Initial commit

README.md

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+---
+license: other
+base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
+tags:
+- llama-factory
+- full
+- generated_from_trainer
+model-index:
+- name: train_2024-07-30-02-47-53_llama3.1_truthqa_bench2
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# train_2024-07-30-02-47-53_llama3.1_truthqa_bench2
+
+This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on the truth_train_0716_2 dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-06
+- train_batch_size: 2
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 128
+- total_eval_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 10
+- num_epochs: 5.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.43.3
+- Pytorch 2.3.0a0+ebedce2
+- Datasets 2.20.0
+- Tokenizers 0.19.1

all_results.json

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+    "num_input_tokens_seen": 1207760,
+    "total_flos": 5.438488809413018e+16,
+    "train_loss": 0.4546951471592796,
+    "train_runtime": 2562.7617,
+    "train_samples_per_second": 9.673,
+    "train_steps_per_second": 0.074
+}

checkpoint-190/config.json

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+    "rope_type": "llama3"
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+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.3",
+  "use_cache": false,
+  "vocab_size": 128256
+}

checkpoint-190/generation_config.json

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+  "transformers_version": "4.43.3"
+}

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checkpoint-190/training_args.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a6ecc3807e5feffecb8b7b6a2bfa5d46fc6256a5d17108d79ca8784a783d158e
+size 6712

checkpoint-190/zero_to_fp32.py

@@ -0,0 +1,604 @@
+#!/usr/bin/env python
+
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
+# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
+# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
+# application.
+#
+# example: python zero_to_fp32.py . pytorch_model.bin
+
+import argparse
+import torch
+import glob
+import math
+import os
+import re
+from collections import OrderedDict
+from dataclasses import dataclass
+
+# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
+# DeepSpeed data structures it has to be available in the current python environment.
+from deepspeed.utils import logger
+from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
+                                            FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
+                                            FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
+
+
+@dataclass
+class zero_model_state:
+    buffers: dict()
+    param_shapes: dict()
+    shared_params: list
+    ds_version: int
+    frozen_param_shapes: dict()
+    frozen_param_fragments: dict()
+
+
+debug = 0
+
+# load to cpu
+device = torch.device('cpu')
+
+
+def atoi(text):
+    return int(text) if text.isdigit() else text
+
+
+def natural_keys(text):
+    '''
+    alist.sort(key=natural_keys) sorts in human order
+    http://nedbatchelder.com/blog/200712/human_sorting.html
+    (See Toothy's implementation in the comments)
+    '''
+    return [atoi(c) for c in re.split(r'(\d+)', text)]
+
+
+def get_model_state_file(checkpoint_dir, zero_stage):
+    if not os.path.isdir(checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
+
+    # there should be only one file
+    if zero_stage <= 2:
+        file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
+    elif zero_stage == 3:
+        file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
+
+    if not os.path.exists(file):
+        raise FileNotFoundError(f"can't find model states file at '{file}'")
+
+    return file
+
+
+def get_checkpoint_files(checkpoint_dir, glob_pattern):
+    # XXX: need to test that this simple glob rule works for multi-node setup too
+    ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
+
+    if len(ckpt_files) == 0:
+        raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
+
+    return ckpt_files
+
+
+def get_optim_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
+
+
+def get_model_state_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device)
+
+        if BUFFER_NAMES not in state_dict:
+            raise ValueError(f"{file} is not a model state checkpoint")
+        buffer_names = state_dict[BUFFER_NAMES]
+        if debug:
+            print("Found buffers:", buffer_names)
+
+        # recover just the buffers while restoring them to fp32 if they were saved in fp16
+        buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
+        param_shapes = state_dict[PARAM_SHAPES]
+
+        # collect parameters that are included in param_shapes
+        param_names = []
+        for s in param_shapes:
+            for name in s.keys():
+                param_names.append(name)
+
+        # update with frozen parameters
+        frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
+        if frozen_param_shapes is not None:
+            if debug:
+                print(f"Found frozen_param_shapes: {frozen_param_shapes}")
+            param_names += list(frozen_param_shapes.keys())
+
+        # handle shared params
+        shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
+
+        ds_version = state_dict.get(DS_VERSION, None)
+
+        frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
+
+        z_model_state = zero_model_state(buffers=buffers,
+                                         param_shapes=param_shapes,
+                                         shared_params=shared_params,
+                                         ds_version=ds_version,
+                                         frozen_param_shapes=frozen_param_shapes,
+                                         frozen_param_fragments=frozen_param_fragments)
+        zero_model_states.append(z_model_state)
+
+    return zero_model_states
+
+
+def parse_optim_states(files, ds_checkpoint_dir):
+
+    total_files = len(files)
+    state_dicts = []
+    for f in files:
+        state_dict = torch.load(f, map_location=device)
+        # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
+        # and also handle the case where it was already removed by another helper script
+        state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
+        state_dicts.append(state_dict)
+
+    if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
+        raise ValueError(f"{files[0]} is not a zero checkpoint")
+    zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
+    world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
+
+    # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
+    # parameters can be different from data parallelism for non-expert parameters. So we can just
+    # use the max of the partition_count to get the dp world_size.
+
+    if type(world_size) is list:
+        world_size = max(world_size)
+
+    if world_size != total_files:
+        raise ValueError(
+            f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
+            "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
+        )
+
+    # the groups are named differently in each stage
+    if zero_stage <= 2:
+        fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
+    elif zero_stage == 3:
+        fp32_groups_key = FP32_FLAT_GROUPS
+    else:
+        raise ValueError(f"unknown zero stage {zero_stage}")
+
+    if zero_stage <= 2:
+        fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
+    elif zero_stage == 3:
+        # if there is more than one param group, there will be multiple flattened tensors - one
+        # flattened tensor per group - for simplicity merge them into a single tensor
+        #
+        # XXX: could make the script more memory efficient for when there are multiple groups - it
+        # will require matching the sub-lists of param_shapes for each param group flattened tensor
+
+        fp32_flat_groups = [
+            torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
+        ]
+
+    return zero_stage, world_size, fp32_flat_groups
+
+
+def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
+    """
+    Returns fp32 state_dict reconstructed from ds checkpoint
+
+    Args:
+        - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
+
+    """
+    print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
+
+    optim_files = get_optim_files(ds_checkpoint_dir)
+    zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
+    print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
+
+    model_files = get_model_state_files(ds_checkpoint_dir)
+
+    zero_model_states = parse_model_states(model_files)
+    print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
+
+    if zero_stage <= 2:
+        return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                                          exclude_frozen_parameters)
+    elif zero_stage == 3:
+        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                                          exclude_frozen_parameters)
+
+
+def _zero2_merge_frozen_params(state_dict, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+    frozen_param_fragments = zero_model_states[0].frozen_param_fragments
+
+    if debug:
+        num_elem = sum(s.numel() for s in frozen_param_shapes.values())
+        print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        state_dict[name] = frozen_param_fragments[name]
+
+        if debug:
+            print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _has_callable(obj, fn):
+    attr = getattr(obj, fn, None)
+    return callable(attr)
+
+
+def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+
+    # Reconstruction protocol:
+    #
+    # XXX: document this
+
+    if debug:
+        for i in range(world_size):
+            for j in range(len(fp32_flat_groups[0])):
+                print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
+
+    # XXX: memory usage doubles here (zero2)
+    num_param_groups = len(fp32_flat_groups[0])
+    merged_single_partition_of_fp32_groups = []
+    for i in range(num_param_groups):
+        merged_partitions = [sd[i] for sd in fp32_flat_groups]
+        full_single_fp32_vector = torch.cat(merged_partitions, 0)
+        merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
+    avail_numel = sum(
+        [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
+
+    if debug:
+        wanted_params = sum([len(shapes) for shapes in param_shapes])
+        wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
+        # not asserting if there is a mismatch due to possible padding
+        print(f"Have {avail_numel} numels to process.")
+        print(f"Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    total_numel = 0
+    total_params = 0
+    for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
+        offset = 0
+        avail_numel = full_single_fp32_vector.numel()
+        for name, shape in shapes.items():
+
+            unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
+            total_numel += unpartitioned_numel
+            total_params += 1
+
+            if debug:
+                print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+            state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
+            offset += unpartitioned_numel
+
+        # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
+        # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
+        # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
+        # live optimizer object, so we are checking that the numbers are within the right range
+        align_to = 2 * world_size
+
+        def zero2_align(x):
+            return align_to * math.ceil(x / align_to)
+
+        if debug:
+            print(f"original offset={offset}, avail_numel={avail_numel}")
+
+        offset = zero2_align(offset)
+        avail_numel = zero2_align(avail_numel)
+
+        if debug:
+            print(f"aligned  offset={offset}, avail_numel={avail_numel}")
+
+        # Sanity check
+        if offset != avail_numel:
+            raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                               exclude_frozen_parameters):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    if not exclude_frozen_parameters:
+        _zero2_merge_frozen_params(state_dict, zero_model_states)
+
+    _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def zero3_partitioned_param_info(unpartitioned_numel, world_size):
+    remainder = unpartitioned_numel % world_size
+    padding_numel = (world_size - remainder) if remainder else 0
+    partitioned_numel = math.ceil(unpartitioned_numel / world_size)
+    return partitioned_numel, padding_numel
+
+
+def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    if debug:
+        for i in range(world_size):
+            num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
+            print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in zero_model_states[0].frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
+        state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
+
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+    avail_numel = fp32_flat_groups[0].numel() * world_size
+    # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
+    # param, re-consolidating each param, while dealing with padding if any
+
+    # merge list of dicts, preserving order
+    param_shapes = {k: v for d in param_shapes for k, v in d.items()}
+
+    if debug:
+        for i in range(world_size):
+            print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
+
+        wanted_params = len(param_shapes)
+        wanted_numel = sum(shape.numel() for shape in param_shapes.values())
+        # not asserting if there is a mismatch due to possible padding
+        avail_numel = fp32_flat_groups[0].numel() * world_size
+        print(f"Trainable params: Have {avail_numel} numels to process.")
+        print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    offset = 0
+    total_numel = 0
+    total_params = 0
+    for name, shape in param_shapes.items():
+
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+        total_params += 1
+
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+        # XXX: memory usage doubles here
+        state_dict[name] = torch.cat(
+            tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
+            0).narrow(0, 0, unpartitioned_numel).view(shape)
+        offset += partitioned_numel
+
+    offset *= world_size
+
+    # Sanity check
+    if offset != avail_numel:
+        raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                               exclude_frozen_parameters):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    if not exclude_frozen_parameters:
+        _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
+
+    _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None, exclude_frozen_parameters=False):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
+    ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
+    via a model hub.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
+        - ``exclude_frozen_parameters``: exclude frozen parameters
+
+    Returns:
+        - pytorch ``state_dict``
+
+    Note: this approach may not work if your application doesn't have sufficient free CPU memory and
+    you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint.
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        # do the training and checkpoint saving
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
+        model = model.cpu() # move to cpu
+        model.load_state_dict(state_dict)
+        # submit to model hub or save the model to share with others
+
+    In this example the ``model`` will no longer be usable in the deepspeed context of the same
+    application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
+
+    """
+    if tag is None:
+        latest_path = os.path.join(checkpoint_dir, 'latest')
+        if os.path.isfile(latest_path):
+            with open(latest_path, 'r') as fd:
+                tag = fd.read().strip()
+        else:
+            raise ValueError(f"Unable to find 'latest' file at {latest_path}")
+
+    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
+
+    if not os.path.isdir(ds_checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
+
+    return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+
+
+def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None, exclude_frozen_parameters=False):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
+    loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+        - ``exclude_frozen_parameters``: exclude frozen parameters
+    """
+
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag, exclude_frozen_parameters)
+    print(f"Saving fp32 state dict to {output_file}")
+    torch.save(state_dict, output_file)
+
+
+def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
+    """
+    1. Put the provided model to cpu
+    2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
+    3. Load it into the provided model
+
+    Args:
+        - ``model``: the model object to update
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+
+    Returns:
+        - ``model`: modified model
+
+    Make sure you have plenty of CPU memory available before you call this function. If you don't
+    have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
+    conveniently placed for you in the checkpoint folder.
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
+        model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
+        # submit to model hub or save the model to share with others
+
+    Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
+    of the same application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    """
+    logger.info(f"Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info(f"Overwriting model with fp32 weights")
+    model = model.cpu()
+    model.load_state_dict(state_dict, strict=False)
+
+    return model
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("checkpoint_dir",
+                        type=str,
+                        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
+    parser.add_argument(
+        "output_file",
+        type=str,
+        help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
+    parser.add_argument("-t",
+                        "--tag",
+                        type=str,
+                        default=None,
+                        help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
+    parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
+    parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
+    args = parser.parse_args()
+
+    debug = args.debug
+
+    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
+                                               args.output_file,
+                                               tag=args.tag,
+                                               exclude_frozen_parameters=args.exclude_frozen_parameters)

config.json

@@ -0,0 +1,39 @@
+{
+  "_name_or_path": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
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running_log.txt

