-04-25 19:30:50] Loaded pretrained: EleutherAI/pythia-1b
+[2026-04-25 19:30:50] Total params: 1,011,781,632
+[2026-04-25 19:30:50] Trainable params: 1,011,781,632
+[2026-04-25 19:30:50] Creating dataloaders...
+[2026-04-25 19:30:50] Train dataset size: 316397
+[2026-04-25 19:30:50] Train batches per epoch (before DDP split): 79100
+[2026-04-25 19:30:50] Validation dataset size: 2000
+[2026-04-25 19:30:50] Validation batches: 167
+[2026-04-25 19:30:50] Creating optimizer...
+[2026-04-25 19:30:50] Total steps: 9887, Steps per epoch: 39550
+[2026-04-25 19:30:50] Preparing model, optimizer, and dataloaders with Accelerate...
+[2026-04-25 19:30:51] Train batches per epoch (after DDP split): 39550
+[2026-04-25 19:30:51] Starting training...
+[2026-04-25 19:30:51]
+============================================================
+[2026-04-25 19:30:51] EPOCH 1/1
+[2026-04-25 19:30:51] ============================================================
+[2026-04-25 19:30:55] Epoch 1 | Step 10 | Loss: 2.1524 | LR: 5.91e-06
+[2026-04-25 19:30:57] Epoch 1 | Step 20 | Loss: 1.8675 | LR: 6.82e-06
+[2026-04-25 19:31:00] Epoch 1 | Step 30 | Loss: 1.6663 | LR: 7.73e-06
+[2026-04-25 19:31:03] Epoch 1 | Step 40 | Loss: 1.5603 | LR: 8.64e-06
+[2026-04-25 19:31:05] Epoch 1 | Step 50 | Loss: 1.4615 | LR: 9.55e-06
+[2026-04-25 19:31:08] Epoch 1 | Step 60 | Loss: 1.3987 | LR: 1.05e-05
+[2026-04-25 19:31:11] Epoch 1 | Step 70 | Loss: 1.3411 | LR: 1.14e-05
+[2026-04-25 19:31:13] Epoch 1 | Step 80 | Loss: 1.3206 | LR: 1.23e-05
+[2026-04-25 19:31:16] Epoch 1 | Step 90 | Loss: 1.2938 | LR: 1.32e-05
+[2026-04-25 19:31:19] Epoch 1 | Step 100 | Loss: 1.2705 | LR: 1.41e-05
+[2026-04-25 19:31:21] Epoch 1 | Step 110 | Loss: 1.2636 | LR: 1.50e-05
+[2026-04-25 19:31:24] Epoch 1 | Step 120 | Loss: 1.2522 | LR: 1.59e-05
+[2026-04-25 19:31:26] Epoch 1 | Step 130 | Loss: 1.2495 | LR: 1.68e-05
+[2026-04-25 19:31:29] Epoch 1 | Step 140 | Loss: 1.2478 | LR: 1.78e-05
+[2026-04-25 19:31:31] Epoch 1 | Step 150 | Loss: 1.2312 | LR: 1.87e-05
+[2026-04-25 19:31:34] Epoch 1 | Step 160 | Loss: 1.2177 | LR: 1.96e-05
+[2026-04-25 19:31:36] Epoch 1 | Step 170 | Loss: 1.2106 | LR: 2.05e-05
+[2026-04-25 19:31:39] Epoch 1 | Step 180 | Loss: 1.1969 | LR: 2.14e-05
+[2026-04-25 19:31:41] Epoch 1 | Step 190 | Loss: 1.1936 | LR: 2.23e-05
+[2026-04-25 19:31:44] Epoch 1 | Step 200 | Loss: 1.1900 | LR: 2.32e-05
+[2026-04-25 19:31:46] Epoch 1 | Step 210 | Loss: 1.1927 | LR: 2.41e-05
+[2026-04-25 19:31:49] Epoch 1 | Step 220 | Loss: 1.1902 | LR: 2.50e-05
+[2026-04-25 19:31:51] Epoch 1 | Step 230 | Loss: 1.1810 | LR: 2.60e-05
+[2026-04-25 19:31:54] Epoch 1 | Step 240 | Loss: 1.1758 | LR: 2.69e-05
+[2026-04-25 19:31:56] Epoch 1 | Step 250 | Loss: 1.1720 | LR: 2.78e-05
+[2026-04-25 19:31:59] Epoch 1 | Step 260 | Loss: 1.1742 | LR: 2.87e-05
+[2026-04-25 19:32:02] Epoch 1 | Step 270 | Loss: 1.1699 | LR: 2.96e-05
+[2026-04-25 19:32:04] Epoch 1 | Step 280 | Loss: 1.1642 | LR: 3.05e-05
+[2026-04-25 19:32:07] Epoch 1 | Step 290 | Loss: 1.1616 | LR: 3.14e-05
+[2026-04-25 19:32:09] Epoch 1 | Step 300 | Loss: 1.1595 | LR: 3.23e-05
+[2026-04-25 19:32:12] Epoch 1 | Step 310 | Loss: 1.1566 | LR: 3.32e-05
+[2026-04-25 19:32:14] Epoch 1 | Step 320 | Loss: 1.1522 | LR: 3.41e-05
+[2026-04-25 19:32:17] Epoch 1 | Step 330 | Loss: 1.1486 | LR: 3.51e-05
+[2026-04-25 19:32:20] Epoch 1 | Step 340 | Loss: 1.1466 | LR: 3.60e-05
+[2026-04-25 19:32:22] Epoch 1 | Step 350 | Loss: 1.1467 | LR: 3.69e-05
+[2026-04-25 19:32:25] Epoch 1 | Step 360 | Loss: 1.1434 | LR: 3.78e-05
+[2026-04-25 19:32:28] Epoch 1 | Step 370 | Loss: 1.1389 | LR: 3.87e-05
+[2026-04-25 19:32:31] Epoch 1 | Step 380 | Loss: 1.1363 | LR: 3.96e-05
+[2026-04-25 19:32:33] Epoch 1 | Step 390 | Loss: 1.1352 | LR: 4.05e-05
+[2026-04-25 19:32:36] Epoch 1 | Step 400 | Loss: 1.1346 | LR: 4.14e-05
+[2026-04-25 19:32:38] Epoch 1 | Step 410 | Loss: 1.1341 | LR: 4.23e-05
+[2026-04-25 19:32:41] Epoch 1 | Step 420 | Loss: 1.1331 | LR: 4.33e-05
+[2026-04-25 19:32:44] Epoch 1 | Step 430 | Loss: 1.1364 | LR: 4.42e-05
+[2026-04-25 19:32:46] Epoch 1 | Step 440 | Loss: 1.1334 | LR: 4.51e-05
+[2026-04-25 19:32:49] Epoch 1 | Step 450 | Loss: 1.1329 | LR: 4.60e-05
+[2026-04-25 19:32:52] Epoch 1 | Step 460 | Loss: 1.1340 | LR: 4.69e-05
+[2026-04-25 19:32:54] Epoch 1 | Step 470 | Loss: 1.1333 | LR: 4.78e-05
+[2026-04-25 19:32:57] Epoch 1 | Step 480 | Loss: 1.1345 | LR: 4.87e-05
+[2026-04-25 19:33:00] Epoch 1 | Step 490 | Loss: 1.1336 | LR: 4.96e-05
+[2026-04-25 19:33:02] Epoch 1 | Step 500 | Loss: 1.1338 | LR: 5.00e-05
+[2026-04-25 19:33:04] Epoch 1 | Step 510 | Loss: 1.1334 | LR: 5.00e-05
+[2026-04-25 19:33:07] Epoch 1 | Step 520 | Loss: 1.1345 | LR: 5.00e-05
+[2026-04-25 19:33:10] Epoch 1 | Step 530 | Loss: 1.1333 | LR: 5.00e-05
+[2026-04-25 19:33:12] Epoch 1 | Step 540 | Loss: 1.1320 | LR: 5.00e-05
+[2026-04-25 19:33:15] Epoch 1 | Step 550 | Loss: 1.1320 | LR: 5.00e-05
+[2026-04-25 19:33:17] Epoch 1 | Step 560 | Loss: 1.1316 | LR: 5.00e-05
+[2026-04-25 19:33:20] Epoch 1 | Step 570 | Loss: 1.1330 | LR: 5.00e-05
+[2026-04-25 19:33:22] Epoch 1 | Step 580 | Loss: 1.1348 | LR: 5.00e-05
+[2026-04-25 19:33:25] Epoch 1 | Step 590 | Loss: 1.1367 | LR: 5.00e-05
+[2026-04-25 19:33:28] Epoch 1 | Step 600 | Loss: 1.1377 | LR: 5.00e-05
+[2026-04-25 19:33:30] Epoch 1 | Step 610 | Loss: 1.1404 | LR: 5.00e-05
+[2026-04-25 19:33:32] Epoch 1 | Step 620 | Loss: 1.1430 | LR: 5.00e-05
+[2026-04-25 19:33:35] Epoch 1 | Step 630 | Loss: 1.1441 | LR: 5.00e-05
+[2026-04-25 19:33:37] Epoch 1 | Step 640 | Loss: 1.1462 | LR: 5.00e-05
+[2026-04-25 19:33:40] Epoch 1 | Step 650 | Loss: 1.1474 | LR: 5.00e-05
+[2026-04-25 19:33:42] Epoch 1 | Step 660 | Loss: 1.1492 | LR: 5.00e-05
+[2026-04-25 19:33:45] Epoch 1 | Step 670 | Loss: 1.1490 | LR: 5.00e-05
+[2026-04-25 19:33:48] Epoch 1 | Step 680 | Loss: 1.1500 | LR: 5.00e-05
+[2026-04-25 19:33:50] Epoch 1 | Step 690 | Loss: 1.1503 | LR: 5.00e-05
+[2026-04-25 19:33:53] Epoch 1 | Step 700 | Loss: 1.1522 | LR: 5.00e-05
+[2026-04-25 19:33:55] Epoch 1 | Step 710 | Loss: 1.1525 | LR: 5.00e-05
+[2026-04-25 19:33:58] Epoch 1 | Step 720 | Loss: 1.1535 | LR: 5.00e-05
+[2026-04-25 19:34:00] Epoch 1 | Step 730 | Loss: 1.1543 | LR: 5.00e-05
+[2026-04-25 19:34:03] Epoch 1 | Step 740 | Loss: 1.1542 | LR: 5.00e-05
+[2026-04-25 19:34:05] Epoch 1 | Step 750 | Loss: 1.1544 | LR: 5.00e-05
+[2026-04-25 19:34:08] Epoch 1 | Step 760 | Loss: 1.1564 | LR: 5.00e-05
+[2026-04-25 19:34:10] Epoch 1 | Step 770 | Loss: 1.1587 | LR: 5.00e-05
+[2026-04-25 19:34:13] Epoch 1 | Step 780 | Loss: 1.1598 | LR: 5.00e-05
+[2026-04-25 19:34:15] Epoch 1 | Step 790 | Loss: 1.1607 | LR: 5.00e-05
+[2026-04-25 19:34:18] Epoch 1 | Step 800 | Loss: 1.1601 | LR: 5.00e-05
+[2026-04-25 19:34:20] Epoch 1 | Step 810 | Loss: 1.1607 | LR: 5.00e-05
+[2026-04-25 19:34:23] Epoch 1 | Step 820 | Loss: 1.1608 | LR: 5.00e-05
+[2026-04-25 19:34:25] Epoch 1 | Step 830 | Loss: 1.1620 | LR: 5.00e-05
+[2026-04-25 19:34:28] Epoch 1 | Step 840 | Loss: 1.1619 | LR: 5.00e-05
+[2026-04-25 19:34:31] Epoch 1 | Step 850 | Loss: 1.1610 | LR: 5.00e-05
+[2026-04-25 19:34:33] Epoch 1 | Step 860 | Loss: 1.1625 | LR: 5.00e-05
+[2026-04-25 19:34:36] Epoch 1 | Step 870 | Loss: 1.1644 | LR: 5.00e-05
+[2026-04-25 19:34:38] Epoch 1 | Step 880 | Loss: 1.1657 | LR: 5.00e-05
+[2026-04-25 19:34:40] Epoch 1 | Step 890 | Loss: 1.1664 | LR: 5.00e-05
+[2026-04-25 19:34:43] Epoch 1 | Step 900 | Loss: 1.1663 | LR: 5.00e-05
+[2026-04-25 19:34:45] Epoch 1 | Step 910 | Loss: 1.1678 | LR: 5.00e-05
+[2026-04-25 19:34:48] Epoch 1 | Step 920 | Loss: 1.1698 | LR: 5.00e-05
+[2026-04-25 19:34:51] Epoch 1 | Step 930 | Loss: 1.1699 | LR: 5.00e-05
+[2026-04-25 19:34:53] Epoch 1 | Step 940 | Loss: 1.1709 | LR: 5.00e-05
+[2026-04-25 19:34:56] Epoch 1 | Step 950 | Loss: 1.1697 | LR: 5.00e-05
+[2026-04-25 19:34:59] Epoch 1 | Step 960 | Loss: 1.1699 | LR: 5.00e-05
+[2026-04-25 19:35:02] Epoch 1 | Step 970 | Loss: 1.1707 | LR: 5.00e-05
+[2026-04-25 19:35:04] Epoch 1 | Step 980 | Loss: 1.1705 | LR: 5.00e-05
+[2026-04-25 19:35:07] Epoch 1 | Step 990 | Loss: 1.1698 | LR: 5.00e-05
+[2026-04-25 19:35:10] Epoch 1 | Step 1000 | Loss: 1.1701 | LR: 5.00e-05
+[2026-04-25 19:35:12] Epoch 1 | Step 1010 | Loss: 1.1715 | LR: 5.00e-05
+[2026-04-25 19:35:14] Epoch 1 | Step 1020 | Loss: 1.1718 | LR: 5.00e-05
+[2026-04-25 19:35:17] Epoch 1 | Step 1030 | Loss: 1.1732 | LR: 5.00e-05
+[2026-04-25 19:35:19] Epoch 1 | Step 1040 | Loss: 1.1720 | LR: 5.00e-05
+[2026-04-25 19:35:22] Epoch 1 | Step 1050 | Loss: 1.1719 | LR: 5.00e-05
+[2026-04-25 19:35:24] Epoch 1 | Step 1060 | Loss: 1.1707 | LR: 5.00e-05
+[2026-04-25 19:35:27] Epoch 1 | Step 1070 | Loss: 1.1707 | LR: 5.00e-05
+[2026-04-25 19:35:29] Epoch 1 | Step 1080 | Loss: 1.1726 | LR: 5.00e-05
+[2026-04-25 19:35:32] Epoch 1 | Step 1090 | Loss: 1.1749 | LR: 5.00e-05
+[2026-04-25 19:35:34] Epoch 1 | Step 1100 | Loss: 1.1750 | LR: 5.00e-05
+[2026-04-25 19:35:37] Epoch 1 | Step 1110 | Loss: 1.1760 | LR: 5.00e-05
+[2026-04-25 19:35:39] Epoch 1 | Step 1120 | Loss: 1.1770 | LR: 5.00e-05
+[2026-04-25 19:35:42] Epoch 1 | Step 1130 | Loss: 1.1777 | LR: 5.00e-05
+[2026-04-25 19:35:45] Epoch 1 | Step 1140 | Loss: 1.1778 | LR: 5.00e-05
+[2026-04-25 19:35:47] Epoch 1 | Step 1150 | Loss: 1.1764 | LR: 5.00e-05
+[2026-04-25 19:35:50] Epoch 1 | Step 1160 | Loss: 1.1777 | LR: 5.00e-05
+[2026-04-25 19:35:52] Epoch 1 | Step 1170 | Loss: 1.1790 | LR: 5.00e-05
