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_2024-07-30-02-00-04_llama3.1_truthqa_bench_1
+  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_2024-07-30-02-00-04_llama3.1_truthqa_bench_1
+
+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 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": 1208400,
+    "total_flos": 5.441370708980531e+16,
+    "train_loss": 0.4588010550536494,
+    "train_runtime": 2569.9216,
+    "train_samples_per_second": 9.666,
+    "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 sha256:2df0deb3faa995dd55cfc222c85d9a98c6edb7a841846f324a172f36bdb0127e
+size 6712

checkpoint-190/zero_to_fp32.py

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+#!/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,
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running_log.txt

@@ -0,0 +1,665 @@
+07/30/2024 02:01:43 - INFO - llamafactory.hparams.parser - Process rank: 2, device: cuda:2, n_gpu: 1, distributed training: True, compute dtype: torch.bfloat16
+
+[INFO|parser.py:344] 2024-07-30 02:01:43,215 >> Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: True, compute dtype: torch.bfloat16
+
+07/30/2024 02:01:43 - INFO - llamafactory.hparams.parser - Process rank: 1, device: cuda:1, n_gpu: 1, distributed training: True, compute dtype: torch.bfloat16
+
+07/30/2024 02:01:43 - INFO - llamafactory.hparams.parser - Process rank: 7, device: cuda:7, n_gpu: 1, distributed training: True, compute dtype: torch.bfloat16
+
+07/30/2024 02:01:43 - INFO - llamafactory.hparams.parser - Process rank: 5, device: cuda:5, n_gpu: 1, distributed training: True, compute dtype: torch.bfloat16
+
+07/30/2024 02:01:43 - INFO - llamafactory.hparams.parser - Process rank: 3, device: cuda:3, n_gpu: 1, distributed training: True, compute dtype: torch.bfloat16
+
+07/30/2024 02:01:43 - INFO - llamafactory.hparams.parser - Process rank: 4, device: cuda:4, n_gpu: 1, distributed training: True, compute dtype: torch.bfloat16
+
+[INFO|tokenization_utils_base.py:2289] 2024-07-30 02:01:43,699 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--meta-llama--Meta-Llama-3.1-8B-Instruct/snapshots/b2a4d0f33b41fcd59a6d31662cc63b8d53367e1e/tokenizer.json
+
+07/30/2024 02:01:43 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
+
+07/30/2024 02:01:43 - INFO - llamafactory.data.template - Add pad token: <|eot_id|>
+
+07/30/2024 02:01:43 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
+
+07/30/2024 02:01:43 - INFO - llamafactory.data.template - Add pad token: <|eot_id|>
+
+07/30/2024 02:01:43 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
+
+07/30/2024 02:01:43 - INFO - llamafactory.data.template - Add pad token: <|eot_id|>
+
+07/30/2024 02:01:43 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
+
+07/30/2024 02:01:43 - INFO - llamafactory.data.template - Add pad token: <|eot_id|>
+
+07/30/2024 02:01:43 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
+
+07/30/2024 02:01:43 - INFO - llamafactory.data.template - Add pad token: <|eot_id|>
+
+07/30/2024 02:01:43 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
+
+07/30/2024 02:01:43 - INFO - llamafactory.data.template - Add pad token: <|eot_id|>
+
+[INFO|tokenization_utils_base.py:2289] 2024-07-30 02:01:43,699 >> loading file added_tokens.json from cache at None
+
+[INFO|tokenization_utils_base.py:2289] 2024-07-30 02:01:43,700 >> 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 02:01:43,700 >> 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 02:01:43,995 >> 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 02:01:43,996 >> Replace eos token: <|eot_id|>
+
+[INFO|template.py:372] 2024-07-30 02:01:43,996 >> Add pad token: <|eot_id|>
+
+[INFO|loader.py:52] 2024-07-30 02:01:43,997 >> Loading dataset 0716_truthfulqa_benchmark_train.json...
+
+07/30/2024 02:01:44 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
+
+07/30/2024 02:01:44 - INFO - llamafactory.data.template - Add pad token: <|eot_id|>
+
+07/30/2024 02:01:45 - INFO - llamafactory.data.loader - Loading dataset 0716_truthfulqa_benchmark_train.json...
+
+07/30/2024 02:01:45 - INFO - llamafactory.data.loader - Loading dataset 0716_truthfulqa_benchmark_train.json...
+
+07/30/2024 02:01:45 - INFO - llamafactory.data.loader - Loading dataset 0716_truthfulqa_benchmark_train.json...
+
+07/30/2024 02:01:45 - INFO - llamafactory.data.loader - Loading dataset 0716_truthfulqa_benchmark_train.json...
+
+07/30/2024 02:01:45 - INFO - llamafactory.data.loader - Loading dataset 0716_truthfulqa_benchmark_train.json...
+
+07/30/2024 02:01:45 - INFO - llamafactory.data.loader - Loading dataset 0716_truthfulqa_benchmark_train.json...
+
+07/30/2024 02:01:45 - INFO - llamafactory.data.loader - Loading dataset 0716_truthfulqa_benchmark_train.json...
+
+[INFO|configuration_utils.py:733] 2024-07-30 02:01:49,540 >> 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 02:01:49,542 >> 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 02:01:49,591 >> 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 02:01:49,593 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16.
+
+[INFO|configuration_utils.py:1038] 2024-07-30 02:01:49,596 >> Generate config GenerationConfig {
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ]
+}
+
+
+[INFO|modeling_utils.py:4463] 2024-07-30 02:01:53,393 >> All model checkpoint weights were used when initializing LlamaForCausalLM.
+
+
+[INFO|modeling_utils.py:4471] 2024-07-30 02:01:53,393 >> 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 02:01:53,572 >> 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 02:01:53,573 >> 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 02:01:53,580 >> Gradient checkpointing enabled.
+
+[INFO|attention.py:84] 2024-07-30 02:01:53,580 >> Using torch SDPA for faster training and inference.
+
+[INFO|adapter.py:302] 2024-07-30 02:01:53,580 >> Upcasting trainable params to float32.
+
+[INFO|adapter.py:48] 2024-07-30 02:01:53,580 >> Fine-tuning method: Full
+
+[INFO|loader.py:196] 2024-07-30 02:01:53,625 >> trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
+
+[INFO|trainer.py:648] 2024-07-30 02:01:53,631 >> Using auto half precision backend
+
+07/30/2024 02:01:53 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
+
+07/30/2024 02:01:53 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
+
+07/30/2024 02:01:53 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
+
+07/30/2024 02:01:53 - INFO - llamafactory.model.adapter - Fine-tuning method: Full
+
+07/30/2024 02:01:53 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.adapter - Fine-tuning method: Full
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.adapter - Fine-tuning method: Full
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.adapter - Fine-tuning method: Full
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.adapter - Fine-tuning method: Full
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.adapter - Fine-tuning method: Full
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.loader - trainable params: 8,030,261,248 || all params: 8,030,261,248 || trainable%: 100.0000
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
+
+07/30/2024 02:01:54 - INFO - llamafactory.model.adapter - Fine-tuning method: Full
+
+07/30/2024 02:01:54 - 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 02:02:17,907 >> ***** Running training *****
+
+[INFO|trainer.py:2135] 2024-07-30 02:02:17,907 >>   Num examples = 4,968
+
+[INFO|trainer.py:2136] 2024-07-30 02:02:17,907 >>   Num Epochs = 5
+
+[INFO|trainer.py:2137] 2024-07-30 02:02:17,907 >>   Instantaneous batch size per device = 2
+
+[INFO|trainer.py:2140] 2024-07-30 02:02:17,907 >>   Total train batch size (w. parallel, distributed & accumulation) = 128
+
+[INFO|trainer.py:2141] 2024-07-30 02:02:17,907 >>   Gradient Accumulation steps = 8
+
+[INFO|trainer.py:2142] 2024-07-30 02:02:17,907 >>   Total optimization steps = 190
+
+[INFO|trainer.py:2143] 2024-07-30 02:02:17,909 >>   Number of trainable parameters = 8,030,261,248
+
+[INFO|callbacks.py:310] 2024-07-30 02:02:32,126 >> {'loss': 12.6483, 'learning_rate': 5.0000e-07, 'epoch': 0.03, 'throughput': 458.09}
+
+[INFO|callbacks.py:310] 2024-07-30 02:02:45,352 >> {'loss': 12.2961, 'learning_rate': 1.0000e-06, 'epoch': 0.05, 'throughput': 468.19}
+
+[INFO|callbacks.py:310] 2024-07-30 02:02:58,589 >> {'loss': 11.9707, 'learning_rate': 1.5000e-06, 'epoch': 0.08, 'throughput': 473.17}
+
+[INFO|callbacks.py:310] 2024-07-30 02:03:11,796 >> {'loss': 11.1284, 'learning_rate': 2.0000e-06, 'epoch': 0.10, 'throughput': 470.33}
+
+[INFO|callbacks.py:310] 2024-07-30 02:03:25,006 >> {'loss': 8.3234, 'learning_rate': 2.5000e-06, 'epoch': 0.13, 'throughput': 476.45}
+
+[INFO|callbacks.py:310] 2024-07-30 02:03:38,195 >> {'loss': 5.8729, 'learning_rate': 3.0000e-06, 'epoch': 0.15, 'throughput': 475.31}
+
+[INFO|callbacks.py:310] 2024-07-30 02:03:51,404 >> {'loss': 4.8266, 'learning_rate': 3.5000e-06, 'epoch': 0.18, 'throughput': 472.16}
+
+[INFO|callbacks.py:310] 2024-07-30 02:04:04,608 >> {'loss': 2.5017, 'learning_rate': 4.0000e-06, 'epoch': 0.21, 'throughput': 472.66}