@@ -0,0 +1,663 @@
+07/30/2024 02:49:08 - INFO - llamafactory.hparams.parser - Process rank: 7, device: cuda:7, n_gpu: 1, distributed training: True, compute dtype: torch.bfloat16
+
+07/30/2024 02:49:08 - INFO - llamafactory.hparams.parser - Process rank: 3, device: cuda:3, n_gpu: 1, distributed training: True, compute dtype: torch.bfloat16
+
+07/30/2024 02:49:08 - INFO - llamafactory.hparams.parser - Process rank: 4, device: cuda:4, n_gpu: 1, distributed training: True, compute dtype: torch.bfloat16
+
+07/30/2024 02:49:08 - INFO - llamafactory.hparams.parser - Process rank: 2, device: cuda:2, n_gpu: 1, distributed training: True, compute dtype: torch.bfloat16
+
+07/30/2024 02:49:08 - INFO - llamafactory.hparams.parser - Process rank: 6, device: cuda:6, n_gpu: 1, distributed training: True, compute dtype: torch.bfloat16
+
+07/30/2024 02:49:08 - INFO - llamafactory.hparams.parser - Process rank: 1, device: cuda:1, n_gpu: 1, distributed training: True, compute dtype: torch.bfloat16
+
+07/30/2024 02:49:09 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
+
+07/30/2024 02:49:09 - INFO - llamafactory.data.template - Add pad token: <|eot_id|>
+
+07/30/2024 02:49:09 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
+
+07/30/2024 02:49:09 - INFO - llamafactory.data.template - Add pad token: <|eot_id|>
+
+07/30/2024 02:49:09 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
+
+07/30/2024 02:49:09 - INFO - llamafactory.data.template - Add pad token: <|eot_id|>
+
+07/30/2024 02:49:09 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
+
+07/30/2024 02:49:09 - INFO - llamafactory.data.template - Add pad token: <|eot_id|>
+
+[INFO|tokenization_utils_base.py:2289] 2024-07-30 02:49:09,083 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--meta-llama--Meta-Llama-3.1-8B-Instruct/snapshots/b2a4d0f33b41fcd59a6d31662cc63b8d53367e1e/tokenizer.json
+
+[INFO|tokenization_utils_base.py:2289] 2024-07-30 02:49:09,083 >> loading file added_tokens.json from cache at None
+
+[INFO|tokenization_utils_base.py:2289] 2024-07-30 02:49:09,083 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--meta-llama--Meta-Llama-3.1-8B-Instruct/snapshots/b2a4d0f33b41fcd59a6d31662cc63b8d53367e1e/special_tokens_map.json
+
+[INFO|tokenization_utils_base.py:2289] 2024-07-30 02:49:09,083 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--meta-llama--Meta-Llama-3.1-8B-Instruct/snapshots/b2a4d0f33b41fcd59a6d31662cc63b8d53367e1e/tokenizer_config.json
+
+[INFO|tokenization_utils_base.py:2533] 2024-07-30 02:49:09,345 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+
+[INFO|template.py:270] 2024-07-30 02:49:09,345 >> Replace eos token: <|eot_id|>
+
+[INFO|template.py:372] 2024-07-30 02:49:09,345 >> Add pad token: <|eot_id|>
+
+[INFO|loader.py:52] 2024-07-30 02:49:09,346 >> Loading dataset 0716_truthfulqa_benchmark_train_2.json...
+
+07/30/2024 02:49:09 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
+
+07/30/2024 02:49:09 - INFO - llamafactory.data.template - Add pad token: <|eot_id|>
+
+07/30/2024 02:49:09 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
+
+07/30/2024 02:49:09 - INFO - llamafactory.data.template - Add pad token: <|eot_id|>
+
+07/30/2024 02:49:09 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
+
+07/30/2024 02:49:09 - INFO - llamafactory.data.template - Add pad token: <|eot_id|>
+
+07/30/2024 02:49:11 - INFO - llamafactory.data.loader - Loading dataset 0716_truthfulqa_benchmark_train_2.json...
+
+07/30/2024 02:49:11 - INFO - llamafactory.data.loader - Loading dataset 0716_truthfulqa_benchmark_train_2.json...
+
+07/30/2024 02:49:11 - INFO - llamafactory.data.loader - Loading dataset 0716_truthfulqa_benchmark_train_2.json...
+
+07/30/2024 02:49:11 - INFO - llamafactory.data.loader - Loading dataset 0716_truthfulqa_benchmark_train_2.json...
+
+07/30/2024 02:49:11 - INFO - llamafactory.data.loader - Loading dataset 0716_truthfulqa_benchmark_train_2.json...
+
+07/30/2024 02:49:11 - INFO - llamafactory.data.loader - Loading dataset 0716_truthfulqa_benchmark_train_2.json...
+
+07/30/2024 02:49:11 - INFO - llamafactory.data.loader - Loading dataset 0716_truthfulqa_benchmark_train_2.json...
+
+[INFO|configuration_utils.py:733] 2024-07-30 02:49:14,919 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--meta-llama--Meta-Llama-3.1-8B-Instruct/snapshots/b2a4d0f33b41fcd59a6d31662cc63b8d53367e1e/config.json
+
+[INFO|configuration_utils.py:800] 2024-07-30 02:49:14,925 >> Model config LlamaConfig {
+  "_name_or_path": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.3",
+  "use_cache": true,
+  "vocab_size": 128256
+}
+
+
+[INFO|modeling_utils.py:3634] 2024-07-30 02:49:14,976 >> loading weights file model.safetensors from cache at /root/.cache/huggingface/hub/models--meta-llama--Meta-Llama-3.1-8B-Instruct/snapshots/b2a4d0f33b41fcd59a6d31662cc63b8d53367e1e/model.safetensors.index.json
+
+[INFO|modeling_utils.py:1572] 2024-07-30 02:49:14,978 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16.
+
+[INFO|configuration_utils.py:1038] 2024-07-30 02:49:14,981 >> Generate config GenerationConfig {
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ]
+}
+
+
+[INFO|modeling_utils.py:4463] 2024-07-30 02:49:19,162 >> All model checkpoint weights were used when initializing LlamaForCausalLM.
+
+
+[INFO|modeling_utils.py:4471] 2024-07-30 02:49:19,162 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at meta-llama/Meta-Llama-3.1-8B-Instruct.
+If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training.
+
+[INFO|configuration_utils.py:993] 2024-07-30 02:49:19,337 >> loading configuration file generation_config.json from cache at /root/.cache/huggingface/hub/models--meta-llama--Meta-Llama-3.1-8B-Instruct/snapshots/b2a4d0f33b41fcd59a6d31662cc63b8d53367e1e/generation_config.json
+
+[INFO|configuration_utils.py:1038] 2024-07-30 02:49:19,337 >> Generate config GenerationConfig {
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9
+}
+
+
+[INFO|checkpointing.py:103] 2024-07-30 02:49:19,344 >> Gradient checkpointing enabled.
+
+[INFO|attention.py:84] 2024-07-30 02:49:19,345 >> Using torch SDPA for faster training and inference.
+
+[INFO|adapter.py:302] 2024-07-30 02:49:19,345 >> Upcasting trainable params to float32.
+
+[INFO|adapter.py:48] 2024-07-30 02:49:19,345 >> Fine-tuning method: Full
+
+[INFO|loader.py:196] 2024-07-30 02:49:19,389 >> trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.adapter - Fine-tuning method: Full
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.adapter - Fine-tuning method: Full
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.adapter - Fine-tuning method: Full
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.adapter - Fine-tuning method: Full
+
+[INFO|trainer.py:648] 2024-07-30 02:49:19,394 >> Using auto half precision backend
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.adapter - Fine-tuning method: Full
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.adapter - Fine-tuning method: Full
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
+
+07/30/2024 02:49:19 - INFO - llamafactory.model.adapter - Fine-tuning method: Full
+
+07/30/2024 02:49:20 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
+
+[INFO|trainer.py:2134] 2024-07-30 02:49:41,827 >> ***** Running training *****
+
+[INFO|trainer.py:2135] 2024-07-30 02:49:41,827 >>   Num examples = 4,958
+
+[INFO|trainer.py:2136] 2024-07-30 02:49:41,827 >>   Num Epochs = 5
+
+[INFO|trainer.py:2137] 2024-07-30 02:49:41,827 >>   Instantaneous batch size per device = 2
+
+[INFO|trainer.py:2140] 2024-07-30 02:49:41,828 >>   Total train batch size (w. parallel, distributed & accumulation) = 128
+
+[INFO|trainer.py:2141] 2024-07-30 02:49:41,828 >>   Gradient Accumulation steps = 8
+
+[INFO|trainer.py:2142] 2024-07-30 02:49:41,828 >>   Total optimization steps = 190
+
+[INFO|trainer.py:2143] 2024-07-30 02:49:41,829 >>   Number of trainable parameters = 8,030,261,248
+
+[INFO|callbacks.py:310] 2024-07-30 02:49:56,428 >> {'loss': 12.4760, 'learning_rate': 5.0000e-07, 'epoch': 0.03, 'throughput': 439.53}
+
+[INFO|callbacks.py:310] 2024-07-30 02:50:09,604 >> {'loss': 12.1047, 'learning_rate': 1.0000e-06, 'epoch': 0.05, 'throughput': 467.79}
+
+[INFO|callbacks.py:310] 2024-07-30 02:50:22,768 >> {'loss': 12.0404, 'learning_rate': 1.5000e-06, 'epoch': 0.08, 'throughput': 475.65}
+
+[INFO|callbacks.py:310] 2024-07-30 02:50:35,923 >> {'loss': 10.5293, 'learning_rate': 2.0000e-06, 'epoch': 0.10, 'throughput': 479.47}
+
+[INFO|callbacks.py:310] 2024-07-30 02:50:49,106 >> {'loss': 8.3117, 'learning_rate': 2.5000e-06, 'epoch': 0.13, 'throughput': 479.70}
+
+[INFO|callbacks.py:310] 2024-07-30 02:51:02,273 >> {'loss': 6.0338, 'learning_rate': 3.0000e-06, 'epoch': 0.15, 'throughput': 480.34}
+
+[INFO|callbacks.py:310] 2024-07-30 02:51:15,440 >> {'loss': 4.8226, 'learning_rate': 3.5000e-06, 'epoch': 0.18, 'throughput': 480.63}
+
+[INFO|callbacks.py:310] 2024-07-30 02:51:28,623 >> {'loss': 2.9485, 'learning_rate': 4.0000e-06, 'epoch': 0.21, 'throughput': 480.63}
+
+[INFO|callbacks.py:310] 2024-07-30 02:51:41,799 >> {'loss': 0.9784, 'learning_rate': 4.5000e-06, 'epoch': 0.23, 'throughput': 478.52}
+
+[INFO|callbacks.py:310] 2024-07-30 02:51:54,970 >> {'loss': 0.5759, 'learning_rate': 5.0000e-06, 'epoch': 0.26, 'throughput': 478.41}
+
+[INFO|callbacks.py:310] 2024-07-30 02:52:08,132 >> {'loss': 1.1284, 'learning_rate': 4.9996e-06, 'epoch': 0.28, 'throughput': 478.35}
+
+[INFO|callbacks.py:310] 2024-07-30 02:52:21,287 >> {'loss': 1.1272, 'learning_rate': 4.9985e-06, 'epoch': 0.31, 'throughput': 479.03}
+
+[INFO|callbacks.py:310] 2024-07-30 02:52:34,460 >> {'loss': 0.9501, 'learning_rate': 4.9966e-06, 'epoch': 0.34, 'throughput': 479.08}
+
+[INFO|callbacks.py:310] 2024-07-30 02:52:47,615 >> {'loss': 0.4610, 'learning_rate': 4.9939e-06, 'epoch': 0.36, 'throughput': 478.92}
+
+[INFO|callbacks.py:310] 2024-07-30 02:53:00,776 >> {'loss': 1.2016, 'learning_rate': 4.9905e-06, 'epoch': 0.39, 'throughput': 479.89}
+
+[INFO|callbacks.py:310] 2024-07-30 02:53:13,956 >> {'loss': 0.3310, 'learning_rate': 4.9863e-06, 'epoch': 0.41, 'throughput': 480.39}
+
+[INFO|callbacks.py:310] 2024-07-30 02:53:27,114 >> {'loss': 0.3565, 'learning_rate': 4.9814e-06, 'epoch': 0.44, 'throughput': 480.60}
+
+[INFO|callbacks.py:310] 2024-07-30 02:53:40,291 >> {'loss': 0.6088, 'learning_rate': 4.9757e-06, 'epoch': 0.46, 'throughput': 480.01}
+
+[INFO|callbacks.py:310] 2024-07-30 02:53:53,463 >> {'loss': 0.2701, 'learning_rate': 4.9692e-06, 'epoch': 0.49, 'throughput': 480.13}
+
+[INFO|callbacks.py:310] 2024-07-30 02:54:06,625 >> {'loss': 0.7005, 'learning_rate': 4.9620e-06, 'epoch': 0.52, 'throughput': 480.07}
+
+[INFO|callbacks.py:310] 2024-07-30 02:54:19,803 >> {'loss': 0.3424, 'learning_rate': 4.9541e-06, 'epoch': 0.54, 'throughput': 481.03}
+
+[INFO|callbacks.py:310] 2024-07-30 02:54:32,946 >> {'loss': 0.6274, 'learning_rate': 4.9454e-06, 'epoch': 0.57, 'throughput': 480.58}
+
+[INFO|callbacks.py:310] 2024-07-30 02:54:46,125 >> {'loss': 0.4183, 'learning_rate': 4.9359e-06, 'epoch': 0.59, 'throughput': 480.64}
+
+[INFO|callbacks.py:310] 2024-07-30 02:54:59,279 >> {'loss': 0.1517, 'learning_rate': 4.9257e-06, 'epoch': 0.62, 'throughput': 481.29}
+
+[INFO|callbacks.py:310] 2024-07-30 02:55:12,444 >> {'loss': 0.1906, 'learning_rate': 4.9148e-06, 'epoch': 0.65, 'throughput': 480.85}
+
+[INFO|callbacks.py:310] 2024-07-30 02:55:25,606 >> {'loss': 0.1537, 'learning_rate': 4.9032e-06, 'epoch': 0.67, 'throughput': 480.78}
+
+[INFO|callbacks.py:310] 2024-07-30 02:55:38,769 >> {'loss': 0.1957, 'learning_rate': 4.8908e-06, 'epoch': 0.70, 'throughput': 481.34}
+
+[INFO|callbacks.py:310] 2024-07-30 02:55:51,936 >> {'loss': 0.3026, 'learning_rate': 4.8776e-06, 'epoch': 0.72, 'throughput': 481.25}
+
+[INFO|callbacks.py:310] 2024-07-30 02:56:05,100 >> {'loss': 0.2031, 'learning_rate': 4.8638e-06, 'epoch': 0.75, 'throughput': 481.25}
+
+[INFO|callbacks.py:310] 2024-07-30 02:56:18,268 >> {'loss': 0.1461, 'learning_rate': 4.8492e-06, 'epoch': 0.77, 'throughput': 481.53}
+
+[INFO|callbacks.py:310] 2024-07-30 02:56:31,447 >> {'loss': 0.1873, 'learning_rate': 4.8340e-06, 'epoch': 0.80, 'throughput': 481.00}
+
+[INFO|callbacks.py:310] 2024-07-30 02:56:44,603 >> {'loss': 0.1388, 'learning_rate': 4.8180e-06, 'epoch': 0.83, 'throughput': 480.75}
+
+[INFO|callbacks.py:310] 2024-07-30 02:56:57,774 >> {'loss': 0.1127, 'learning_rate': 4.8013e-06, 'epoch': 0.85, 'throughput': 481.16}
