Add files using upload-large-folder tool

vbert-siglip2-slbert-30_210/latest_opt_step_dir

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+/lustre/fsn1/projects/rech/nwd/uyn61im/checkpoints/experiments/vbert/slbert/vbert-siglip2-slbert-30_210/opt_step-30000

vbert-siglip2-slbert-30_210/opt_step-12000__merged/special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<global-img>",
+    "<row_1_col_1>",
+    "<row_1_col_2>",
+    "<row_1_col_3>",
+    "<row_1_col_4>",
+    "<row_1_col_5>",
+    "<row_1_col_6>",
+    "<row_2_col_1>",
+    "<row_2_col_2>",
+    "<row_2_col_3>",
+    "<row_2_col_4>",
+    "<row_2_col_5>",
+    "<row_2_col_6>",
+    "<row_3_col_1>",
+    "<row_3_col_2>",
+    "<row_3_col_3>",
+    "<row_3_col_4>",
+    "<row_3_col_5>",
+    "<row_3_col_6>",
+    "<row_4_col_1>",
+    "<row_4_col_2>",
+    "<row_4_col_3>",
+    "<row_4_col_4>",
+    "<row_4_col_5>",
+    "<row_4_col_6>",
+    "<row_5_col_1>",
+    "<row_5_col_2>",
+    "<row_5_col_3>",
+    "<row_5_col_4>",
+    "<row_5_col_5>",
+    "<row_5_col_6>",
+    "<row_6_col_1>",
+    "<row_6_col_2>",
+    "<row_6_col_3>",
+    "<row_6_col_4>",
+    "<row_6_col_5>",
+    "<row_6_col_6>",
+    "<end_of_utterance>",
+    "<fake_token_around_image>",
+    "<image>"
+  ],
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<|reserved_special_token_0|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

vbert-siglip2-slbert-30_210/train_logs.json

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+{"lr": 0.0, "num_opt_steps": 30000, "num_epochs": 0, "per_token_loss": {"sft": 0.6581978052854538, "all": 0.6581978052854538}, "z_loss": {"sft": 0.0, "all": 0.0}, "watt/s": {"sft": 305.36119306689926, "all": 305.36119306689926}, "tflops": {"sft": 10.922609116859737, "all": 10.922609116859737}, "tflop_counter": {"sft": 27412154.399291992, "all": 27412154.399291992}, "fwd_bwd_time": {"sft": 2275998.743268013, "all": 2275998.743268013}, "tflops_acc": {"sft": 12.044011219413948, "all": 12.044011219413948}, "num_per_device_batches": {"sft": 1920000, "all": 1920000}, "num_images": {"sft": 110833007, "all": 110833007}, "num_image_tokens": {"sft": 7093312448, "all": 7093312448}, "num_tokens": {"sft": 3529578821, "all": 3529578821}, "image_to_text_ratio": {"sft": 0.051525361835956573, "all": 0.051525361835956573}, "pixel_values_sum": {"sft": 28847235316920.0, "all": 28847235316920.0}, "num_padding": {"sft": 1916452372, "all": 1916452372}, "num_per_device_batches_in_curr_epoch": {"sft": 1920000, "all": 1920000}, "num_batches": {"all": 1920000}, "num_batches_in_curr_epoch": {"all": 1920000}, "per_token_loss_acc": {}, "z_loss_acc": {}, "num_batches_since_training_logged": {}, "num_per_device_batches_since_training_logged": {}, "tflop_counter_since_training_logged": {}, "total_energy_delta_since_training_logged": {}, "fwd_bwd_time_since_training_logged": {}, "global_batch_size_current": 256}

vbert-siglip2-slbert-30_210__im2048/latest_opt_step_dir

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+/lustre/fsn1/projects/rech/nwd/uyn61im/checkpoints/experiments/vbert/ablations/image_upscaling/siglip2-slbert-30_210__im2048/opt_step-30000