+[2026-04-25 19:35:55] Epoch 1 | Step 1180 | Loss: 1.1791 | LR: 5.00e-05
+[2026-04-25 19:35:58] Epoch 1 | Step 1190 | Loss: 1.1799 | LR: 5.00e-05
+[2026-04-25 19:36:00] Epoch 1 | Step 1200 | Loss: 1.1797 | LR: 5.00e-05
+[2026-04-25 19:36:03] Epoch 1 | Step 1210 | Loss: 1.1787 | LR: 5.00e-05
+[2026-04-25 19:36:05] Epoch 1 | Step 1220 | Loss: 1.1775 | LR: 5.00e-05
+[2026-04-25 19:36:08] Epoch 1 | Step 1230 | Loss: 1.1783 | LR: 5.00e-05
+[2026-04-25 19:36:11] Epoch 1 | Step 1240 | Loss: 1.1790 | LR: 5.00e-05
+[2026-04-25 19:36:13] Epoch 1 | Step 1250 | Loss: 1.1786 | LR: 5.00e-05
+[2026-04-25 19:36:16] Epoch 1 | Step 1260 | Loss: 1.1788 | LR: 5.00e-05
+[2026-04-25 19:36:18] Epoch 1 | Step 1270 | Loss: 1.1777 | LR: 5.00e-05
+[2026-04-25 19:36:20] Epoch 1 | Step 1280 | Loss: 1.1787 | LR: 5.00e-05
+[2026-04-25 19:36:23] Epoch 1 | Step 1290 | Loss: 1.1795 | LR: 5.00e-05
+[2026-04-25 19:36:26] Epoch 1 | Step 1300 | Loss: 1.1790 | LR: 5.00e-05
+[2026-04-25 19:36:28] Epoch 1 | Step 1310 | Loss: 1.1792 | LR: 5.00e-05
+[2026-04-25 19:36:31] Epoch 1 | Step 1320 | Loss: 1.1798 | LR: 5.00e-05
+[2026-04-25 19:36:33] Epoch 1 | Step 1330 | Loss: 1.1793 | LR: 5.00e-05
+[2026-04-25 19:36:35] Epoch 1 | Step 1340 | Loss: 1.1796 | LR: 5.00e-05
+[2026-04-25 19:36:38] Epoch 1 | Step 1350 | Loss: 1.1804 | LR: 5.00e-05
+[2026-04-25 19:36:41] Epoch 1 | Step 1360 | Loss: 1.1804 | LR: 5.00e-05
+[2026-04-25 19:36:43] Epoch 1 | Step 1370 | Loss: 1.1805 | LR: 5.00e-05
+[2026-04-25 19:36:46] Epoch 1 | Step 1380 | Loss: 1.1817 | LR: 5.00e-05
+[2026-04-25 19:36:48] Epoch 1 | Step 1390 | Loss: 1.1826 | LR: 5.00e-05
+[2026-04-25 19:36:51] Epoch 1 | Step 1400 | Loss: 1.1828 | LR: 5.00e-05
+[2026-04-25 19:36:54] Epoch 1 | Step 1410 | Loss: 1.1819 | LR: 5.00e-05
+[2026-04-25 19:36:56] Epoch 1 | Step 1420 | Loss: 1.1821 | LR: 5.00e-05
+[2026-04-25 19:36:59] Epoch 1 | Step 1430 | Loss: 1.1823 | LR: 5.00e-05
+[2026-04-25 19:37:01] Epoch 1 | Step 1440 | Loss: 1.1822 | LR: 5.00e-05
+[2026-04-25 19:37:04] Epoch 1 | Step 1450 | Loss: 1.1824 | LR: 5.00e-05
+[2026-04-25 19:37:06] Epoch 1 | Step 1460 | Loss: 1.1814 | LR: 5.00e-05
+[2026-04-25 19:37:09] Epoch 1 | Step 1470 | Loss: 1.1823 | LR: 5.00e-05
+[2026-04-25 19:37:12] Epoch 1 | Step 1480 | Loss: 1.1825 | LR: 5.00e-05
+[2026-04-25 19:37:14] Epoch 1 | Step 1490 | Loss: 1.1834 | LR: 5.00e-05
+[2026-04-25 19:37:16] Epoch 1 | Step 1500 | Loss: 1.1832 | LR: 5.00e-05
+[2026-04-25 19:37:19] Epoch 1 | Step 1510 | Loss: 1.1836 | LR: 5.00e-05
+[2026-04-25 19:37:22] Epoch 1 | Step 1520 | Loss: 1.1842 | LR: 5.00e-05
+[2026-04-25 19:37:24] Epoch 1 | Step 1530 | Loss: 1.1842 | LR: 5.00e-05
+[2026-04-25 19:37:26] Epoch 1 | Step 1540 | Loss: 1.1850 | LR: 5.00e-05
+[2026-04-25 19:37:29] Epoch 1 | Step 1550 | Loss: 1.1855 | LR: 5.00e-05
+[2026-04-25 19:37:31] Epoch 1 | Step 1560 | Loss: 1.1850 | LR: 5.00e-05
+[2026-04-25 19:37:34] Epoch 1 | Step 1570 | Loss: 1.1859 | LR: 5.00e-05
+[2026-04-25 19:37:37] Epoch 1 | Step 1580 | Loss: 1.1856 | LR: 5.00e-05
+[2026-04-25 19:37:39] Epoch 1 | Step 1590 | Loss: 1.1859 | LR: 5.00e-05
+[2026-04-25 19:37:41] Epoch 1 | Step 1600 | Loss: 1.1862 | LR: 5.00e-05
+[2026-04-25 19:37:44] Epoch 1 | Step 1610 | Loss: 1.1852 | LR: 5.00e-05
+[2026-04-25 19:37:46] Epoch 1 | Step 1620 | Loss: 1.1843 | LR: 5.00e-05
+[2026-04-25 19:37:49] Epoch 1 | Step 1630 | Loss: 1.1851 | LR: 5.00e-05
+[2026-04-25 19:37:51] Epoch 1 | Step 1640 | Loss: 1.1852 | LR: 5.00e-05
+[2026-04-25 19:37:54] Epoch 1 | Step 1650 | Loss: 1.1847 | LR: 5.00e-05
+[2026-04-25 19:37:56] Epoch 1 | Step 1660 | Loss: 1.1842 | LR: 5.00e-05
+[2026-04-25 19:37:58] Epoch 1 | Step 1670 | Loss: 1.1854 | LR: 5.00e-05
+[2026-04-25 19:38:01] Epoch 1 | Step 1680 | Loss: 1.1857 | LR: 5.00e-05
+[2026-04-25 19:38:04] Epoch 1 | Step 1690 | Loss: 1.1855 | LR: 5.00e-05
+[2026-04-25 19:38:06] Epoch 1 | Step 1700 | Loss: 1.1848 | LR: 5.00e-05
+[2026-04-25 19:38:09] Epoch 1 | Step 1710 | Loss: 1.1846 | LR: 5.00e-05
+[2026-04-25 19:38:11] Epoch 1 | Step 1720 | Loss: 1.1844 | LR: 5.00e-05
+[2026-04-25 19:38:14] Epoch 1 | Step 1730 | Loss: 1.1842 | LR: 5.00e-05
+[2026-04-25 19:38:16] Epoch 1 | Step 1740 | Loss: 1.1845 | LR: 5.00e-05
+[2026-04-25 19:38:19] Epoch 1 | Step 1750 | Loss: 1.1859 | LR: 5.00e-05
+[2026-04-25 19:38:21] Epoch 1 | Step 1760 | Loss: 1.1856 | LR: 5.00e-05
+[2026-04-25 19:38:24] Epoch 1 | Step 1770 | Loss: 1.1861 | LR: 5.00e-05
+[2026-04-25 19:38:26] Epoch 1 | Step 1780 | Loss: 1.1860 | LR: 5.00e-05
+[2026-04-25 19:38:29] Epoch 1 | Step 1790 | Loss: 1.1865 | LR: 5.00e-05
+[2026-04-25 19:38:31] Epoch 1 | Step 1800 | Loss: 1.1860 | LR: 5.00e-05
+[2026-04-25 19:38:34] Epoch 1 | Step 1810 | Loss: 1.1864 | LR: 5.00e-05
+[2026-04-25 19:38:37] Epoch 1 | Step 1820 | Loss: 1.1870 | LR: 5.00e-05
+[2026-04-25 19:38:39] Epoch 1 | Step 1830 | Loss: 1.1873 | LR: 5.00e-05
+[2026-04-25 19:38:42] Epoch 1 | Step 1840 | Loss: 1.1877 | LR: 5.00e-05
+[2026-04-25 19:38:44] Epoch 1 | Step 1850 | Loss: 1.1874 | LR: 5.00e-05
+[2026-04-25 19:38:47] Epoch 1 | Step 1860 | Loss: 1.1879 | LR: 5.00e-05
+[2026-04-25 19:38:49] Epoch 1 | Step 1870 | Loss: 1.1876 | LR: 5.00e-05
+[2026-04-25 19:38:52] Epoch 1 | Step 1880 | Loss: 1.1871 | LR: 5.00e-05
+[2026-04-25 19:38:55] Epoch 1 | Step 1890 | Loss: 1.1877 | LR: 5.00e-05
+[2026-04-25 19:38:58] Epoch 1 | Step 1900 | Loss: 1.1877 | LR: 5.00e-05
+[2026-04-25 19:39:00] Epoch 1 | Step 1910 | Loss: 1.1881 | LR: 5.00e-05
+[2026-04-25 19:39:02] Epoch 1 | Step 1920 | Loss: 1.1887 | LR: 5.00e-05
+[2026-04-25 19:39:05] Epoch 1 | Step 1930 | Loss: 1.1888 | LR: 5.00e-05
+[2026-04-25 19:39:08] Epoch 1 | Step 1940 | Loss: 1.1886 | LR: 5.00e-05
+[2026-04-25 19:39:10] Epoch 1 | Step 1950 | Loss: 1.1883 | LR: 5.00e-05
+[2026-04-25 19:39:13] Epoch 1 | Step 1960 | Loss: 1.1885 | LR: 5.00e-05
+[2026-04-25 19:39:15] Epoch 1 | Step 1970 | Loss: 1.1887 | LR: 5.00e-05
+[2026-04-25 19:39:17] Epoch 1 | Step 1980 | Loss: 1.1894 | LR: 5.00e-05
+[2026-04-25 19:39:20] Epoch 1 | Step 1990 | Loss: 1.1897 | LR: 5.00e-05
+[2026-04-25 19:39:22] Epoch 1 | Step 2000 | Loss: 1.1897 | LR: 5.00e-05
+[2026-04-25 19:39:23] Validation | Batch 10/84 | Loss: 1.1552
+[2026-04-25 19:39:23] Validation | Batch 20/84 | Loss: 1.1688
+[2026-04-25 19:39:24] Validation | Batch 30/84 | Loss: 1.2551
+[2026-04-25 19:39:24] Validation | Batch 40/84 | Loss: 1.2580
+[2026-04-25 19:39:24] Validation | Batch 50/84 | Loss: 1.2545
+[2026-04-25 19:39:25] Validation | Batch 60/84 | Loss: 1.2285
+[2026-04-25 19:39:26] Validation | Batch 70/84 | Loss: 1.2086
+[2026-04-25 19:39:26] Validation | Batch 80/84 | Loss: 1.2153
+[2026-04-25 19:39:26] Validation | Batch 84/84 | Loss: 1.2082
+[2026-04-25 19:39:27] Validation | Loss: 1.2082 | PPL: 3.43 | Time: 3.85s
+[2026-04-25 19:39:29] New best model saved! Val loss: 1.2082
+[2026-04-25 19:39:31] Epoch 1 | Step 2010 | Loss: 1.1900 | LR: 5.00e-05
+[2026-04-25 19:39:34] Epoch 1 | Step 2020 | Loss: 1.1900 | LR: 5.00e-05
+[2026-04-25 19:39:36] Epoch 1 | Step 2030 | Loss: 1.1905 | LR: 5.00e-05
+[2026-04-25 19:39:39] Epoch 1 | Step 2040 | Loss: 1.1907 | LR: 5.00e-05
+[2026-04-25 19:39:41] Epoch 1 | Step 2050 | Loss: 1.1909 | LR: 5.00e-05
+[2026-04-25 19:39:44] Epoch 1 | Step 2060 | Loss: 1.1907 | LR: 5.00e-05
+[2026-04-25 19:39:47] Epoch 1 | Step 2070 | Loss: 1.1899 | LR: 5.00e-05
+[2026-04-25 19:39:49] Epoch 1 | Step 2080 | Loss: 1.1895 | LR: 5.00e-05
+[2026-04-25 19:39:51] Epoch 1 | Step 2090 | Loss: 1.1900 | LR: 5.00e-05
+[2026-04-25 19:39:54] Epoch 1 | Step 2100 | Loss: 1.1903 | LR: 5.00e-05
+[2026-04-25 19:39:57] Epoch 1 | Step 2110 | Loss: 1.1904 | LR: 5.00e-05
+[2026-04-25 19:39:59] Epoch 1 | Step 2120 | Loss: 1.1902 | LR: 5.00e-05
+[2026-04-25 19:40:02] Epoch 1 | Step 2130 | Loss: 1.1906 | LR: 5.00e-05
+[2026-04-25 19:40:04] Epoch 1 | Step 2140 | Loss: 1.1907 | LR: 5.00e-05
+[2026-04-25 19:40:07] Epoch 1 | Step 2150 | Loss: 1.1907 | LR: 5.00e-05
+[2026-04-25 19:40:09] Epoch 1 | Step 2160 | Loss: 1.1912 | LR: 5.00e-05
+[2026-04-25 19:40:12] Epoch 1 | Step 2170 | Loss: 1.1910 | LR: 5.00e-05
+[2026-04-25 19:40:14] Epoch 1 | Step 2180 | Loss: 1.1907 | LR: 5.00e-05
+[2026-04-25 19:40:16] Epoch 1 | Step 2190 | Loss: 1.1910 | LR: 5.00e-05
+[2026-04-25 19:40:19] Epoch 1 | Step 2200 | Loss: 1.1911 | LR: 5.00e-05
+[2026-04-25 19:40:21] Epoch 1 | Step 2210 | Loss: 1.1912 | LR: 5.00e-05
+[2026-04-25 19:40:24] Epoch 1 | Step 2220 | Loss: 1.1920 | LR: 5.00e-05
+[2026-04-25 19:40:26] Epoch 1 | Step 2230 | Loss: 1.1929 | LR: 5.00e-05
+[2026-04-25 19:40:29] Epoch 1 | Step 2240 | Loss: 1.1936 | LR: 5.00e-05
+[2026-04-25 19:40:31] Epoch 1 | Step 2250 | Loss: 1.1940 | LR: 5.00e-05
+[2026-04-25 19:40:34] Epoch 1 | Step 2260 | Loss: 1.1939 | LR: 5.00e-05
+[2026-04-25 19:40:37] Epoch 1 | Step 2270 | Loss: 1.1941 | LR: 5.00e-05
+[2026-04-25 19:40:39] Epoch 1 | Step 2280 | Loss: 1.1945 | LR: 5.00e-05
+[2026-04-25 19:40:42] Epoch 1 | Step 2290 | Loss: 1.1954 | LR: 5.00e-05
+[2026-04-25 19:40:44] Epoch 1 | Step 2300 | Loss: 1.1957 | LR: 5.00e-05
+[2026-04-25 19:40:46] Epoch 1 | Step 2310 | Loss: 1.1956 | LR: 5.00e-05
+[2026-04-25 19:40:49] Epoch 1 | Step 2320 | Loss: 1.1958 | LR: 5.00e-05
+[2026-04-25 19:40:51] Epoch 1 | Step 2330 | Loss: 1.1959 | LR: 5.00e-05
+[2026-04-25 19:40:54] Epoch 1 | Step 2340 | Loss: 1.1958 | LR: 5.00e-05
+[2026-04-25 19:40:56] Epoch 1 | Step 2350 | Loss: 1.1956 | LR: 5.00e-05