+
+[INFO|callbacks.py:310] 2024-07-30 02:57:10,949 >> {'loss': 0.1243, 'learning_rate': 4.7839e-06, 'epoch': 0.88, 'throughput': 480.87}
+
+[INFO|callbacks.py:310] 2024-07-30 02:57:24,122 >> {'loss': 0.0969, 'learning_rate': 4.7658e-06, 'epoch': 0.90, 'throughput': 480.63}
+
+[INFO|callbacks.py:310] 2024-07-30 02:57:37,282 >> {'loss': 0.0890, 'learning_rate': 4.7470e-06, 'epoch': 0.93, 'throughput': 480.62}
+
+[INFO|callbacks.py:310] 2024-07-30 02:57:50,444 >> {'loss': 0.1703, 'learning_rate': 4.7275e-06, 'epoch': 0.95, 'throughput': 481.20}
+
+[INFO|callbacks.py:310] 2024-07-30 02:58:03,606 >> {'loss': 0.1132, 'learning_rate': 4.7074e-06, 'epoch': 0.98, 'throughput': 481.46}
+
+[INFO|callbacks.py:310] 2024-07-30 02:58:16,767 >> {'loss': 0.1294, 'learning_rate': 4.6865e-06, 'epoch': 1.01, 'throughput': 481.77}
+
+[INFO|callbacks.py:310] 2024-07-30 02:58:29,908 >> {'loss': 0.0881, 'learning_rate': 4.6651e-06, 'epoch': 1.03, 'throughput': 481.78}
+
+[INFO|callbacks.py:310] 2024-07-30 02:58:43,077 >> {'loss': 0.0504, 'learning_rate': 4.6429e-06, 'epoch': 1.06, 'throughput': 481.55}
+
+[INFO|callbacks.py:310] 2024-07-30 02:58:56,238 >> {'loss': 0.0723, 'learning_rate': 4.6201e-06, 'epoch': 1.08, 'throughput': 481.72}
+
+[INFO|callbacks.py:310] 2024-07-30 02:59:09,398 >> {'loss': 0.0726, 'learning_rate': 4.5967e-06, 'epoch': 1.11, 'throughput': 481.69}
+
+[INFO|callbacks.py:310] 2024-07-30 02:59:22,561 >> {'loss': 0.1355, 'learning_rate': 4.5726e-06, 'epoch': 1.14, 'throughput': 481.60}
+
+[INFO|callbacks.py:310] 2024-07-30 02:59:35,735 >> {'loss': 0.0713, 'learning_rate': 4.5479e-06, 'epoch': 1.16, 'throughput': 481.51}
+
+[INFO|callbacks.py:310] 2024-07-30 02:59:48,896 >> {'loss': 0.0796, 'learning_rate': 4.5225e-06, 'epoch': 1.19, 'throughput': 481.50}
+
+[INFO|callbacks.py:310] 2024-07-30 03:00:02,045 >> {'loss': 0.0778, 'learning_rate': 4.4966e-06, 'epoch': 1.21, 'throughput': 481.46}
+
+[INFO|callbacks.py:310] 2024-07-30 03:00:15,214 >> {'loss': 0.0606, 'learning_rate': 4.4700e-06, 'epoch': 1.24, 'throughput': 481.40}
+
+[INFO|callbacks.py:310] 2024-07-30 03:00:28,372 >> {'loss': 0.0411, 'learning_rate': 4.4429e-06, 'epoch': 1.26, 'throughput': 481.53}
+
+[INFO|callbacks.py:310] 2024-07-30 03:00:41,534 >> {'loss': 0.0773, 'learning_rate': 4.4151e-06, 'epoch': 1.29, 'throughput': 481.53}
+
+[INFO|callbacks.py:310] 2024-07-30 03:00:54,678 >> {'loss': 0.0355, 'learning_rate': 4.3868e-06, 'epoch': 1.32, 'throughput': 481.73}
+
+[INFO|callbacks.py:310] 2024-07-30 03:01:07,849 >> {'loss': 0.0607, 'learning_rate': 4.3579e-06, 'epoch': 1.34, 'throughput': 481.48}
+
+[INFO|callbacks.py:310] 2024-07-30 03:01:21,013 >> {'loss': 0.0542, 'learning_rate': 4.3284e-06, 'epoch': 1.37, 'throughput': 481.43}
+
+[INFO|callbacks.py:310] 2024-07-30 03:01:34,182 >> {'loss': 0.0629, 'learning_rate': 4.2983e-06, 'epoch': 1.39, 'throughput': 481.42}
+
+[INFO|callbacks.py:310] 2024-07-30 03:01:47,353 >> {'loss': 0.0519, 'learning_rate': 4.2678e-06, 'epoch': 1.42, 'throughput': 481.73}
+
+[INFO|callbacks.py:310] 2024-07-30 03:02:00,520 >> {'loss': 0.0481, 'learning_rate': 4.2366e-06, 'epoch': 1.45, 'throughput': 481.67}
+
+[INFO|callbacks.py:310] 2024-07-30 03:02:13,678 >> {'loss': 0.0659, 'learning_rate': 4.2050e-06, 'epoch': 1.47, 'throughput': 481.67}
+
+[INFO|callbacks.py:310] 2024-07-30 03:02:26,831 >> {'loss': 0.0980, 'learning_rate': 4.1728e-06, 'epoch': 1.50, 'throughput': 482.09}
+
+[INFO|callbacks.py:310] 2024-07-30 03:02:40,005 >> {'loss': 0.0411, 'learning_rate': 4.1401e-06, 'epoch': 1.52, 'throughput': 482.24}
+
+[INFO|callbacks.py:310] 2024-07-30 03:02:53,178 >> {'loss': 0.0396, 'learning_rate': 4.1070e-06, 'epoch': 1.55, 'throughput': 481.97}
+
+[INFO|callbacks.py:310] 2024-07-30 03:03:06,330 >> {'loss': 0.0413, 'learning_rate': 4.0733e-06, 'epoch': 1.57, 'throughput': 481.73}
+
+[INFO|callbacks.py:310] 2024-07-30 03:03:19,497 >> {'loss': 0.1195, 'learning_rate': 4.0392e-06, 'epoch': 1.60, 'throughput': 482.02}
+
+[INFO|callbacks.py:310] 2024-07-30 03:03:32,670 >> {'loss': 0.0534, 'learning_rate': 4.0045e-06, 'epoch': 1.63, 'throughput': 482.06}
+
+[INFO|callbacks.py:310] 2024-07-30 03:03:45,839 >> {'loss': 0.0662, 'learning_rate': 3.9695e-06, 'epoch': 1.65, 'throughput': 481.93}
+
+[INFO|callbacks.py:310] 2024-07-30 03:03:59,009 >> {'loss': 0.0462, 'learning_rate': 3.9339e-06, 'epoch': 1.68, 'throughput': 481.86}
+
+[INFO|callbacks.py:310] 2024-07-30 03:04:12,160 >> {'loss': 0.0899, 'learning_rate': 3.8980e-06, 'epoch': 1.70, 'throughput': 481.90}
+
+[INFO|callbacks.py:310] 2024-07-30 03:04:25,334 >> {'loss': 0.0691, 'learning_rate': 3.8616e-06, 'epoch': 1.73, 'throughput': 482.08}
+
+[INFO|callbacks.py:310] 2024-07-30 03:04:38,487 >> {'loss': 0.1022, 'learning_rate': 3.8248e-06, 'epoch': 1.75, 'throughput': 482.24}
+
+[INFO|callbacks.py:310] 2024-07-30 03:04:51,658 >> {'loss': 0.1062, 'learning_rate': 3.7876e-06, 'epoch': 1.78, 'throughput': 482.17}
+
+[INFO|callbacks.py:310] 2024-07-30 03:05:04,814 >> {'loss': 0.0491, 'learning_rate': 3.7500e-06, 'epoch': 1.81, 'throughput': 482.44}
+
+[INFO|callbacks.py:310] 2024-07-30 03:05:17,972 >> {'loss': 0.1507, 'learning_rate': 3.7120e-06, 'epoch': 1.83, 'throughput': 482.42}
+
+[INFO|callbacks.py:310] 2024-07-30 03:05:31,123 >> {'loss': 0.1234, 'learning_rate': 3.6737e-06, 'epoch': 1.86, 'throughput': 482.31}
+
+[INFO|callbacks.py:310] 2024-07-30 03:05:44,271 >> {'loss': 0.0450, 'learning_rate': 3.6350e-06, 'epoch': 1.88, 'throughput': 482.26}
+
+[INFO|callbacks.py:310] 2024-07-30 03:05:57,439 >> {'loss': 0.0615, 'learning_rate': 3.5959e-06, 'epoch': 1.91, 'throughput': 482.50}
+
+[INFO|callbacks.py:310] 2024-07-30 03:06:10,604 >> {'loss': 0.1961, 'learning_rate': 3.5565e-06, 'epoch': 1.94, 'throughput': 482.59}
+
+[INFO|callbacks.py:310] 2024-07-30 03:06:23,764 >> {'loss': 0.2311, 'learning_rate': 3.5168e-06, 'epoch': 1.96, 'throughput': 482.60}
+
+[INFO|callbacks.py:310] 2024-07-30 03:06:36,916 >> {'loss': 0.1556, 'learning_rate': 3.4768e-06, 'epoch': 1.99, 'throughput': 482.48}
+
+[INFO|callbacks.py:310] 2024-07-30 03:06:50,068 >> {'loss': 0.0626, 'learning_rate': 3.4365e-06, 'epoch': 2.01, 'throughput': 482.38}
+
+[INFO|callbacks.py:310] 2024-07-30 03:07:03,233 >> {'loss': 0.0197, 'learning_rate': 3.3959e-06, 'epoch': 2.04, 'throughput': 482.41}
+
+[INFO|callbacks.py:310] 2024-07-30 03:07:16,401 >> {'loss': 0.0057, 'learning_rate': 3.3551e-06, 'epoch': 2.06, 'throughput': 482.65}
+
+[INFO|callbacks.py:310] 2024-07-30 03:07:29,571 >> {'loss': 0.0290, 'learning_rate': 3.3139e-06, 'epoch': 2.09, 'throughput': 482.53}
+
+[INFO|callbacks.py:310] 2024-07-30 03:07:42,734 >> {'loss': 0.0593, 'learning_rate': 3.2725e-06, 'epoch': 2.12, 'throughput': 482.74}
+
+[INFO|callbacks.py:310] 2024-07-30 03:07:55,878 >> {'loss': 0.0455, 'learning_rate': 3.2309e-06, 'epoch': 2.14, 'throughput': 482.77}
+
+[INFO|callbacks.py:310] 2024-07-30 03:08:09,034 >> {'loss': 0.0325, 'learning_rate': 3.1891e-06, 'epoch': 2.17, 'throughput': 482.74}
+
+[INFO|callbacks.py:310] 2024-07-30 03:08:22,192 >> {'loss': 0.0071, 'learning_rate': 3.1470e-06, 'epoch': 2.19, 'throughput': 482.90}
+
+[INFO|callbacks.py:310] 2024-07-30 03:08:35,352 >> {'loss': 0.0336, 'learning_rate': 3.1048e-06, 'epoch': 2.22, 'throughput': 482.86}
+
+[INFO|callbacks.py:310] 2024-07-30 03:08:48,514 >> {'loss': 0.0389, 'learning_rate': 3.0624e-06, 'epoch': 2.25, 'throughput': 482.92}
+
+[INFO|callbacks.py:310] 2024-07-30 03:09:01,669 >> {'loss': 0.0016, 'learning_rate': 3.0198e-06, 'epoch': 2.27, 'throughput': 482.87}
+
+[INFO|callbacks.py:310] 2024-07-30 03:09:14,848 >> {'loss': 0.0625, 'learning_rate': 2.9770e-06, 'epoch': 2.30, 'throughput': 483.05}
+
+[INFO|callbacks.py:310] 2024-07-30 03:09:27,996 >> {'loss': 0.0201, 'learning_rate': 2.9341e-06, 'epoch': 2.32, 'throughput': 482.95}
+
+[INFO|callbacks.py:310] 2024-07-30 03:09:41,158 >> {'loss': 0.0126, 'learning_rate': 2.8911e-06, 'epoch': 2.35, 'throughput': 482.79}
+
+[INFO|callbacks.py:310] 2024-07-30 03:09:54,341 >> {'loss': 0.0148, 'learning_rate': 2.8479e-06, 'epoch': 2.37, 'throughput': 482.91}
+
+[INFO|callbacks.py:310] 2024-07-30 03:10:07,511 >> {'loss': 0.0140, 'learning_rate': 2.8047e-06, 'epoch': 2.40, 'throughput': 482.83}
+
+[INFO|callbacks.py:310] 2024-07-30 03:10:20,674 >> {'loss': 0.0096, 'learning_rate': 2.7613e-06, 'epoch': 2.43, 'throughput': 482.82}
+
+[INFO|callbacks.py:310] 2024-07-30 03:10:33,834 >> {'loss': 0.0249, 'learning_rate': 2.7179e-06, 'epoch': 2.45, 'throughput': 482.82}
+
+[INFO|callbacks.py:310] 2024-07-30 03:10:47,009 >> {'loss': 0.0358, 'learning_rate': 2.6744e-06, 'epoch': 2.48, 'throughput': 482.76}
+
+[INFO|callbacks.py:310] 2024-07-30 03:11:00,178 >> {'loss': 0.0494, 'learning_rate': 2.6308e-06, 'epoch': 2.50, 'throughput': 482.67}
+
+[INFO|callbacks.py:310] 2024-07-30 03:11:13,329 >> {'loss': 0.0092, 'learning_rate': 2.5872e-06, 'epoch': 2.53, 'throughput': 482.63}
+
+[INFO|callbacks.py:310] 2024-07-30 03:11:26,479 >> {'loss': 0.0215, 'learning_rate': 2.5436e-06, 'epoch': 2.55, 'throughput': 482.59}
+
+[INFO|callbacks.py:310] 2024-07-30 03:11:39,631 >> {'loss': 0.0122, 'learning_rate': 2.5000e-06, 'epoch': 2.58, 'throughput': 482.73}
+
+[INFO|callbacks.py:310] 2024-07-30 03:11:52,780 >> {'loss': 0.0296, 'learning_rate': 2.4564e-06, 'epoch': 2.61, 'throughput': 482.73}
+
+[INFO|callbacks.py:310] 2024-07-30 03:12:05,936 >> {'loss': 0.0089, 'learning_rate': 2.4128e-06, 'epoch': 2.63, 'throughput': 482.78}
+
+[INFO|callbacks.py:310] 2024-07-30 03:12:19,112 >> {'loss': 0.0406, 'learning_rate': 2.3692e-06, 'epoch': 2.66, 'throughput': 482.65}
+
+[INFO|callbacks.py:310] 2024-07-30 03:12:32,273 >> {'loss': 0.0114, 'learning_rate': 2.3256e-06, 'epoch': 2.68, 'throughput': 482.82}
+
+[INFO|callbacks.py:310] 2024-07-30 03:12:45,431 >> {'loss': 0.0396, 'learning_rate': 2.2821e-06, 'epoch': 2.71, 'throughput': 482.81}
+
+[INFO|callbacks.py:310] 2024-07-30 03:12:58,595 >> {'loss': 0.0077, 'learning_rate': 2.2387e-06, 'epoch': 2.74, 'throughput': 482.67}
+
+[INFO|callbacks.py:310] 2024-07-30 03:13:11,749 >> {'loss': 0.0044, 'learning_rate': 2.1953e-06, 'epoch': 2.76, 'throughput': 482.63}
+
+[INFO|callbacks.py:310] 2024-07-30 03:13:24,906 >> {'loss': 0.0045, 'learning_rate': 2.1521e-06, 'epoch': 2.79, 'throughput': 482.54}
+
+[INFO|callbacks.py:310] 2024-07-30 03:13:38,052 >> {'loss': 0.0405, 'learning_rate': 2.1089e-06, 'epoch': 2.81, 'throughput': 482.51}
+
+[INFO|callbacks.py:310] 2024-07-30 03:13:51,205 >> {'loss': 0.0225, 'learning_rate': 2.0659e-06, 'epoch': 2.84, 'throughput': 482.55}
+
+[INFO|callbacks.py:310] 2024-07-30 03:14:04,369 >> {'loss': 0.0415, 'learning_rate': 2.0230e-06, 'epoch': 2.86, 'throughput': 482.50}
+
+[INFO|callbacks.py:310] 2024-07-30 03:14:17,523 >> {'loss': 0.0173, 'learning_rate': 1.9802e-06, 'epoch': 2.89, 'throughput': 482.45}
+
+[INFO|callbacks.py:310] 2024-07-30 03:14:30,685 >> {'loss': 0.0005, 'learning_rate': 1.9376e-06, 'epoch': 2.92, 'throughput': 482.38}
+
+[INFO|callbacks.py:310] 2024-07-30 03:14:43,845 >> {'loss': 0.0306, 'learning_rate': 1.8952e-06, 'epoch': 2.94, 'throughput': 482.57}
+
+[INFO|callbacks.py:310] 2024-07-30 03:14:57,012 >> {'loss': 0.0422, 'learning_rate': 1.8530e-06, 'epoch': 2.97, 'throughput': 482.66}
+
+[INFO|callbacks.py:310] 2024-07-30 03:15:10,157 >> {'loss': 0.0472, 'learning_rate': 1.8109e-06, 'epoch': 2.99, 'throughput': 482.55}
+
+[INFO|callbacks.py:310] 2024-07-30 03:15:23,306 >> {'loss': 0.0259, 'learning_rate': 1.7691e-06, 'epoch': 3.02, 'throughput': 482.50}
+
+[INFO|callbacks.py:310] 2024-07-30 03:15:36,475 >> {'loss': 0.0029, 'learning_rate': 1.7275e-06, 'epoch': 3.05, 'throughput': 482.46}
+
+[INFO|callbacks.py:310] 2024-07-30 03:15:49,636 >> {'loss': 0.0350, 'learning_rate': 1.6861e-06, 'epoch': 3.07, 'throughput': 482.46}
+
+[INFO|callbacks.py:310] 2024-07-30 03:16:02,800 >> {'loss': 0.0015, 'learning_rate': 1.6449e-06, 'epoch': 3.10, 'throughput': 482.26}
+
+[INFO|callbacks.py:310] 2024-07-30 03:16:15,954 >> {'loss': 0.0006, 'learning_rate': 1.6041e-06, 'epoch': 3.12, 'throughput': 482.20}
+
+[INFO|callbacks.py:310] 2024-07-30 03:16:29,114 >> {'loss': 0.0143, 'learning_rate': 1.5635e-06, 'epoch': 3.15, 'throughput': 482.19}
+
+[INFO|callbacks.py:310] 2024-07-30 03:16:42,268 >> {'loss': 0.0219, 'learning_rate': 1.5232e-06, 'epoch': 3.17, 'throughput': 482.19}
+