vbert-siglip2-slbert-30_210__im2048/opt_step-26000/accelerator_state/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 . output_dir/
+#   or
+#   python zero_to_fp32.py . output_dir/ --safe_serialization
+
+import argparse
+import torch
+import glob
+import math
+import os
+import re
+import json
+from tqdm import tqdm
+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 tqdm(param_shapes.items(), desc='Gathering Sharded Weights'):
+        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_dir,
+                                               max_shard_size="5GB",
+                                               safe_serialization=False,
+                                               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_dir``: directory to the pytorch fp32 state_dict output files
+        - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
+        - ``safe_serialization``:  whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
+        - ``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
+    """
+    # Dependency pre-check
+    if safe_serialization:
+        try:
+            from safetensors.torch import save_file
+        except ImportError:
+            print('If you want to use `safe_serialization`, please `pip install safetensors`')
+            raise
+    if max_shard_size is not None:
+        try:
+            from huggingface_hub import split_torch_state_dict_into_shards
+        except ImportError:
+            print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
+            raise
+
+    # Convert zero checkpoint to state_dict
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag, exclude_frozen_parameters)
+
+    # Shard the model if it is too big.
+    weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
+    if max_shard_size is not None:
+        filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
+        state_dict_split = split_torch_state_dict_into_shards(state_dict,
+                                                              filename_pattern=filename_pattern,
+                                                              max_shard_size=max_shard_size)
+    else:
+        from collections import namedtuple
+        StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
+        state_dict_split = StateDictSplit(is_sharded=False,
+                                          filename_to_tensors={weights_name: list(state_dict.keys())})
+
+    # Save the model
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard = {tensor: state_dict[tensor].contiguous() for tensor in tensors}
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard, output_path)
+
+    # Save index if sharded
+    if state_dict_split.is_sharded:
+        index = {
+            "metadata": state_dict_split.metadata,
+            "weight_map": state_dict_split.tensor_to_filename,
+        }
+        save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
+        save_index_file = os.path.join(output_dir, save_index_file)
+        with open(save_index_file, "w", encoding="utf-8") as f:
+            content = json.dumps(index, indent=2, sort_keys=True) + "\n"
+            f.write(content)
+
+
+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_dir",
+                        type=str,
+                        help="directory to the pytorch fp32 state_dict output files"
+                        "(e.g. path/checkpoint-12-output/)")
+    parser.add_argument(
+        "--max_shard_size",
+        type=str,
+        default="5GB",
+        help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
+        "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
+        "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
+        "without CPU OOM issues.")
+    parser.add_argument(
+        "--safe_serialization",
+        default=False,
+        action='store_true',
+        help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
+    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_dir,
+                                               max_shard_size=args.max_shard_size,
+                                               safe_serialization=args.safe_serialization,
+                                               tag=args.tag,
+                                               exclude_frozen_parameters=args.exclude_frozen_parameters)