+
+[INFO|callbacks.py:310] 2024-07-30 02:04:17,826 >> {'loss': 0.5637, 'learning_rate': 4.5000e-06, 'epoch': 0.23, 'throughput': 474.73}
+
+[INFO|callbacks.py:310] 2024-07-30 02:04:31,021 >> {'loss': 0.2757, 'learning_rate': 5.0000e-06, 'epoch': 0.26, 'throughput': 475.87}
+
+[INFO|callbacks.py:310] 2024-07-30 02:04:44,208 >> {'loss': 1.4131, 'learning_rate': 4.9996e-06, 'epoch': 0.28, 'throughput': 475.52}
+
+[INFO|callbacks.py:310] 2024-07-30 02:04:57,404 >> {'loss': 0.2805, 'learning_rate': 4.9985e-06, 'epoch': 0.31, 'throughput': 475.40}
+
+[INFO|callbacks.py:310] 2024-07-30 02:05:10,617 >> {'loss': 1.1385, 'learning_rate': 4.9966e-06, 'epoch': 0.33, 'throughput': 475.72}
+
+[INFO|callbacks.py:310] 2024-07-30 02:05:23,802 >> {'loss': 1.5128, 'learning_rate': 4.9939e-06, 'epoch': 0.36, 'throughput': 476.75}
+
+[INFO|callbacks.py:310] 2024-07-30 02:05:37,011 >> {'loss': 0.2268, 'learning_rate': 4.9905e-06, 'epoch': 0.39, 'throughput': 476.14}
+
+[INFO|callbacks.py:310] 2024-07-30 02:05:50,209 >> {'loss': 1.0743, 'learning_rate': 4.9863e-06, 'epoch': 0.41, 'throughput': 477.44}
+
+[INFO|callbacks.py:310] 2024-07-30 02:06:03,432 >> {'loss': 0.5564, 'learning_rate': 4.9814e-06, 'epoch': 0.44, 'throughput': 476.62}
+
+[INFO|callbacks.py:310] 2024-07-30 02:06:16,606 >> {'loss': 1.0090, 'learning_rate': 4.9757e-06, 'epoch': 0.46, 'throughput': 477.26}
+
+[INFO|callbacks.py:310] 2024-07-30 02:06:29,795 >> {'loss': 0.7292, 'learning_rate': 4.9692e-06, 'epoch': 0.49, 'throughput': 477.81}
+
+[INFO|callbacks.py:310] 2024-07-30 02:06:42,989 >> {'loss': 0.3204, 'learning_rate': 4.9620e-06, 'epoch': 0.51, 'throughput': 478.65}
+
+[INFO|callbacks.py:310] 2024-07-30 02:06:56,204 >> {'loss': 0.2741, 'learning_rate': 4.9541e-06, 'epoch': 0.54, 'throughput': 479.15}
+
+[INFO|callbacks.py:310] 2024-07-30 02:07:09,386 >> {'loss': 0.1732, 'learning_rate': 4.9454e-06, 'epoch': 0.57, 'throughput': 479.44}
+
+[INFO|callbacks.py:310] 2024-07-30 02:07:22,590 >> {'loss': 0.4089, 'learning_rate': 4.9359e-06, 'epoch': 0.59, 'throughput': 479.66}
+
+[INFO|callbacks.py:310] 2024-07-30 02:07:35,787 >> {'loss': 0.4201, 'learning_rate': 4.9257e-06, 'epoch': 0.62, 'throughput': 478.93}
+
+[INFO|callbacks.py:310] 2024-07-30 02:07:48,982 >> {'loss': 0.1772, 'learning_rate': 4.9148e-06, 'epoch': 0.64, 'throughput': 478.78}
+
+[INFO|callbacks.py:310] 2024-07-30 02:08:02,171 >> {'loss': 0.5611, 'learning_rate': 4.9032e-06, 'epoch': 0.67, 'throughput': 478.66}
+
+[INFO|callbacks.py:310] 2024-07-30 02:08:15,354 >> {'loss': 0.4709, 'learning_rate': 4.8908e-06, 'epoch': 0.69, 'throughput': 479.27}
+
+[INFO|callbacks.py:310] 2024-07-30 02:08:28,552 >> {'loss': 0.3355, 'learning_rate': 4.8776e-06, 'epoch': 0.72, 'throughput': 479.86}
+
+[INFO|callbacks.py:310] 2024-07-30 02:08:41,745 >> {'loss': 0.1259, 'learning_rate': 4.8638e-06, 'epoch': 0.75, 'throughput': 480.33}
+
+[INFO|callbacks.py:310] 2024-07-30 02:08:54,952 >> {'loss': 0.1584, 'learning_rate': 4.8492e-06, 'epoch': 0.77, 'throughput': 480.88}
+
+[INFO|callbacks.py:310] 2024-07-30 02:09:08,155 >> {'loss': 0.1765, 'learning_rate': 4.8340e-06, 'epoch': 0.80, 'throughput': 480.77}
+
+[INFO|callbacks.py:310] 2024-07-30 02:09:21,349 >> {'loss': 0.1372, 'learning_rate': 4.8180e-06, 'epoch': 0.82, 'throughput': 480.79}
+
+[INFO|callbacks.py:310] 2024-07-30 02:09:34,545 >> {'loss': 0.1321, 'learning_rate': 4.8013e-06, 'epoch': 0.85, 'throughput': 481.24}
+
+[INFO|callbacks.py:310] 2024-07-30 02:09:47,741 >> {'loss': 0.2427, 'learning_rate': 4.7839e-06, 'epoch': 0.87, 'throughput': 481.32}
+
+[INFO|callbacks.py:310] 2024-07-30 02:10:00,942 >> {'loss': 0.2354, 'learning_rate': 4.7658e-06, 'epoch': 0.90, 'throughput': 481.45}
+
+[INFO|callbacks.py:310] 2024-07-30 02:10:14,134 >> {'loss': 0.0977, 'learning_rate': 4.7470e-06, 'epoch': 0.93, 'throughput': 481.35}
+
+[INFO|callbacks.py:310] 2024-07-30 02:10:27,351 >> {'loss': 0.1405, 'learning_rate': 4.7275e-06, 'epoch': 0.95, 'throughput': 481.53}
+
+[INFO|callbacks.py:310] 2024-07-30 02:10:40,545 >> {'loss': 0.2396, 'learning_rate': 4.7074e-06, 'epoch': 0.98, 'throughput': 481.84}
+
+[INFO|callbacks.py:310] 2024-07-30 02:10:53,741 >> {'loss': 0.1272, 'learning_rate': 4.6865e-06, 'epoch': 1.00, 'throughput': 482.08}
+
+[INFO|callbacks.py:310] 2024-07-30 02:11:06,936 >> {'loss': 0.0815, 'learning_rate': 4.6651e-06, 'epoch': 1.03, 'throughput': 482.15}
+
+[INFO|callbacks.py:310] 2024-07-30 02:11:20,135 >> {'loss': 0.0771, 'learning_rate': 4.6429e-06, 'epoch': 1.05, 'throughput': 482.43}
+
+[INFO|callbacks.py:310] 2024-07-30 02:11:33,315 >> {'loss': 0.0821, 'learning_rate': 4.6201e-06, 'epoch': 1.08, 'throughput': 482.59}
+
+[INFO|callbacks.py:310] 2024-07-30 02:11:46,521 >> {'loss': 0.0597, 'learning_rate': 4.5967e-06, 'epoch': 1.11, 'throughput': 482.86}
+
+[INFO|callbacks.py:310] 2024-07-30 02:11:59,700 >> {'loss': 0.0356, 'learning_rate': 4.5726e-06, 'epoch': 1.13, 'throughput': 482.92}
+
+[INFO|callbacks.py:310] 2024-07-30 02:12:12,896 >> {'loss': 0.0895, 'learning_rate': 4.5479e-06, 'epoch': 1.16, 'throughput': 482.54}
+
+[INFO|callbacks.py:310] 2024-07-30 02:12:26,093 >> {'loss': 0.0402, 'learning_rate': 4.5225e-06, 'epoch': 1.18, 'throughput': 482.62}
+
+[INFO|callbacks.py:310] 2024-07-30 02:12:39,286 >> {'loss': 0.0659, 'learning_rate': 4.4966e-06, 'epoch': 1.21, 'throughput': 482.70}
+
+[INFO|callbacks.py:310] 2024-07-30 02:12:52,488 >> {'loss': 0.0910, 'learning_rate': 4.4700e-06, 'epoch': 1.23, 'throughput': 482.11}
+
+[INFO|callbacks.py:310] 2024-07-30 02:13:05,678 >> {'loss': 0.0380, 'learning_rate': 4.4429e-06, 'epoch': 1.26, 'throughput': 482.47}
+
+[INFO|callbacks.py:310] 2024-07-30 02:13:18,858 >> {'loss': 0.0998, 'learning_rate': 4.4151e-06, 'epoch': 1.29, 'throughput': 482.27}
+
+[INFO|callbacks.py:310] 2024-07-30 02:13:32,052 >> {'loss': 0.1125, 'learning_rate': 4.3868e-06, 'epoch': 1.31, 'throughput': 482.22}
+
+[INFO|callbacks.py:310] 2024-07-30 02:13:45,252 >> {'loss': 0.0330, 'learning_rate': 4.3579e-06, 'epoch': 1.34, 'throughput': 482.18}
+
+[INFO|callbacks.py:310] 2024-07-30 02:13:58,458 >> {'loss': 0.0688, 'learning_rate': 4.3284e-06, 'epoch': 1.36, 'throughput': 482.16}
+
+[INFO|callbacks.py:310] 2024-07-30 02:14:11,657 >> {'loss': 0.0433, 'learning_rate': 4.2983e-06, 'epoch': 1.39, 'throughput': 481.96}
+
+[INFO|callbacks.py:310] 2024-07-30 02:14:24,864 >> {'loss': 0.0116, 'learning_rate': 4.2678e-06, 'epoch': 1.41, 'throughput': 482.19}
+
+[INFO|callbacks.py:310] 2024-07-30 02:14:38,054 >> {'loss': 0.0634, 'learning_rate': 4.2366e-06, 'epoch': 1.44, 'throughput': 482.20}
+
+[INFO|callbacks.py:310] 2024-07-30 02:14:51,257 >> {'loss': 0.0729, 'learning_rate': 4.2050e-06, 'epoch': 1.47, 'throughput': 482.35}
+
+[INFO|callbacks.py:310] 2024-07-30 02:15:04,437 >> {'loss': 0.1315, 'learning_rate': 4.1728e-06, 'epoch': 1.49, 'throughput': 482.28}
+
+[INFO|callbacks.py:310] 2024-07-30 02:15:17,637 >> {'loss': 0.0500, 'learning_rate': 4.1401e-06, 'epoch': 1.52, 'throughput': 482.10}
+
+[INFO|callbacks.py:310] 2024-07-30 02:15:30,845 >> {'loss': 0.0838, 'learning_rate': 4.1070e-06, 'epoch': 1.54, 'throughput': 482.14}
+
+[INFO|callbacks.py:310] 2024-07-30 02:15:44,056 >> {'loss': 0.0577, 'learning_rate': 4.0733e-06, 'epoch': 1.57, 'throughput': 482.16}
+
+[INFO|callbacks.py:310] 2024-07-30 02:15:57,241 >> {'loss': 0.0465, 'learning_rate': 4.0392e-06, 'epoch': 1.59, 'throughput': 482.29}
+
+[INFO|callbacks.py:310] 2024-07-30 02:16:10,432 >> {'loss': 0.0497, 'learning_rate': 4.0045e-06, 'epoch': 1.62, 'throughput': 482.54}
+
+[INFO|callbacks.py:310] 2024-07-30 02:16:23,621 >> {'loss': 0.0559, 'learning_rate': 3.9695e-06, 'epoch': 1.65, 'throughput': 482.32}
+
+[INFO|callbacks.py:310] 2024-07-30 02:16:36,808 >> {'loss': 0.0394, 'learning_rate': 3.9339e-06, 'epoch': 1.67, 'throughput': 482.29}
+
+[INFO|callbacks.py:310] 2024-07-30 02:16:49,989 >> {'loss': 0.0737, 'learning_rate': 3.8980e-06, 'epoch': 1.70, 'throughput': 482.12}
+
+[INFO|callbacks.py:310] 2024-07-30 02:17:03,202 >> {'loss': 0.0466, 'learning_rate': 3.8616e-06, 'epoch': 1.72, 'throughput': 482.08}
+
+[INFO|callbacks.py:310] 2024-07-30 02:17:16,390 >> {'loss': 0.0619, 'learning_rate': 3.8248e-06, 'epoch': 1.75, 'throughput': 481.72}
+
+[INFO|callbacks.py:310] 2024-07-30 02:17:29,587 >> {'loss': 0.0749, 'learning_rate': 3.7876e-06, 'epoch': 1.77, 'throughput': 481.78}
+
+[INFO|callbacks.py:310] 2024-07-30 02:17:42,784 >> {'loss': 0.0328, 'learning_rate': 3.7500e-06, 'epoch': 1.80, 'throughput': 482.00}
+
+[INFO|callbacks.py:310] 2024-07-30 02:17:55,968 >> {'loss': 0.0896, 'learning_rate': 3.7120e-06, 'epoch': 1.83, 'throughput': 482.27}
+
+[INFO|callbacks.py:310] 2024-07-30 02:18:09,156 >> {'loss': 0.0489, 'learning_rate': 3.6737e-06, 'epoch': 1.85, 'throughput': 482.03}
+
+[INFO|callbacks.py:310] 2024-07-30 02:18:22,353 >> {'loss': 0.0305, 'learning_rate': 3.6350e-06, 'epoch': 1.88, 'throughput': 482.12}
+
+[INFO|callbacks.py:310] 2024-07-30 02:18:35,561 >> {'loss': 0.0547, 'learning_rate': 3.5959e-06, 'epoch': 1.90, 'throughput': 482.22}