+[INFO|callbacks.py:310] 2024-07-30 03:16:55,411 >> {'loss': 0.0074, 'learning_rate': 1.4832e-06, 'epoch': 3.20, 'throughput': 482.31}
+
+[INFO|callbacks.py:310] 2024-07-30 03:17:08,589 >> {'loss': 0.0052, 'learning_rate': 1.4435e-06, 'epoch': 3.23, 'throughput': 482.23}
+
+[INFO|callbacks.py:310] 2024-07-30 03:17:21,739 >> {'loss': 0.0013, 'learning_rate': 1.4041e-06, 'epoch': 3.25, 'throughput': 482.19}
+
+[INFO|callbacks.py:310] 2024-07-30 03:17:34,901 >> {'loss': 0.0018, 'learning_rate': 1.3650e-06, 'epoch': 3.28, 'throughput': 482.26}
+
+[INFO|callbacks.py:310] 2024-07-30 03:17:48,057 >> {'loss': 0.0077, 'learning_rate': 1.3263e-06, 'epoch': 3.30, 'throughput': 482.22}
+
+[INFO|callbacks.py:310] 2024-07-30 03:18:01,209 >> {'loss': 0.0138, 'learning_rate': 1.2880e-06, 'epoch': 3.33, 'throughput': 482.24}
+
+[INFO|callbacks.py:310] 2024-07-30 03:18:14,360 >> {'loss': 0.0102, 'learning_rate': 1.2500e-06, 'epoch': 3.35, 'throughput': 482.29}
+
+[INFO|callbacks.py:310] 2024-07-30 03:18:27,523 >> {'loss': 0.0067, 'learning_rate': 1.2124e-06, 'epoch': 3.38, 'throughput': 482.41}
+
+[INFO|callbacks.py:310] 2024-07-30 03:18:40,673 >> {'loss': 0.0056, 'learning_rate': 1.1752e-06, 'epoch': 3.41, 'throughput': 482.43}
+
+[INFO|callbacks.py:310] 2024-07-30 03:18:53,846 >> {'loss': 0.0066, 'learning_rate': 1.1384e-06, 'epoch': 3.43, 'throughput': 482.59}
+
+[INFO|callbacks.py:310] 2024-07-30 03:19:07,002 >> {'loss': 0.0033, 'learning_rate': 1.1020e-06, 'epoch': 3.46, 'throughput': 482.61}
+
+[INFO|callbacks.py:310] 2024-07-30 03:19:20,156 >> {'loss': 0.0008, 'learning_rate': 1.0661e-06, 'epoch': 3.48, 'throughput': 482.56}
+
+[INFO|callbacks.py:310] 2024-07-30 03:19:33,324 >> {'loss': 0.0027, 'learning_rate': 1.0305e-06, 'epoch': 3.51, 'throughput': 482.56}
+
+[INFO|callbacks.py:310] 2024-07-30 03:19:46,477 >> {'loss': 0.0021, 'learning_rate': 9.9546e-07, 'epoch': 3.54, 'throughput': 482.46}
+
+[INFO|callbacks.py:310] 2024-07-30 03:19:59,648 >> {'loss': 0.0008, 'learning_rate': 9.6085e-07, 'epoch': 3.56, 'throughput': 482.42}
+
+[INFO|callbacks.py:310] 2024-07-30 03:20:12,805 >> {'loss': 0.0051, 'learning_rate': 9.2670e-07, 'epoch': 3.59, 'throughput': 482.52}
+
+[INFO|callbacks.py:310] 2024-07-30 03:20:25,967 >> {'loss': 0.0026, 'learning_rate': 8.9303e-07, 'epoch': 3.61, 'throughput': 482.52}
+
+[INFO|callbacks.py:310] 2024-07-30 03:20:39,143 >> {'loss': 0.0041, 'learning_rate': 8.5985e-07, 'epoch': 3.64, 'throughput': 482.51}
+
+[INFO|callbacks.py:310] 2024-07-30 03:20:52,302 >> {'loss': 0.0230, 'learning_rate': 8.2717e-07, 'epoch': 3.66, 'throughput': 482.38}
+
+[INFO|callbacks.py:310] 2024-07-30 03:21:05,454 >> {'loss': 0.0106, 'learning_rate': 7.9500e-07, 'epoch': 3.69, 'throughput': 482.32}
+
+[INFO|callbacks.py:310] 2024-07-30 03:21:18,598 >> {'loss': 0.0238, 'learning_rate': 7.6335e-07, 'epoch': 3.72, 'throughput': 482.48}
+
+[INFO|callbacks.py:310] 2024-07-30 03:21:31,757 >> {'loss': 0.0088, 'learning_rate': 7.3223e-07, 'epoch': 3.74, 'throughput': 482.51}
+
+[INFO|callbacks.py:310] 2024-07-30 03:21:44,919 >> {'loss': 0.0391, 'learning_rate': 7.0165e-07, 'epoch': 3.77, 'throughput': 482.58}
+
+[INFO|callbacks.py:310] 2024-07-30 03:21:58,081 >> {'loss': 0.0008, 'learning_rate': 6.7162e-07, 'epoch': 3.79, 'throughput': 482.64}
+
+[INFO|callbacks.py:310] 2024-07-30 03:22:11,241 >> {'loss': 0.0177, 'learning_rate': 6.4214e-07, 'epoch': 3.82, 'throughput': 482.62}
+
+[INFO|callbacks.py:310] 2024-07-30 03:22:24,389 >> {'loss': 0.0001, 'learning_rate': 6.1323e-07, 'epoch': 3.85, 'throughput': 482.53}
+
+[INFO|callbacks.py:310] 2024-07-30 03:22:37,539 >> {'loss': 0.0002, 'learning_rate': 5.8489e-07, 'epoch': 3.87, 'throughput': 482.57}
+
+[INFO|callbacks.py:310] 2024-07-30 03:22:50,689 >> {'loss': 0.0044, 'learning_rate': 5.5714e-07, 'epoch': 3.90, 'throughput': 482.50}
+
+[INFO|callbacks.py:310] 2024-07-30 03:23:03,841 >> {'loss': 0.0015, 'learning_rate': 5.2997e-07, 'epoch': 3.92, 'throughput': 482.56}
+
+[INFO|callbacks.py:310] 2024-07-30 03:23:16,993 >> {'loss': 0.0003, 'learning_rate': 5.0341e-07, 'epoch': 3.95, 'throughput': 482.53}
+
+[INFO|callbacks.py:310] 2024-07-30 03:23:30,143 >> {'loss': 0.0361, 'learning_rate': 4.7746e-07, 'epoch': 3.97, 'throughput': 482.59}
+
+[INFO|callbacks.py:310] 2024-07-30 03:23:43,318 >> {'loss': 0.0005, 'learning_rate': 4.5212e-07, 'epoch': 4.00, 'throughput': 482.75}
+
+[INFO|callbacks.py:310] 2024-07-30 03:23:56,477 >> {'loss': 0.0022, 'learning_rate': 4.2741e-07, 'epoch': 4.03, 'throughput': 482.76}
+
+[INFO|callbacks.py:310] 2024-07-30 03:24:09,624 >> {'loss': 0.0212, 'learning_rate': 4.0332e-07, 'epoch': 4.05, 'throughput': 482.78}
+
+[INFO|callbacks.py:310] 2024-07-30 03:24:22,793 >> {'loss': 0.0003, 'learning_rate': 3.7988e-07, 'epoch': 4.08, 'throughput': 482.68}
+
+[INFO|callbacks.py:310] 2024-07-30 03:24:35,959 >> {'loss': 0.0047, 'learning_rate': 3.5708e-07, 'epoch': 4.10, 'throughput': 482.59}
+
+[INFO|callbacks.py:310] 2024-07-30 03:24:49,103 >> {'loss': 0.0014, 'learning_rate': 3.3494e-07, 'epoch': 4.13, 'throughput': 482.54}
+
+[INFO|callbacks.py:310] 2024-07-30 03:25:02,253 >> {'loss': 0.0006, 'learning_rate': 3.1345e-07, 'epoch': 4.15, 'throughput': 482.62}
+
+[INFO|callbacks.py:310] 2024-07-30 03:25:15,421 >> {'loss': 0.0003, 'learning_rate': 2.9263e-07, 'epoch': 4.18, 'throughput': 482.66}
+
+[INFO|callbacks.py:310] 2024-07-30 03:25:28,590 >> {'loss': 0.0021, 'learning_rate': 2.7248e-07, 'epoch': 4.21, 'throughput': 482.63}
+
+[INFO|callbacks.py:310] 2024-07-30 03:25:41,746 >> {'loss': 0.0001, 'learning_rate': 2.5301e-07, 'epoch': 4.23, 'throughput': 482.54}
+
+[INFO|callbacks.py:310] 2024-07-30 03:25:54,902 >> {'loss': 0.0007, 'learning_rate': 2.3423e-07, 'epoch': 4.26, 'throughput': 482.58}
+
+[INFO|callbacks.py:310] 2024-07-30 03:26:08,056 >> {'loss': 0.0013, 'learning_rate': 2.1614e-07, 'epoch': 4.28, 'throughput': 482.49}
+
+[INFO|callbacks.py:310] 2024-07-30 03:26:21,216 >> {'loss': 0.0002, 'learning_rate': 1.9874e-07, 'epoch': 4.31, 'throughput': 482.55}
+
+[INFO|callbacks.py:310] 2024-07-30 03:26:34,365 >> {'loss': 0.0011, 'learning_rate': 1.8204e-07, 'epoch': 4.34, 'throughput': 482.47}
+
+[INFO|callbacks.py:310] 2024-07-30 03:26:47,530 >> {'loss': 0.0001, 'learning_rate': 1.6605e-07, 'epoch': 4.36, 'throughput': 482.39}
+
+[INFO|callbacks.py:310] 2024-07-30 03:27:00,675 >> {'loss': 0.0006, 'learning_rate': 1.5077e-07, 'epoch': 4.39, 'throughput': 482.46}
+
+[INFO|callbacks.py:310] 2024-07-30 03:27:13,849 >> {'loss': 0.0003, 'learning_rate': 1.3620e-07, 'epoch': 4.41, 'throughput': 482.60}
+
+[INFO|callbacks.py:310] 2024-07-30 03:27:27,011 >> {'loss': 0.0002, 'learning_rate': 1.2236e-07, 'epoch': 4.44, 'throughput': 482.60}
+
+[INFO|callbacks.py:310] 2024-07-30 03:27:40,171 >> {'loss': 0.0027, 'learning_rate': 1.0924e-07, 'epoch': 4.46, 'throughput': 482.69}
+
+[INFO|callbacks.py:310] 2024-07-30 03:27:53,338 >> {'loss': 0.0002, 'learning_rate': 9.6846e-08, 'epoch': 4.49, 'throughput': 482.67}
+
+[INFO|callbacks.py:310] 2024-07-30 03:28:06,490 >> {'loss': 0.0001, 'learning_rate': 8.5185e-08, 'epoch': 4.52, 'throughput': 482.72}
+
+[INFO|callbacks.py:310] 2024-07-30 03:28:19,653 >> {'loss': 0.0109, 'learning_rate': 7.4261e-08, 'epoch': 4.54, 'throughput': 482.85}
+
+[INFO|callbacks.py:310] 2024-07-30 03:28:32,819 >> {'loss': 0.0039, 'learning_rate': 6.4075e-08, 'epoch': 4.57, 'throughput': 482.78}
+
+[INFO|callbacks.py:310] 2024-07-30 03:28:45,970 >> {'loss': 0.0026, 'learning_rate': 5.4631e-08, 'epoch': 4.59, 'throughput': 482.83}
+
+[INFO|callbacks.py:310] 2024-07-30 03:28:59,127 >> {'loss': 0.0002, 'learning_rate': 4.5932e-08, 'epoch': 4.62, 'throughput': 482.85}
+
+[INFO|callbacks.py:310] 2024-07-30 03:29:12,277 >> {'loss': 0.0044, 'learning_rate': 3.7981e-08, 'epoch': 4.65, 'throughput': 482.83}
+
+[INFO|callbacks.py:310] 2024-07-30 03:29:25,426 >> {'loss': 0.0001, 'learning_rate': 3.0779e-08, 'epoch': 4.67, 'throughput': 482.83}
+
+[INFO|callbacks.py:310] 2024-07-30 03:29:38,563 >> {'loss': 0.0103, 'learning_rate': 2.4330e-08, 'epoch': 4.70, 'throughput': 482.82}
+
+[INFO|callbacks.py:310] 2024-07-30 03:29:51,712 >> {'loss': 0.0002, 'learning_rate': 1.8635e-08, 'epoch': 4.72, 'throughput': 482.82}
+
+[INFO|callbacks.py:310] 2024-07-30 03:30:04,875 >> {'loss': 0.0038, 'learning_rate': 1.3695e-08, 'epoch': 4.75, 'throughput': 482.82}
+
+[INFO|callbacks.py:310] 2024-07-30 03:30:18,027 >> {'loss': 0.0039, 'learning_rate': 9.5133e-09, 'epoch': 4.77, 'throughput': 482.88}
+
+[INFO|callbacks.py:310] 2024-07-30 03:30:31,175 >> {'loss': 0.0005, 'learning_rate': 6.0899e-09, 'epoch': 4.80, 'throughput': 482.83}
+
+[INFO|callbacks.py:310] 2024-07-30 03:30:44,329 >> {'loss': 0.0002, 'learning_rate': 3.4262e-09, 'epoch': 4.83, 'throughput': 482.85}
+
+[INFO|callbacks.py:310] 2024-07-30 03:30:57,472 >> {'loss': 0.0011, 'learning_rate': 1.5229e-09, 'epoch': 4.85, 'throughput': 482.82}
+
+[INFO|callbacks.py:310] 2024-07-30 03:31:10,622 >> {'loss': 0.0007, 'learning_rate': 3.8076e-10, 'epoch': 4.88, 'throughput': 482.75}
+
+[INFO|callbacks.py:310] 2024-07-30 03:31:23,759 >> {'loss': 0.0002, 'learning_rate': 0.0000e+00, 'epoch': 4.90, 'throughput': 482.73}
+
+[INFO|trainer.py:3503] 2024-07-30 03:31:31,698 >> Saving model checkpoint to saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-47-53_llama3.1_truthqa_bench2/checkpoint-190
+
+[INFO|configuration_utils.py:472] 2024-07-30 03:31:31,701 >> Configuration saved in saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-47-53_llama3.1_truthqa_bench2/checkpoint-190/config.json
+
+[INFO|configuration_utils.py:807] 2024-07-30 03:31:31,701 >> Configuration saved in saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-47-53_llama3.1_truthqa_bench2/checkpoint-190/generation_config.json
+
+[INFO|modeling_utils.py:2763] 2024-07-30 03:31:48,048 >> The model is bigger than the maximum size per checkpoint (5GB) and is going to be split in 4 checkpoint shards. You can find where each parameters has been saved in the index located at saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-47-53_llama3.1_truthqa_bench2/checkpoint-190/model.safetensors.index.json.
+
+[INFO|tokenization_utils_base.py:2702] 2024-07-30 03:31:48,052 >> tokenizer config file saved in saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-47-53_llama3.1_truthqa_bench2/checkpoint-190/tokenizer_config.json
+
+[INFO|tokenization_utils_base.py:2711] 2024-07-30 03:31:48,052 >> Special tokens file saved in saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-47-53_llama3.1_truthqa_bench2/checkpoint-190/special_tokens_map.json
+
+[INFO|trainer.py:2394] 2024-07-30 03:32:24,590 >> 
+
+Training completed. Do not forget to share your model on huggingface.co/models =)
+
+
+
+[INFO|trainer.py:3503] 2024-07-30 03:32:32,434 >> Saving model checkpoint to saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-47-53_llama3.1_truthqa_bench2
+
+[INFO|configuration_utils.py:472] 2024-07-30 03:32:32,437 >> Configuration saved in saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-47-53_llama3.1_truthqa_bench2/config.json
+
+[INFO|configuration_utils.py:807] 2024-07-30 03:32:32,437 >> Configuration saved in saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-47-53_llama3.1_truthqa_bench2/generation_config.json
+
+[INFO|modeling_utils.py:2763] 2024-07-30 03:32:49,870 >> The model is bigger than the maximum size per checkpoint (5GB) and is going to be split in 4 checkpoint shards. You can find where each parameters has been saved in the index located at saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-47-53_llama3.1_truthqa_bench2/model.safetensors.index.json.
+
+[INFO|tokenization_utils_base.py:2702] 2024-07-30 03:32:49,874 >> tokenizer config file saved in saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-47-53_llama3.1_truthqa_bench2/tokenizer_config.json
+
+[INFO|tokenization_utils_base.py:2711] 2024-07-30 03:32:49,874 >> Special tokens file saved in saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-47-53_llama3.1_truthqa_bench2/special_tokens_map.json
+
+[WARNING|ploting.py:89] 2024-07-30 03:32:51,207 >> No metric eval_loss to plot.
+
+[WARNING|ploting.py:89] 2024-07-30 03:32:51,207 >> No metric eval_accuracy to plot.
+
+[INFO|modelcard.py:449] 2024-07-30 03:32:51,207 >> Dropping the following result as it does not have all the necessary fields:
+{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}
+