+[2026-04-25 19:40:59] Epoch 1 | Step 2360 | Loss: 1.1959 | LR: 5.00e-05
+[2026-04-25 19:41:01] Epoch 1 | Step 2370 | Loss: 1.1958 | LR: 5.00e-05
+[2026-04-25 19:41:04] Epoch 1 | Step 2380 | Loss: 1.1957 | LR: 5.00e-05
+[2026-04-25 19:41:06] Epoch 1 | Step 2390 | Loss: 1.1960 | LR: 5.00e-05
+[2026-04-25 19:41:09] Epoch 1 | Step 2400 | Loss: 1.1957 | LR: 5.00e-05
+[2026-04-25 19:41:11] Epoch 1 | Step 2410 | Loss: 1.1962 | LR: 5.00e-05
+[2026-04-25 19:41:14] Epoch 1 | Step 2420 | Loss: 1.1964 | LR: 5.00e-05
+[2026-04-25 19:41:16] Epoch 1 | Step 2430 | Loss: 1.1966 | LR: 5.00e-05
+[2026-04-25 19:41:19] Epoch 1 | Step 2440 | Loss: 1.1962 | LR: 5.00e-05
+[2026-04-25 19:41:22] Epoch 1 | Step 2450 | Loss: 1.1961 | LR: 5.00e-05
+[2026-04-25 19:41:24] Epoch 1 | Step 2460 | Loss: 1.1961 | LR: 5.00e-05
+[2026-04-25 19:41:27] Epoch 1 | Step 2470 | Loss: 1.1963 | LR: 5.00e-05
+[2026-04-25 19:41:29] Epoch 1 | Step 2480 | Loss: 1.1964 | LR: 5.00e-05
+[2026-04-25 19:41:32] Epoch 1 | Step 2490 | Loss: 1.1960 | LR: 5.00e-05
+[2026-04-25 19:41:35] Epoch 1 | Step 2500 | Loss: 1.1957 | LR: 5.00e-05
+[2026-04-25 19:41:37] Epoch 1 | Step 2510 | Loss: 1.1959 | LR: 5.00e-05
+[2026-04-25 19:41:39] Epoch 1 | Step 2520 | Loss: 1.1954 | LR: 5.00e-05
+[2026-04-25 19:41:42] Epoch 1 | Step 2530 | Loss: 1.1952 | LR: 5.00e-05
+[2026-04-25 19:41:44] Epoch 1 | Step 2540 | Loss: 1.1952 | LR: 5.00e-05
+[2026-04-25 19:41:47] Epoch 1 | Step 2550 | Loss: 1.1947 | LR: 5.00e-05
+[2026-04-25 19:41:50] Epoch 1 | Step 2560 | Loss: 1.1948 | LR: 5.00e-05
+[2026-04-25 19:41:52] Epoch 1 | Step 2570 | Loss: 1.1953 | LR: 5.00e-05
+[2026-04-25 19:41:55] Epoch 1 | Step 2580 | Loss: 1.1958 | LR: 5.00e-05
+[2026-04-25 19:41:58] Epoch 1 | Step 2590 | Loss: 1.1960 | LR: 5.00e-05
+[2026-04-25 19:42:00] Epoch 1 | Step 2600 | Loss: 1.1962 | LR: 5.00e-05
+[2026-04-25 19:42:02] Epoch 1 | Step 2610 | Loss: 1.1963 | LR: 5.00e-05
+[2026-04-25 19:42:05] Epoch 1 | Step 2620 | Loss: 1.1960 | LR: 5.00e-05
+[2026-04-25 19:42:07] Epoch 1 | Step 2630 | Loss: 1.1958 | LR: 5.00e-05
+[2026-04-25 19:42:10] Epoch 1 | Step 2640 | Loss: 1.1960 | LR: 5.00e-05
+[2026-04-25 19:42:13] Epoch 1 | Step 2650 | Loss: 1.1958 | LR: 5.00e-05
+[2026-04-25 19:42:15] Epoch 1 | Step 2660 | Loss: 1.1960 | LR: 5.00e-05
+[2026-04-25 19:42:18] Epoch 1 | Step 2670 | Loss: 1.1957 | LR: 5.00e-05
+[2026-04-25 19:42:20] Epoch 1 | Step 2680 | Loss: 1.1956 | LR: 5.00e-05
+[2026-04-25 19:42:23] Epoch 1 | Step 2690 | Loss: 1.1955 | LR: 5.00e-05
+[2026-04-25 19:42:25] Epoch 1 | Step 2700 | Loss: 1.1953 | LR: 5.00e-05
+[2026-04-25 19:42:28] Epoch 1 | Step 2710 | Loss: 1.1948 | LR: 5.00e-05
+[2026-04-25 19:42:31] Epoch 1 | Step 2720 | Loss: 1.1951 | LR: 5.00e-05
+[2026-04-25 19:42:33] Epoch 1 | Step 2730 | Loss: 1.1949 | LR: 5.00e-05
+[2026-04-25 19:42:35] Epoch 1 | Step 2740 | Loss: 1.1955 | LR: 5.00e-05
+[2026-04-25 19:42:38] Epoch 1 | Step 2750 | Loss: 1.1958 | LR: 5.00e-05
+[2026-04-25 19:42:40] Epoch 1 | Step 2760 | Loss: 1.1954 | LR: 5.00e-05
+[2026-04-25 19:42:43] Epoch 1 | Step 2770 | Loss: 1.1953 | LR: 5.00e-05
+[2026-04-25 19:42:45] Epoch 1 | Step 2780 | Loss: 1.1957 | LR: 5.00e-05
+[2026-04-25 19:42:48] Epoch 1 | Step 2790 | Loss: 1.1957 | LR: 5.00e-05
+[2026-04-25 19:42:50] Epoch 1 | Step 2800 | Loss: 1.1956 | LR: 5.00e-05
+[2026-04-25 19:42:53] Epoch 1 | Step 2810 | Loss: 1.1959 | LR: 5.00e-05
+[2026-04-25 19:42:55] Epoch 1 | Step 2820 | Loss: 1.1959 | LR: 5.00e-05
+[2026-04-25 19:42:58] Epoch 1 | Step 2830 | Loss: 1.1957 | LR: 5.00e-05
+[2026-04-25 19:43:00] Epoch 1 | Step 2840 | Loss: 1.1965 | LR: 5.00e-05
+[2026-04-25 19:43:03] Epoch 1 | Step 2850 | Loss: 1.1966 | LR: 5.00e-05
+[2026-04-25 19:43:05] Epoch 1 | Step 2860 | Loss: 1.1966 | LR: 5.00e-05
+[2026-04-25 19:43:08] Epoch 1 | Step 2870 | Loss: 1.1968 | LR: 5.00e-05
+[2026-04-25 19:43:11] Epoch 1 | Step 2880 | Loss: 1.1965 | LR: 5.00e-05
+[2026-04-25 19:43:13] Epoch 1 | Step 2890 | Loss: 1.1964 | LR: 5.00e-05
+[2026-04-25 19:43:16] Epoch 1 | Step 2900 | Loss: 1.1959 | LR: 5.00e-05
+[2026-04-25 19:43:18] Epoch 1 | Step 2910 | Loss: 1.1958 | LR: 5.00e-05
+[2026-04-25 19:43:21] Epoch 1 | Step 2920 | Loss: 1.1961 | LR: 5.00e-05
+[2026-04-25 19:43:24] Epoch 1 | Step 2930 | Loss: 1.1960 | LR: 5.00e-05
+[2026-04-25 19:43:27] Epoch 1 | Step 2940 | Loss: 1.1958 | LR: 5.00e-05
+[2026-04-25 19:43:29] Epoch 1 | Step 2950 | Loss: 1.1961 | LR: 5.00e-05
+[2026-04-25 19:43:32] Epoch 1 | Step 2960 | Loss: 1.1962 | LR: 5.00e-05
+[2026-04-25 19:43:34] Epoch 1 | Step 2970 | Loss: 1.1963 | LR: 5.00e-05
+[2026-04-25 19:43:37] Epoch 1 | Step 2980 | Loss: 1.1962 | LR: 5.00e-05
+[2026-04-25 19:43:39] Epoch 1 | Step 2990 | Loss: 1.1965 | LR: 5.00e-05
+[2026-04-25 19:43:42] Epoch 1 | Step 3000 | Loss: 1.1964 | LR: 5.00e-05
+[2026-04-25 19:43:45] Epoch 1 | Step 3010 | Loss: 1.1965 | LR: 5.00e-05
+[2026-04-25 19:43:47] Epoch 1 | Step 3020 | Loss: 1.1962 | LR: 5.00e-05
+[2026-04-25 19:43:50] Epoch 1 | Step 3030 | Loss: 1.1961 | LR: 5.00e-05
+[2026-04-25 19:43:52] Epoch 1 | Step 3040 | Loss: 1.1955 | LR: 5.00e-05
+[2026-04-25 19:43:55] Epoch 1 | Step 3050 | Loss: 1.1951 | LR: 5.00e-05
+[2026-04-25 19:43:57] Epoch 1 | Step 3060 | Loss: 1.1952 | LR: 5.00e-05
+[2026-04-25 19:44:00] Epoch 1 | Step 3070 | Loss: 1.1950 | LR: 5.00e-05
+[2026-04-25 19:44:03] Epoch 1 | Step 3080 | Loss: 1.1951 | LR: 5.00e-05
+[2026-04-25 19:44:05] Epoch 1 | Step 3090 | Loss: 1.1949 | LR: 5.00e-05
+[2026-04-25 19:44:07] Epoch 1 | Step 3100 | Loss: 1.1948 | LR: 5.00e-05
+[2026-04-25 19:44:10] Epoch 1 | Step 3110 | Loss: 1.1946 | LR: 5.00e-05
+[2026-04-25 19:44:12] Epoch 1 | Step 3120 | Loss: 1.1952 | LR: 5.00e-05
+[2026-04-25 19:44:15] Epoch 1 | Step 3130 | Loss: 1.1949 | LR: 5.00e-05
+[2026-04-25 19:44:18] Epoch 1 | Step 3140 | Loss: 1.1951 | LR: 5.00e-05
+[2026-04-25 19:44:20] Epoch 1 | Step 3150 | Loss: 1.1954 | LR: 5.00e-05
+[2026-04-25 19:44:23] Epoch 1 | Step 3160 | Loss: 1.1954 | LR: 5.00e-05
+[2026-04-25 19:44:25] Epoch 1 | Step 3170 | Loss: 1.1955 | LR: 5.00e-05
+[2026-04-25 19:44:28] Epoch 1 | Step 3180 | Loss: 1.1956 | LR: 5.00e-05
+[2026-04-25 19:44:30] Epoch 1 | Step 3190 | Loss: 1.1952 | LR: 5.00e-05
+[2026-04-25 19:44:33] Epoch 1 | Step 3200 | Loss: 1.1952 | LR: 5.00e-05
+[2026-04-25 19:44:35] Epoch 1 | Step 3210 | Loss: 1.1951 | LR: 5.00e-05
+[2026-04-25 19:44:38] Epoch 1 | Step 3220 | Loss: 1.1946 | LR: 5.00e-05
+[2026-04-25 19:44:40] Epoch 1 | Step 3230 | Loss: 1.1951 | LR: 5.00e-05
+[2026-04-25 19:44:43] Epoch 1 | Step 3240 | Loss: 1.1950 | LR: 5.00e-05
+[2026-04-25 19:44:45] Epoch 1 | Step 3250 | Loss: 1.1951 | LR: 5.00e-05
+[2026-04-25 19:44:48] Epoch 1 | Step 3260 | Loss: 1.1953 | LR: 5.00e-05
+[2026-04-25 19:44:50] Epoch 1 | Step 3270 | Loss: 1.1952 | LR: 5.00e-05
+[2026-04-25 19:44:53] Epoch 1 | Step 3280 | Loss: 1.1948 | LR: 5.00e-05
+[2026-04-25 19:44:55] Epoch 1 | Step 3290 | Loss: 1.1948 | LR: 5.00e-05
+[2026-04-25 19:44:58] Epoch 1 | Step 3300 | Loss: 1.1950 | LR: 5.00e-05
+[2026-04-25 19:45:00] Epoch 1 | Step 3310 | Loss: 1.1949 | LR: 5.00e-05
+[2026-04-25 19:45:03] Epoch 1 | Step 3320 | Loss: 1.1951 | LR: 5.00e-05
+[2026-04-25 19:45:06] Epoch 1 | Step 3330 | Loss: 1.1950 | LR: 5.00e-05
+[2026-04-25 19:45:09] Epoch 1 | Step 3340 | Loss: 1.1951 | LR: 5.00e-05
+[2026-04-25 19:45:11] Epoch 1 | Step 3350 | Loss: 1.1948 | LR: 5.00e-05
+[2026-04-25 19:45:13] Epoch 1 | Step 3360 | Loss: 1.1946 | LR: 5.00e-05
+[2026-04-25 19:45:16] Epoch 1 | Step 3370 | Loss: 1.1949 | LR: 5.00e-05
+[2026-04-25 19:45:19] Epoch 1 | Step 3380 | Loss: 1.1946 | LR: 5.00e-05
+[2026-04-25 19:45:22] Epoch 1 | Step 3390 | Loss: 1.1949 | LR: 5.00e-05
+[2026-04-25 19:45:24] Epoch 1 | Step 3400 | Loss: 1.1954 | LR: 5.00e-05
+[2026-04-25 19:45:27] Epoch 1 | Step 3410 | Loss: 1.1952 | LR: 5.00e-05
+[2026-04-25 19:45:29] Epoch 1 | Step 3420 | Loss: 1.1949 | LR: 5.00e-05
+[2026-04-25 19:45:32] Epoch 1 | Step 3430 | Loss: 1.1949 | LR: 5.00e-05
+[2026-04-25 19:45:35] Epoch 1 | Step 3440 | Loss: 1.1951 | LR: 5.00e-05
+[2026-04-25 19:45:37] Epoch 1 | Step 3450 | Loss: 1.1950 | LR: 5.00e-05
+[2026-04-25 19:45:40] Epoch 1 | Step 3460 | Loss: 1.1949 | LR: 5.00e-05
+[2026-04-25 19:45:42] Epoch 1 | Step 3470 | Loss: 1.1949 | LR: 5.00e-05
+[2026-04-25 19:45:45] Epoch 1 | Step 3480 | Loss: 1.1949 | LR: 5.00e-05
+[2026-04-25 19:45:47] Epoch 1 | Step 3490 | Loss: 1.1947 | LR: 5.00e-05
+[2026-04-25 19:45:50] Epoch 1 | Step 3500 | Loss: 1.1943 | LR: 5.00e-05
+[2026-04-25 19:45:53] Epoch 1 | Step 3510 | Loss: 1.1947 | LR: 5.00e-05
+[2026-04-25 19:45:55] Epoch 1 | Step 3520 | Loss: 1.1944 | LR: 5.00e-05
+[2026-04-25 19:45:58] Epoch 1 | Step 3530 | Loss: 1.1947 | LR: 5.00e-05
+[2026-04-25 19:46:00] Epoch 1 | Step 3540 | Loss: 1.1944 | LR: 5.00e-05
+[2026-04-25 19:46:03] Epoch 1 | Step 3550 | Loss: 1.1944 | LR: 5.00e-05
+[2026-04-25 19:46:05] Epoch 1 | Step 3560 | Loss: 1.1944 | LR: 5.00e-05
+[2026-04-25 19:46:08] Epoch 1 | Step 3570 | Loss: 1.1943 | LR: 5.00e-05
+[2026-04-25 19:46:11] Epoch 1 | Step 3580 | Loss: 1.1942 | LR: 5.00e-05
+[2026-04-25 19:46:13] Epoch 1 | Step 3590 | Loss: 1.1942 | LR: 5.00e-05
+[2026-04-25 19:46:16] Epoch 1 | Step 3600 | Loss: 1.1940 | LR: 5.00e-05
+[2026-04-25 19:46:18] Epoch 1 | Step 3610 | Loss: 1.1939 | LR: 5.00e-05
+[2026-04-25 19:46:21] Epoch 1 | Step 3620 | Loss: 1.1938 | LR: 5.00e-05
+[2026-04-25 19:46:23] Epoch 1 | Step 3630 | Loss: 1.1942 | LR: 5.00e-05