+
+[INFO|callbacks.py:310] 2024-07-30 02:18:48,783 >> {'loss': 0.0554, 'learning_rate': 3.5565e-06, 'epoch': 1.93, 'throughput': 482.18}
+
+[INFO|callbacks.py:310] 2024-07-30 02:19:01,977 >> {'loss': 0.1169, 'learning_rate': 3.5168e-06, 'epoch': 1.95, 'throughput': 482.14}
+
+[INFO|callbacks.py:310] 2024-07-30 02:19:15,182 >> {'loss': 0.0587, 'learning_rate': 3.4768e-06, 'epoch': 1.98, 'throughput': 481.85}
+
+[INFO|callbacks.py:310] 2024-07-30 02:19:28,367 >> {'loss': 0.0624, 'learning_rate': 3.4365e-06, 'epoch': 2.01, 'throughput': 481.99}
+
+[INFO|callbacks.py:310] 2024-07-30 02:19:41,570 >> {'loss': 0.0152, 'learning_rate': 3.3959e-06, 'epoch': 2.03, 'throughput': 482.04}
+
+[INFO|callbacks.py:310] 2024-07-30 02:19:54,767 >> {'loss': 0.0224, 'learning_rate': 3.3551e-06, 'epoch': 2.06, 'throughput': 482.06}
+
+[INFO|callbacks.py:310] 2024-07-30 02:20:07,979 >> {'loss': 0.0084, 'learning_rate': 3.3139e-06, 'epoch': 2.08, 'throughput': 481.96}
+
+[INFO|callbacks.py:310] 2024-07-30 02:20:21,187 >> {'loss': 0.0293, 'learning_rate': 3.2725e-06, 'epoch': 2.11, 'throughput': 481.78}
+
+[INFO|callbacks.py:310] 2024-07-30 02:20:34,397 >> {'loss': 0.0167, 'learning_rate': 3.2309e-06, 'epoch': 2.14, 'throughput': 481.49}
+
+[INFO|callbacks.py:310] 2024-07-30 02:20:47,579 >> {'loss': 0.0094, 'learning_rate': 3.1891e-06, 'epoch': 2.16, 'throughput': 481.79}
+
+[INFO|callbacks.py:310] 2024-07-30 02:21:00,789 >> {'loss': 0.0551, 'learning_rate': 3.1470e-06, 'epoch': 2.19, 'throughput': 481.95}
+
+[INFO|callbacks.py:310] 2024-07-30 02:21:13,980 >> {'loss': 0.0684, 'learning_rate': 3.1048e-06, 'epoch': 2.21, 'throughput': 481.91}
+
+[INFO|callbacks.py:310] 2024-07-30 02:21:27,180 >> {'loss': 0.0178, 'learning_rate': 3.0624e-06, 'epoch': 2.24, 'throughput': 482.28}
+
+[INFO|callbacks.py:310] 2024-07-30 02:21:40,367 >> {'loss': 0.0515, 'learning_rate': 3.0198e-06, 'epoch': 2.26, 'throughput': 482.19}
+
+[INFO|callbacks.py:310] 2024-07-30 02:21:53,575 >> {'loss': 0.0116, 'learning_rate': 2.9770e-06, 'epoch': 2.29, 'throughput': 482.10}
+
+[INFO|callbacks.py:310] 2024-07-30 02:22:06,773 >> {'loss': 0.0010, 'learning_rate': 2.9341e-06, 'epoch': 2.32, 'throughput': 482.26}
+
+[INFO|callbacks.py:310] 2024-07-30 02:22:19,949 >> {'loss': 0.0061, 'learning_rate': 2.8911e-06, 'epoch': 2.34, 'throughput': 482.33}
+
+[INFO|callbacks.py:310] 2024-07-30 02:22:33,140 >> {'loss': 0.0099, 'learning_rate': 2.8479e-06, 'epoch': 2.37, 'throughput': 482.29}
+
+[INFO|callbacks.py:310] 2024-07-30 02:22:46,325 >> {'loss': 0.0246, 'learning_rate': 2.8047e-06, 'epoch': 2.39, 'throughput': 482.35}
+
+[INFO|callbacks.py:310] 2024-07-30 02:22:59,519 >> {'loss': 0.0235, 'learning_rate': 2.7613e-06, 'epoch': 2.42, 'throughput': 482.41}
+
+[INFO|callbacks.py:310] 2024-07-30 02:23:12,716 >> {'loss': 0.0563, 'learning_rate': 2.7179e-06, 'epoch': 2.44, 'throughput': 482.33}
+
+[INFO|callbacks.py:310] 2024-07-30 02:23:25,929 >> {'loss': 0.0436, 'learning_rate': 2.6744e-06, 'epoch': 2.47, 'throughput': 482.13}
+
+[INFO|callbacks.py:310] 2024-07-30 02:23:39,130 >> {'loss': 0.0139, 'learning_rate': 2.6308e-06, 'epoch': 2.50, 'throughput': 482.13}
+
+[INFO|callbacks.py:310] 2024-07-30 02:23:52,320 >> {'loss': 0.0301, 'learning_rate': 2.5872e-06, 'epoch': 2.52, 'throughput': 482.23}
+
+[INFO|callbacks.py:310] 2024-07-30 02:24:05,516 >> {'loss': 0.0154, 'learning_rate': 2.5436e-06, 'epoch': 2.55, 'throughput': 482.18}
+
+[INFO|callbacks.py:310] 2024-07-30 02:24:18,713 >> {'loss': 0.0167, 'learning_rate': 2.5000e-06, 'epoch': 2.57, 'throughput': 482.31}
+
+[INFO|callbacks.py:310] 2024-07-30 02:24:31,913 >> {'loss': 0.0171, 'learning_rate': 2.4564e-06, 'epoch': 2.60, 'throughput': 482.36}
+
+[INFO|callbacks.py:310] 2024-07-30 02:24:45,107 >> {'loss': 0.0219, 'learning_rate': 2.4128e-06, 'epoch': 2.62, 'throughput': 482.31}
+
+[INFO|callbacks.py:310] 2024-07-30 02:24:58,305 >> {'loss': 0.0108, 'learning_rate': 2.3692e-06, 'epoch': 2.65, 'throughput': 482.28}
+
+[INFO|callbacks.py:310] 2024-07-30 02:25:11,491 >> {'loss': 0.0124, 'learning_rate': 2.3256e-06, 'epoch': 2.68, 'throughput': 482.41}
+
+[INFO|callbacks.py:310] 2024-07-30 02:25:24,706 >> {'loss': 0.0489, 'learning_rate': 2.2821e-06, 'epoch': 2.70, 'throughput': 482.24}
+
+[INFO|callbacks.py:310] 2024-07-30 02:25:37,892 >> {'loss': 0.0131, 'learning_rate': 2.2387e-06, 'epoch': 2.73, 'throughput': 482.14}
+
+[INFO|callbacks.py:310] 2024-07-30 02:25:51,092 >> {'loss': 0.0148, 'learning_rate': 2.1953e-06, 'epoch': 2.75, 'throughput': 481.98}
+
+[INFO|callbacks.py:310] 2024-07-30 02:26:04,293 >> {'loss': 0.0279, 'learning_rate': 2.1521e-06, 'epoch': 2.78, 'throughput': 481.90}
+
+[INFO|callbacks.py:310] 2024-07-30 02:26:17,491 >> {'loss': 0.0177, 'learning_rate': 2.1089e-06, 'epoch': 2.80, 'throughput': 482.07}
+
+[INFO|callbacks.py:310] 2024-07-30 02:26:30,687 >> {'loss': 0.0362, 'learning_rate': 2.0659e-06, 'epoch': 2.83, 'throughput': 482.08}
+
+[INFO|callbacks.py:310] 2024-07-30 02:26:43,893 >> {'loss': 0.0380, 'learning_rate': 2.0230e-06, 'epoch': 2.86, 'throughput': 482.11}
+
+[INFO|callbacks.py:310] 2024-07-30 02:26:57,098 >> {'loss': 0.0249, 'learning_rate': 1.9802e-06, 'epoch': 2.88, 'throughput': 482.19}
+
+[INFO|callbacks.py:310] 2024-07-30 02:27:10,288 >> {'loss': 0.0074, 'learning_rate': 1.9376e-06, 'epoch': 2.91, 'throughput': 482.16}
+
+[INFO|callbacks.py:310] 2024-07-30 02:27:23,478 >> {'loss': 0.0147, 'learning_rate': 1.8952e-06, 'epoch': 2.93, 'throughput': 482.20}
+
+[INFO|callbacks.py:310] 2024-07-30 02:27:36,691 >> {'loss': 0.0168, 'learning_rate': 1.8530e-06, 'epoch': 2.96, 'throughput': 482.11}
+
+[INFO|callbacks.py:310] 2024-07-30 02:27:49,881 >> {'loss': 0.0083, 'learning_rate': 1.8109e-06, 'epoch': 2.98, 'throughput': 482.06}
+
+[INFO|callbacks.py:310] 2024-07-30 02:28:03,079 >> {'loss': 0.0086, 'learning_rate': 1.7691e-06, 'epoch': 3.01, 'throughput': 482.07}
+
+[INFO|callbacks.py:310] 2024-07-30 02:28:16,292 >> {'loss': 0.0025, 'learning_rate': 1.7275e-06, 'epoch': 3.04, 'throughput': 482.17}
+
+[INFO|callbacks.py:310] 2024-07-30 02:28:29,513 >> {'loss': 0.0067, 'learning_rate': 1.6861e-06, 'epoch': 3.06, 'throughput': 482.08}
+
+[INFO|callbacks.py:310] 2024-07-30 02:28:42,701 >> {'loss': 0.0020, 'learning_rate': 1.6449e-06, 'epoch': 3.09, 'throughput': 482.04}
+
+[INFO|callbacks.py:310] 2024-07-30 02:28:55,898 >> {'loss': 0.0003, 'learning_rate': 1.6041e-06, 'epoch': 3.11, 'throughput': 481.90}
+
+[INFO|callbacks.py:310] 2024-07-30 02:29:09,091 >> {'loss': 0.0012, 'learning_rate': 1.5635e-06, 'epoch': 3.14, 'throughput': 481.74}
+
+[INFO|callbacks.py:310] 2024-07-30 02:29:22,287 >> {'loss': 0.0001, 'learning_rate': 1.5232e-06, 'epoch': 3.16, 'throughput': 481.70}
+
+[INFO|callbacks.py:310] 2024-07-30 02:29:35,461 >> {'loss': 0.0098, 'learning_rate': 1.4832e-06, 'epoch': 3.19, 'throughput': 481.86}
+
+[INFO|callbacks.py:310] 2024-07-30 02:29:48,673 >> {'loss': 0.0029, 'learning_rate': 1.4435e-06, 'epoch': 3.22, 'throughput': 481.92}
+
+[INFO|callbacks.py:310] 2024-07-30 02:30:01,861 >> {'loss': 0.0000, 'learning_rate': 1.4041e-06, 'epoch': 3.24, 'throughput': 481.92}
+
+[INFO|callbacks.py:310] 2024-07-30 02:30:15,066 >> {'loss': 0.0000, 'learning_rate': 1.3650e-06, 'epoch': 3.27, 'throughput': 482.03}
+
+[INFO|callbacks.py:310] 2024-07-30 02:30:28,255 >> {'loss': 0.0002, 'learning_rate': 1.3263e-06, 'epoch': 3.29, 'throughput': 482.09}
+
+[INFO|callbacks.py:310] 2024-07-30 02:30:41,464 >> {'loss': 0.0331, 'learning_rate': 1.2880e-06, 'epoch': 3.32, 'throughput': 482.05}
+
+[INFO|callbacks.py:310] 2024-07-30 02:30:54,652 >> {'loss': 0.0000, 'learning_rate': 1.2500e-06, 'epoch': 3.34, 'throughput': 482.07}
+
+[INFO|callbacks.py:310] 2024-07-30 02:31:07,849 >> {'loss': 0.0038, 'learning_rate': 1.2124e-06, 'epoch': 3.37, 'throughput': 482.13}
+
+[INFO|callbacks.py:310] 2024-07-30 02:31:21,051 >> {'loss': 0.0000, 'learning_rate': 1.1752e-06, 'epoch': 3.40, 'throughput': 482.03}
+
+[INFO|callbacks.py:310] 2024-07-30 02:31:34,257 >> {'loss': 0.0051, 'learning_rate': 1.1384e-06, 'epoch': 3.42, 'throughput': 481.94}
+
+[INFO|callbacks.py:310] 2024-07-30 02:31:47,439 >> {'loss': 0.0589, 'learning_rate': 1.1020e-06, 'epoch': 3.45, 'throughput': 482.01}
+
+[INFO|callbacks.py:310] 2024-07-30 02:32:00,633 >> {'loss': 0.0003, 'learning_rate': 1.0661e-06, 'epoch': 3.47, 'throughput': 482.02}
+
+[INFO|callbacks.py:310] 2024-07-30 02:32:13,818 >> {'loss': 0.0355, 'learning_rate': 1.0305e-06, 'epoch': 3.50, 'throughput': 481.90}
+
+[INFO|callbacks.py:310] 2024-07-30 02:32:27,019 >> {'loss': 0.0402, 'learning_rate': 9.9546e-07, 'epoch': 3.52, 'throughput': 481.99}
+
+[INFO|callbacks.py:310] 2024-07-30 02:32:40,217 >> {'loss': 0.0000, 'learning_rate': 9.6085e-07, 'epoch': 3.55, 'throughput': 482.00}
+
+[INFO|callbacks.py:310] 2024-07-30 02:32:53,422 >> {'loss': 0.0083, 'learning_rate': 9.2670e-07, 'epoch': 3.58, 'throughput': 482.01}
+
+[INFO|callbacks.py:310] 2024-07-30 02:33:06,623 >> {'loss': 0.0000, 'learning_rate': 8.9303e-07, 'epoch': 3.60, 'throughput': 481.96}