special_tokens_map.json

@@ -0,0 +1,17 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|eot_id|>"
+}

tokenizer_config.json

@@ -0,0 +1,2065 @@
+{
+  "added_tokens_decoder": {
+    "128000": {
+      "content": "<|begin_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128001": {
+      "content": "<|end_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128002": {
+      "content": "<|reserved_special_token_0|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128003": {
+      "content": "<|reserved_special_token_1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128004": {
+      "content": "<|finetune_right_pad_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128005": {
+      "content": "<|reserved_special_token_2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128006": {
+      "content": "<|start_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128007": {
+      "content": "<|end_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128008": {
+      "content": "<|eom_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128009": {
+      "content": "<|eot_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+  "chat_template": "{{ '<|begin_of_text|>' }}{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ '<|start_header_id|>system<|end_header_id|>\n\n' + system_message + '<|eot_id|>' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|start_header_id|>user<|end_header_id|>\n\n' + content + '<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|eot_id|>' }}{% endif %}{% endfor %}",
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+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

train_results.json

@@ -0,0 +1,9 @@
+{
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+    "num_input_tokens_seen": 1207760,
+    "total_flos": 5.438488809413018e+16,
+    "train_loss": 0.4546951471592796,
+    "train_runtime": 2562.7617,
+    "train_samples_per_second": 9.673,
+    "train_steps_per_second": 0.074
+}