+[2026-04-25 19:46:26] Epoch 1 | Step 3640 | Loss: 1.1945 | LR: 5.00e-05
+[2026-04-25 19:46:28] Epoch 1 | Step 3650 | Loss: 1.1946 | LR: 5.00e-05
+[2026-04-25 19:46:31] Epoch 1 | Step 3660 | Loss: 1.1944 | LR: 5.00e-05
+[2026-04-25 19:46:33] Epoch 1 | Step 3670 | Loss: 1.1942 | LR: 5.00e-05
+[2026-04-25 19:46:36] Epoch 1 | Step 3680 | Loss: 1.1943 | LR: 5.00e-05
+[2026-04-25 19:46:38] Epoch 1 | Step 3690 | Loss: 1.1941 | LR: 5.00e-05
+[2026-04-25 19:46:41] Epoch 1 | Step 3700 | Loss: 1.1939 | LR: 5.00e-05
+[2026-04-25 19:46:43] Epoch 1 | Step 3710 | Loss: 1.1939 | LR: 5.00e-05
+[2026-04-25 19:46:46] Epoch 1 | Step 3720 | Loss: 1.1939 | LR: 5.00e-05
+[2026-04-25 19:46:48] Epoch 1 | Step 3730 | Loss: 1.1941 | LR: 5.00e-05
+[2026-04-25 19:46:51] Epoch 1 | Step 3740 | Loss: 1.1942 | LR: 5.00e-05
+[2026-04-25 19:46:53] Epoch 1 | Step 3750 | Loss: 1.1940 | LR: 5.00e-05
+[2026-04-25 19:46:56] Epoch 1 | Step 3760 | Loss: 1.1942 | LR: 5.00e-05
+[2026-04-25 19:46:59] Epoch 1 | Step 3770 | Loss: 1.1944 | LR: 5.00e-05
+[2026-04-25 19:47:01] Epoch 1 | Step 3780 | Loss: 1.1945 | LR: 5.00e-05
+[2026-04-25 19:47:04] Epoch 1 | Step 3790 | Loss: 1.1946 | LR: 5.00e-05
+[2026-04-25 19:47:06] Epoch 1 | Step 3800 | Loss: 1.1948 | LR: 5.00e-05
+[2026-04-25 19:47:09] Epoch 1 | Step 3810 | Loss: 1.1942 | LR: 5.00e-05
+[2026-04-25 19:47:11] Epoch 1 | Step 3820 | Loss: 1.1940 | LR: 5.00e-05
+[2026-04-25 19:47:14] Epoch 1 | Step 3830 | Loss: 1.1939 | LR: 5.00e-05
+[2026-04-25 19:47:16] Epoch 1 | Step 3840 | Loss: 1.1941 | LR: 5.00e-05
+[2026-04-25 19:47:19] Epoch 1 | Step 3850 | Loss: 1.1938 | LR: 5.00e-05
+[2026-04-25 19:47:21] Epoch 1 | Step 3860 | Loss: 1.1937 | LR: 5.00e-05
+[2026-04-25 19:47:24] Epoch 1 | Step 3870 | Loss: 1.1936 | LR: 5.00e-05
+[2026-04-25 19:47:27] Epoch 1 | Step 3880 | Loss: 1.1932 | LR: 5.00e-05
+[2026-04-25 19:47:29] Epoch 1 | Step 3890 | Loss: 1.1931 | LR: 5.00e-05
+[2026-04-25 19:47:32] Epoch 1 | Step 3900 | Loss: 1.1932 | LR: 5.00e-05
+[2026-04-25 19:47:34] Epoch 1 | Step 3910 | Loss: 1.1934 | LR: 5.00e-05
+[2026-04-25 19:47:37] Epoch 1 | Step 3920 | Loss: 1.1936 | LR: 5.00e-05
+[2026-04-25 19:47:39] Epoch 1 | Step 3930 | Loss: 1.1935 | LR: 5.00e-05
+[2026-04-25 19:47:42] Epoch 1 | Step 3940 | Loss: 1.1935 | LR: 5.00e-05
+[2026-04-25 19:47:45] Epoch 1 | Step 3950 | Loss: 1.1933 | LR: 5.00e-05
+[2026-04-25 19:47:47] Epoch 1 | Step 3960 | Loss: 1.1932 | LR: 5.00e-05
+[2026-04-25 19:47:50] Epoch 1 | Step 3970 | Loss: 1.1931 | LR: 5.00e-05
+[2026-04-25 19:47:53] Epoch 1 | Step 3980 | Loss: 1.1932 | LR: 4.99e-05
+[2026-04-25 19:47:55] Epoch 1 | Step 3990 | Loss: 1.1930 | LR: 4.99e-05
+[2026-04-25 19:47:57] Epoch 1 | Step 4000 | Loss: 1.1930 | LR: 4.98e-05
+[2026-04-25 19:47:58] Validation | Batch 10/84 | Loss: 1.1351
+[2026-04-25 19:47:58] Validation | Batch 20/84 | Loss: 1.1323
+[2026-04-25 19:47:59] Validation | Batch 30/84 | Loss: 1.2164
+[2026-04-25 19:47:59] Validation | Batch 40/84 | Loss: 1.2215
+[2026-04-25 19:48:00] Validation | Batch 50/84 | Loss: 1.2167
+[2026-04-25 19:48:00] Validation | Batch 60/84 | Loss: 1.1898
+[2026-04-25 19:48:01] Validation | Batch 70/84 | Loss: 1.1702
+[2026-04-25 19:48:01] Validation | Batch 80/84 | Loss: 1.1787
+[2026-04-25 19:48:01] Validation | Batch 84/84 | Loss: 1.1692
+[2026-04-25 19:48:02] Validation | Loss: 1.1692 | PPL: 3.30 | Time: 3.78s
+[2026-04-25 19:48:04] New best model saved! Val loss: 1.1692
+[2026-04-25 19:48:06] Epoch 1 | Step 4010 | Loss: 1.1929 | LR: 4.97e-05
+[2026-04-25 19:48:09] Epoch 1 | Step 4020 | Loss: 1.1930 | LR: 4.95e-05
+[2026-04-25 19:48:11] Epoch 1 | Step 4030 | Loss: 1.1927 | LR: 4.94e-05
+[2026-04-25 19:48:14] Epoch 1 | Step 4040 | Loss: 1.1924 | LR: 4.92e-05
+[2026-04-25 19:48:16] Epoch 1 | Step 4050 | Loss: 1.1924 | LR: 4.90e-05
+[2026-04-25 19:48:18] Epoch 1 | Step 4060 | Loss: 1.1919 | LR: 4.88e-05
+[2026-04-25 19:48:21] Epoch 1 | Step 4070 | Loss: 1.1920 | LR: 4.85e-05
+[2026-04-25 19:48:23] Epoch 1 | Step 4080 | Loss: 1.1921 | LR: 4.82e-05
+[2026-04-25 19:48:26] Epoch 1 | Step 4090 | Loss: 1.1921 | LR: 4.80e-05
+[2026-04-25 19:48:28] Epoch 1 | Step 4100 | Loss: 1.1923 | LR: 4.77e-05
+[2026-04-25 19:48:31] Epoch 1 | Step 4110 | Loss: 1.1922 | LR: 4.73e-05
+[2026-04-25 19:48:34] Epoch 1 | Step 4120 | Loss: 1.1924 | LR: 4.70e-05
+[2026-04-25 19:48:36] Epoch 1 | Step 4130 | Loss: 1.1922 | LR: 4.66e-05
+[2026-04-25 19:48:39] Epoch 1 | Step 4140 | Loss: 1.1923 | LR: 4.62e-05
+[2026-04-25 19:48:42] Epoch 1 | Step 4150 | Loss: 1.1929 | LR: 4.58e-05
+[2026-04-25 19:48:44] Epoch 1 | Step 4160 | Loss: 1.1932 | LR: 4.54e-05
+[2026-04-25 19:48:47] Epoch 1 | Step 4170 | Loss: 1.1931 | LR: 4.49e-05
+[2026-04-25 19:48:50] Epoch 1 | Step 4180 | Loss: 1.1931 | LR: 4.45e-05
+[2026-04-25 19:48:52] Epoch 1 | Step 4190 | Loss: 1.1930 | LR: 4.40e-05
+[2026-04-25 19:48:55] Epoch 1 | Step 4200 | Loss: 1.1934 | LR: 4.35e-05
+[2026-04-25 19:48:57] Epoch 1 | Step 4210 | Loss: 1.1932 | LR: 4.30e-05
+[2026-04-25 19:49:00] Epoch 1 | Step 4220 | Loss: 1.1937 | LR: 4.25e-05
+[2026-04-25 19:49:03] Epoch 1 | Step 4230 | Loss: 1.1937 | LR: 4.19e-05
+[2026-04-25 19:49:06] Epoch 1 | Step 4240 | Loss: 1.1939 | LR: 4.14e-05
+[2026-04-25 19:49:08] Epoch 1 | Step 4250 | Loss: 1.1939 | LR: 4.08e-05
+[2026-04-25 19:49:11] Epoch 1 | Step 4260 | Loss: 1.1935 | LR: 4.02e-05
+[2026-04-25 19:49:13] Epoch 1 | Step 4270 | Loss: 1.1937 | LR: 3.96e-05
+[2026-04-25 19:49:16] Epoch 1 | Step 4280 | Loss: 1.1936 | LR: 3.90e-05
+[2026-04-25 19:49:18] Epoch 1 | Step 4290 | Loss: 1.1933 | LR: 3.84e-05
+[2026-04-25 19:49:21] Epoch 1 | Step 4300 | Loss: 1.1932 | LR: 3.78e-05
+[2026-04-25 19:49:23] Epoch 1 | Step 4310 | Loss: 1.1935 | LR: 3.71e-05
+[2026-04-25 19:49:26] Epoch 1 | Step 4320 | Loss: 1.1936 | LR: 3.65e-05
+[2026-04-25 19:49:28] Epoch 1 | Step 4330 | Loss: 1.1934 | LR: 3.58e-05
+[2026-04-25 19:49:31] Epoch 1 | Step 4340 | Loss: 1.1932 | LR: 3.52e-05
+[2026-04-25 19:49:33] Epoch 1 | Step 4350 | Loss: 1.1929 | LR: 3.45e-05
+[2026-04-25 19:49:36] Epoch 1 | Step 4360 | Loss: 1.1928 | LR: 3.38e-05
+[2026-04-25 19:49:38] Epoch 1 | Step 4370 | Loss: 1.1928 | LR: 3.31e-05
+[2026-04-25 19:49:41] Epoch 1 | Step 4380 | Loss: 1.1927 | LR: 3.24e-05
+[2026-04-25 19:49:43] Epoch 1 | Step 4390 | Loss: 1.1927 | LR: 3.17e-05
+[2026-04-25 19:49:46] Epoch 1 | Step 4400 | Loss: 1.1924 | LR: 3.10e-05
+[2026-04-25 19:49:48] Epoch 1 | Step 4410 | Loss: 1.1919 | LR: 3.03e-05
+[2026-04-25 19:49:51] Epoch 1 | Step 4420 | Loss: 1.1922 | LR: 2.96e-05
+[2026-04-25 19:49:53] Epoch 1 | Step 4430 | Loss: 1.1920 | LR: 2.89e-05
+[2026-04-25 19:49:56] Epoch 1 | Step 4440 | Loss: 1.1923 | LR: 2.82e-05
+[2026-04-25 19:49:59] Epoch 1 | Step 4450 | Loss: 1.1921 | LR: 2.74e-05
+[2026-04-25 19:50:01] Epoch 1 | Step 4460 | Loss: 1.1924 | LR: 2.67e-05
+[2026-04-25 19:50:04] Epoch 1 | Step 4470 | Loss: 1.1921 | LR: 2.60e-05
+[2026-04-25 19:50:06] Epoch 1 | Step 4480 | Loss: 1.1918 | LR: 2.53e-05
+[2026-04-25 19:50:09] Epoch 1 | Step 4490 | Loss: 1.1916 | LR: 2.46e-05
+[2026-04-25 19:50:12] Epoch 1 | Step 4500 | Loss: 1.1916 | LR: 2.39e-05
+[2026-04-25 19:50:14] Epoch 1 | Step 4510 | Loss: 1.1911 | LR: 2.32e-05
+[2026-04-25 19:50:16] Epoch 1 | Step 4520 | Loss: 1.1908 | LR: 2.25e-05
+[2026-04-25 19:50:20] Epoch 1 | Step 4530 | Loss: 1.1904 | LR: 2.18e-05
+[2026-04-25 19:50:22] Epoch 1 | Step 4540 | Loss: 1.1902 | LR: 2.11e-05
+[2026-04-25 19:50:25] Epoch 1 | Step 4550 | Loss: 1.1898 | LR: 2.04e-05
+[2026-04-25 19:50:28] Epoch 1 | Step 4560 | Loss: 1.1897 | LR: 1.97e-05
+[2026-04-25 19:50:30] Epoch 1 | Step 4570 | Loss: 1.1896 | LR: 1.91e-05
+[2026-04-25 19:50:32] Epoch 1 | Step 4580 | Loss: 1.1894 | LR: 1.84e-05
+[2026-04-25 19:50:35] Epoch 1 | Step 4590 | Loss: 1.1892 | LR: 1.78e-05
+[2026-04-25 19:50:37] Epoch 1 | Step 4600 | Loss: 1.1889 | LR: 1.71e-05
+[2026-04-25 19:50:40] Epoch 1 | Step 4610 | Loss: 1.1886 | LR: 1.65e-05
+[2026-04-25 19:50:42] Epoch 1 | Step 4620 | Loss: 1.1886 | LR: 1.59e-05
+[2026-04-25 19:50:45] Epoch 1 | Step 4630 | Loss: 1.1884 | LR: 1.53e-05
+[2026-04-25 19:50:47] Epoch 1 | Step 4640 | Loss: 1.1883 | LR: 1.47e-05
+[2026-04-25 19:50:50] Epoch 1 | Step 4650 | Loss: 1.1882 | LR: 1.41e-05
+[2026-04-25 19:50:52] Epoch 1 | Step 4660 | Loss: 1.1880 | LR: 1.35e-05
+[2026-04-25 19:50:55] Epoch 1 | Step 4670 | Loss: 1.1877 | LR: 1.30e-05
+[2026-04-25 19:50:58] Epoch 1 | Step 4680 | Loss: 1.1877 | LR: 1.24e-05
+[2026-04-25 19:51:00] Epoch 1 | Step 4690 | Loss: 1.1874 | LR: 1.19e-05
+[2026-04-25 19:51:03] Epoch 1 | Step 4700 | Loss: 1.1875 | LR: 1.14e-05
+[2026-04-25 19:51:05] Epoch 1 | Step 4710 | Loss: 1.1872 | LR: 1.09e-05
+[2026-04-25 19:51:08] Epoch 1 | Step 4720 | Loss: 1.1871 | LR: 1.04e-05
+[2026-04-25 19:51:10] Epoch 1 | Step 4730 | Loss: 1.1870 | LR: 9.98e-06
+[2026-04-25 19:51:12] Epoch 1 | Step 4740 | Loss: 1.1867 | LR: 9.54e-06
+[2026-04-25 19:51:15] Epoch 1 | Step 4750 | Loss: 1.1866 | LR: 9.12e-06
+[2026-04-25 19:51:17] Epoch 1 | Step 4760 | Loss: 1.1863 | LR: 8.72e-06
+[2026-04-25 19:51:20] Epoch 1 | Step 4770 | Loss: 1.1858 | LR: 8.33e-06
+[2026-04-25 19:51:22] Epoch 1 | Step 4780 | Loss: 1.1859 | LR: 7.97e-06
+[2026-04-25 19:51:25] Epoch 1 | Step 4790 | Loss: 1.1858 | LR: 7.62e-06
+[2026-04-25 19:51:28] Epoch 1 | Step 4800 | Loss: 1.1854 | LR: 7.30e-06
+[2026-04-25 19:51:30] Epoch 1 | Step 4810 | Loss: 1.1849 | LR: 6.99e-06
+[2026-04-25 19:51:33] Epoch 1 | Step 4820 | Loss: 1.1846 | LR: 6.71e-06