+
+[INFO|callbacks.py:310] 2024-07-30 02:33:19,818 >> {'loss': 0.0012, 'learning_rate': 8.5985e-07, 'epoch': 3.63, 'throughput': 481.83}
+
+[INFO|callbacks.py:310] 2024-07-30 02:33:33,015 >> {'loss': 0.0342, 'learning_rate': 8.2717e-07, 'epoch': 3.65, 'throughput': 481.86}
+
+[INFO|callbacks.py:310] 2024-07-30 02:33:46,211 >> {'loss': 0.0723, 'learning_rate': 7.9500e-07, 'epoch': 3.68, 'throughput': 481.82}
+
+[INFO|callbacks.py:310] 2024-07-30 02:33:59,393 >> {'loss': 0.0000, 'learning_rate': 7.6335e-07, 'epoch': 3.70, 'throughput': 481.79}
+
+[INFO|callbacks.py:310] 2024-07-30 02:34:12,587 >> {'loss': 0.0410, 'learning_rate': 7.3223e-07, 'epoch': 3.73, 'throughput': 481.85}
+
+[INFO|callbacks.py:310] 2024-07-30 02:34:25,760 >> {'loss': 0.0000, 'learning_rate': 7.0165e-07, 'epoch': 3.76, 'throughput': 481.70}
+
+[INFO|callbacks.py:310] 2024-07-30 02:34:38,959 >> {'loss': 0.0001, 'learning_rate': 6.7162e-07, 'epoch': 3.78, 'throughput': 481.95}
+
+[INFO|callbacks.py:310] 2024-07-30 02:34:52,147 >> {'loss': 0.0220, 'learning_rate': 6.4214e-07, 'epoch': 3.81, 'throughput': 482.01}
+
+[INFO|callbacks.py:310] 2024-07-30 02:35:05,342 >> {'loss': 0.0000, 'learning_rate': 6.1323e-07, 'epoch': 3.83, 'throughput': 482.03}
+
+[INFO|callbacks.py:310] 2024-07-30 02:35:18,550 >> {'loss': 0.0095, 'learning_rate': 5.8489e-07, 'epoch': 3.86, 'throughput': 482.04}
+
+[INFO|callbacks.py:310] 2024-07-30 02:35:31,746 >> {'loss': 0.0002, 'learning_rate': 5.5714e-07, 'epoch': 3.88, 'throughput': 482.11}
+
+[INFO|callbacks.py:310] 2024-07-30 02:35:44,947 >> {'loss': 0.0000, 'learning_rate': 5.2997e-07, 'epoch': 3.91, 'throughput': 482.02}
+
+[INFO|callbacks.py:310] 2024-07-30 02:35:58,134 >> {'loss': 0.0274, 'learning_rate': 5.0341e-07, 'epoch': 3.94, 'throughput': 481.97}
+
+[INFO|callbacks.py:310] 2024-07-30 02:36:11,327 >> {'loss': 0.0284, 'learning_rate': 4.7746e-07, 'epoch': 3.96, 'throughput': 482.02}
+
+[INFO|callbacks.py:310] 2024-07-30 02:36:24,527 >> {'loss': 0.0001, 'learning_rate': 4.5212e-07, 'epoch': 3.99, 'throughput': 481.94}
+
+[INFO|callbacks.py:310] 2024-07-30 02:36:37,725 >> {'loss': 0.0000, 'learning_rate': 4.2741e-07, 'epoch': 4.01, 'throughput': 482.02}
+
+[INFO|callbacks.py:310] 2024-07-30 02:36:50,932 >> {'loss': 0.0001, 'learning_rate': 4.0332e-07, 'epoch': 4.04, 'throughput': 482.00}
+
+[INFO|callbacks.py:310] 2024-07-30 02:37:04,125 >> {'loss': 0.0001, 'learning_rate': 3.7988e-07, 'epoch': 4.06, 'throughput': 482.01}
+
+[INFO|callbacks.py:310] 2024-07-30 02:37:17,326 >> {'loss': 0.0121, 'learning_rate': 3.5708e-07, 'epoch': 4.09, 'throughput': 481.91}
+
+[INFO|callbacks.py:310] 2024-07-30 02:37:30,524 >> {'loss': 0.0000, 'learning_rate': 3.3494e-07, 'epoch': 4.12, 'throughput': 482.00}
+
+[INFO|callbacks.py:310] 2024-07-30 02:37:43,706 >> {'loss': 0.0001, 'learning_rate': 3.1345e-07, 'epoch': 4.14, 'throughput': 482.03}
+
+[INFO|callbacks.py:310] 2024-07-30 02:37:56,913 >> {'loss': 0.0001, 'learning_rate': 2.9263e-07, 'epoch': 4.17, 'throughput': 482.07}
+
+[INFO|callbacks.py:310] 2024-07-30 02:38:10,110 >> {'loss': 0.0002, 'learning_rate': 2.7248e-07, 'epoch': 4.19, 'throughput': 482.13}
+
+[INFO|callbacks.py:310] 2024-07-30 02:38:23,292 >> {'loss': 0.0005, 'learning_rate': 2.5301e-07, 'epoch': 4.22, 'throughput': 482.10}
+
+[INFO|callbacks.py:310] 2024-07-30 02:38:36,504 >> {'loss': 0.0001, 'learning_rate': 2.3423e-07, 'epoch': 4.24, 'throughput': 481.98}
+
+[INFO|callbacks.py:310] 2024-07-30 02:38:49,692 >> {'loss': 0.0001, 'learning_rate': 2.1614e-07, 'epoch': 4.27, 'throughput': 482.03}
+
+[INFO|callbacks.py:310] 2024-07-30 02:39:02,905 >> {'loss': 0.0001, 'learning_rate': 1.9874e-07, 'epoch': 4.30, 'throughput': 482.02}
+
+[INFO|callbacks.py:310] 2024-07-30 02:39:16,096 >> {'loss': 0.0001, 'learning_rate': 1.8204e-07, 'epoch': 4.32, 'throughput': 481.99}
+
+[INFO|callbacks.py:310] 2024-07-30 02:39:29,295 >> {'loss': 0.0001, 'learning_rate': 1.6605e-07, 'epoch': 4.35, 'throughput': 481.91}
+
+[INFO|callbacks.py:310] 2024-07-30 02:39:42,485 >> {'loss': 0.0002, 'learning_rate': 1.5077e-07, 'epoch': 4.37, 'throughput': 481.92}
+
+[INFO|callbacks.py:310] 2024-07-30 02:39:55,679 >> {'loss': 0.0026, 'learning_rate': 1.3620e-07, 'epoch': 4.40, 'throughput': 481.94}
+
+[INFO|callbacks.py:310] 2024-07-30 02:40:08,863 >> {'loss': 0.0032, 'learning_rate': 1.2236e-07, 'epoch': 4.42, 'throughput': 481.96}
+
+[INFO|callbacks.py:310] 2024-07-30 02:40:22,060 >> {'loss': 0.0004, 'learning_rate': 1.0924e-07, 'epoch': 4.45, 'throughput': 482.04}
+
+[INFO|callbacks.py:310] 2024-07-30 02:40:35,246 >> {'loss': 0.0001, 'learning_rate': 9.6846e-08, 'epoch': 4.48, 'throughput': 481.94}
+
+[INFO|callbacks.py:310] 2024-07-30 02:40:48,451 >> {'loss': 0.0074, 'learning_rate': 8.5185e-08, 'epoch': 4.50, 'throughput': 481.88}
+
+[INFO|callbacks.py:310] 2024-07-30 02:41:01,650 >> {'loss': 0.0029, 'learning_rate': 7.4261e-08, 'epoch': 4.53, 'throughput': 481.80}
+
+[INFO|callbacks.py:310] 2024-07-30 02:41:14,857 >> {'loss': 0.0001, 'learning_rate': 6.4075e-08, 'epoch': 4.55, 'throughput': 481.85}
+
+[INFO|callbacks.py:310] 2024-07-30 02:41:28,042 >> {'loss': 0.0000, 'learning_rate': 5.4631e-08, 'epoch': 4.58, 'throughput': 481.85}
+
+[INFO|callbacks.py:310] 2024-07-30 02:41:41,246 >> {'loss': 0.0082, 'learning_rate': 4.5932e-08, 'epoch': 4.60, 'throughput': 481.87}
+
+[INFO|callbacks.py:310] 2024-07-30 02:41:54,426 >> {'loss': 0.0001, 'learning_rate': 3.7981e-08, 'epoch': 4.63, 'throughput': 481.94}
+
+[INFO|callbacks.py:310] 2024-07-30 02:42:07,625 >> {'loss': 0.0000, 'learning_rate': 3.0779e-08, 'epoch': 4.66, 'throughput': 481.87}
+
+[INFO|callbacks.py:310] 2024-07-30 02:42:20,822 >> {'loss': 0.0001, 'learning_rate': 2.4330e-08, 'epoch': 4.68, 'throughput': 481.79}
+
+[INFO|callbacks.py:310] 2024-07-30 02:42:34,026 >> {'loss': 0.0001, 'learning_rate': 1.8635e-08, 'epoch': 4.71, 'throughput': 481.65}
+
+[INFO|callbacks.py:310] 2024-07-30 02:42:47,237 >> {'loss': 0.0052, 'learning_rate': 1.3695e-08, 'epoch': 4.73, 'throughput': 481.57}
+
+[INFO|callbacks.py:310] 2024-07-30 02:43:00,435 >> {'loss': 0.0009, 'learning_rate': 9.5133e-09, 'epoch': 4.76, 'throughput': 481.58}
+
+[INFO|callbacks.py:310] 2024-07-30 02:43:13,635 >> {'loss': 0.0001, 'learning_rate': 6.0899e-09, 'epoch': 4.78, 'throughput': 481.60}
+
+[INFO|callbacks.py:310] 2024-07-30 02:43:26,831 >> {'loss': 0.0010, 'learning_rate': 3.4262e-09, 'epoch': 4.81, 'throughput': 481.73}
+
+[INFO|callbacks.py:310] 2024-07-30 02:43:40,013 >> {'loss': 0.0013, 'learning_rate': 1.5229e-09, 'epoch': 4.84, 'throughput': 481.70}
+
+[INFO|callbacks.py:310] 2024-07-30 02:43:53,203 >> {'loss': 0.0001, 'learning_rate': 3.8076e-10, 'epoch': 4.86, 'throughput': 481.71}
+
+[INFO|callbacks.py:310] 2024-07-30 02:44:06,388 >> {'loss': 0.0004, 'learning_rate': 0.0000e+00, 'epoch': 4.89, 'throughput': 481.73}
+
+[INFO|trainer.py:3503] 2024-07-30 02:44:14,015 >> Saving model checkpoint to saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-00-04_llama3.1_truthqa_bench_1/checkpoint-190
+
+[INFO|configuration_utils.py:472] 2024-07-30 02:44:14,018 >> Configuration saved in saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-00-04_llama3.1_truthqa_bench_1/checkpoint-190/config.json
+
+[INFO|configuration_utils.py:807] 2024-07-30 02:44:14,018 >> Configuration saved in saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-00-04_llama3.1_truthqa_bench_1/checkpoint-190/generation_config.json
+
+[INFO|modeling_utils.py:2763] 2024-07-30 02:44:30,740 >> 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_2024-07-30-02-00-04_llama3.1_truthqa_bench_1/checkpoint-190/model.safetensors.index.json.
+
+[INFO|tokenization_utils_base.py:2702] 2024-07-30 02:44:30,743 >> tokenizer config file saved in saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-00-04_llama3.1_truthqa_bench_1/checkpoint-190/tokenizer_config.json
+
+[INFO|tokenization_utils_base.py:2711] 2024-07-30 02:44:30,743 >> Special tokens file saved in saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-00-04_llama3.1_truthqa_bench_1/checkpoint-190/special_tokens_map.json
+
+[INFO|trainer.py:2394] 2024-07-30 02:45:07,830 >> 
+
+Training completed. Do not forget to share your model on huggingface.co/models =)
+
+
+
+[INFO|trainer.py:3503] 2024-07-30 02:45:15,199 >> Saving model checkpoint to saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-00-04_llama3.1_truthqa_bench_1
+
+[INFO|configuration_utils.py:472] 2024-07-30 02:45:15,201 >> Configuration saved in saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-00-04_llama3.1_truthqa_bench_1/config.json
+
+[INFO|configuration_utils.py:807] 2024-07-30 02:45:15,202 >> Configuration saved in saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-00-04_llama3.1_truthqa_bench_1/generation_config.json
+
+[INFO|modeling_utils.py:2763] 2024-07-30 02:45:32,161 >> 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_2024-07-30-02-00-04_llama3.1_truthqa_bench_1/model.safetensors.index.json.