trainer_log.jsonl

@@ -0,0 +1,191 @@
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+{"current_steps": 2, "total_steps": 190, "loss": 12.1047, "learning_rate": 1.0000000000000002e-06, "epoch": 0.05161290322580645, "percentage": 1.05, "elapsed_time": "0:00:27", "remaining_time": "0:43:30", "throughput": "467.79", "total_tokens": 12992}
+{"current_steps": 3, "total_steps": 190, "loss": 12.0404, "learning_rate": 1.5e-06, "epoch": 0.07741935483870968, "percentage": 1.58, "elapsed_time": "0:00:40", "remaining_time": "0:42:31", "throughput": "475.65", "total_tokens": 19472}
+{"current_steps": 4, "total_steps": 190, "loss": 10.5293, "learning_rate": 2.0000000000000003e-06, "epoch": 0.1032258064516129, "percentage": 2.11, "elapsed_time": "0:00:54", "remaining_time": "0:41:55", "throughput": "479.47", "total_tokens": 25936}
+{"current_steps": 5, "total_steps": 190, "loss": 8.3117, "learning_rate": 2.5e-06, "epoch": 0.12903225806451613, "percentage": 2.63, "elapsed_time": "0:01:07", "remaining_time": "0:41:29", "throughput": "479.70", "total_tokens": 32272}
+{"current_steps": 6, "total_steps": 190, "loss": 6.0338, "learning_rate": 3e-06, "epoch": 0.15483870967741936, "percentage": 3.16, "elapsed_time": "0:01:20", "remaining_time": "0:41:06", "throughput": "480.34", "total_tokens": 38640}
+{"current_steps": 7, "total_steps": 190, "loss": 4.8226, "learning_rate": 3.5e-06, "epoch": 0.18064516129032257, "percentage": 3.68, "elapsed_time": "0:01:33", "remaining_time": "0:40:47", "throughput": "480.63", "total_tokens": 44992}
+{"current_steps": 8, "total_steps": 190, "loss": 2.9485, "learning_rate": 4.000000000000001e-06, "epoch": 0.2064516129032258, "percentage": 4.21, "elapsed_time": "0:01:46", "remaining_time": "0:40:29", "throughput": "480.63", "total_tokens": 51328}
+{"current_steps": 9, "total_steps": 190, "loss": 0.9784, "learning_rate": 4.5e-06, "epoch": 0.23225806451612904, "percentage": 4.74, "elapsed_time": "0:01:59", "remaining_time": "0:40:12", "throughput": "478.52", "total_tokens": 57408}
+{"current_steps": 10, "total_steps": 190, "loss": 0.5759, "learning_rate": 5e-06, "epoch": 0.25806451612903225, "percentage": 5.26, "elapsed_time": "0:02:13", "remaining_time": "0:39:56", "throughput": "478.41", "total_tokens": 63696}
+{"current_steps": 11, "total_steps": 190, "loss": 1.1284, "learning_rate": 4.9996192378909785e-06, "epoch": 0.2838709677419355, "percentage": 5.79, "elapsed_time": "0:02:26", "remaining_time": "0:39:40", "throughput": "478.35", "total_tokens": 69984}
+{"current_steps": 12, "total_steps": 190, "loss": 1.1272, "learning_rate": 4.99847706754774e-06, "epoch": 0.3096774193548387, "percentage": 6.32, "elapsed_time": "0:02:39", "remaining_time": "0:39:25", "throughput": "479.03", "total_tokens": 76384}
+{"current_steps": 13, "total_steps": 190, "loss": 0.9501, "learning_rate": 4.9965738368864345e-06, "epoch": 0.33548387096774196, "percentage": 6.84, "elapsed_time": "0:02:52", "remaining_time": "0:39:10", "throughput": "479.08", "total_tokens": 82704}
+{"current_steps": 14, "total_steps": 190, "loss": 0.461, "learning_rate": 4.993910125649561e-06, "epoch": 0.36129032258064514, "percentage": 7.37, "elapsed_time": "0:03:05", "remaining_time": "0:38:55", "throughput": "478.92", "total_tokens": 88976}
+{"current_steps": 15, "total_steps": 190, "loss": 1.2016, "learning_rate": 4.990486745229364e-06, "epoch": 0.3870967741935484, "percentage": 7.89, "elapsed_time": "0:03:18", "remaining_time": "0:38:41", "throughput": "479.89", "total_tokens": 95472}
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+{"current_steps": 17, "total_steps": 190, "loss": 0.3565, "learning_rate": 4.981365379103306e-06, "epoch": 0.43870967741935485, "percentage": 8.95, "elapsed_time": "0:03:45", "remaining_time": "0:38:12", "throughput": "480.60", "total_tokens": 108272}
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+{"current_steps": 20, "total_steps": 190, "loss": 0.7005, "learning_rate": 4.962019382530521e-06, "epoch": 0.5161290322580645, "percentage": 10.53, "elapsed_time": "0:04:24", "remaining_time": "0:37:30", "throughput": "480.07", "total_tokens": 127120}
+{"current_steps": 21, "total_steps": 190, "loss": 0.3424, "learning_rate": 4.9540679586191605e-06, "epoch": 0.5419354838709678, "percentage": 11.05, "elapsed_time": "0:04:37", "remaining_time": "0:37:17", "throughput": "481.03", "total_tokens": 133712}
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+{"current_steps": 23, "total_steps": 190, "loss": 0.4183, "learning_rate": 4.935925161963089e-06, "epoch": 0.5935483870967742, "percentage": 12.11, "elapsed_time": "0:05:04", "remaining_time": "0:36:49", "throughput": "480.64", "total_tokens": 146256}
+{"current_steps": 24, "total_steps": 190, "loss": 0.1517, "learning_rate": 4.925739315689991e-06, "epoch": 0.6193548387096774, "percentage": 12.63, "elapsed_time": "0:05:17", "remaining_time": "0:36:35", "throughput": "481.29", "total_tokens": 152784}
+{"current_steps": 25, "total_steps": 190, "loss": 0.1906, "learning_rate": 4.914814565722671e-06, "epoch": 0.6451612903225806, "percentage": 13.16, "elapsed_time": "0:05:30", "remaining_time": "0:36:22", "throughput": "480.85", "total_tokens": 158976}
+{"current_steps": 26, "total_steps": 190, "loss": 0.1537, "learning_rate": 4.903154239845798e-06, "epoch": 0.6709677419354839, "percentage": 13.68, "elapsed_time": "0:05:43", "remaining_time": "0:36:08", "throughput": "480.78", "total_tokens": 165280}
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+{"current_steps": 37, "total_steps": 190, "loss": 0.1703, "learning_rate": 4.72751631047092e-06, "epoch": 0.9548387096774194, "percentage": 19.47, "elapsed_time": "0:08:08", "remaining_time": "0:33:40", "throughput": "481.20", "total_tokens": 235120}
+{"current_steps": 38, "total_steps": 190, "loss": 0.1132, "learning_rate": 4.707368982147318e-06, "epoch": 0.9806451612903225, "percentage": 20.0, "elapsed_time": "0:08:21", "remaining_time": "0:33:27", "throughput": "481.46", "total_tokens": 241584}
+{"current_steps": 39, "total_steps": 190, "loss": 0.1294, "learning_rate": 4.68654926784849e-06, "epoch": 1.0064516129032257, "percentage": 20.53, "elapsed_time": "0:08:34", "remaining_time": "0:33:13", "throughput": "481.77", "total_tokens": 248080}
+{"current_steps": 40, "total_steps": 190, "loss": 0.0881, "learning_rate": 4.665063509461098e-06, "epoch": 1.032258064516129, "percentage": 21.05, "elapsed_time": "0:08:48", "remaining_time": "0:33:00", "throughput": "481.78", "total_tokens": 254416}
+{"current_steps": 41, "total_steps": 190, "loss": 0.0504, "learning_rate": 4.642918251755281e-06, "epoch": 1.0580645161290323, "percentage": 21.58, "elapsed_time": "0:09:01", "remaining_time": "0:32:46", "throughput": "481.55", "total_tokens": 260640}
+{"current_steps": 42, "total_steps": 190, "loss": 0.0723, "learning_rate": 4.620120240391065e-06, "epoch": 1.0838709677419356, "percentage": 22.11, "elapsed_time": "0:09:14", "remaining_time": "0:32:33", "throughput": "481.72", "total_tokens": 267072}
+{"current_steps": 43, "total_steps": 190, "loss": 0.0726, "learning_rate": 4.596676419863561e-06, "epoch": 1.1096774193548387, "percentage": 22.63, "elapsed_time": "0:09:27", "remaining_time": "0:32:20", "throughput": "481.69", "total_tokens": 273392}
+{"current_steps": 44, "total_steps": 190, "loss": 0.1355, "learning_rate": 4.572593931387604e-06, "epoch": 1.135483870967742, "percentage": 23.16, "elapsed_time": "0:09:40", "remaining_time": "0:32:06", "throughput": "481.60", "total_tokens": 279680}
+{"current_steps": 45, "total_steps": 190, "loss": 0.0713, "learning_rate": 4.54788011072248e-06, "epoch": 1.1612903225806452, "percentage": 23.68, "elapsed_time": "0:09:53", "remaining_time": "0:31:53", "throughput": "481.51", "total_tokens": 285968}
+{"current_steps": 46, "total_steps": 190, "loss": 0.0796, "learning_rate": 4.522542485937369e-06, "epoch": 1.1870967741935483, "percentage": 24.21, "elapsed_time": "0:10:07", "remaining_time": "0:31:40", "throughput": "481.50", "total_tokens": 292304}
+{"current_steps": 47, "total_steps": 190, "loss": 0.0778, "learning_rate": 4.496588775118232e-06, "epoch": 1.2129032258064516, "percentage": 24.74, "elapsed_time": "0:10:20", "remaining_time": "0:31:27", "throughput": "481.46", "total_tokens": 298608}
+{"current_steps": 48, "total_steps": 190, "loss": 0.0606, "learning_rate": 4.470026884016805e-06, "epoch": 1.238709677419355, "percentage": 25.26, "elapsed_time": "0:10:33", "remaining_time": "0:31:13", "throughput": "481.40", "total_tokens": 304912}
+{"current_steps": 49, "total_steps": 190, "loss": 0.0411, "learning_rate": 4.442864903642428e-06, "epoch": 1.2645161290322582, "percentage": 25.79, "elapsed_time": "0:10:46", "remaining_time": "0:31:00", "throughput": "481.53", "total_tokens": 311328}
+{"current_steps": 50, "total_steps": 190, "loss": 0.0773, "learning_rate": 4.415111107797445e-06, "epoch": 1.2903225806451613, "percentage": 26.32, "elapsed_time": "0:10:59", "remaining_time": "0:30:47", "throughput": "481.53", "total_tokens": 317664}
+{"current_steps": 51, "total_steps": 190, "loss": 0.0355, "learning_rate": 4.386773950556931e-06, "epoch": 1.3161290322580645, "percentage": 26.84, "elapsed_time": "0:11:12", "remaining_time": "0:30:33", "throughput": "481.73", "total_tokens": 324128}
+{"current_steps": 52, "total_steps": 190, "loss": 0.0607, "learning_rate": 4.357862063693486e-06, "epoch": 1.3419354838709676, "percentage": 27.37, "elapsed_time": "0:11:26", "remaining_time": "0:30:20", "throughput": "481.48", "total_tokens": 330304}
+{"current_steps": 53, "total_steps": 190, "loss": 0.0542, "learning_rate": 4.328384254047927e-06, "epoch": 1.367741935483871, "percentage": 27.89, "elapsed_time": "0:11:39", "remaining_time": "0:30:07", "throughput": "481.43", "total_tokens": 336608}
+{"current_steps": 54, "total_steps": 190, "loss": 0.0629, "learning_rate": 4.2983495008466285e-06, "epoch": 1.3935483870967742, "percentage": 28.42, "elapsed_time": "0:11:52", "remaining_time": "0:29:54", "throughput": "481.42", "total_tokens": 342944}
+{"current_steps": 55, "total_steps": 190, "loss": 0.0519, "learning_rate": 4.267766952966369e-06, "epoch": 1.4193548387096775, "percentage": 28.95, "elapsed_time": "0:12:05", "remaining_time": "0:29:40", "throughput": "481.73", "total_tokens": 349504}
+{"current_steps": 56, "total_steps": 190, "loss": 0.0481, "learning_rate": 4.236645926147493e-06, "epoch": 1.4451612903225808, "percentage": 29.47, "elapsed_time": "0:12:18", "remaining_time": "0:29:27", "throughput": "481.67", "total_tokens": 355808}
+{"current_steps": 57, "total_steps": 190, "loss": 0.0659, "learning_rate": 4.204995900156247e-06, "epoch": 1.4709677419354839, "percentage": 30.0, "elapsed_time": "0:12:31", "remaining_time": "0:29:14", "throughput": "481.67", "total_tokens": 362144}
+{"current_steps": 58, "total_steps": 190, "loss": 0.098, "learning_rate": 4.172826515897146e-06, "epoch": 1.4967741935483871, "percentage": 30.53, "elapsed_time": "0:12:45", "remaining_time": "0:29:01", "throughput": "482.09", "total_tokens": 368800}
+{"current_steps": 59, "total_steps": 190, "loss": 0.0411, "learning_rate": 4.140147572476269e-06, "epoch": 1.5225806451612902, "percentage": 31.05, "elapsed_time": "0:12:58", "remaining_time": "0:28:47", "throughput": "482.24", "total_tokens": 375264}
+{"current_steps": 60, "total_steps": 190, "loss": 0.0396, "learning_rate": 4.106969024216348e-06, "epoch": 1.5483870967741935, "percentage": 31.58, "elapsed_time": "0:13:11", "remaining_time": "0:28:34", "throughput": "481.97", "total_tokens": 381408}
+{"current_steps": 61, "total_steps": 190, "loss": 0.0413, "learning_rate": 4.073300977624594e-06, "epoch": 1.5741935483870968, "percentage": 32.11, "elapsed_time": "0:13:24", "remaining_time": "0:28:21", "throughput": "481.73", "total_tokens": 387552}
+{"current_steps": 62, "total_steps": 190, "loss": 0.1195, "learning_rate": 4.039153688314146e-06, "epoch": 1.6, "percentage": 32.63, "elapsed_time": "0:13:37", "remaining_time": "0:28:08", "throughput": "482.02", "total_tokens": 394128}
+{"current_steps": 63, "total_steps": 190, "loss": 0.0534, "learning_rate": 4.0045375578801216e-06, "epoch": 1.6258064516129034, "percentage": 33.16, "elapsed_time": "0:13:50", "remaining_time": "0:27:54", "throughput": "482.06", "total_tokens": 400512}
+{"current_steps": 64, "total_steps": 190, "loss": 0.0662, "learning_rate": 3.969463130731183e-06, "epoch": 1.6516129032258065, "percentage": 33.68, "elapsed_time": "0:14:04", "remaining_time": "0:27:41", "throughput": "481.93", "total_tokens": 406752}
+{"current_steps": 65, "total_steps": 190, "loss": 0.0462, "learning_rate": 3.933941090877615e-06, "epoch": 1.6774193548387095, "percentage": 34.21, "elapsed_time": "0:14:17", "remaining_time": "0:27:28", "throughput": "481.86", "total_tokens": 413040}
+{"current_steps": 66, "total_steps": 190, "loss": 0.0899, "learning_rate": 3.897982258676867e-06, "epoch": 1.7032258064516128, "percentage": 34.74, "elapsed_time": "0:14:30", "remaining_time": "0:27:15", "throughput": "481.90", "total_tokens": 419408}
+{"current_steps": 67, "total_steps": 190, "loss": 0.0691, "learning_rate": 3.861597587537568e-06, "epoch": 1.729032258064516, "percentage": 35.26, "elapsed_time": "0:14:43", "remaining_time": "0:27:01", "throughput": "482.08", "total_tokens": 425920}
+{"current_steps": 68, "total_steps": 190, "loss": 0.1022, "learning_rate": 3.824798160583012e-06, "epoch": 1.7548387096774194, "percentage": 35.79, "elapsed_time": "0:14:56", "remaining_time": "0:26:48", "throughput": "482.24", "total_tokens": 432400}
+{"current_steps": 69, "total_steps": 190, "loss": 0.1062, "learning_rate": 3.787595187275136e-06, "epoch": 1.7806451612903227, "percentage": 36.32, "elapsed_time": "0:15:09", "remaining_time": "0:26:35", "throughput": "482.17", "total_tokens": 438688}
+{"current_steps": 70, "total_steps": 190, "loss": 0.0491, "learning_rate": 3.7500000000000005e-06, "epoch": 1.8064516129032258, "percentage": 36.84, "elapsed_time": "0:15:22", "remaining_time": "0:26:22", "throughput": "482.44", "total_tokens": 445280}
+{"current_steps": 71, "total_steps": 190, "loss": 0.1507, "learning_rate": 3.7120240506158433e-06, "epoch": 1.832258064516129, "percentage": 37.37, "elapsed_time": "0:15:36", "remaining_time": "0:26:09", "throughput": "482.42", "total_tokens": 451616}