+[2026-04-25 19:51:35] Epoch 1 | Step 4830 | Loss: 1.1842 | LR: 6.45e-06
+[2026-04-25 19:51:38] Epoch 1 | Step 4840 | Loss: 1.1840 | LR: 6.21e-06
+[2026-04-25 19:51:41] Epoch 1 | Step 4850 | Loss: 1.1842 | LR: 5.99e-06
+[2026-04-25 19:51:43] Epoch 1 | Step 4860 | Loss: 1.1842 | LR: 5.79e-06
+[2026-04-25 19:51:46] Epoch 1 | Step 4870 | Loss: 1.1843 | LR: 5.61e-06
+[2026-04-25 19:51:48] Epoch 1 | Step 4880 | Loss: 1.1840 | LR: 5.46e-06
+[2026-04-25 19:51:51] Epoch 1 | Step 4890 | Loss: 1.1837 | LR: 5.32e-06
+[2026-04-25 19:51:54] Epoch 1 | Step 4900 | Loss: 1.1836 | LR: 5.21e-06
+[2026-04-25 19:51:56] Epoch 1 | Step 4910 | Loss: 1.1834 | LR: 5.13e-06
+[2026-04-25 19:51:59] Epoch 1 | Step 4920 | Loss: 1.1832 | LR: 5.06e-06
+[2026-04-25 19:52:01] Epoch 1 | Step 4930 | Loss: 1.1830 | LR: 5.02e-06
+[2026-04-25 19:52:04] Epoch 1 | Step 4940 | Loss: 1.1829 | LR: 5.00e-06
+[2026-04-25 19:52:06] Epoch 1 | Step 4950 | Loss: 1.1828 | LR: 5.00e-06
+[2026-04-25 19:52:09] Epoch 1 | Step 4960 | Loss: 1.1828 | LR: 5.00e-06
+[2026-04-25 19:52:11] Epoch 1 | Step 4970 | Loss: 1.1825 | LR: 5.00e-06
+[2026-04-25 19:52:14] Epoch 1 | Step 4980 | Loss: 1.1824 | LR: 5.00e-06
+[2026-04-25 19:52:16] Epoch 1 | Step 4990 | Loss: 1.1820 | LR: 5.00e-06
+[2026-04-25 19:52:19] Epoch 1 | Step 5000 | Loss: 1.1821 | LR: 5.00e-06
+[2026-04-25 19:52:21] Epoch 1 | Step 5010 | Loss: 1.1819 | LR: 5.00e-06
+[2026-04-25 19:52:24] Epoch 1 | Step 5020 | Loss: 1.1816 | LR: 5.00e-06
+[2026-04-25 19:52:26] Epoch 1 | Step 5030 | Loss: 1.1814 | LR: 5.00e-06
+[2026-04-25 19:52:28] Epoch 1 | Step 5040 | Loss: 1.1811 | LR: 5.00e-06
+[2026-04-25 19:52:31] Epoch 1 | Step 5050 | Loss: 1.1809 | LR: 5.00e-06
+[2026-04-25 19:52:33] Epoch 1 | Step 5060 | Loss: 1.1807 | LR: 5.00e-06
+[2026-04-25 19:52:36] Epoch 1 | Step 5070 | Loss: 1.1805 | LR: 5.00e-06
+[2026-04-25 19:52:38] Epoch 1 | Step 5080 | Loss: 1.1805 | LR: 5.00e-06
+[2026-04-25 19:52:41] Epoch 1 | Step 5090 | Loss: 1.1804 | LR: 5.00e-06
+[2026-04-25 19:52:44] Epoch 1 | Step 5100 | Loss: 1.1800 | LR: 5.00e-06
+[2026-04-25 19:52:46] Epoch 1 | Step 5110 | Loss: 1.1798 | LR: 5.00e-06
+[2026-04-25 19:52:49] Epoch 1 | Step 5120 | Loss: 1.1799 | LR: 5.00e-06
+[2026-04-25 19:52:52] Epoch 1 | Step 5130 | Loss: 1.1796 | LR: 5.00e-06
+[2026-04-25 19:52:54] Epoch 1 | Step 5140 | Loss: 1.1794 | LR: 5.00e-06
+[2026-04-25 19:52:57] Epoch 1 | Step 5150 | Loss: 1.1791 | LR: 5.00e-06
+[2026-04-25 19:52:59] Epoch 1 | Step 5160 | Loss: 1.1786 | LR: 5.00e-06
+[2026-04-25 19:53:02] Epoch 1 | Step 5170 | Loss: 1.1785 | LR: 5.00e-06
+[2026-04-25 19:53:04] Epoch 1 | Step 5180 | Loss: 1.1783 | LR: 5.00e-06
+[2026-04-25 19:53:07] Epoch 1 | Step 5190 | Loss: 1.1783 | LR: 5.00e-06
+[2026-04-25 19:53:10] Epoch 1 | Step 5200 | Loss: 1.1782 | LR: 5.00e-06
+[2026-04-25 19:53:12] Epoch 1 | Step 5210 | Loss: 1.1780 | LR: 5.00e-06
+[2026-04-25 19:53:15] Epoch 1 | Step 5220 | Loss: 1.1779 | LR: 5.00e-06
+[2026-04-25 19:53:18] Epoch 1 | Step 5230 | Loss: 1.1778 | LR: 5.00e-06
+[2026-04-25 19:53:20] Epoch 1 | Step 5240 | Loss: 1.1777 | LR: 5.00e-06
+[2026-04-25 19:53:23] Epoch 1 | Step 5250 | Loss: 1.1777 | LR: 5.00e-06
+[2026-04-25 19:53:25] Epoch 1 | Step 5260 | Loss: 1.1775 | LR: 5.00e-06
+[2026-04-25 19:53:28] Epoch 1 | Step 5270 | Loss: 1.1773 | LR: 5.00e-06
+[2026-04-25 19:53:31] Epoch 1 | Step 5280 | Loss: 1.1770 | LR: 5.00e-06
+[2026-04-25 19:53:33] Epoch 1 | Step 5290 | Loss: 1.1766 | LR: 5.00e-06
+[2026-04-25 19:53:36] Epoch 1 | Step 5300 | Loss: 1.1764 | LR: 5.00e-06
+[2026-04-25 19:53:38] Epoch 1 | Step 5310 | Loss: 1.1764 | LR: 5.00e-06
+[2026-04-25 19:53:41] Epoch 1 | Step 5320 | Loss: 1.1761 | LR: 5.00e-06
+[2026-04-25 19:53:43] Epoch 1 | Step 5330 | Loss: 1.1760 | LR: 5.00e-06
+[2026-04-25 19:53:46] Epoch 1 | Step 5340 | Loss: 1.1758 | LR: 5.00e-06
+[2026-04-25 19:53:48] Epoch 1 | Step 5350 | Loss: 1.1756 | LR: 5.00e-06
+[2026-04-25 19:53:51] Epoch 1 | Step 5360 | Loss: 1.1756 | LR: 5.00e-06
+[2026-04-25 19:53:53] Epoch 1 | Step 5370 | Loss: 1.1754 | LR: 5.00e-06
+[2026-04-25 19:53:55] Epoch 1 | Step 5380 | Loss: 1.1752 | LR: 5.00e-06
+[2026-04-25 19:53:58] Epoch 1 | Step 5390 | Loss: 1.1749 | LR: 5.00e-06
+[2026-04-25 19:54:01] Epoch 1 | Step 5400 | Loss: 1.1746 | LR: 5.00e-06
+[2026-04-25 19:54:03] Epoch 1 | Step 5410 | Loss: 1.1745 | LR: 5.00e-06
+[2026-04-25 19:54:06] Epoch 1 | Step 5420 | Loss: 1.1742 | LR: 5.00e-06
+[2026-04-25 19:54:08] Epoch 1 | Step 5430 | Loss: 1.1741 | LR: 5.00e-06
+[2026-04-25 19:54:11] Epoch 1 | Step 5440 | Loss: 1.1741 | LR: 5.00e-06
+[2026-04-25 19:54:13] Epoch 1 | Step 5450 | Loss: 1.1742 | LR: 5.00e-06
+[2026-04-25 19:54:16] Epoch 1 | Step 5460 | Loss: 1.1739 | LR: 5.00e-06
+[2026-04-25 19:54:18] Epoch 1 | Step 5470 | Loss: 1.1736 | LR: 5.00e-06
+[2026-04-25 19:54:21] Epoch 1 | Step 5480 | Loss: 1.1736 | LR: 5.00e-06
+[2026-04-25 19:54:23] Epoch 1 | Step 5490 | Loss: 1.1735 | LR: 5.00e-06
+[2026-04-25 19:54:26] Epoch 1 | Step 5500 | Loss: 1.1734 | LR: 5.00e-06
+[2026-04-25 19:54:28] Epoch 1 | Step 5510 | Loss: 1.1735 | LR: 5.00e-06
+[2026-04-25 19:54:31] Epoch 1 | Step 5520 | Loss: 1.1733 | LR: 5.00e-06
+[2026-04-25 19:54:33] Epoch 1 | Step 5530 | Loss: 1.1732 | LR: 5.00e-06
+[2026-04-25 19:54:36] Epoch 1 | Step 5540 | Loss: 1.1727 | LR: 5.00e-06
+[2026-04-25 19:54:38] Epoch 1 | Step 5550 | Loss: 1.1726 | LR: 5.00e-06
+[2026-04-25 19:54:41] Epoch 1 | Step 5560 | Loss: 1.1723 | LR: 5.00e-06
+[2026-04-25 19:54:44] Epoch 1 | Step 5570 | Loss: 1.1724 | LR: 5.00e-06
+[2026-04-25 19:54:46] Epoch 1 | Step 5580 | Loss: 1.1721 | LR: 5.00e-06
+[2026-04-25 19:54:49] Epoch 1 | Step 5590 | Loss: 1.1718 | LR: 5.00e-06
+[2026-04-25 19:54:51] Epoch 1 | Step 5600 | Loss: 1.1719 | LR: 5.00e-06
+[2026-04-25 19:54:54] Epoch 1 | Step 5610 | Loss: 1.1719 | LR: 5.00e-06
+[2026-04-25 19:54:56] Epoch 1 | Step 5620 | Loss: 1.1716 | LR: 5.00e-06
+[2026-04-25 19:54:59] Epoch 1 | Step 5630 | Loss: 1.1715 | LR: 5.00e-06
+[2026-04-25 19:55:02] Epoch 1 | Step 5640 | Loss: 1.1714 | LR: 5.00e-06
+[2026-04-25 19:55:04] Epoch 1 | Step 5650 | Loss: 1.1713 | LR: 5.00e-06
+[2026-04-25 19:55:07] Epoch 1 | Step 5660 | Loss: 1.1709 | LR: 5.00e-06
+[2026-04-25 19:55:09] Epoch 1 | Step 5670 | Loss: 1.1708 | LR: 5.00e-06
+[2026-04-25 19:55:12] Epoch 1 | Step 5680 | Loss: 1.1704 | LR: 5.00e-06
+[2026-04-25 19:55:14] Epoch 1 | Step 5690 | Loss: 1.1704 | LR: 5.00e-06
+[2026-04-25 19:55:16] Epoch 1 | Step 5700 | Loss: 1.1702 | LR: 5.00e-06
+[2026-04-25 19:55:19] Epoch 1 | Step 5710 | Loss: 1.1702 | LR: 5.00e-06
+[2026-04-25 19:55:22] Epoch 1 | Step 5720 | Loss: 1.1701 | LR: 5.00e-06
+[2026-04-25 19:55:24] Epoch 1 | Step 5730 | Loss: 1.1700 | LR: 5.00e-06
+[2026-04-25 19:55:27] Epoch 1 | Step 5740 | Loss: 1.1700 | LR: 5.00e-06
+[2026-04-25 19:55:30] Epoch 1 | Step 5750 | Loss: 1.1698 | LR: 5.00e-06
+[2026-04-25 19:55:33] Epoch 1 | Step 5760 | Loss: 1.1697 | LR: 5.00e-06
+[2026-04-25 19:55:35] Epoch 1 | Step 5770 | Loss: 1.1697 | LR: 5.00e-06
+[2026-04-25 19:55:37] Epoch 1 | Step 5780 | Loss: 1.1694 | LR: 5.00e-06
+[2026-04-25 19:55:40] Epoch 1 | Step 5790 | Loss: 1.1695 | LR: 5.00e-06
+[2026-04-25 19:55:42] Epoch 1 | Step 5800 | Loss: 1.1697 | LR: 5.00e-06
+[2026-04-25 19:55:45] Epoch 1 | Step 5810 | Loss: 1.1696 | LR: 5.00e-06
+[2026-04-25 19:55:48] Epoch 1 | Step 5820 | Loss: 1.1693 | LR: 5.00e-06
+[2026-04-25 19:55:50] Epoch 1 | Step 5830 | Loss: 1.1690 | LR: 5.00e-06
+[2026-04-25 19:55:52] Epoch 1 | Step 5840 | Loss: 1.1691 | LR: 5.00e-06
+[2026-04-25 19:55:55] Epoch 1 | Step 5850 | Loss: 1.1690 | LR: 5.00e-06
+[2026-04-25 19:55:57] Epoch 1 | Step 5860 | Loss: 1.1688 | LR: 5.00e-06
+[2026-04-25 19:56:00] Epoch 1 | Step 5870 | Loss: 1.1688 | LR: 5.00e-06
+[2026-04-25 19:56:03] Epoch 1 | Step 5880 | Loss: 1.1688 | LR: 5.00e-06
+[2026-04-25 19:56:05] Epoch 1 | Step 5890 | Loss: 1.1687 | LR: 5.00e-06
+[2026-04-25 19:56:08] Epoch 1 | Step 5900 | Loss: 1.1685 | LR: 5.00e-06
+[2026-04-25 19:56:11] Epoch 1 | Step 5910 | Loss: 1.1684 | LR: 5.00e-06
+[2026-04-25 19:56:13] Epoch 1 | Step 5920 | Loss: 1.1681 | LR: 5.00e-06
+[2026-04-25 19:56:16] Epoch 1 | Step 5930 | Loss: 1.1681 | LR: 5.00e-06
+[2026-04-25 19:56:19] Epoch 1 | Step 5940 | Loss: 1.1679 | LR: 5.00e-06
+[2026-04-25 19:56:21] Epoch 1 | Step 5950 | Loss: 1.1680 | LR: 5.00e-06
+[2026-04-25 19:56:24] Epoch 1 | Step 5960 | Loss: 1.1679 | LR: 5.00e-06
+[2026-04-25 19:56:26] Epoch 1 | Step 5970 | Loss: 1.1679 | LR: 5.00e-06
+[2026-04-25 19:56:29] Epoch 1 | Step 5980 | Loss: 1.1677 | LR: 5.00e-06
+[2026-04-25 19:56:32] Epoch 1 | Step 5990 | Loss: 1.1678 | LR: 5.00e-06
+[2026-04-25 19:56:34] Epoch 1 | Step 6000 | Loss: 1.1676 | LR: 5.00e-06
+[2026-04-25 19:56:35] Validation | Batch 10/84 | Loss: 1.0538
+[2026-04-25 19:56:35] Validation | Batch 20/84 | Loss: 1.0550
+[2026-04-25 19:56:35] Validation | Batch 30/84 | Loss: 1.1355
+[2026-04-25 19:56:36] Validation | Batch 40/84 | Loss: 1.1377
+[2026-04-25 19:56:36] Validation | Batch 50/84 | Loss: 1.1304
+[2026-04-25 19:56:37] Validation | Batch 60/84 | Loss: 1.1020
+[2026-04-25 19:56:37] Validation | Batch 70/84 | Loss: 1.0857
+[2026-04-25 19:56:38] Validation | Batch 80/84 | Loss: 1.0935
+[2026-04-25 19:56:38] Validation | Batch 84/84 | Loss: 1.0843
+[2026-04-25 19:56:38] Validation | Loss: 1.0843 | PPL: 3.02 | Time: 3.76s
+[2026-04-25 19:56:41] New best model saved! Val loss: 1.0843