+
+[INFO|tokenization_utils_base.py:2702] 2024-07-30 02:45:32,164 >> tokenizer config file saved in saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-00-04_llama3.1_truthqa_bench_1/tokenizer_config.json
+
+[INFO|tokenization_utils_base.py:2711] 2024-07-30 02:45:32,165 >> Special tokens file saved in saves/LLaMA3.1-8B-Chat/full/train_2024-07-30-02-00-04_llama3.1_truthqa_bench_1/special_tokens_map.json
+
+[WARNING|ploting.py:89] 2024-07-30 02:45:33,509 >> No metric eval_loss to plot.
+
+[WARNING|ploting.py:89] 2024-07-30 02:45:33,510 >> No metric eval_accuracy to plot.
+
+[INFO|modelcard.py:449] 2024-07-30 02:45:33,510 >> 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,
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+  "padding_side": "right",
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+}

train_results.json

@@ -0,0 +1,9 @@
+{
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+    "total_flos": 5.441370708980531e+16,
+    "train_loss": 0.4588010550536494,
+    "train_runtime": 2569.9216,
+    "train_samples_per_second": 9.666,
+    "train_steps_per_second": 0.074
+}

trainer_log.jsonl

@@ -0,0 +1,191 @@
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+{"current_steps": 2, "total_steps": 190, "loss": 12.2961, "learning_rate": 1.0000000000000002e-06, "epoch": 0.05144694533762058, "percentage": 1.05, "elapsed_time": "0:00:27", "remaining_time": "0:42:59", "throughput": "468.19", "total_tokens": 12848}
+{"current_steps": 3, "total_steps": 190, "loss": 11.9707, "learning_rate": 1.5e-06, "epoch": 0.07717041800643087, "percentage": 1.58, "elapsed_time": "0:00:40", "remaining_time": "0:42:15", "throughput": "473.17", "total_tokens": 19248}
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+{"current_steps": 5, "total_steps": 190, "loss": 8.3234, "learning_rate": 2.5e-06, "epoch": 0.12861736334405144, "percentage": 2.63, "elapsed_time": "0:01:07", "remaining_time": "0:41:22", "throughput": "476.45", "total_tokens": 31968}
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+{"current_steps": 7, "total_steps": 190, "loss": 4.8266, "learning_rate": 3.5e-06, "epoch": 0.18006430868167203, "percentage": 3.68, "elapsed_time": "0:01:33", "remaining_time": "0:40:44", "throughput": "472.16", "total_tokens": 44144}
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+{"current_steps": 9, "total_steps": 190, "loss": 0.5637, "learning_rate": 4.5e-06, "epoch": 0.2315112540192926, "percentage": 4.74, "elapsed_time": "0:01:59", "remaining_time": "0:40:11", "throughput": "474.73", "total_tokens": 56928}
+{"current_steps": 10, "total_steps": 190, "loss": 0.2757, "learning_rate": 5e-06, "epoch": 0.2572347266881029, "percentage": 5.26, "elapsed_time": "0:02:13", "remaining_time": "0:39:56", "throughput": "475.87", "total_tokens": 63344}
+{"current_steps": 11, "total_steps": 190, "loss": 1.4131, "learning_rate": 4.9996192378909785e-06, "epoch": 0.2829581993569132, "percentage": 5.79, "elapsed_time": "0:02:26", "remaining_time": "0:39:40", "throughput": "475.52", "total_tokens": 69568}
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+{"current_steps": 13, "total_steps": 190, "loss": 1.1385, "learning_rate": 4.9965738368864345e-06, "epoch": 0.33440514469453375, "percentage": 6.84, "elapsed_time": "0:02:52", "remaining_time": "0:39:11", "throughput": "475.72", "total_tokens": 82160}
+{"current_steps": 14, "total_steps": 190, "loss": 1.5128, "learning_rate": 4.993910125649561e-06, "epoch": 0.36012861736334406, "percentage": 7.37, "elapsed_time": "0:03:05", "remaining_time": "0:38:56", "throughput": "476.75", "total_tokens": 88624}
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+{"current_steps": 24, "total_steps": 190, "loss": 0.4201, "learning_rate": 4.925739315689991e-06, "epoch": 0.617363344051447, "percentage": 12.63, "elapsed_time": "0:05:17", "remaining_time": "0:36:38", "throughput": "478.93", "total_tokens": 152240}
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+{"current_steps": 37, "total_steps": 190, "loss": 0.1405, "learning_rate": 4.72751631047092e-06, "epoch": 0.9517684887459807, "percentage": 19.47, "elapsed_time": "0:08:09", "remaining_time": "0:33:43", "throughput": "481.53", "total_tokens": 235680}
+{"current_steps": 38, "total_steps": 190, "loss": 0.2396, "learning_rate": 4.707368982147318e-06, "epoch": 0.977491961414791, "percentage": 20.0, "elapsed_time": "0:08:22", "remaining_time": "0:33:30", "throughput": "481.84", "total_tokens": 242192}
+{"current_steps": 39, "total_steps": 190, "loss": 0.1272, "learning_rate": 4.68654926784849e-06, "epoch": 1.0032154340836013, "percentage": 20.53, "elapsed_time": "0:08:35", "remaining_time": "0:33:17", "throughput": "482.08", "total_tokens": 248672}
+{"current_steps": 40, "total_steps": 190, "loss": 0.0815, "learning_rate": 4.665063509461098e-06, "epoch": 1.0289389067524115, "percentage": 21.05, "elapsed_time": "0:08:49", "remaining_time": "0:33:03", "throughput": "482.15", "total_tokens": 255072}
+{"current_steps": 41, "total_steps": 190, "loss": 0.0771, "learning_rate": 4.642918251755281e-06, "epoch": 1.0546623794212218, "percentage": 21.58, "elapsed_time": "0:09:02", "remaining_time": "0:32:50", "throughput": "482.43", "total_tokens": 261584}
+{"current_steps": 42, "total_steps": 190, "loss": 0.0821, "learning_rate": 4.620120240391065e-06, "epoch": 1.0803858520900322, "percentage": 22.11, "elapsed_time": "0:09:15", "remaining_time": "0:32:37", "throughput": "482.59", "total_tokens": 268032}
+{"current_steps": 43, "total_steps": 190, "loss": 0.0597, "learning_rate": 4.596676419863561e-06, "epoch": 1.1061093247588425, "percentage": 22.63, "elapsed_time": "0:09:28", "remaining_time": "0:32:23", "throughput": "482.86", "total_tokens": 274560}
+{"current_steps": 44, "total_steps": 190, "loss": 0.0356, "learning_rate": 4.572593931387604e-06, "epoch": 1.1318327974276527, "percentage": 23.16, "elapsed_time": "0:09:41", "remaining_time": "0:32:10", "throughput": "482.92", "total_tokens": 280960}
+{"current_steps": 45, "total_steps": 190, "loss": 0.0895, "learning_rate": 4.54788011072248e-06, "epoch": 1.157556270096463, "percentage": 23.68, "elapsed_time": "0:09:54", "remaining_time": "0:31:57", "throughput": "482.54", "total_tokens": 287104}
+{"current_steps": 46, "total_steps": 190, "loss": 0.0402, "learning_rate": 4.522542485937369e-06, "epoch": 1.1832797427652733, "percentage": 24.21, "elapsed_time": "0:10:08", "remaining_time": "0:31:43", "throughput": "482.62", "total_tokens": 293520}
+{"current_steps": 47, "total_steps": 190, "loss": 0.0659, "learning_rate": 4.496588775118232e-06, "epoch": 1.2090032154340835, "percentage": 24.74, "elapsed_time": "0:10:21", "remaining_time": "0:31:30", "throughput": "482.70", "total_tokens": 299936}
+{"current_steps": 48, "total_steps": 190, "loss": 0.091, "learning_rate": 4.470026884016805e-06, "epoch": 1.234726688102894, "percentage": 25.26, "elapsed_time": "0:10:34", "remaining_time": "0:31:17", "throughput": "482.11", "total_tokens": 305936}
+{"current_steps": 49, "total_steps": 190, "loss": 0.038, "learning_rate": 4.442864903642428e-06, "epoch": 1.2604501607717042, "percentage": 25.79, "elapsed_time": "0:10:47", "remaining_time": "0:31:03", "throughput": "482.47", "total_tokens": 312528}
+{"current_steps": 50, "total_steps": 190, "loss": 0.0998, "learning_rate": 4.415111107797445e-06, "epoch": 1.2861736334405145, "percentage": 26.32, "elapsed_time": "0:11:00", "remaining_time": "0:30:50", "throughput": "482.27", "total_tokens": 318752}
+{"current_steps": 51, "total_steps": 190, "loss": 0.1125, "learning_rate": 4.386773950556931e-06, "epoch": 1.3118971061093248, "percentage": 26.84, "elapsed_time": "0:11:14", "remaining_time": "0:30:37", "throughput": "482.22", "total_tokens": 325088}
+{"current_steps": 52, "total_steps": 190, "loss": 0.033, "learning_rate": 4.357862063693486e-06, "epoch": 1.337620578778135, "percentage": 27.37, "elapsed_time": "0:11:27", "remaining_time": "0:30:24", "throughput": "482.18", "total_tokens": 331424}
+{"current_steps": 53, "total_steps": 190, "loss": 0.0688, "learning_rate": 4.328384254047927e-06, "epoch": 1.3633440514469453, "percentage": 27.89, "elapsed_time": "0:11:40", "remaining_time": "0:30:10", "throughput": "482.16", "total_tokens": 337776}
+{"current_steps": 54, "total_steps": 190, "loss": 0.0433, "learning_rate": 4.2983495008466285e-06, "epoch": 1.3890675241157555, "percentage": 28.42, "elapsed_time": "0:11:53", "remaining_time": "0:29:57", "throughput": "481.96", "total_tokens": 344000}
+{"current_steps": 55, "total_steps": 190, "loss": 0.0116, "learning_rate": 4.267766952966369e-06, "epoch": 1.414790996784566, "percentage": 28.95, "elapsed_time": "0:12:06", "remaining_time": "0:29:44", "throughput": "482.19", "total_tokens": 350528}
+{"current_steps": 56, "total_steps": 190, "loss": 0.0634, "learning_rate": 4.236645926147493e-06, "epoch": 1.4405144694533762, "percentage": 29.47, "elapsed_time": "0:12:20", "remaining_time": "0:29:31", "throughput": "482.20", "total_tokens": 356896}
+{"current_steps": 57, "total_steps": 190, "loss": 0.0729, "learning_rate": 4.204995900156247e-06, "epoch": 1.4662379421221865, "percentage": 30.0, "elapsed_time": "0:12:33", "remaining_time": "0:29:17", "throughput": "482.35", "total_tokens": 363376}
+{"current_steps": 58, "total_steps": 190, "loss": 0.1315, "learning_rate": 4.172826515897146e-06, "epoch": 1.4919614147909968, "percentage": 30.53, "elapsed_time": "0:12:46", "remaining_time": "0:29:04", "throughput": "482.28", "total_tokens": 369680}
+{"current_steps": 59, "total_steps": 190, "loss": 0.05, "learning_rate": 4.140147572476269e-06, "epoch": 1.517684887459807, "percentage": 31.05, "elapsed_time": "0:12:59", "remaining_time": "0:28:51", "throughput": "482.10", "total_tokens": 375904}
+{"current_steps": 60, "total_steps": 190, "loss": 0.0838, "learning_rate": 4.106969024216348e-06, "epoch": 1.5434083601286175, "percentage": 31.58, "elapsed_time": "0:13:12", "remaining_time": "0:28:38", "throughput": "482.14", "total_tokens": 382304}
+{"current_steps": 61, "total_steps": 190, "loss": 0.0577, "learning_rate": 4.073300977624594e-06, "epoch": 1.5691318327974275, "percentage": 32.11, "elapsed_time": "0:13:26", "remaining_time": "0:28:24", "throughput": "482.16", "total_tokens": 388688}