+{"current_steps": 72, "total_steps": 190, "loss": 0.1234, "learning_rate": 3.6736789069647273e-06, "epoch": 1.8580645161290321, "percentage": 37.89, "elapsed_time": "0:15:49", "remaining_time": "0:25:55", "throughput": "482.31", "total_tokens": 457856}
+{"current_steps": 73, "total_steps": 190, "loss": 0.045, "learning_rate": 3.634976249348867e-06, "epoch": 1.8838709677419354, "percentage": 38.42, "elapsed_time": "0:16:02", "remaining_time": "0:25:42", "throughput": "482.26", "total_tokens": 464144}
+{"current_steps": 74, "total_steps": 190, "loss": 0.0615, "learning_rate": 3.595927866972694e-06, "epoch": 1.9096774193548387, "percentage": 38.95, "elapsed_time": "0:16:15", "remaining_time": "0:25:29", "throughput": "482.50", "total_tokens": 470736}
+{"current_steps": 75, "total_steps": 190, "loss": 0.1961, "learning_rate": 3.556545654351749e-06, "epoch": 1.935483870967742, "percentage": 39.47, "elapsed_time": "0:16:28", "remaining_time": "0:25:16", "throughput": "482.59", "total_tokens": 477168}
+{"current_steps": 76, "total_steps": 190, "loss": 0.2311, "learning_rate": 3.516841607689501e-06, "epoch": 1.9612903225806453, "percentage": 40.0, "elapsed_time": "0:16:41", "remaining_time": "0:25:02", "throughput": "482.60", "total_tokens": 483536}
+{"current_steps": 77, "total_steps": 190, "loss": 0.1556, "learning_rate": 3.476827821223184e-06, "epoch": 1.9870967741935484, "percentage": 40.53, "elapsed_time": "0:16:55", "remaining_time": "0:24:49", "throughput": "482.48", "total_tokens": 489760}
+{"current_steps": 78, "total_steps": 190, "loss": 0.0626, "learning_rate": 3.436516483539781e-06, "epoch": 2.0129032258064514, "percentage": 41.05, "elapsed_time": "0:17:08", "remaining_time": "0:24:36", "throughput": "482.38", "total_tokens": 496000}
+{"current_steps": 79, "total_steps": 190, "loss": 0.0197, "learning_rate": 3.39591987386325e-06, "epoch": 2.0387096774193547, "percentage": 41.58, "elapsed_time": "0:17:21", "remaining_time": "0:24:23", "throughput": "482.41", "total_tokens": 502384}
+{"current_steps": 80, "total_steps": 190, "loss": 0.0057, "learning_rate": 3.3550503583141726e-06, "epoch": 2.064516129032258, "percentage": 42.11, "elapsed_time": "0:17:34", "remaining_time": "0:24:10", "throughput": "482.65", "total_tokens": 508992}
+{"current_steps": 81, "total_steps": 190, "loss": 0.029, "learning_rate": 3.313920386142892e-06, "epoch": 2.0903225806451613, "percentage": 42.63, "elapsed_time": "0:17:47", "remaining_time": "0:23:56", "throughput": "482.53", "total_tokens": 515216}
+{"current_steps": 82, "total_steps": 190, "loss": 0.0593, "learning_rate": 3.272542485937369e-06, "epoch": 2.1161290322580646, "percentage": 43.16, "elapsed_time": "0:18:00", "remaining_time": "0:23:43", "throughput": "482.74", "total_tokens": 521792}
+{"current_steps": 83, "total_steps": 190, "loss": 0.0455, "learning_rate": 3.230929261806842e-06, "epoch": 2.141935483870968, "percentage": 43.68, "elapsed_time": "0:18:14", "remaining_time": "0:23:30", "throughput": "482.77", "total_tokens": 528176}
+{"current_steps": 84, "total_steps": 190, "loss": 0.0325, "learning_rate": 3.189093389542498e-06, "epoch": 2.167741935483871, "percentage": 44.21, "elapsed_time": "0:18:27", "remaining_time": "0:23:17", "throughput": "482.74", "total_tokens": 534496}
+{"current_steps": 85, "total_steps": 190, "loss": 0.0071, "learning_rate": 3.147047612756302e-06, "epoch": 2.193548387096774, "percentage": 44.74, "elapsed_time": "0:18:40", "remaining_time": "0:23:03", "throughput": "482.90", "total_tokens": 541024}
+{"current_steps": 86, "total_steps": 190, "loss": 0.0336, "learning_rate": 3.1048047389991693e-06, "epoch": 2.2193548387096773, "percentage": 45.26, "elapsed_time": "0:18:53", "remaining_time": "0:22:50", "throughput": "482.86", "total_tokens": 547328}
+{"current_steps": 87, "total_steps": 190, "loss": 0.0389, "learning_rate": 3.062377635859663e-06, "epoch": 2.2451612903225806, "percentage": 45.79, "elapsed_time": "0:19:06", "remaining_time": "0:22:37", "throughput": "482.92", "total_tokens": 553760}
+{"current_steps": 88, "total_steps": 190, "loss": 0.0016, "learning_rate": 3.019779227044398e-06, "epoch": 2.270967741935484, "percentage": 46.32, "elapsed_time": "0:19:19", "remaining_time": "0:22:24", "throughput": "482.87", "total_tokens": 560048}
+{"current_steps": 89, "total_steps": 190, "loss": 0.0625, "learning_rate": 2.9770224884413625e-06, "epoch": 2.296774193548387, "percentage": 46.84, "elapsed_time": "0:19:33", "remaining_time": "0:22:11", "throughput": "483.05", "total_tokens": 566624}
+{"current_steps": 90, "total_steps": 190, "loss": 0.0201, "learning_rate": 2.9341204441673267e-06, "epoch": 2.3225806451612905, "percentage": 47.37, "elapsed_time": "0:19:46", "remaining_time": "0:21:57", "throughput": "482.95", "total_tokens": 572864}
+{"current_steps": 91, "total_steps": 190, "loss": 0.0126, "learning_rate": 2.8910861626005774e-06, "epoch": 2.3483870967741938, "percentage": 47.89, "elapsed_time": "0:19:59", "remaining_time": "0:21:44", "throughput": "482.79", "total_tokens": 579024}
+{"current_steps": 92, "total_steps": 190, "loss": 0.0148, "learning_rate": 2.847932752400164e-06, "epoch": 2.3741935483870966, "percentage": 48.42, "elapsed_time": "0:20:12", "remaining_time": "0:21:31", "throughput": "482.91", "total_tokens": 585536}
+{"current_steps": 93, "total_steps": 190, "loss": 0.014, "learning_rate": 2.804673358512869e-06, "epoch": 2.4, "percentage": 48.95, "elapsed_time": "0:20:25", "remaining_time": "0:21:18", "throughput": "482.83", "total_tokens": 591792}
+{"current_steps": 94, "total_steps": 190, "loss": 0.0096, "learning_rate": 2.761321158169134e-06, "epoch": 2.425806451612903, "percentage": 49.47, "elapsed_time": "0:20:38", "remaining_time": "0:21:05", "throughput": "482.82", "total_tokens": 598144}
+{"current_steps": 95, "total_steps": 190, "loss": 0.0249, "learning_rate": 2.717889356869146e-06, "epoch": 2.4516129032258065, "percentage": 50.0, "elapsed_time": "0:20:52", "remaining_time": "0:20:52", "throughput": "482.82", "total_tokens": 604496}
+{"current_steps": 96, "total_steps": 190, "loss": 0.0358, "learning_rate": 2.6743911843603134e-06, "epoch": 2.47741935483871, "percentage": 50.53, "elapsed_time": "0:21:05", "remaining_time": "0:20:38", "throughput": "482.76", "total_tokens": 610784}
+{"current_steps": 97, "total_steps": 190, "loss": 0.0494, "learning_rate": 2.6308398906073603e-06, "epoch": 2.5032258064516126, "percentage": 51.05, "elapsed_time": "0:21:18", "remaining_time": "0:20:25", "throughput": "482.67", "total_tokens": 617024}
+{"current_steps": 98, "total_steps": 190, "loss": 0.0092, "learning_rate": 2.587248741756253e-06, "epoch": 2.5290322580645164, "percentage": 51.58, "elapsed_time": "0:21:31", "remaining_time": "0:20:12", "throughput": "482.63", "total_tokens": 623312}
+{"current_steps": 99, "total_steps": 190, "loss": 0.0215, "learning_rate": 2.543631016093209e-06, "epoch": 2.554838709677419, "percentage": 52.11, "elapsed_time": "0:21:44", "remaining_time": "0:19:59", "throughput": "482.59", "total_tokens": 629616}
+{"current_steps": 100, "total_steps": 190, "loss": 0.0122, "learning_rate": 2.5e-06, "epoch": 2.5806451612903225, "percentage": 52.63, "elapsed_time": "0:21:57", "remaining_time": "0:19:46", "throughput": "482.73", "total_tokens": 636144}
+{"current_steps": 101, "total_steps": 190, "loss": 0.0296, "learning_rate": 2.4563689839067913e-06, "epoch": 2.606451612903226, "percentage": 53.16, "elapsed_time": "0:22:10", "remaining_time": "0:19:32", "throughput": "482.73", "total_tokens": 642496}
+{"current_steps": 102, "total_steps": 190, "loss": 0.0089, "learning_rate": 2.4127512582437486e-06, "epoch": 2.632258064516129, "percentage": 53.68, "elapsed_time": "0:22:24", "remaining_time": "0:19:19", "throughput": "482.78", "total_tokens": 648912}
+{"current_steps": 103, "total_steps": 190, "loss": 0.0406, "learning_rate": 2.3691601093926406e-06, "epoch": 2.6580645161290324, "percentage": 54.21, "elapsed_time": "0:22:37", "remaining_time": "0:19:06", "throughput": "482.65", "total_tokens": 655088}
+{"current_steps": 104, "total_steps": 190, "loss": 0.0114, "learning_rate": 2.325608815639687e-06, "epoch": 2.6838709677419352, "percentage": 54.74, "elapsed_time": "0:22:50", "remaining_time": "0:18:53", "throughput": "482.82", "total_tokens": 661680}
+{"current_steps": 105, "total_steps": 190, "loss": 0.0396, "learning_rate": 2.2821106431308546e-06, "epoch": 2.709677419354839, "percentage": 55.26, "elapsed_time": "0:23:03", "remaining_time": "0:18:40", "throughput": "482.81", "total_tokens": 668016}
+{"current_steps": 106, "total_steps": 190, "loss": 0.0077, "learning_rate": 2.238678841830867e-06, "epoch": 2.735483870967742, "percentage": 55.79, "elapsed_time": "0:23:16", "remaining_time": "0:18:26", "throughput": "482.67", "total_tokens": 674176}
+{"current_steps": 107, "total_steps": 190, "loss": 0.0044, "learning_rate": 2.195326641487132e-06, "epoch": 2.761290322580645, "percentage": 56.32, "elapsed_time": "0:23:29", "remaining_time": "0:18:13", "throughput": "482.63", "total_tokens": 680464}
+{"current_steps": 108, "total_steps": 190, "loss": 0.0045, "learning_rate": 2.1520672475998374e-06, "epoch": 2.7870967741935484, "percentage": 56.84, "elapsed_time": "0:23:43", "remaining_time": "0:18:00", "throughput": "482.54", "total_tokens": 686688}
+{"current_steps": 109, "total_steps": 190, "loss": 0.0405, "learning_rate": 2.1089138373994226e-06, "epoch": 2.8129032258064517, "percentage": 57.37, "elapsed_time": "0:23:56", "remaining_time": "0:17:47", "throughput": "482.51", "total_tokens": 692992}
+{"current_steps": 110, "total_steps": 190, "loss": 0.0225, "learning_rate": 2.0658795558326745e-06, "epoch": 2.838709677419355, "percentage": 57.89, "elapsed_time": "0:24:09", "remaining_time": "0:17:34", "throughput": "482.55", "total_tokens": 699392}
+{"current_steps": 111, "total_steps": 190, "loss": 0.0415, "learning_rate": 2.022977511558638e-06, "epoch": 2.864516129032258, "percentage": 58.42, "elapsed_time": "0:24:22", "remaining_time": "0:17:20", "throughput": "482.50", "total_tokens": 705680}
+{"current_steps": 112, "total_steps": 190, "loss": 0.0173, "learning_rate": 1.9802207729556023e-06, "epoch": 2.8903225806451616, "percentage": 58.95, "elapsed_time": "0:24:35", "remaining_time": "0:17:07", "throughput": "482.45", "total_tokens": 711952}
+{"current_steps": 113, "total_steps": 190, "loss": 0.0005, "learning_rate": 1.937622364140338e-06, "epoch": 2.9161290322580644, "percentage": 59.47, "elapsed_time": "0:24:48", "remaining_time": "0:16:54", "throughput": "482.38", "total_tokens": 718192}
+{"current_steps": 114, "total_steps": 190, "loss": 0.0306, "learning_rate": 1.895195261000831e-06, "epoch": 2.9419354838709677, "percentage": 60.0, "elapsed_time": "0:25:02", "remaining_time": "0:16:41", "throughput": "482.57", "total_tokens": 724832}
+{"current_steps": 115, "total_steps": 190, "loss": 0.0422, "learning_rate": 1.852952387243698e-06, "epoch": 2.967741935483871, "percentage": 60.53, "elapsed_time": "0:25:15", "remaining_time": "0:16:28", "throughput": "482.66", "total_tokens": 731312}
+{"current_steps": 116, "total_steps": 190, "loss": 0.0472, "learning_rate": 1.8109066104575023e-06, "epoch": 2.9935483870967743, "percentage": 61.05, "elapsed_time": "0:25:28", "remaining_time": "0:16:14", "throughput": "482.55", "total_tokens": 737488}
+{"current_steps": 117, "total_steps": 190, "loss": 0.0259, "learning_rate": 1.7690707381931585e-06, "epoch": 3.0193548387096776, "percentage": 61.58, "elapsed_time": "0:25:41", "remaining_time": "0:16:01", "throughput": "482.50", "total_tokens": 743760}
+{"current_steps": 118, "total_steps": 190, "loss": 0.0029, "learning_rate": 1.7274575140626318e-06, "epoch": 3.0451612903225804, "percentage": 62.11, "elapsed_time": "0:25:54", "remaining_time": "0:15:48", "throughput": "482.46", "total_tokens": 750048}
+{"current_steps": 119, "total_steps": 190, "loss": 0.035, "learning_rate": 1.686079613857109e-06, "epoch": 3.0709677419354837, "percentage": 62.63, "elapsed_time": "0:26:07", "remaining_time": "0:15:35", "throughput": "482.46", "total_tokens": 756400}
+{"current_steps": 120, "total_steps": 190, "loss": 0.0015, "learning_rate": 1.6449496416858285e-06, "epoch": 3.096774193548387, "percentage": 63.16, "elapsed_time": "0:26:20", "remaining_time": "0:15:22", "throughput": "482.26", "total_tokens": 762432}
+{"current_steps": 121, "total_steps": 190, "loss": 0.0006, "learning_rate": 1.6040801261367494e-06, "epoch": 3.1225806451612903, "percentage": 63.68, "elapsed_time": "0:26:34", "remaining_time": "0:15:09", "throughput": "482.20", "total_tokens": 768688}
+{"current_steps": 122, "total_steps": 190, "loss": 0.0143, "learning_rate": 1.56348351646022e-06, "epoch": 3.1483870967741936, "percentage": 64.21, "elapsed_time": "0:26:47", "remaining_time": "0:14:55", "throughput": "482.19", "total_tokens": 775024}
+{"current_steps": 123, "total_steps": 190, "loss": 0.0219, "learning_rate": 1.5231721787768162e-06, "epoch": 3.174193548387097, "percentage": 64.74, "elapsed_time": "0:27:00", "remaining_time": "0:14:42", "throughput": "482.19", "total_tokens": 781360}
+{"current_steps": 124, "total_steps": 190, "loss": 0.0074, "learning_rate": 1.4831583923105e-06, "epoch": 3.2, "percentage": 65.26, "elapsed_time": "0:27:13", "remaining_time": "0:14:29", "throughput": "482.31", "total_tokens": 787888}
+{"current_steps": 125, "total_steps": 190, "loss": 0.0052, "learning_rate": 1.443454345648252e-06, "epoch": 3.225806451612903, "percentage": 65.79, "elapsed_time": "0:27:26", "remaining_time": "0:14:16", "throughput": "482.23", "total_tokens": 794112}
+{"current_steps": 126, "total_steps": 190, "loss": 0.0013, "learning_rate": 1.4040721330273063e-06, "epoch": 3.2516129032258063, "percentage": 66.32, "elapsed_time": "0:27:39", "remaining_time": "0:14:03", "throughput": "482.19", "total_tokens": 800384}
+{"current_steps": 127, "total_steps": 190, "loss": 0.0018, "learning_rate": 1.3650237506511333e-06, "epoch": 3.2774193548387096, "percentage": 66.84, "elapsed_time": "0:27:53", "remaining_time": "0:13:49", "throughput": "482.26", "total_tokens": 806848}
+{"current_steps": 128, "total_steps": 190, "loss": 0.0077, "learning_rate": 1.3263210930352737e-06, "epoch": 3.303225806451613, "percentage": 67.37, "elapsed_time": "0:28:06", "remaining_time": "0:13:36", "throughput": "482.22", "total_tokens": 813136}