+[2026-04-25 19:56:43] Epoch 1 | Step 6010 | Loss: 1.1676 | LR: 5.00e-06
+[2026-04-25 19:56:46] Epoch 1 | Step 6020 | Loss: 1.1674 | LR: 5.00e-06
+[2026-04-25 19:56:48] Epoch 1 | Step 6030 | Loss: 1.1675 | LR: 5.00e-06
+[2026-04-25 19:56:51] Epoch 1 | Step 6040 | Loss: 1.1674 | LR: 5.00e-06
+[2026-04-25 19:56:54] Epoch 1 | Step 6050 | Loss: 1.1674 | LR: 5.00e-06
+[2026-04-25 19:56:56] Epoch 1 | Step 6060 | Loss: 1.1673 | LR: 5.00e-06
+[2026-04-25 19:56:59] Epoch 1 | Step 6070 | Loss: 1.1670 | LR: 5.00e-06
+[2026-04-25 19:57:01] Epoch 1 | Step 6080 | Loss: 1.1670 | LR: 5.00e-06
+[2026-04-25 19:57:04] Epoch 1 | Step 6090 | Loss: 1.1671 | LR: 5.00e-06
+[2026-04-25 19:57:07] Epoch 1 | Step 6100 | Loss: 1.1672 | LR: 5.00e-06
+[2026-04-25 19:57:09] Epoch 1 | Step 6110 | Loss: 1.1671 | LR: 5.00e-06
+[2026-04-25 19:57:12] Epoch 1 | Step 6120 | Loss: 1.1669 | LR: 5.00e-06
+[2026-04-25 19:57:14] Epoch 1 | Step 6130 | Loss: 1.1667 | LR: 5.00e-06
+[2026-04-25 19:57:17] Epoch 1 | Step 6140 | Loss: 1.1662 | LR: 5.00e-06
+[2026-04-25 19:57:19] Epoch 1 | Step 6150 | Loss: 1.1660 | LR: 5.00e-06
+[2026-04-25 19:57:22] Epoch 1 | Step 6160 | Loss: 1.1659 | LR: 5.00e-06
+[2026-04-25 19:57:24] Epoch 1 | Step 6170 | Loss: 1.1660 | LR: 5.00e-06
+[2026-04-25 19:57:27] Epoch 1 | Step 6180 | Loss: 1.1657 | LR: 5.00e-06
+[2026-04-25 19:57:29] Epoch 1 | Step 6190 | Loss: 1.1655 | LR: 5.00e-06
+[2026-04-25 19:57:32] Epoch 1 | Step 6200 | Loss: 1.1653 | LR: 5.00e-06
+[2026-04-25 19:57:35] Epoch 1 | Step 6210 | Loss: 1.1653 | LR: 5.00e-06
+[2026-04-25 19:57:38] Epoch 1 | Step 6220 | Loss: 1.1653 | LR: 5.00e-06
+[2026-04-25 19:57:40] Epoch 1 | Step 6230 | Loss: 1.1650 | LR: 5.00e-06
+[2026-04-25 19:57:43] Epoch 1 | Step 6240 | Loss: 1.1650 | LR: 5.00e-06
+[2026-04-25 19:57:45] Epoch 1 | Step 6250 | Loss: 1.1646 | LR: 5.00e-06
+[2026-04-25 19:57:48] Epoch 1 | Step 6260 | Loss: 1.1646 | LR: 5.00e-06
+[2026-04-25 19:57:50] Epoch 1 | Step 6270 | Loss: 1.1645 | LR: 5.00e-06
+[2026-04-25 19:57:53] Epoch 1 | Step 6280 | Loss: 1.1641 | LR: 5.00e-06
+[2026-04-25 19:57:55] Epoch 1 | Step 6290 | Loss: 1.1640 | LR: 5.00e-06
+[2026-04-25 19:57:58] Epoch 1 | Step 6300 | Loss: 1.1639 | LR: 5.00e-06
+[2026-04-25 19:58:01] Epoch 1 | Step 6310 | Loss: 1.1639 | LR: 5.00e-06
+[2026-04-25 19:58:03] Epoch 1 | Step 6320 | Loss: 1.1638 | LR: 5.00e-06
+[2026-04-25 19:58:06] Epoch 1 | Step 6330 | Loss: 1.1640 | LR: 5.00e-06
+[2026-04-25 19:58:08] Epoch 1 | Step 6340 | Loss: 1.1640 | LR: 5.00e-06
+[2026-04-25 19:58:11] Epoch 1 | Step 6350 | Loss: 1.1639 | LR: 5.00e-06
+[2026-04-25 19:58:13] Epoch 1 | Step 6360 | Loss: 1.1640 | LR: 5.00e-06
+[2026-04-25 19:58:16] Epoch 1 | Step 6370 | Loss: 1.1639 | LR: 5.00e-06
+[2026-04-25 19:58:18] Epoch 1 | Step 6380 | Loss: 1.1638 | LR: 5.00e-06
+[2026-04-25 19:58:21] Epoch 1 | Step 6390 | Loss: 1.1635 | LR: 5.00e-06
+[2026-04-25 19:58:23] Epoch 1 | Step 6400 | Loss: 1.1633 | LR: 5.00e-06
+[2026-04-25 19:58:25] Epoch 1 | Step 6410 | Loss: 1.1632 | LR: 5.00e-06
+[2026-04-25 19:58:28] Epoch 1 | Step 6420 | Loss: 1.1630 | LR: 5.00e-06
+[2026-04-25 19:58:30] Epoch 1 | Step 6430 | Loss: 1.1629 | LR: 5.00e-06
+[2026-04-25 19:58:33] Epoch 1 | Step 6440 | Loss: 1.1628 | LR: 5.00e-06
+[2026-04-25 19:58:35] Epoch 1 | Step 6450 | Loss: 1.1627 | LR: 5.00e-06
+[2026-04-25 19:58:38] Epoch 1 | Step 6460 | Loss: 1.1623 | LR: 5.00e-06
+[2026-04-25 19:58:40] Epoch 1 | Step 6470 | Loss: 1.1622 | LR: 5.00e-06
+[2026-04-25 19:58:43] Epoch 1 | Step 6480 | Loss: 1.1622 | LR: 5.00e-06
+[2026-04-25 19:58:45] Epoch 1 | Step 6490 | Loss: 1.1623 | LR: 5.00e-06
+[2026-04-25 19:58:48] Epoch 1 | Step 6500 | Loss: 1.1620 | LR: 5.00e-06
+[2026-04-25 19:58:50] Epoch 1 | Step 6510 | Loss: 1.1618 | LR: 5.00e-06
+[2026-04-25 19:58:53] Epoch 1 | Step 6520 | Loss: 1.1615 | LR: 5.00e-06
+[2026-04-25 19:58:55] Epoch 1 | Step 6530 | Loss: 1.1612 | LR: 5.00e-06
+[2026-04-25 19:58:58] Epoch 1 | Step 6540 | Loss: 1.1610 | LR: 5.00e-06
+[2026-04-25 19:59:00] Epoch 1 | Step 6550 | Loss: 1.1608 | LR: 5.00e-06
+[2026-04-25 19:59:03] Epoch 1 | Step 6560 | Loss: 1.1607 | LR: 5.00e-06
+[2026-04-25 19:59:05] Epoch 1 | Step 6570 | Loss: 1.1606 | LR: 5.00e-06
+[2026-04-25 19:59:08] Epoch 1 | Step 6580 | Loss: 1.1606 | LR: 5.00e-06
+[2026-04-25 19:59:10] Epoch 1 | Step 6590 | Loss: 1.1603 | LR: 5.00e-06
+[2026-04-25 19:59:13] Epoch 1 | Step 6600 | Loss: 1.1602 | LR: 5.00e-06
+[2026-04-25 19:59:16] Epoch 1 | Step 6610 | Loss: 1.1601 | LR: 5.00e-06
+[2026-04-25 19:59:18] Epoch 1 | Step 6620 | Loss: 1.1600 | LR: 5.00e-06
+[2026-04-25 19:59:21] Epoch 1 | Step 6630 | Loss: 1.1598 | LR: 5.00e-06
+[2026-04-25 19:59:23] Epoch 1 | Step 6640 | Loss: 1.1598 | LR: 5.00e-06
+[2026-04-25 19:59:25] Epoch 1 | Step 6650 | Loss: 1.1598 | LR: 5.00e-06
+[2026-04-25 19:59:28] Epoch 1 | Step 6660 | Loss: 1.1595 | LR: 5.00e-06
+[2026-04-25 19:59:31] Epoch 1 | Step 6670 | Loss: 1.1594 | LR: 5.00e-06
+[2026-04-25 19:59:33] Epoch 1 | Step 6680 | Loss: 1.1593 | LR: 5.00e-06
+[2026-04-25 19:59:36] Epoch 1 | Step 6690 | Loss: 1.1593 | LR: 5.00e-06
+[2026-04-25 19:59:38] Epoch 1 | Step 6700 | Loss: 1.1592 | LR: 5.00e-06
+[2026-04-25 19:59:41] Epoch 1 | Step 6710 | Loss: 1.1591 | LR: 5.00e-06
+[2026-04-25 19:59:43] Epoch 1 | Step 6720 | Loss: 1.1590 | LR: 5.00e-06
+[2026-04-25 19:59:46] Epoch 1 | Step 6730 | Loss: 1.1591 | LR: 5.00e-06
+[2026-04-25 19:59:48] Epoch 1 | Step 6740 | Loss: 1.1589 | LR: 5.00e-06
+[2026-04-25 19:59:51] Epoch 1 | Step 6750 | Loss: 1.1587 | LR: 5.00e-06
+[2026-04-25 19:59:53] Epoch 1 | Step 6760 | Loss: 1.1587 | LR: 5.00e-06
+[2026-04-25 19:59:56] Epoch 1 | Step 6770 | Loss: 1.1586 | LR: 5.00e-06
+[2026-04-25 19:59:58] Epoch 1 | Step 6780 | Loss: 1.1585 | LR: 5.00e-06
+[2026-04-25 20:00:01] Epoch 1 | Step 6790 | Loss: 1.1585 | LR: 5.00e-06
+[2026-04-25 20:00:03] Epoch 1 | Step 6800 | Loss: 1.1586 | LR: 5.00e-06
+[2026-04-25 20:00:06] Epoch 1 | Step 6810 | Loss: 1.1585 | LR: 5.00e-06
+[2026-04-25 20:00:08] Epoch 1 | Step 6820 | Loss: 1.1586 | LR: 5.00e-06
+[2026-04-25 20:00:11] Epoch 1 | Step 6830 | Loss: 1.1586 | LR: 5.00e-06
+[2026-04-25 20:00:14] Epoch 1 | Step 6840 | Loss: 1.1586 | LR: 5.00e-06
+[2026-04-25 20:00:16] Epoch 1 | Step 6850 | Loss: 1.1585 | LR: 5.00e-06
+[2026-04-25 20:00:19] Epoch 1 | Step 6860 | Loss: 1.1584 | LR: 5.00e-06
+[2026-04-25 20:00:21] Epoch 1 | Step 6870 | Loss: 1.1583 | LR: 5.00e-06
+[2026-04-25 20:00:24] Epoch 1 | Step 6880 | Loss: 1.1581 | LR: 5.00e-06
+[2026-04-25 20:00:26] Epoch 1 | Step 6890 | Loss: 1.1582 | LR: 5.00e-06
+[2026-04-25 20:00:29] Epoch 1 | Step 6900 | Loss: 1.1582 | LR: 5.00e-06
+[2026-04-25 20:00:31] Epoch 1 | Step 6910 | Loss: 1.1578 | LR: 5.00e-06
+[2026-04-25 20:00:34] Epoch 1 | Step 6920 | Loss: 1.1577 | LR: 5.00e-06
+[2026-04-25 20:00:36] Epoch 1 | Step 6930 | Loss: 1.1577 | LR: 5.00e-06
+[2026-04-25 20:00:39] Epoch 1 | Step 6940 | Loss: 1.1575 | LR: 5.00e-06
+[2026-04-25 20:00:41] Epoch 1 | Step 6950 | Loss: 1.1574 | LR: 5.00e-06
+[2026-04-25 20:00:44] Epoch 1 | Step 6960 | Loss: 1.1574 | LR: 5.00e-06
+[2026-04-25 20:00:47] Epoch 1 | Step 6970 | Loss: 1.1573 | LR: 5.00e-06
+[2026-04-25 20:00:49] Epoch 1 | Step 6980 | Loss: 1.1572 | LR: 5.00e-06
+[2026-04-25 20:00:51] Epoch 1 | Step 6990 | Loss: 1.1570 | LR: 5.00e-06
+[2026-04-25 20:00:54] Epoch 1 | Step 7000 | Loss: 1.1568 | LR: 5.00e-06
+[2026-04-25 20:00:56] Epoch 1 | Step 7010 | Loss: 1.1567 | LR: 5.00e-06
+[2026-04-25 20:00:59] Epoch 1 | Step 7020 | Loss: 1.1567 | LR: 5.00e-06
+[2026-04-25 20:01:01] Epoch 1 | Step 7030 | Loss: 1.1566 | LR: 5.00e-06
+[2026-04-25 20:01:04] Epoch 1 | Step 7040 | Loss: 1.1566 | LR: 5.00e-06
+[2026-04-25 20:01:06] Epoch 1 | Step 7050 | Loss: 1.1564 | LR: 5.00e-06
+[2026-04-25 20:01:09] Epoch 1 | Step 7060 | Loss: 1.1563 | LR: 5.00e-06
+[2026-04-25 20:01:11] Epoch 1 | Step 7070 | Loss: 1.1564 | LR: 5.00e-06
+[2026-04-25 20:01:14] Epoch 1 | Step 7080 | Loss: 1.1561 | LR: 5.00e-06
+[2026-04-25 20:01:16] Epoch 1 | Step 7090 | Loss: 1.1561 | LR: 5.00e-06
+[2026-04-25 20:01:19] Epoch 1 | Step 7100 | Loss: 1.1558 | LR: 5.00e-06
+[2026-04-25 20:01:22] Epoch 1 | Step 7110 | Loss: 1.1557 | LR: 5.00e-06
+[2026-04-25 20:01:24] Epoch 1 | Step 7120 | Loss: 1.1558 | LR: 5.00e-06
+[2026-04-25 20:01:27] Epoch 1 | Step 7130 | Loss: 1.1555 | LR: 5.00e-06
+[2026-04-25 20:01:29] Epoch 1 | Step 7140 | Loss: 1.1553 | LR: 5.00e-06
+[2026-04-25 20:01:31] Epoch 1 | Step 7150 | Loss: 1.1555 | LR: 5.00e-06
+[2026-04-25 20:01:34] Epoch 1 | Step 7160 | Loss: 1.1552 | LR: 5.00e-06
+[2026-04-25 20:01:37] Epoch 1 | Step 7170 | Loss: 1.1552 | LR: 5.00e-06
+[2026-04-25 20:01:39] Epoch 1 | Step 7180 | Loss: 1.1551 | LR: 5.00e-06
+[2026-04-25 20:01:42] Epoch 1 | Step 7190 | Loss: 1.1552 | LR: 5.00e-06
+[2026-04-25 20:01:44] Epoch 1 | Step 7200 | Loss: 1.1550 | LR: 5.00e-06
+[2026-04-25 20:01:47] Epoch 1 | Step 7210 | Loss: 1.1548 | LR: 5.00e-06
+[2026-04-25 20:01:50] Epoch 1 | Step 7220 | Loss: 1.1548 | LR: 5.00e-06
+[2026-04-25 20:01:52] Epoch 1 | Step 7230 | Loss: 1.1548 | LR: 5.00e-06
+[2026-04-25 20:01:55] Epoch 1 | Step 7240 | Loss: 1.1547 | LR: 5.00e-06
+[2026-04-25 20:01:58] Epoch 1 | Step 7250 | Loss: 1.1546 | LR: 5.00e-06
+[2026-04-25 20:02:00] Epoch 1 | Step 7260 | Loss: 1.1545 | LR: 5.00e-06
+[2026-04-25 20:02:03] Epoch 1 | Step 7270 | Loss: 1.1545 | LR: 5.00e-06
+[2026-04-25 20:02:05] Epoch 1 | Step 7280 | Loss: 1.1545 | LR: 5.00e-06