+{"current_steps": 62, "total_steps": 190, "loss": 0.0465, "learning_rate": 4.039153688314146e-06, "epoch": 1.594855305466238, "percentage": 32.63, "elapsed_time": "0:13:39", "remaining_time": "0:28:11", "throughput": "482.29", "total_tokens": 395152}
+{"current_steps": 63, "total_steps": 190, "loss": 0.0497, "learning_rate": 4.0045375578801216e-06, "epoch": 1.6205787781350482, "percentage": 33.16, "elapsed_time": "0:13:52", "remaining_time": "0:27:58", "throughput": "482.54", "total_tokens": 401728}
+{"current_steps": 64, "total_steps": 190, "loss": 0.0559, "learning_rate": 3.969463130731183e-06, "epoch": 1.6463022508038585, "percentage": 33.68, "elapsed_time": "0:14:05", "remaining_time": "0:27:44", "throughput": "482.32", "total_tokens": 407904}
+{"current_steps": 65, "total_steps": 190, "loss": 0.0394, "learning_rate": 3.933941090877615e-06, "epoch": 1.6720257234726688, "percentage": 34.21, "elapsed_time": "0:14:18", "remaining_time": "0:27:31", "throughput": "482.29", "total_tokens": 414240}
+{"current_steps": 66, "total_steps": 190, "loss": 0.0737, "learning_rate": 3.897982258676867e-06, "epoch": 1.697749196141479, "percentage": 34.74, "elapsed_time": "0:14:32", "remaining_time": "0:27:18", "throughput": "482.12", "total_tokens": 420448}
+{"current_steps": 67, "total_steps": 190, "loss": 0.0466, "learning_rate": 3.861597587537568e-06, "epoch": 1.7234726688102895, "percentage": 35.26, "elapsed_time": "0:14:45", "remaining_time": "0:27:05", "throughput": "482.08", "total_tokens": 426784}
+{"current_steps": 68, "total_steps": 190, "loss": 0.0619, "learning_rate": 3.824798160583012e-06, "epoch": 1.7491961414790995, "percentage": 35.79, "elapsed_time": "0:14:58", "remaining_time": "0:26:51", "throughput": "481.72", "total_tokens": 432816}
+{"current_steps": 69, "total_steps": 190, "loss": 0.0749, "learning_rate": 3.787595187275136e-06, "epoch": 1.77491961414791, "percentage": 36.32, "elapsed_time": "0:15:11", "remaining_time": "0:26:38", "throughput": "481.78", "total_tokens": 439232}
+{"current_steps": 70, "total_steps": 190, "loss": 0.0328, "learning_rate": 3.7500000000000005e-06, "epoch": 1.8006430868167203, "percentage": 36.84, "elapsed_time": "0:15:24", "remaining_time": "0:26:25", "throughput": "482.00", "total_tokens": 445792}
+{"current_steps": 71, "total_steps": 190, "loss": 0.0896, "learning_rate": 3.7120240506158433e-06, "epoch": 1.8263665594855305, "percentage": 37.37, "elapsed_time": "0:15:38", "remaining_time": "0:26:12", "throughput": "482.27", "total_tokens": 452400}
+{"current_steps": 72, "total_steps": 190, "loss": 0.0489, "learning_rate": 3.6736789069647273e-06, "epoch": 1.852090032154341, "percentage": 37.89, "elapsed_time": "0:15:51", "remaining_time": "0:25:58", "throughput": "482.03", "total_tokens": 458528}
+{"current_steps": 73, "total_steps": 190, "loss": 0.0305, "learning_rate": 3.634976249348867e-06, "epoch": 1.877813504823151, "percentage": 38.42, "elapsed_time": "0:16:04", "remaining_time": "0:25:45", "throughput": "482.12", "total_tokens": 464976}
+{"current_steps": 74, "total_steps": 190, "loss": 0.0547, "learning_rate": 3.595927866972694e-06, "epoch": 1.9035369774919615, "percentage": 38.95, "elapsed_time": "0:16:17", "remaining_time": "0:25:32", "throughput": "482.22", "total_tokens": 471440}
+{"current_steps": 75, "total_steps": 190, "loss": 0.0554, "learning_rate": 3.556545654351749e-06, "epoch": 1.9292604501607717, "percentage": 39.47, "elapsed_time": "0:16:30", "remaining_time": "0:25:19", "throughput": "482.18", "total_tokens": 477776}
+{"current_steps": 76, "total_steps": 190, "loss": 0.1169, "learning_rate": 3.516841607689501e-06, "epoch": 1.954983922829582, "percentage": 40.0, "elapsed_time": "0:16:44", "remaining_time": "0:25:06", "throughput": "482.14", "total_tokens": 484096}
+{"current_steps": 77, "total_steps": 190, "loss": 0.0587, "learning_rate": 3.476827821223184e-06, "epoch": 1.9807073954983923, "percentage": 40.53, "elapsed_time": "0:16:57", "remaining_time": "0:24:52", "throughput": "481.85", "total_tokens": 490176}
+{"current_steps": 78, "total_steps": 190, "loss": 0.0624, "learning_rate": 3.436516483539781e-06, "epoch": 2.0064308681672025, "percentage": 41.05, "elapsed_time": "0:17:10", "remaining_time": "0:24:39", "throughput": "481.99", "total_tokens": 496672}
+{"current_steps": 79, "total_steps": 190, "loss": 0.0152, "learning_rate": 3.39591987386325e-06, "epoch": 2.032154340836013, "percentage": 41.58, "elapsed_time": "0:17:23", "remaining_time": "0:24:26", "throughput": "482.04", "total_tokens": 503088}
+{"current_steps": 80, "total_steps": 190, "loss": 0.0224, "learning_rate": 3.3550503583141726e-06, "epoch": 2.057877813504823, "percentage": 42.11, "elapsed_time": "0:17:36", "remaining_time": "0:24:13", "throughput": "482.06", "total_tokens": 509472}
+{"current_steps": 81, "total_steps": 190, "loss": 0.0084, "learning_rate": 3.313920386142892e-06, "epoch": 2.0836012861736335, "percentage": 42.63, "elapsed_time": "0:17:50", "remaining_time": "0:23:59", "throughput": "481.96", "total_tokens": 515728}
+{"current_steps": 82, "total_steps": 190, "loss": 0.0293, "learning_rate": 3.272542485937369e-06, "epoch": 2.1093247588424435, "percentage": 43.16, "elapsed_time": "0:18:03", "remaining_time": "0:23:46", "throughput": "481.78", "total_tokens": 521904}
+{"current_steps": 83, "total_steps": 190, "loss": 0.0167, "learning_rate": 3.230929261806842e-06, "epoch": 2.135048231511254, "percentage": 43.68, "elapsed_time": "0:18:16", "remaining_time": "0:23:33", "throughput": "481.49", "total_tokens": 527952}
+{"current_steps": 84, "total_steps": 190, "loss": 0.0094, "learning_rate": 3.189093389542498e-06, "epoch": 2.1607717041800645, "percentage": 44.21, "elapsed_time": "0:18:29", "remaining_time": "0:23:20", "throughput": "481.79", "total_tokens": 534624}
+{"current_steps": 85, "total_steps": 190, "loss": 0.0551, "learning_rate": 3.147047612756302e-06, "epoch": 2.1864951768488745, "percentage": 44.74, "elapsed_time": "0:18:42", "remaining_time": "0:23:07", "throughput": "481.95", "total_tokens": 541168}
+{"current_steps": 86, "total_steps": 190, "loss": 0.0684, "learning_rate": 3.1048047389991693e-06, "epoch": 2.212218649517685, "percentage": 45.26, "elapsed_time": "0:18:56", "remaining_time": "0:22:53", "throughput": "481.91", "total_tokens": 547488}
+{"current_steps": 87, "total_steps": 190, "loss": 0.0178, "learning_rate": 3.062377635859663e-06, "epoch": 2.237942122186495, "percentage": 45.79, "elapsed_time": "0:19:09", "remaining_time": "0:22:40", "throughput": "482.28", "total_tokens": 554272}
+{"current_steps": 88, "total_steps": 190, "loss": 0.0515, "learning_rate": 3.019779227044398e-06, "epoch": 2.2636655948553055, "percentage": 46.32, "elapsed_time": "0:19:22", "remaining_time": "0:22:27", "throughput": "482.19", "total_tokens": 560528}
+{"current_steps": 89, "total_steps": 190, "loss": 0.0116, "learning_rate": 2.9770224884413625e-06, "epoch": 2.289389067524116, "percentage": 46.84, "elapsed_time": "0:19:35", "remaining_time": "0:22:14", "throughput": "482.10", "total_tokens": 566784}
+{"current_steps": 90, "total_steps": 190, "loss": 0.001, "learning_rate": 2.9341204441673267e-06, "epoch": 2.315112540192926, "percentage": 47.37, "elapsed_time": "0:19:48", "remaining_time": "0:22:00", "throughput": "482.26", "total_tokens": 573344}
+{"current_steps": 91, "total_steps": 190, "loss": 0.0061, "learning_rate": 2.8910861626005774e-06, "epoch": 2.3408360128617365, "percentage": 47.89, "elapsed_time": "0:20:02", "remaining_time": "0:21:47", "throughput": "482.33", "total_tokens": 579776}
+{"current_steps": 92, "total_steps": 190, "loss": 0.0099, "learning_rate": 2.847932752400164e-06, "epoch": 2.3665594855305465, "percentage": 48.42, "elapsed_time": "0:20:15", "remaining_time": "0:21:34", "throughput": "482.29", "total_tokens": 586096}
+{"current_steps": 93, "total_steps": 190, "loss": 0.0246, "learning_rate": 2.804673358512869e-06, "epoch": 2.392282958199357, "percentage": 48.95, "elapsed_time": "0:20:28", "remaining_time": "0:21:21", "throughput": "482.35", "total_tokens": 592528}
+{"current_steps": 94, "total_steps": 190, "loss": 0.0235, "learning_rate": 2.761321158169134e-06, "epoch": 2.418006430868167, "percentage": 49.47, "elapsed_time": "0:20:41", "remaining_time": "0:21:08", "throughput": "482.41", "total_tokens": 598960}
+{"current_steps": 95, "total_steps": 190, "loss": 0.0563, "learning_rate": 2.717889356869146e-06, "epoch": 2.4437299035369775, "percentage": 50.0, "elapsed_time": "0:20:54", "remaining_time": "0:20:54", "throughput": "482.33", "total_tokens": 605232}
+{"current_steps": 96, "total_steps": 190, "loss": 0.0436, "learning_rate": 2.6743911843603134e-06, "epoch": 2.469453376205788, "percentage": 50.53, "elapsed_time": "0:21:08", "remaining_time": "0:20:41", "throughput": "482.13", "total_tokens": 611344}
+{"current_steps": 97, "total_steps": 190, "loss": 0.0139, "learning_rate": 2.6308398906073603e-06, "epoch": 2.495176848874598, "percentage": 51.05, "elapsed_time": "0:21:21", "remaining_time": "0:20:28", "throughput": "482.13", "total_tokens": 617712}
+{"current_steps": 98, "total_steps": 190, "loss": 0.0301, "learning_rate": 2.587248741756253e-06, "epoch": 2.5209003215434085, "percentage": 51.58, "elapsed_time": "0:21:34", "remaining_time": "0:20:15", "throughput": "482.23", "total_tokens": 624208}
+{"current_steps": 99, "total_steps": 190, "loss": 0.0154, "learning_rate": 2.543631016093209e-06, "epoch": 2.5466237942122185, "percentage": 52.11, "elapsed_time": "0:21:47", "remaining_time": "0:20:01", "throughput": "482.18", "total_tokens": 630496}
+{"current_steps": 100, "total_steps": 190, "loss": 0.0167, "learning_rate": 2.5e-06, "epoch": 2.572347266881029, "percentage": 52.63, "elapsed_time": "0:22:00", "remaining_time": "0:19:48", "throughput": "482.31", "total_tokens": 637040}
+{"current_steps": 101, "total_steps": 190, "loss": 0.0171, "learning_rate": 2.4563689839067913e-06, "epoch": 2.598070739549839, "percentage": 53.16, "elapsed_time": "0:22:14", "remaining_time": "0:19:35", "throughput": "482.36", "total_tokens": 643472}