+{"current_steps": 129, "total_steps": 190, "loss": 0.0138, "learning_rate": 1.2879759493841577e-06, "epoch": 3.329032258064516, "percentage": 67.89, "elapsed_time": "0:28:19", "remaining_time": "0:13:23", "throughput": "482.24", "total_tokens": 819504}
+{"current_steps": 130, "total_steps": 190, "loss": 0.0102, "learning_rate": 1.2500000000000007e-06, "epoch": 3.3548387096774195, "percentage": 68.42, "elapsed_time": "0:28:32", "remaining_time": "0:13:10", "throughput": "482.29", "total_tokens": 825936}
+{"current_steps": 131, "total_steps": 190, "loss": 0.0067, "learning_rate": 1.2124048127248644e-06, "epoch": 3.3806451612903228, "percentage": 68.95, "elapsed_time": "0:28:45", "remaining_time": "0:12:57", "throughput": "482.41", "total_tokens": 832496}
+{"current_steps": 132, "total_steps": 190, "loss": 0.0056, "learning_rate": 1.1752018394169882e-06, "epoch": 3.4064516129032256, "percentage": 69.47, "elapsed_time": "0:28:58", "remaining_time": "0:12:44", "throughput": "482.43", "total_tokens": 838864}
+{"current_steps": 133, "total_steps": 190, "loss": 0.0066, "learning_rate": 1.1384024124624324e-06, "epoch": 3.432258064516129, "percentage": 70.0, "elapsed_time": "0:29:12", "remaining_time": "0:12:30", "throughput": "482.59", "total_tokens": 845504}
+{"current_steps": 134, "total_steps": 190, "loss": 0.0033, "learning_rate": 1.1020177413231334e-06, "epoch": 3.458064516129032, "percentage": 70.53, "elapsed_time": "0:29:25", "remaining_time": "0:12:17", "throughput": "482.61", "total_tokens": 851888}
+{"current_steps": 135, "total_steps": 190, "loss": 0.0008, "learning_rate": 1.0660589091223854e-06, "epoch": 3.4838709677419355, "percentage": 71.05, "elapsed_time": "0:29:38", "remaining_time": "0:12:04", "throughput": "482.56", "total_tokens": 858144}
+{"current_steps": 136, "total_steps": 190, "loss": 0.0027, "learning_rate": 1.0305368692688175e-06, "epoch": 3.509677419354839, "percentage": 71.58, "elapsed_time": "0:29:51", "remaining_time": "0:11:51", "throughput": "482.56", "total_tokens": 864496}
+{"current_steps": 137, "total_steps": 190, "loss": 0.0021, "learning_rate": 9.95462442119879e-07, "epoch": 3.535483870967742, "percentage": 72.11, "elapsed_time": "0:30:04", "remaining_time": "0:11:38", "throughput": "482.46", "total_tokens": 870672}
+{"current_steps": 138, "total_steps": 190, "loss": 0.0008, "learning_rate": 9.608463116858544e-07, "epoch": 3.5612903225806454, "percentage": 72.63, "elapsed_time": "0:30:17", "remaining_time": "0:11:24", "throughput": "482.42", "total_tokens": 876944}
+{"current_steps": 139, "total_steps": 190, "loss": 0.0051, "learning_rate": 9.266990223754069e-07, "epoch": 3.587096774193548, "percentage": 73.16, "elapsed_time": "0:30:30", "remaining_time": "0:11:11", "throughput": "482.52", "total_tokens": 883488}
+{"current_steps": 140, "total_steps": 190, "loss": 0.0026, "learning_rate": 8.930309757836517e-07, "epoch": 3.6129032258064515, "percentage": 73.68, "elapsed_time": "0:30:44", "remaining_time": "0:10:58", "throughput": "482.52", "total_tokens": 889824}
+{"current_steps": 141, "total_steps": 190, "loss": 0.0041, "learning_rate": 8.598524275237321e-07, "epoch": 3.638709677419355, "percentage": 74.21, "elapsed_time": "0:30:57", "remaining_time": "0:10:45", "throughput": "482.51", "total_tokens": 896176}
+{"current_steps": 142, "total_steps": 190, "loss": 0.023, "learning_rate": 8.271734841028553e-07, "epoch": 3.664516129032258, "percentage": 74.74, "elapsed_time": "0:31:10", "remaining_time": "0:10:32", "throughput": "482.38", "total_tokens": 902272}
+{"current_steps": 143, "total_steps": 190, "loss": 0.0106, "learning_rate": 7.950040998437541e-07, "epoch": 3.6903225806451614, "percentage": 75.26, "elapsed_time": "0:31:23", "remaining_time": "0:10:19", "throughput": "482.32", "total_tokens": 908512}
+{"current_steps": 144, "total_steps": 190, "loss": 0.0238, "learning_rate": 7.633540738525066e-07, "epoch": 3.7161290322580647, "percentage": 75.79, "elapsed_time": "0:31:36", "remaining_time": "0:10:05", "throughput": "482.48", "total_tokens": 915152}
+{"current_steps": 145, "total_steps": 190, "loss": 0.0088, "learning_rate": 7.322330470336314e-07, "epoch": 3.741935483870968, "percentage": 76.32, "elapsed_time": "0:31:49", "remaining_time": "0:09:52", "throughput": "482.51", "total_tokens": 921552}
+{"current_steps": 146, "total_steps": 190, "loss": 0.0391, "learning_rate": 7.016504991533727e-07, "epoch": 3.767741935483871, "percentage": 76.84, "elapsed_time": "0:32:03", "remaining_time": "0:09:39", "throughput": "482.58", "total_tokens": 928048}
+{"current_steps": 147, "total_steps": 190, "loss": 0.0008, "learning_rate": 6.716157459520739e-07, "epoch": 3.793548387096774, "percentage": 77.37, "elapsed_time": "0:32:16", "remaining_time": "0:09:26", "throughput": "482.64", "total_tokens": 934512}
+{"current_steps": 148, "total_steps": 190, "loss": 0.0177, "learning_rate": 6.421379363065142e-07, "epoch": 3.8193548387096774, "percentage": 77.89, "elapsed_time": "0:32:29", "remaining_time": "0:09:13", "throughput": "482.62", "total_tokens": 940816}
+{"current_steps": 149, "total_steps": 190, "loss": 0.0001, "learning_rate": 6.1322604944307e-07, "epoch": 3.8451612903225807, "percentage": 78.42, "elapsed_time": "0:32:42", "remaining_time": "0:09:00", "throughput": "482.53", "total_tokens": 946992}
+{"current_steps": 150, "total_steps": 190, "loss": 0.0002, "learning_rate": 5.848888922025553e-07, "epoch": 3.870967741935484, "percentage": 78.95, "elapsed_time": "0:32:55", "remaining_time": "0:08:46", "throughput": "482.57", "total_tokens": 953424}
+{"current_steps": 151, "total_steps": 190, "loss": 0.0044, "learning_rate": 5.571350963575728e-07, "epoch": 3.896774193548387, "percentage": 79.47, "elapsed_time": "0:33:08", "remaining_time": "0:08:33", "throughput": "482.50", "total_tokens": 959616}
+{"current_steps": 152, "total_steps": 190, "loss": 0.0015, "learning_rate": 5.299731159831953e-07, "epoch": 3.9225806451612906, "percentage": 80.0, "elapsed_time": "0:33:22", "remaining_time": "0:08:20", "throughput": "482.56", "total_tokens": 966096}
+{"current_steps": 153, "total_steps": 190, "loss": 0.0003, "learning_rate": 5.034112248817685e-07, "epoch": 3.9483870967741934, "percentage": 80.53, "elapsed_time": "0:33:35", "remaining_time": "0:08:07", "throughput": "482.53", "total_tokens": 972368}
+{"current_steps": 154, "total_steps": 190, "loss": 0.0361, "learning_rate": 4.774575140626317e-07, "epoch": 3.9741935483870967, "percentage": 81.05, "elapsed_time": "0:33:48", "remaining_time": "0:07:54", "throughput": "482.59", "total_tokens": 978848}
+{"current_steps": 155, "total_steps": 190, "loss": 0.0005, "learning_rate": 4.5211988927752026e-07, "epoch": 4.0, "percentage": 81.58, "elapsed_time": "0:34:01", "remaining_time": "0:07:40", "throughput": "482.75", "total_tokens": 985520}
+{"current_steps": 156, "total_steps": 190, "loss": 0.0022, "learning_rate": 4.27406068612396e-07, "epoch": 4.025806451612903, "percentage": 82.11, "elapsed_time": "0:34:14", "remaining_time": "0:07:27", "throughput": "482.76", "total_tokens": 991904}
+{"current_steps": 157, "total_steps": 190, "loss": 0.0212, "learning_rate": 4.033235801364402e-07, "epoch": 4.051612903225807, "percentage": 82.63, "elapsed_time": "0:34:27", "remaining_time": "0:07:14", "throughput": "482.78", "total_tokens": 998288}
+{"current_steps": 158, "total_steps": 190, "loss": 0.0003, "learning_rate": 3.798797596089351e-07, "epoch": 4.077419354838709, "percentage": 83.16, "elapsed_time": "0:34:40", "remaining_time": "0:07:01", "throughput": "482.68", "total_tokens": 1004432}
+{"current_steps": 159, "total_steps": 190, "loss": 0.0047, "learning_rate": 3.5708174824471947e-07, "epoch": 4.103225806451613, "percentage": 83.68, "elapsed_time": "0:34:54", "remaining_time": "0:06:48", "throughput": "482.59", "total_tokens": 1010608}
+{"current_steps": 160, "total_steps": 190, "loss": 0.0014, "learning_rate": 3.3493649053890325e-07, "epoch": 4.129032258064516, "percentage": 84.21, "elapsed_time": "0:35:07", "remaining_time": "0:06:35", "throughput": "482.54", "total_tokens": 1016848}
+{"current_steps": 161, "total_steps": 190, "loss": 0.0006, "learning_rate": 3.134507321515107e-07, "epoch": 4.15483870967742, "percentage": 84.74, "elapsed_time": "0:35:20", "remaining_time": "0:06:21", "throughput": "482.62", "total_tokens": 1023360}
+{"current_steps": 162, "total_steps": 190, "loss": 0.0003, "learning_rate": 2.9263101785268253e-07, "epoch": 4.180645161290323, "percentage": 85.26, "elapsed_time": "0:35:33", "remaining_time": "0:06:08", "throughput": "482.66", "total_tokens": 1029808}
+{"current_steps": 163, "total_steps": 190, "loss": 0.0021, "learning_rate": 2.7248368952908055e-07, "epoch": 4.2064516129032254, "percentage": 85.79, "elapsed_time": "0:35:46", "remaining_time": "0:05:55", "throughput": "482.63", "total_tokens": 1036080}
+{"current_steps": 164, "total_steps": 190, "loss": 0.0001, "learning_rate": 2.53014884252083e-07, "epoch": 4.232258064516129, "percentage": 86.32, "elapsed_time": "0:35:59", "remaining_time": "0:05:42", "throughput": "482.54", "total_tokens": 1042240}
+{"current_steps": 165, "total_steps": 190, "loss": 0.0007, "learning_rate": 2.3423053240837518e-07, "epoch": 4.258064516129032, "percentage": 86.84, "elapsed_time": "0:36:13", "remaining_time": "0:05:29", "throughput": "482.58", "total_tokens": 1048672}
+{"current_steps": 166, "total_steps": 190, "loss": 0.0013, "learning_rate": 2.1613635589349756e-07, "epoch": 4.283870967741936, "percentage": 87.37, "elapsed_time": "0:36:26", "remaining_time": "0:05:16", "throughput": "482.49", "total_tokens": 1054832}
+{"current_steps": 167, "total_steps": 190, "loss": 0.0002, "learning_rate": 1.9873786636889908e-07, "epoch": 4.309677419354839, "percentage": 87.89, "elapsed_time": "0:36:39", "remaining_time": "0:05:02", "throughput": "482.55", "total_tokens": 1061312}
+{"current_steps": 168, "total_steps": 190, "loss": 0.0011, "learning_rate": 1.8204036358303173e-07, "epoch": 4.335483870967742, "percentage": 88.42, "elapsed_time": "0:36:52", "remaining_time": "0:04:49", "throughput": "482.47", "total_tokens": 1067488}
+{"current_steps": 169, "total_steps": 190, "loss": 0.0001, "learning_rate": 1.6604893375699594e-07, "epoch": 4.361290322580645, "percentage": 88.95, "elapsed_time": "0:37:05", "remaining_time": "0:04:36", "throughput": "482.39", "total_tokens": 1073648}
+{"current_steps": 170, "total_steps": 190, "loss": 0.0006, "learning_rate": 1.507684480352292e-07, "epoch": 4.387096774193548, "percentage": 89.47, "elapsed_time": "0:37:18", "remaining_time": "0:04:23", "throughput": "482.46", "total_tokens": 1080160}
+{"current_steps": 171, "total_steps": 190, "loss": 0.0003, "learning_rate": 1.362035610017079e-07, "epoch": 4.412903225806452, "percentage": 90.0, "elapsed_time": "0:37:32", "remaining_time": "0:04:10", "throughput": "482.60", "total_tokens": 1086832}
+{"current_steps": 172, "total_steps": 190, "loss": 0.0002, "learning_rate": 1.223587092621162e-07, "epoch": 4.438709677419355, "percentage": 90.53, "elapsed_time": "0:37:45", "remaining_time": "0:03:57", "throughput": "482.60", "total_tokens": 1093184}
+{"current_steps": 173, "total_steps": 190, "loss": 0.0027, "learning_rate": 1.0923811009241142e-07, "epoch": 4.464516129032258, "percentage": 91.05, "elapsed_time": "0:37:58", "remaining_time": "0:03:43", "throughput": "482.69", "total_tokens": 1099728}
+{"current_steps": 174, "total_steps": 190, "loss": 0.0002, "learning_rate": 9.684576015420277e-08, "epoch": 4.490322580645161, "percentage": 91.58, "elapsed_time": "0:38:11", "remaining_time": "0:03:30", "throughput": "482.67", "total_tokens": 1106032}
+{"current_steps": 175, "total_steps": 190, "loss": 0.0001, "learning_rate": 8.518543427732951e-08, "epoch": 4.516129032258064, "percentage": 92.11, "elapsed_time": "0:38:24", "remaining_time": "0:03:17", "throughput": "482.72", "total_tokens": 1112496}
+{"current_steps": 176, "total_steps": 190, "loss": 0.0109, "learning_rate": 7.426068431000883e-08, "epoch": 4.541935483870968, "percentage": 92.63, "elapsed_time": "0:38:37", "remaining_time": "0:03:04", "throughput": "482.85", "total_tokens": 1119152}
+{"current_steps": 177, "total_steps": 190, "loss": 0.0039, "learning_rate": 6.407483803691216e-08, "epoch": 4.567741935483871, "percentage": 93.16, "elapsed_time": "0:38:50", "remaining_time": "0:02:51", "throughput": "482.78", "total_tokens": 1125360}
+{"current_steps": 178, "total_steps": 190, "loss": 0.0026, "learning_rate": 5.463099816548578e-08, "epoch": 4.593548387096774, "percentage": 93.68, "elapsed_time": "0:39:04", "remaining_time": "0:02:38", "throughput": "482.83", "total_tokens": 1131824}
+{"current_steps": 179, "total_steps": 190, "loss": 0.0002, "learning_rate": 4.593204138084006e-08, "epoch": 4.619354838709677, "percentage": 94.21, "elapsed_time": "0:39:17", "remaining_time": "0:02:24", "throughput": "482.85", "total_tokens": 1138224}
+{"current_steps": 180, "total_steps": 190, "loss": 0.0044, "learning_rate": 3.798061746947995e-08, "epoch": 4.645161290322581, "percentage": 94.74, "elapsed_time": "0:39:30", "remaining_time": "0:02:11", "throughput": "482.83", "total_tokens": 1144528}
+{"current_steps": 181, "total_steps": 190, "loss": 0.0001, "learning_rate": 3.077914851215585e-08, "epoch": 4.670967741935484, "percentage": 95.26, "elapsed_time": "0:39:43", "remaining_time": "0:01:58", "throughput": "482.83", "total_tokens": 1150880}
+{"current_steps": 182, "total_steps": 190, "loss": 0.0103, "learning_rate": 2.4329828146074096e-08, "epoch": 4.6967741935483875, "percentage": 95.79, "elapsed_time": "0:39:56", "remaining_time": "0:01:45", "throughput": "482.82", "total_tokens": 1157184}
+{"current_steps": 183, "total_steps": 190, "loss": 0.0002, "learning_rate": 1.8634620896695044e-08, "epoch": 4.72258064516129, "percentage": 96.32, "elapsed_time": "0:40:09", "remaining_time": "0:01:32", "throughput": "482.82", "total_tokens": 1163536}
+{"current_steps": 184, "total_steps": 190, "loss": 0.0038, "learning_rate": 1.3695261579316776e-08, "epoch": 4.748387096774193, "percentage": 96.84, "elapsed_time": "0:40:23", "remaining_time": "0:01:19", "throughput": "482.82", "total_tokens": 1169888}
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