+[2026-04-25 20:02:08] Epoch 1 | Step 7290 | Loss: 1.1542 | LR: 5.00e-06
+[2026-04-25 20:02:10] Epoch 1 | Step 7300 | Loss: 1.1541 | LR: 5.00e-06
+[2026-04-25 20:02:13] Epoch 1 | Step 7310 | Loss: 1.1539 | LR: 5.00e-06
+[2026-04-25 20:02:16] Epoch 1 | Step 7320 | Loss: 1.1536 | LR: 5.00e-06
+[2026-04-25 20:02:18] Epoch 1 | Step 7330 | Loss: 1.1536 | LR: 5.00e-06
+[2026-04-25 20:02:21] Epoch 1 | Step 7340 | Loss: 1.1537 | LR: 5.00e-06
+[2026-04-25 20:02:23] Epoch 1 | Step 7350 | Loss: 1.1537 | LR: 5.00e-06
+[2026-04-25 20:02:26] Epoch 1 | Step 7360 | Loss: 1.1535 | LR: 5.00e-06
+[2026-04-25 20:02:29] Epoch 1 | Step 7370 | Loss: 1.1532 | LR: 5.00e-06
+[2026-04-25 20:02:31] Epoch 1 | Step 7380 | Loss: 1.1530 | LR: 5.00e-06
+[2026-04-25 20:02:34] Epoch 1 | Step 7390 | Loss: 1.1528 | LR: 5.00e-06
+[2026-04-25 20:02:36] Epoch 1 | Step 7400 | Loss: 1.1527 | LR: 5.00e-06
+[2026-04-25 20:02:39] Epoch 1 | Step 7410 | Loss: 1.1528 | LR: 5.00e-06
+[2026-04-25 20:02:41] Epoch 1 | Step 7420 | Loss: 1.1527 | LR: 5.00e-06
+[2026-04-25 20:02:44] Epoch 1 | Step 7430 | Loss: 1.1525 | LR: 5.00e-06
+[2026-04-25 20:02:46] Epoch 1 | Step 7440 | Loss: 1.1525 | LR: 5.00e-06
+[2026-04-25 20:02:49] Epoch 1 | Step 7450 | Loss: 1.1523 | LR: 5.00e-06
+[2026-04-25 20:02:51] Epoch 1 | Step 7460 | Loss: 1.1522 | LR: 5.00e-06
+[2026-04-25 20:02:54] Epoch 1 | Step 7470 | Loss: 1.1521 | LR: 5.00e-06
+[2026-04-25 20:02:56] Epoch 1 | Step 7480 | Loss: 1.1521 | LR: 5.00e-06
+[2026-04-25 20:02:59] Epoch 1 | Step 7490 | Loss: 1.1521 | LR: 5.00e-06
+[2026-04-25 20:03:01] Epoch 1 | Step 7500 | Loss: 1.1521 | LR: 5.00e-06
+[2026-04-25 20:03:04] Epoch 1 | Step 7510 | Loss: 1.1521 | LR: 5.00e-06
+[2026-04-25 20:03:06] Epoch 1 | Step 7520 | Loss: 1.1520 | LR: 5.00e-06
+[2026-04-25 20:03:09] Epoch 1 | Step 7530 | Loss: 1.1518 | LR: 5.00e-06
+[2026-04-25 20:03:11] Epoch 1 | Step 7540 | Loss: 1.1517 | LR: 5.00e-06
+[2026-04-25 20:03:14] Epoch 1 | Step 7550 | Loss: 1.1517 | LR: 5.00e-06
+[2026-04-25 20:03:16] Epoch 1 | Step 7560 | Loss: 1.1516 | LR: 5.00e-06
+[2026-04-25 20:03:19] Epoch 1 | Step 7570 | Loss: 1.1515 | LR: 5.00e-06
+[2026-04-25 20:03:21] Epoch 1 | Step 7580 | Loss: 1.1514 | LR: 5.00e-06
+[2026-04-25 20:03:24] Epoch 1 | Step 7590 | Loss: 1.1512 | LR: 5.00e-06
+[2026-04-25 20:03:26] Epoch 1 | Step 7600 | Loss: 1.1511 | LR: 5.00e-06
+[2026-04-25 20:03:28] Epoch 1 | Step 7610 | Loss: 1.1510 | LR: 5.00e-06
+[2026-04-25 20:03:31] Epoch 1 | Step 7620 | Loss: 1.1508 | LR: 5.00e-06
+[2026-04-25 20:03:33] Epoch 1 | Step 7630 | Loss: 1.1507 | LR: 5.00e-06
+[2026-04-25 20:03:36] Epoch 1 | Step 7640 | Loss: 1.1506 | LR: 5.00e-06
+[2026-04-25 20:03:39] Epoch 1 | Step 7650 | Loss: 1.1504 | LR: 5.00e-06
+[2026-04-25 20:03:41] Epoch 1 | Step 7660 | Loss: 1.1502 | LR: 5.00e-06
+[2026-04-25 20:03:44] Epoch 1 | Step 7670 | Loss: 1.1500 | LR: 5.00e-06
+[2026-04-25 20:03:46] Epoch 1 | Step 7680 | Loss: 1.1499 | LR: 5.00e-06
+[2026-04-25 20:03:49] Epoch 1 | Step 7690 | Loss: 1.1500 | LR: 5.00e-06
+[2026-04-25 20:03:52] Epoch 1 | Step 7700 | Loss: 1.1498 | LR: 5.00e-06
+[2026-04-25 20:03:55] Epoch 1 | Step 7710 | Loss: 1.1495 | LR: 5.00e-06
+[2026-04-25 20:03:57] Epoch 1 | Step 7720 | Loss: 1.1496 | LR: 5.00e-06
+[2026-04-25 20:04:00] Epoch 1 | Step 7730 | Loss: 1.1497 | LR: 5.00e-06
+[2026-04-25 20:04:02] Epoch 1 | Step 7740 | Loss: 1.1498 | LR: 5.00e-06
+[2026-04-25 20:04:05] Epoch 1 | Step 7750 | Loss: 1.1498 | LR: 5.00e-06
+[2026-04-25 20:04:08] Epoch 1 | Step 7760 | Loss: 1.1496 | LR: 5.00e-06
+[2026-04-25 20:04:10] Epoch 1 | Step 7770 | Loss: 1.1494 | LR: 5.00e-06
+[2026-04-25 20:04:13] Epoch 1 | Step 7780 | Loss: 1.1493 | LR: 5.00e-06
+[2026-04-25 20:04:15] Epoch 1 | Step 7790 | Loss: 1.1492 | LR: 5.00e-06
+[2026-04-25 20:04:18] Epoch 1 | Step 7800 | Loss: 1.1490 | LR: 5.00e-06
+[2026-04-25 20:04:20] Epoch 1 | Step 7810 | Loss: 1.1491 | LR: 5.00e-06
+[2026-04-25 20:04:23] Epoch 1 | Step 7820 | Loss: 1.1491 | LR: 5.00e-06
+[2026-04-25 20:04:25] Epoch 1 | Step 7830 | Loss: 1.1490 | LR: 5.00e-06
+[2026-04-25 20:04:28] Epoch 1 | Step 7840 | Loss: 1.1488 | LR: 5.00e-06
+[2026-04-25 20:04:31] Epoch 1 | Step 7850 | Loss: 1.1485 | LR: 5.00e-06
+[2026-04-25 20:04:33] Epoch 1 | Step 7860 | Loss: 1.1485 | LR: 5.00e-06
+[2026-04-25 20:04:36] Epoch 1 | Step 7870 | Loss: 1.1484 | LR: 5.00e-06
+[2026-04-25 20:04:38] Epoch 1 | Step 7880 | Loss: 1.1484 | LR: 5.00e-06
+[2026-04-25 20:04:41] Epoch 1 | Step 7890 | Loss: 1.1483 | LR: 5.00e-06
+[2026-04-25 20:04:44] Epoch 1 | Step 7900 | Loss: 1.1483 | LR: 5.00e-06
+[2026-04-25 20:04:47] Epoch 1 | Step 7910 | Loss: 1.1483 | LR: 5.00e-06
+[2026-04-25 20:04:50] Epoch 1 | Step 7920 | Loss: 1.1482 | LR: 5.00e-06
+[2026-04-25 20:04:52] Epoch 1 | Step 7930 | Loss: 1.1483 | LR: 5.00e-06
+[2026-04-25 20:04:55] Epoch 1 | Step 7940 | Loss: 1.1482 | LR: 5.00e-06
+[2026-04-25 20:04:57] Epoch 1 | Step 7950 | Loss: 1.1484 | LR: 5.00e-06
+[2026-04-25 20:05:00] Epoch 1 | Step 7960 | Loss: 1.1484 | LR: 5.00e-06
+[2026-04-25 20:05:02] Epoch 1 | Step 7970 | Loss: 1.1484 | LR: 5.00e-06
+[2026-04-25 20:05:05] Epoch 1 | Step 7980 | Loss: 1.1481 | LR: 5.00e-06
+[2026-04-25 20:05:07] Epoch 1 | Step 7990 | Loss: 1.1481 | LR: 5.00e-06
+[2026-04-25 20:05:10] Epoch 1 | Step 8000 | Loss: 1.1480 | LR: 5.00e-06
+[2026-04-25 20:05:10] Validation | Batch 10/84 | Loss: 1.0434
+[2026-04-25 20:05:11] Validation | Batch 20/84 | Loss: 1.0455
+[2026-04-25 20:05:11] Validation | Batch 30/84 | Loss: 1.1267
+[2026-04-25 20:05:12] Validation | Batch 40/84 | Loss: 1.1299
+[2026-04-25 20:05:12] Validation | Batch 50/84 | Loss: 1.1227
+[2026-04-25 20:05:12] Validation | Batch 60/84 | Loss: 1.0947
+[2026-04-25 20:05:13] Validation | Batch 70/84 | Loss: 1.0789
+[2026-04-25 20:05:13] Validation | Batch 80/84 | Loss: 1.0866
+[2026-04-25 20:05:13] Validation | Batch 84/84 | Loss: 1.0775
+[2026-04-25 20:05:14] Validation | Loss: 1.0775 | PPL: 3.00 | Time: 3.76s
+[2026-04-25 20:05:16] New best model saved! Val loss: 1.0775
+[2026-04-25 20:05:19] Epoch 1 | Step 8010 | Loss: 1.1479 | LR: 5.00e-06
+[2026-04-25 20:05:22] Epoch 1 | Step 8020 | Loss: 1.1477 | LR: 5.00e-06
+[2026-04-25 20:05:25] Epoch 1 | Step 8030 | Loss: 1.1475 | LR: 5.00e-06
+[2026-04-25 20:05:28] Epoch 1 | Step 8040 | Loss: 1.1476 | LR: 5.00e-06
+[2026-04-25 20:05:30] Epoch 1 | Step 8050 | Loss: 1.1474 | LR: 5.00e-06
+[2026-04-25 20:05:33] Epoch 1 | Step 8060 | Loss: 1.1473 | LR: 5.00e-06
+[2026-04-25 20:05:35] Epoch 1 | Step 8070 | Loss: 1.1472 | LR: 5.00e-06
+[2026-04-25 20:05:38] Epoch 1 | Step 8080 | Loss: 1.1471 | LR: 5.00e-06
+[2026-04-25 20:05:40] Epoch 1 | Step 8090 | Loss: 1.1469 | LR: 5.00e-06
+[2026-04-25 20:05:43] Epoch 1 | Step 8100 | Loss: 1.1468 | LR: 5.00e-06
+[2026-04-25 20:05:46] Epoch 1 | Step 8110 | Loss: 1.1469 | LR: 5.00e-06
+[2026-04-25 20:05:48] Epoch 1 | Step 8120 | Loss: 1.1468 | LR: 5.00e-06
+[2026-04-25 20:05:51] Epoch 1 | Step 8130 | Loss: 1.1467 | LR: 5.00e-06
+[2026-04-25 20:05:53] Epoch 1 | Step 8140 | Loss: 1.1467 | LR: 5.00e-06
+[2026-04-25 20:05:56] Epoch 1 | Step 8150 | Loss: 1.1467 | LR: 5.00e-06
+[2026-04-25 20:05:58] Epoch 1 | Step 8160 | Loss: 1.1465 | LR: 5.00e-06
+[2026-04-25 20:06:01] Epoch 1 | Step 8170 | Loss: 1.1464 | LR: 5.00e-06
+[2026-04-25 20:06:03] Epoch 1 | Step 8180 | Loss: 1.1463 | LR: 5.00e-06
+[2026-04-25 20:06:06] Epoch 1 | Step 8190 | Loss: 1.1461 | LR: 5.00e-06
+[2026-04-25 20:06:08] Epoch 1 | Step 8200 | Loss: 1.1461 | LR: 5.00e-06
+[2026-04-25 20:06:11] Epoch 1 | Step 8210 | Loss: 1.1460 | LR: 5.00e-06
+[2026-04-25 20:06:13] Epoch 1 | Step 8220 | Loss: 1.1460 | LR: 5.00e-06
+[2026-04-25 20:06:16] Epoch 1 | Step 8230 | Loss: 1.1460 | LR: 5.00e-06
+[2026-04-25 20:06:18] Epoch 1 | Step 8240 | Loss: 1.1460 | LR: 5.00e-06
+[2026-04-25 20:06:21] Epoch 1 | Step 8250 | Loss: 1.1459 | LR: 5.00e-06
+[2026-04-25 20:06:23] Epoch 1 | Step 8260 | Loss: 1.1460 | LR: 5.00e-06
+[2026-04-25 20:06:26] Epoch 1 | Step 8270 | Loss: 1.1460 | LR: 5.00e-06
+[2026-04-25 20:06:29] Epoch 1 | Step 8280 | Loss: 1.1460 | LR: 5.00e-06
+[2026-04-25 20:06:31] Epoch 1 | Step 8290 | Loss: 1.1461 | LR: 5.00e-06
+[2026-04-25 20:06:34] Epoch 1 | Step 8300 | Loss: 1.1459 | LR: 5.00e-06
+[2026-04-25 20:06:36] Epoch 1 | Step 8310 | Loss: 1.1460 | LR: 5.00e-06
+[2026-04-25 20:06:38] Epoch 1 | Step 8320 | Loss: 1.1459 | LR: 5.00e-06
+[2026-04-25 20:06:41] Epoch 1 | Step 8330 | Loss: 1.1459 | LR: 5.00e-06
+[2026-04-25 20:06:43] Epoch 1 | Step 8340 | Loss: 1.1458 | LR: 5.00e-06
+[2026-04-25 20:06:46] Epoch 1 | Step 8350 | Loss: 1.1457 | LR: 5.00e-06
+[2026-04-25 20:06:48] Epoch 1 | Step 8360 | Loss: 1.1456 | LR: 5.00e-06
+[2026-04-25 20:06:51] Epoch 1 | Step 8370 | Loss: 1.1455 | LR: 5.00e-06
+[2026-04-25 20:06:53] Epoch 1 | Step 8380 | Loss: 1.1454 | LR: 5.00e-06
+[2026-04-25 20:06:55] Epoch 1 | Step 8390 | Loss: 1.1454 | LR: 5.00e-06
+[2026-04-25 20:06:58] Epoch 1 | Step 8400 | Loss: 1.1453 | LR: 5.00e-06
+[2026-04-25 20:07:01] Epoch 1 | Step 8410 | Loss: 1.1453 | LR: 5.00e-06
+[2026-04-25 20:07:03] Epoch 1 | Step 8420 | Loss: 1.1454 | LR: 5.00e-06
+[2026-04-25 20:07:06] Epoch 1 | Step 8430 | Loss: 1.1451 | LR: 5.00e-06
+[2026-04-25 20:07:08] Epoch 1 | Step 8440 | Loss: 1.1451 | LR: 5.00e-06
+[2026-04-25 20:07:11] Epoch 1 | Step 8450 | Loss: 1.1450 | LR: 5.00e-06
+[2026-04-25 20:07:13] Epoch 1 | Step 8460 | Loss: 1.1450 | LR: 5.00e-06
+[2026-04-25 20:07:16] Epoch 1 | Step 8470 | Loss: 1.1449 | LR: 5.00e-06