+{"current_steps": 102, "total_steps": 190, "loss": 0.0219, "learning_rate": 2.4127512582437486e-06, "epoch": 2.6237942122186495, "percentage": 53.68, "elapsed_time": "0:22:27", "remaining_time": "0:19:22", "throughput": "482.31", "total_tokens": 649760}
+{"current_steps": 103, "total_steps": 190, "loss": 0.0108, "learning_rate": 2.3691601093926406e-06, "epoch": 2.64951768488746, "percentage": 54.21, "elapsed_time": "0:22:40", "remaining_time": "0:19:09", "throughput": "482.28", "total_tokens": 656096}
+{"current_steps": 104, "total_steps": 190, "loss": 0.0124, "learning_rate": 2.325608815639687e-06, "epoch": 2.67524115755627, "percentage": 54.74, "elapsed_time": "0:22:53", "remaining_time": "0:18:55", "throughput": "482.41", "total_tokens": 662624}
+{"current_steps": 105, "total_steps": 190, "loss": 0.0489, "learning_rate": 2.2821106431308546e-06, "epoch": 2.7009646302250805, "percentage": 55.26, "elapsed_time": "0:23:06", "remaining_time": "0:18:42", "throughput": "482.24", "total_tokens": 668768}
+{"current_steps": 106, "total_steps": 190, "loss": 0.0131, "learning_rate": 2.238678841830867e-06, "epoch": 2.7266881028938905, "percentage": 55.79, "elapsed_time": "0:23:19", "remaining_time": "0:18:29", "throughput": "482.14", "total_tokens": 674992}
+{"current_steps": 107, "total_steps": 190, "loss": 0.0148, "learning_rate": 2.195326641487132e-06, "epoch": 2.752411575562701, "percentage": 56.32, "elapsed_time": "0:23:33", "remaining_time": "0:18:16", "throughput": "481.98", "total_tokens": 681120}
+{"current_steps": 108, "total_steps": 190, "loss": 0.0279, "learning_rate": 2.1520672475998374e-06, "epoch": 2.778135048231511, "percentage": 56.84, "elapsed_time": "0:23:46", "remaining_time": "0:18:02", "throughput": "481.90", "total_tokens": 687376}
+{"current_steps": 109, "total_steps": 190, "loss": 0.0177, "learning_rate": 2.1089138373994226e-06, "epoch": 2.8038585209003215, "percentage": 57.37, "elapsed_time": "0:23:59", "remaining_time": "0:17:49", "throughput": "482.07", "total_tokens": 693984}
+{"current_steps": 110, "total_steps": 190, "loss": 0.0362, "learning_rate": 2.0658795558326745e-06, "epoch": 2.829581993569132, "percentage": 57.89, "elapsed_time": "0:24:12", "remaining_time": "0:17:36", "throughput": "482.08", "total_tokens": 700352}
+{"current_steps": 111, "total_steps": 190, "loss": 0.038, "learning_rate": 2.022977511558638e-06, "epoch": 2.855305466237942, "percentage": 58.42, "elapsed_time": "0:24:25", "remaining_time": "0:17:23", "throughput": "482.11", "total_tokens": 706768}
+{"current_steps": 112, "total_steps": 190, "loss": 0.0249, "learning_rate": 1.9802207729556023e-06, "epoch": 2.8810289389067525, "percentage": 58.95, "elapsed_time": "0:24:39", "remaining_time": "0:17:10", "throughput": "482.19", "total_tokens": 713248}
+{"current_steps": 113, "total_steps": 190, "loss": 0.0074, "learning_rate": 1.937622364140338e-06, "epoch": 2.906752411575563, "percentage": 59.47, "elapsed_time": "0:24:52", "remaining_time": "0:16:56", "throughput": "482.16", "total_tokens": 719568}
+{"current_steps": 114, "total_steps": 190, "loss": 0.0147, "learning_rate": 1.895195261000831e-06, "epoch": 2.932475884244373, "percentage": 60.0, "elapsed_time": "0:25:05", "remaining_time": "0:16:43", "throughput": "482.20", "total_tokens": 725984}
+{"current_steps": 115, "total_steps": 190, "loss": 0.0168, "learning_rate": 1.852952387243698e-06, "epoch": 2.958199356913183, "percentage": 60.53, "elapsed_time": "0:25:18", "remaining_time": "0:16:30", "throughput": "482.11", "total_tokens": 732224}
+{"current_steps": 116, "total_steps": 190, "loss": 0.0083, "learning_rate": 1.8109066104575023e-06, "epoch": 2.9839228295819935, "percentage": 61.05, "elapsed_time": "0:25:31", "remaining_time": "0:16:17", "throughput": "482.06", "total_tokens": 738496}
+{"current_steps": 117, "total_steps": 190, "loss": 0.0086, "learning_rate": 1.7690707381931585e-06, "epoch": 3.009646302250804, "percentage": 61.58, "elapsed_time": "0:25:45", "remaining_time": "0:16:04", "throughput": "482.07", "total_tokens": 744880}
+{"current_steps": 118, "total_steps": 190, "loss": 0.0025, "learning_rate": 1.7274575140626318e-06, "epoch": 3.035369774919614, "percentage": 62.11, "elapsed_time": "0:25:58", "remaining_time": "0:15:50", "throughput": "482.17", "total_tokens": 751408}
+{"current_steps": 119, "total_steps": 190, "loss": 0.0067, "learning_rate": 1.686079613857109e-06, "epoch": 3.0610932475884245, "percentage": 62.63, "elapsed_time": "0:26:11", "remaining_time": "0:15:37", "throughput": "482.08", "total_tokens": 757632}
+{"current_steps": 120, "total_steps": 190, "loss": 0.002, "learning_rate": 1.6449496416858285e-06, "epoch": 3.0868167202572345, "percentage": 63.16, "elapsed_time": "0:26:24", "remaining_time": "0:15:24", "throughput": "482.04", "total_tokens": 763936}
+{"current_steps": 121, "total_steps": 190, "loss": 0.0003, "learning_rate": 1.6040801261367494e-06, "epoch": 3.112540192926045, "percentage": 63.68, "elapsed_time": "0:26:37", "remaining_time": "0:15:11", "throughput": "481.90", "total_tokens": 770064}
+{"current_steps": 122, "total_steps": 190, "loss": 0.0012, "learning_rate": 1.56348351646022e-06, "epoch": 3.1382636655948555, "percentage": 64.21, "elapsed_time": "0:26:51", "remaining_time": "0:14:58", "throughput": "481.74", "total_tokens": 776176}
+{"current_steps": 123, "total_steps": 190, "loss": 0.0001, "learning_rate": 1.5231721787768162e-06, "epoch": 3.1639871382636655, "percentage": 64.74, "elapsed_time": "0:27:04", "remaining_time": "0:14:44", "throughput": "481.70", "total_tokens": 782464}
+{"current_steps": 124, "total_steps": 190, "loss": 0.0098, "learning_rate": 1.4831583923105e-06, "epoch": 3.189710610932476, "percentage": 65.26, "elapsed_time": "0:27:17", "remaining_time": "0:14:31", "throughput": "481.86", "total_tokens": 789072}
+{"current_steps": 125, "total_steps": 190, "loss": 0.0029, "learning_rate": 1.443454345648252e-06, "epoch": 3.215434083601286, "percentage": 65.79, "elapsed_time": "0:27:30", "remaining_time": "0:14:18", "throughput": "481.92", "total_tokens": 795536}
+{"current_steps": 126, "total_steps": 190, "loss": 0.0, "learning_rate": 1.4040721330273063e-06, "epoch": 3.2411575562700965, "percentage": 66.32, "elapsed_time": "0:27:43", "remaining_time": "0:14:05", "throughput": "481.92", "total_tokens": 801888}
+{"current_steps": 127, "total_steps": 190, "loss": 0.0, "learning_rate": 1.3650237506511333e-06, "epoch": 3.266881028938907, "percentage": 66.84, "elapsed_time": "0:27:57", "remaining_time": "0:13:51", "throughput": "482.03", "total_tokens": 808432}
+{"current_steps": 128, "total_steps": 190, "loss": 0.0002, "learning_rate": 1.3263210930352737e-06, "epoch": 3.292604501607717, "percentage": 67.37, "elapsed_time": "0:28:10", "remaining_time": "0:13:38", "throughput": "482.09", "total_tokens": 814896}
+{"current_steps": 129, "total_steps": 190, "loss": 0.0331, "learning_rate": 1.2879759493841577e-06, "epoch": 3.3183279742765275, "percentage": 67.89, "elapsed_time": "0:28:23", "remaining_time": "0:13:25", "throughput": "482.05", "total_tokens": 821200}
+{"current_steps": 130, "total_steps": 190, "loss": 0.0, "learning_rate": 1.2500000000000007e-06, "epoch": 3.3440514469453375, "percentage": 68.42, "elapsed_time": "0:28:36", "remaining_time": "0:13:12", "throughput": "482.07", "total_tokens": 827584}
+{"current_steps": 131, "total_steps": 190, "loss": 0.0038, "learning_rate": 1.2124048127248644e-06, "epoch": 3.369774919614148, "percentage": 68.95, "elapsed_time": "0:28:49", "remaining_time": "0:12:59", "throughput": "482.13", "total_tokens": 834048}
+{"current_steps": 132, "total_steps": 190, "loss": 0.0, "learning_rate": 1.1752018394169882e-06, "epoch": 3.395498392282958, "percentage": 69.47, "elapsed_time": "0:29:03", "remaining_time": "0:12:45", "throughput": "482.03", "total_tokens": 840240}
+{"current_steps": 133, "total_steps": 190, "loss": 0.0051, "learning_rate": 1.1384024124624324e-06, "epoch": 3.4212218649517685, "percentage": 70.0, "elapsed_time": "0:29:16", "remaining_time": "0:12:32", "throughput": "481.94", "total_tokens": 846448}
+{"current_steps": 134, "total_steps": 190, "loss": 0.0589, "learning_rate": 1.1020177413231334e-06, "epoch": 3.446945337620579, "percentage": 70.53, "elapsed_time": "0:29:29", "remaining_time": "0:12:19", "throughput": "482.01", "total_tokens": 852928}
+{"current_steps": 135, "total_steps": 190, "loss": 0.0003, "learning_rate": 1.0660589091223854e-06, "epoch": 3.472668810289389, "percentage": 71.05, "elapsed_time": "0:29:42", "remaining_time": "0:12:06", "throughput": "482.02", "total_tokens": 859312}
+{"current_steps": 136, "total_steps": 190, "loss": 0.0355, "learning_rate": 1.0305368692688175e-06, "epoch": 3.4983922829581995, "percentage": 71.58, "elapsed_time": "0:29:55", "remaining_time": "0:11:53", "throughput": "481.90", "total_tokens": 865440}
+{"current_steps": 137, "total_steps": 190, "loss": 0.0402, "learning_rate": 9.95462442119879e-07, "epoch": 3.5241157556270095, "percentage": 72.11, "elapsed_time": "0:30:09", "remaining_time": "0:11:39", "throughput": "481.99", "total_tokens": 871968}
+{"current_steps": 138, "total_steps": 190, "loss": 0.0, "learning_rate": 9.608463116858544e-07, "epoch": 3.54983922829582, "percentage": 72.63, "elapsed_time": "0:30:22", "remaining_time": "0:11:26", "throughput": "482.00", "total_tokens": 878352}
+{"current_steps": 139, "total_steps": 190, "loss": 0.0083, "learning_rate": 9.266990223754069e-07, "epoch": 3.57556270096463, "percentage": 73.16, "elapsed_time": "0:30:35", "remaining_time": "0:11:13", "throughput": "482.01", "total_tokens": 884736}
+{"current_steps": 140, "total_steps": 190, "loss": 0.0, "learning_rate": 8.930309757836517e-07, "epoch": 3.6012861736334405, "percentage": 73.68, "elapsed_time": "0:30:48", "remaining_time": "0:11:00", "throughput": "481.96", "total_tokens": 891008}
+{"current_steps": 141, "total_steps": 190, "loss": 0.0012, "learning_rate": 8.598524275237321e-07, "epoch": 3.627009646302251, "percentage": 74.21, "elapsed_time": "0:31:01", "remaining_time": "0:10:47", "throughput": "481.83", "total_tokens": 897120}
+{"current_steps": 142, "total_steps": 190, "loss": 0.0342, "learning_rate": 8.271734841028553e-07, "epoch": 3.652733118971061, "percentage": 74.74, "elapsed_time": "0:31:15", "remaining_time": "0:10:33", "throughput": "481.86", "total_tokens": 903536}
+{"current_steps": 143, "total_steps": 190, "loss": 0.0723, "learning_rate": 7.950040998437541e-07, "epoch": 3.6784565916398715, "percentage": 75.26, "elapsed_time": "0:31:28", "remaining_time": "0:10:20", "throughput": "481.82", "total_tokens": 909824}
+{"current_steps": 144, "total_steps": 190, "loss": 0.0, "learning_rate": 7.633540738525066e-07, "epoch": 3.7041800643086815, "percentage": 75.79, "elapsed_time": "0:31:41", "remaining_time": "0:10:07", "throughput": "481.79", "total_tokens": 916112}
+{"current_steps": 145, "total_steps": 190, "loss": 0.041, "learning_rate": 7.322330470336314e-07, "epoch": 3.729903536977492, "percentage": 76.32, "elapsed_time": "0:31:54", "remaining_time": "0:09:54", "throughput": "481.85", "total_tokens": 922592}
+{"current_steps": 146, "total_steps": 190, "loss": 0.0, "learning_rate": 7.016504991533727e-07, "epoch": 3.755627009646302, "percentage": 76.84, "elapsed_time": "0:32:07", "remaining_time": "0:09:40", "throughput": "481.70", "total_tokens": 928640}
+{"current_steps": 147, "total_steps": 190, "loss": 0.0001, "learning_rate": 6.716157459520739e-07, "epoch": 3.7813504823151125, "percentage": 77.37, "elapsed_time": "0:32:21", "remaining_time": "0:09:27", "throughput": "481.95", "total_tokens": 935488}
+{"current_steps": 148, "total_steps": 190, "loss": 0.022, "learning_rate": 6.421379363065142e-07, "epoch": 3.807073954983923, "percentage": 77.89, "elapsed_time": "0:32:34", "remaining_time": "0:09:14", "throughput": "482.01", "total_tokens": 941952}
+{"current_steps": 149, "total_steps": 190, "loss": 0.0, "learning_rate": 6.1322604944307e-07, "epoch": 3.832797427652733, "percentage": 78.42, "elapsed_time": "0:32:47", "remaining_time": "0:09:01", "throughput": "482.03", "total_tokens": 948368}
+{"current_steps": 150, "total_steps": 190, "loss": 0.0095, "learning_rate": 5.848888922025553e-07, "epoch": 3.8585209003215435, "percentage": 78.95, "elapsed_time": "0:33:00", "remaining_time": "0:08:48", "throughput": "482.04", "total_tokens": 954752}
+{"current_steps": 151, "total_steps": 190, "loss": 0.0002, "learning_rate": 5.571350963575728e-07, "epoch": 3.884244372990354, "percentage": 79.47, "elapsed_time": "0:33:13", "remaining_time": "0:08:34", "throughput": "482.11", "total_tokens": 961248}
+{"current_steps": 152, "total_steps": 190, "loss": 0.0, "learning_rate": 5.299731159831953e-07, "epoch": 3.909967845659164, "percentage": 80.0, "elapsed_time": "0:33:27", "remaining_time": "0:08:21", "throughput": "482.02", "total_tokens": 967424}
+{"current_steps": 153, "total_steps": 190, "loss": 0.0274, "learning_rate": 5.034112248817685e-07, "epoch": 3.935691318327974, "percentage": 80.53, "elapsed_time": "0:33:40", "remaining_time": "0:08:08", "throughput": "481.97", "total_tokens": 973696}
+{"current_steps": 154, "total_steps": 190, "loss": 0.0284, "learning_rate": 4.774575140626317e-07, "epoch": 3.9614147909967845, "percentage": 81.05, "elapsed_time": "0:33:53", "remaining_time": "0:07:55", "throughput": "482.02", "total_tokens": 980144}
+{"current_steps": 155, "total_steps": 190, "loss": 0.0001, "learning_rate": 4.5211988927752026e-07, "epoch": 3.987138263665595, "percentage": 81.58, "elapsed_time": "0:34:06", "remaining_time": "0:07:42", "throughput": "481.94", "total_tokens": 986352}
+{"current_steps": 156, "total_steps": 190, "loss": 0.0, "learning_rate": 4.27406068612396e-07, "epoch": 4.012861736334405, "percentage": 82.11, "elapsed_time": "0:34:19", "remaining_time": "0:07:28", "throughput": "482.02", "total_tokens": 992880}
+{"current_steps": 157, "total_steps": 190, "loss": 0.0001, "learning_rate": 4.033235801364402e-07, "epoch": 4.038585209003215, "percentage": 82.63, "elapsed_time": "0:34:33", "remaining_time": "0:07:15", "throughput": "482.00", "total_tokens": 999200}
+{"current_steps": 158, "total_steps": 190, "loss": 0.0001, "learning_rate": 3.798797596089351e-07, "epoch": 4.064308681672026, "percentage": 83.16, "elapsed_time": "0:34:46", "remaining_time": "0:07:02", "throughput": "482.01", "total_tokens": 1005568}
+{"current_steps": 159, "total_steps": 190, "loss": 0.0121, "learning_rate": 3.5708174824471947e-07, "epoch": 4.090032154340836, "percentage": 83.68, "elapsed_time": "0:34:59", "remaining_time": "0:06:49", "throughput": "481.91", "total_tokens": 1011728}
+{"current_steps": 160, "total_steps": 190, "loss": 0.0, "learning_rate": 3.3493649053890325e-07, "epoch": 4.115755627009646, "percentage": 84.21, "elapsed_time": "0:35:12", "remaining_time": "0:06:36", "throughput": "482.00", "total_tokens": 1018272}
+{"current_steps": 161, "total_steps": 190, "loss": 0.0001, "learning_rate": 3.134507321515107e-07, "epoch": 4.141479099678457, "percentage": 84.74, "elapsed_time": "0:35:25", "remaining_time": "0:06:22", "throughput": "482.03", "total_tokens": 1024688}
+{"current_steps": 162, "total_steps": 190, "loss": 0.0001, "learning_rate": 2.9263101785268253e-07, "epoch": 4.167202572347267, "percentage": 85.26, "elapsed_time": "0:35:39", "remaining_time": "0:06:09", "throughput": "482.07", "total_tokens": 1031152}
+{"current_steps": 163, "total_steps": 190, "loss": 0.0002, "learning_rate": 2.7248368952908055e-07, "epoch": 4.192926045016077, "percentage": 85.79, "elapsed_time": "0:35:52", "remaining_time": "0:05:56", "throughput": "482.13", "total_tokens": 1037632}
+{"current_steps": 164, "total_steps": 190, "loss": 0.0005, "learning_rate": 2.53014884252083e-07, "epoch": 4.218649517684887, "percentage": 86.32, "elapsed_time": "0:36:05", "remaining_time": "0:05:43", "throughput": "482.10", "total_tokens": 1043936}
+{"current_steps": 165, "total_steps": 190, "loss": 0.0001, "learning_rate": 2.3423053240837518e-07, "epoch": 4.244372990353698, "percentage": 86.84, "elapsed_time": "0:36:18", "remaining_time": "0:05:30", "throughput": "481.98", "total_tokens": 1050048}
+{"current_steps": 166, "total_steps": 190, "loss": 0.0001, "learning_rate": 2.1613635589349756e-07, "epoch": 4.270096463022508, "percentage": 87.37, "elapsed_time": "0:36:31", "remaining_time": "0:05:16", "throughput": "482.03", "total_tokens": 1056496}
+{"current_steps": 167, "total_steps": 190, "loss": 0.0001, "learning_rate": 1.9873786636889908e-07, "epoch": 4.295819935691318, "percentage": 87.89, "elapsed_time": "0:36:44", "remaining_time": "0:05:03", "throughput": "482.02", "total_tokens": 1062848}
+{"current_steps": 168, "total_steps": 190, "loss": 0.0001, "learning_rate": 1.8204036358303173e-07, "epoch": 4.321543408360129, "percentage": 88.42, "elapsed_time": "0:36:58", "remaining_time": "0:04:50", "throughput": "481.99", "total_tokens": 1069136}
+{"current_steps": 169, "total_steps": 190, "loss": 0.0001, "learning_rate": 1.6604893375699594e-07, "epoch": 4.347266881028939, "percentage": 88.95, "elapsed_time": "0:37:11", "remaining_time": "0:04:37", "throughput": "481.91", "total_tokens": 1075328}
+{"current_steps": 170, "total_steps": 190, "loss": 0.0002, "learning_rate": 1.507684480352292e-07, "epoch": 4.372990353697749, "percentage": 89.47, "elapsed_time": "0:37:24", "remaining_time": "0:04:24", "throughput": "481.92", "total_tokens": 1081696}
+{"current_steps": 171, "total_steps": 190, "loss": 0.0026, "learning_rate": 1.362035610017079e-07, "epoch": 4.39871382636656, "percentage": 90.0, "elapsed_time": "0:37:37", "remaining_time": "0:04:10", "throughput": "481.94", "total_tokens": 1088112}
+{"current_steps": 172, "total_steps": 190, "loss": 0.0032, "learning_rate": 1.223587092621162e-07, "epoch": 4.42443729903537, "percentage": 90.53, "elapsed_time": "0:37:50", "remaining_time": "0:03:57", "throughput": "481.96", "total_tokens": 1094512}
+{"current_steps": 173, "total_steps": 190, "loss": 0.0004, "learning_rate": 1.0923811009241142e-07, "epoch": 4.45016077170418, "percentage": 91.05, "elapsed_time": "0:38:04", "remaining_time": "0:03:44", "throughput": "482.04", "total_tokens": 1101040}
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+{"current_steps": 175, "total_steps": 190, "loss": 0.0074, "learning_rate": 8.518543427732951e-08, "epoch": 4.501607717041801, "percentage": 92.11, "elapsed_time": "0:38:30", "remaining_time": "0:03:18", "throughput": "481.88", "total_tokens": 1113408}
+{"current_steps": 176, "total_steps": 190, "loss": 0.0029, "learning_rate": 7.426068431000883e-08, "epoch": 4.527331189710611, "percentage": 92.63, "elapsed_time": "0:38:43", "remaining_time": "0:03:04", "throughput": "481.80", "total_tokens": 1119568}
+{"current_steps": 177, "total_steps": 190, "loss": 0.0001, "learning_rate": 6.407483803691216e-08, "epoch": 4.553054662379421, "percentage": 93.16, "elapsed_time": "0:38:56", "remaining_time": "0:02:51", "throughput": "481.85", "total_tokens": 1126048}
+{"current_steps": 178, "total_steps": 190, "loss": 0.0, "learning_rate": 5.463099816548578e-08, "epoch": 4.578778135048232, "percentage": 93.68, "elapsed_time": "0:39:10", "remaining_time": "0:02:38", "throughput": "481.85", "total_tokens": 1132400}
+{"current_steps": 179, "total_steps": 190, "loss": 0.0082, "learning_rate": 4.593204138084006e-08, "epoch": 4.604501607717042, "percentage": 94.21, "elapsed_time": "0:39:23", "remaining_time": "0:02:25", "throughput": "481.87", "total_tokens": 1138832}
+{"current_steps": 180, "total_steps": 190, "loss": 0.0001, "learning_rate": 3.798061746947995e-08, "epoch": 4.630225080385852, "percentage": 94.74, "elapsed_time": "0:39:36", "remaining_time": "0:02:12", "throughput": "481.94", "total_tokens": 1145344}
+{"current_steps": 181, "total_steps": 190, "loss": 0.0, "learning_rate": 3.077914851215585e-08, "epoch": 4.655948553054662, "percentage": 95.26, "elapsed_time": "0:39:49", "remaining_time": "0:01:58", "throughput": "481.87", "total_tokens": 1151536}
+{"current_steps": 182, "total_steps": 190, "loss": 0.0001, "learning_rate": 2.4329828146074096e-08, "epoch": 4.681672025723473, "percentage": 95.79, "elapsed_time": "0:40:02", "remaining_time": "0:01:45", "throughput": "481.79", "total_tokens": 1157696}
+{"current_steps": 183, "total_steps": 190, "loss": 0.0001, "learning_rate": 1.8634620896695044e-08, "epoch": 4.707395498392283, "percentage": 96.32, "elapsed_time": "0:40:16", "remaining_time": "0:01:32", "throughput": "481.65", "total_tokens": 1163712}
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