+[2026-04-25 20:07:18] Epoch 1 | Step 8480 | Loss: 1.1448 | LR: 5.00e-06
+[2026-04-25 20:07:21] Epoch 1 | Step 8490 | Loss: 1.1446 | LR: 5.00e-06
+[2026-04-25 20:07:23] Epoch 1 | Step 8500 | Loss: 1.1446 | LR: 5.00e-06
+[2026-04-25 20:07:26] Epoch 1 | Step 8510 | Loss: 1.1444 | LR: 5.00e-06
+[2026-04-25 20:07:29] Epoch 1 | Step 8520 | Loss: 1.1444 | LR: 5.00e-06
+[2026-04-25 20:07:31] Epoch 1 | Step 8530 | Loss: 1.1443 | LR: 5.00e-06
+[2026-04-25 20:07:34] Epoch 1 | Step 8540 | Loss: 1.1445 | LR: 5.00e-06
+[2026-04-25 20:07:36] Epoch 1 | Step 8550 | Loss: 1.1445 | LR: 5.00e-06
+[2026-04-25 20:07:39] Epoch 1 | Step 8560 | Loss: 1.1444 | LR: 5.00e-06
+[2026-04-25 20:07:41] Epoch 1 | Step 8570 | Loss: 1.1443 | LR: 5.00e-06
+[2026-04-25 20:07:44] Epoch 1 | Step 8580 | Loss: 1.1441 | LR: 5.00e-06
+[2026-04-25 20:07:46] Epoch 1 | Step 8590 | Loss: 1.1439 | LR: 5.00e-06
+[2026-04-25 20:07:49] Epoch 1 | Step 8600 | Loss: 1.1438 | LR: 5.00e-06
+[2026-04-25 20:07:52] Epoch 1 | Step 8610 | Loss: 1.1439 | LR: 5.00e-06
+[2026-04-25 20:07:54] Epoch 1 | Step 8620 | Loss: 1.1437 | LR: 5.00e-06
+[2026-04-25 20:07:57] Epoch 1 | Step 8630 | Loss: 1.1435 | LR: 5.00e-06
+[2026-04-25 20:07:59] Epoch 1 | Step 8640 | Loss: 1.1436 | LR: 5.00e-06
+[2026-04-25 20:08:02] Epoch 1 | Step 8650 | Loss: 1.1436 | LR: 5.00e-06
+[2026-04-25 20:08:04] Epoch 1 | Step 8660 | Loss: 1.1434 | LR: 5.00e-06
+[2026-04-25 20:08:07] Epoch 1 | Step 8670 | Loss: 1.1435 | LR: 5.00e-06
+[2026-04-25 20:08:09] Epoch 1 | Step 8680 | Loss: 1.1435 | LR: 5.00e-06
+[2026-04-25 20:08:12] Epoch 1 | Step 8690 | Loss: 1.1433 | LR: 5.00e-06
+[2026-04-25 20:08:14] Epoch 1 | Step 8700 | Loss: 1.1433 | LR: 5.00e-06
+[2026-04-25 20:08:17] Epoch 1 | Step 8710 | Loss: 1.1430 | LR: 5.00e-06
+[2026-04-25 20:08:19] Epoch 1 | Step 8720 | Loss: 1.1429 | LR: 5.00e-06
+[2026-04-25 20:08:22] Epoch 1 | Step 8730 | Loss: 1.1429 | LR: 5.00e-06
+[2026-04-25 20:08:24] Epoch 1 | Step 8740 | Loss: 1.1429 | LR: 5.00e-06
+[2026-04-25 20:08:27] Epoch 1 | Step 8750 | Loss: 1.1429 | LR: 5.00e-06
+[2026-04-25 20:08:29] Epoch 1 | Step 8760 | Loss: 1.1428 | LR: 5.00e-06
+[2026-04-25 20:08:32] Epoch 1 | Step 8770 | Loss: 1.1426 | LR: 5.00e-06
+[2026-04-25 20:08:35] Epoch 1 | Step 8780 | Loss: 1.1425 | LR: 5.00e-06
+[2026-04-25 20:08:37] Epoch 1 | Step 8790 | Loss: 1.1425 | LR: 5.00e-06
+[2026-04-25 20:08:40] Epoch 1 | Step 8800 | Loss: 1.1422 | LR: 5.00e-06
+[2026-04-25 20:08:43] Epoch 1 | Step 8810 | Loss: 1.1422 | LR: 5.00e-06
+[2026-04-25 20:08:45] Epoch 1 | Step 8820 | Loss: 1.1421 | LR: 5.00e-06
+[2026-04-25 20:08:48] Epoch 1 | Step 8830 | Loss: 1.1420 | LR: 5.00e-06
+[2026-04-25 20:08:51] Epoch 1 | Step 8840 | Loss: 1.1419 | LR: 5.00e-06
+[2026-04-25 20:08:53] Epoch 1 | Step 8850 | Loss: 1.1419 | LR: 5.00e-06
+[2026-04-25 20:08:56] Epoch 1 | Step 8860 | Loss: 1.1419 | LR: 5.00e-06
+[2026-04-25 20:08:58] Epoch 1 | Step 8870 | Loss: 1.1419 | LR: 5.00e-06
+[2026-04-25 20:09:01] Epoch 1 | Step 8880 | Loss: 1.1418 | LR: 5.00e-06
+[2026-04-25 20:09:03] Epoch 1 | Step 8890 | Loss: 1.1416 | LR: 5.00e-06
+[2026-04-25 20:09:05] Epoch 1 | Step 8900 | Loss: 1.1413 | LR: 5.00e-06
+[2026-04-25 20:09:08] Epoch 1 | Step 8910 | Loss: 1.1414 | LR: 5.00e-06
+[2026-04-25 20:09:11] Epoch 1 | Step 8920 | Loss: 1.1411 | LR: 5.00e-06
+[2026-04-25 20:09:13] Epoch 1 | Step 8930 | Loss: 1.1410 | LR: 5.00e-06
+[2026-04-25 20:09:16] Epoch 1 | Step 8940 | Loss: 1.1410 | LR: 5.00e-06
+[2026-04-25 20:09:18] Epoch 1 | Step 8950 | Loss: 1.1410 | LR: 5.00e-06
+[2026-04-25 20:09:21] Epoch 1 | Step 8960 | Loss: 1.1410 | LR: 5.00e-06
+[2026-04-25 20:09:23] Epoch 1 | Step 8970 | Loss: 1.1409 | LR: 5.00e-06
+[2026-04-25 20:09:26] Epoch 1 | Step 8980 | Loss: 1.1408 | LR: 5.00e-06
+[2026-04-25 20:09:28] Epoch 1 | Step 8990 | Loss: 1.1406 | LR: 5.00e-06
+[2026-04-25 20:09:31] Epoch 1 | Step 9000 | Loss: 1.1406 | LR: 5.00e-06
+[2026-04-25 20:09:34] Epoch 1 | Step 9010 | Loss: 1.1406 | LR: 5.00e-06
+[2026-04-25 20:09:36] Epoch 1 | Step 9020 | Loss: 1.1407 | LR: 5.00e-06
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+[2026-04-25 20:09:44] Epoch 1 | Step 9050 | Loss: 1.1404 | LR: 5.00e-06
+[2026-04-25 20:09:46] Epoch 1 | Step 9060 | Loss: 1.1405 | LR: 5.00e-06
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+[2026-04-25 20:10:08] Epoch 1 | Step 9150 | Loss: 1.1401 | LR: 5.00e-06
+[2026-04-25 20:10:11] Epoch 1 | Step 9160 | Loss: 1.1400 | LR: 5.00e-06
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+[2026-04-25 20:10:16] Epoch 1 | Step 9180 | Loss: 1.1396 | LR: 5.00e-06
+[2026-04-25 20:10:19] Epoch 1 | Step 9190 | Loss: 1.1393 | LR: 5.00e-06
+[2026-04-25 20:10:21] Epoch 1 | Step 9200 | Loss: 1.1393 | LR: 5.00e-06
+[2026-04-25 20:10:24] Epoch 1 | Step 9210 | Loss: 1.1393 | LR: 5.00e-06
+[2026-04-25 20:10:26] Epoch 1 | Step 9220 | Loss: 1.1392 | LR: 5.00e-06
+[2026-04-25 20:10:29] Epoch 1 | Step 9230 | Loss: 1.1391 | LR: 5.00e-06
+[2026-04-25 20:10:31] Epoch 1 | Step 9240 | Loss: 1.1389 | LR: 5.00e-06
+[2026-04-25 20:10:34] Epoch 1 | Step 9250 | Loss: 1.1388 | LR: 5.00e-06
+[2026-04-25 20:10:37] Epoch 1 | Step 9260 | Loss: 1.1386 | LR: 5.00e-06
+[2026-04-25 20:10:39] Epoch 1 | Step 9270 | Loss: 1.1385 | LR: 5.00e-06
+[2026-04-25 20:10:42] Epoch 1 | Step 9280 | Loss: 1.1385 | LR: 5.00e-06
+[2026-04-25 20:10:44] Epoch 1 | Step 9290 | Loss: 1.1384 | LR: 5.00e-06
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+[2026-04-25 20:11:10] Epoch 1 | Step 9390 | Loss: 1.1375 | LR: 5.00e-06
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+[2026-04-25 20:11:15] Epoch 1 | Step 9410 | Loss: 1.1376 | LR: 5.00e-06
+[2026-04-25 20:11:17] Epoch 1 | Step 9420 | Loss: 1.1377 | LR: 5.00e-06
+[2026-04-25 20:11:20] Epoch 1 | Step 9430 | Loss: 1.1377 | LR: 5.00e-06
+[2026-04-25 20:11:22] Epoch 1 | Step 9440 | Loss: 1.1376 | LR: 5.00e-06
+[2026-04-25 20:11:25] Epoch 1 | Step 9450 | Loss: 1.1377 | LR: 5.00e-06
+[2026-04-25 20:11:27] Epoch 1 | Step 9460 | Loss: 1.1375 | LR: 5.00e-06
+[2026-04-25 20:11:30] Epoch 1 | Step 9470 | Loss: 1.1373 | LR: 5.00e-06
+[2026-04-25 20:11:33] Epoch 1 | Step 9480 | Loss: 1.1371 | LR: 5.00e-06
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+[2026-04-25 20:11:48] Epoch 1 | Step 9540 | Loss: 1.1367 | LR: 5.00e-06
+[2026-04-25 20:11:50] Epoch 1 | Step 9550 | Loss: 1.1367 | LR: 5.00e-06
+[2026-04-25 20:11:53] Epoch 1 | Step 9560 | Loss: 1.1367 | LR: 5.00e-06
+[2026-04-25 20:11:55] Epoch 1 | Step 9570 | Loss: 1.1367 | LR: 5.00e-06
+[2026-04-25 20:11:58] Epoch 1 | Step 9580 | Loss: 1.1368 | LR: 5.00e-06
+[2026-04-25 20:12:00] Epoch 1 | Step 9590 | Loss: 1.1367 | LR: 5.00e-06
+[2026-04-25 20:12:03] Epoch 1 | Step 9600 | Loss: 1.1366 | LR: 5.00e-06
+[2026-04-25 20:12:06] Epoch 1 | Step 9610 | Loss: 1.1365 | LR: 5.00e-06
+[2026-04-25 20:12:09] Epoch 1 | Step 9620 | Loss: 1.1365 | LR: 5.00e-06
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+[2026-04-25 20:12:14] Epoch 1 | Step 9640 | Loss: 1.1365 | LR: 5.00e-06
+[2026-04-25 20:12:17] Epoch 1 | Step 9650 | Loss: 1.1365 | LR: 5.00e-06
+[2026-04-25 20:12:20] Epoch 1 | Step 9660 | Loss: 1.1364 | LR: 5.00e-06
+[2026-04-25 20:12:22] Epoch 1 | Step 9670 | Loss: 1.1364 | LR: 5.00e-06
+[2026-04-25 20:12:25] Epoch 1 | Step 9680 | Loss: 1.1364 | LR: 5.00e-06
+[2026-04-25 20:12:27] Epoch 1 | Step 9690 | Loss: 1.1363 | LR: 5.00e-06
+[2026-04-25 20:12:30] Epoch 1 | Step 9700 | Loss: 1.1363 | LR: 5.00e-06
+[2026-04-25 20:12:32] Epoch 1 | Step 9710 | Loss: 1.1362 | LR: 5.00e-06
+[2026-04-25 20:12:35] Epoch 1 | Step 9720 | Loss: 1.1362 | LR: 5.00e-06
+[2026-04-25 20:12:37] Epoch 1 | Step 9730 | Loss: 1.1362 | LR: 5.00e-06
+[2026-04-25 20:12:40] Epoch 1 | Step 9740 | Loss: 1.1361 | LR: 5.00e-06
+[2026-04-25 20:12:42] Epoch 1 | Step 9750 | Loss: 1.1360 | LR: 5.00e-06
+[2026-04-25 20:12:45] Epoch 1 | Step 9760 | Loss: 1.1359 | LR: 5.00e-06
+[2026-04-25 20:12:47] Epoch 1 | Step 9770 | Loss: 1.1359 | LR: 5.00e-06
+[2026-04-25 20:12:50] Epoch 1 | Step 9780 | Loss: 1.1358 | LR: 5.00e-06
+[2026-04-25 20:12:52] Epoch 1 | Step 9790 | Loss: 1.1357 | LR: 5.00e-06
+[2026-04-25 20:12:55] Epoch 1 | Step 9800 | Loss: 1.1357 | LR: 5.00e-06
+[2026-04-25 20:12:58] Epoch 1 | Step 9810 | Loss: 1.1355 | LR: 5.00e-06
+[2026-04-25 20:13:00] Epoch 1 | Step 9820 | Loss: 1.1354 | LR: 5.00e-06
+[2026-04-25 20:13:03] Epoch 1 | Step 9830 | Loss: 1.1354 | LR: 5.00e-06
+[2026-04-25 20:13:06] Epoch 1 | Step 9840 | Loss: 1.1355 | LR: 5.00e-06
+[2026-04-25 20:13:08] Epoch 1 | Step 9850 | Loss: 1.1354 | LR: 5.00e-06
+[2026-04-25 20:13:11] Epoch 1 | Step 9860 | Loss: 1.1352 | LR: 5.00e-06
+[2026-04-25 20:13:13] Epoch 1 | Step 9870 | Loss: 1.1353 | LR: 5.00e-06
+[2026-04-25 20:13:16] Epoch 1 | Step 9880 | Loss: 1.1353 | LR: 5.00e-06
+[2026-04-25 20:13:18] Epoch 1 completed in 2546.41s | Loss: 1.1353
+[2026-04-25 20:13:18]
+Training completed!
+[2026-04-25 20:13:20] Final model: /workspace/byte-llms-code/outputs/lr_sweep/pythia_1b_lr_5e-5/model_final.pt

lr_sweep/pythia_1b_lr_5e-5/wandb/run-20260425_193045-vg3if73m/files/requirements.txt

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lr_sweep/pythia_1b_lr_5e-5/wandb/run-20260425_193045-vg3if73m/files/wandb-metadata.json

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lr_sweep/pythia_1b_lr_5e-5/wandb/run-20260425_193045-vg3if73m/files/wandb-summary.json

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