vbert-siglip2-slbert-30_210__im2048/opt_step-26000/resume_run_infos.json

@@ -0,0 +1,91 @@
+{
+  "train_logs": {
+    "lr": 4.2264973081037426e-05,
+    "num_opt_steps": 26000,
+    "num_epochs": 0,
+    "per_token_loss": {
+      "sft": 0.8268339484930038,
+      "all": 0.8268339484930038
+    },
+    "z_loss": {
+      "sft": 0.0,
+      "all": 0.0
+    },
+    "watt/s": {
+      "sft": 375.9488208023041,
+      "all": 375.9488208023041
+    },
+    "tflops": {
+      "sft": 11.25311146724861,
+      "all": 11.25311146724861
+    },
+    "tflop_counter": {
+      "sft": 27307846.75453186,
+      "all": 27307846.75453186
+    },
+    "fwd_bwd_time": {
+      "sft": 2318396.5683317184,
+      "all": 2318396.5683317184
+    },
+    "tflops_acc": {
+      "sft": 11.778764309585808,
+      "all": 11.778764309585808
+    },
+    "num_per_device_batches": {
+      "sft": 1664000,
+      "all": 1664000
+    },
+    "num_images": {
+      "sft": 113143169,
+      "all": 113143169
+    },
+    "num_image_tokens": {
+      "sft": 7241162816,
+      "all": 7241162816
+    },
+    "num_tokens": {
+      "sft": 3122763684,
+      "all": 3122763684
+    },
+    "image_to_text_ratio": {
+      "sft": 0.09197408705949783,
+      "all": 0.09197408705949783
+    },
+    "pixel_values_sum": {
+      "sft": 29497643338840.0,
+      "all": 29497643338840.0
+    },
+    "num_padding": {
+      "sft": 1725040721,
+      "all": 1725040721
+    },
+    "num_per_device_batches_in_curr_epoch": {
+      "sft": 1664000,
+      "all": 1664000
+    },
+    "num_batches": {
+      "all": 1664000
+    },
+    "num_batches_in_curr_epoch": {
+      "all": 1664000
+    },
+    "per_token_loss_acc": {},
+    "z_loss_acc": {},
+    "num_batches_since_training_logged": {},
+    "num_per_device_batches_since_training_logged": {},
+    "tflop_counter_since_training_logged": {},
+    "total_energy_delta_since_training_logged": {},
+    "fwd_bwd_time_since_training_logged": {},
+    "global_batch_size_current": 256
+  },
+  "wandb_run_id": "eyif7ogo",
+  "seed": 42,
+  "resume_opt_step": 26000,
+  "resume_epoch": 0,
+  "gbs_running": {
+    "global_seen_samples": 6656000,
+    "global_batch_size_current": 256,
+    "next_goal_samples": 0,
+    "grad_acc_size_current": 4
+  }
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-26000/tokenizer/special_tokens_map.json

@@ -0,0 +1,312 @@
+{
+  "additional_special_tokens": [
+    {
+      "content": "<global-img>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_1_col_1>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_1_col_2>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_1_col_3>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_1_col_4>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_1_col_5>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_1_col_6>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_2_col_1>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_2_col_2>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_2_col_3>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_2_col_4>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_2_col_5>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_2_col_6>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_3_col_1>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_3_col_2>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_3_col_3>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_3_col_4>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_3_col_5>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_3_col_6>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_4_col_1>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_4_col_2>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_4_col_3>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_4_col_4>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_4_col_5>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_4_col_6>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_5_col_1>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_5_col_2>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_5_col_3>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_5_col_4>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_5_col_5>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_5_col_6>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_6_col_1>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_6_col_2>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_6_col_3>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_6_col_4>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_6_col_5>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<row_6_col_6>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<end_of_utterance>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<fake_token_around_image>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<image>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    }
+  ],
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<|reserved_special_token_0|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-26000/tokenizer/tokenizer_config.json

@@ -0,0 +1,2428 @@
+{
+  "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": {
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+  ],
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|end_of_text|>",
+  "extra_special_tokens": {},
+  "legacy": false,
+  "mask_token": "<|reserved_special_token_0|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|end_of_text|>",
+  "tokenizer_class": "PreTrainedTokenizer"
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-26000__merged/chat_template.jinja

@@ -0,0 +1,2 @@
+<|begin_of_text|>{% for message in messages %}{{message['role'] | capitalize}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{line['text']}}{% elif line['type'] == 'image' %}{{ '<image>' }}{% endif %}{% endfor %}<end_of_utterance>
+{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}

vbert-siglip2-slbert-30_210__im2048/opt_step-26000__merged/chat_template.json

@@ -0,0 +1,3 @@
+{
+    "chat_template": "<|begin_of_text|>{% for message in messages %}{{message['role'] | capitalize}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{line['text']}}{% elif line['type'] == 'image' %}{{ '<image>' }}{% endif %}{% endfor %}<end_of_utterance>\n{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}"
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-26000__merged/config.json

@@ -0,0 +1,50 @@
+{
+  "additional_vocab_size": 40,
+  "architectures": [
+    "VBertForMaskedLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_vbert.VBertConfig",
+    "AutoModel": "modeling_vbert.VBertModel",
+    "AutoModelForMaskedLM": "modeling_vbert.VBertForMaskedLM"
+  },
+  "freeze_config": {
+    "freeze_lm_head": true,
+    "freeze_text_layers": true,
+    "freeze_vision_layers": true
+  },
+  "hidden_size": 768,
+  "image_token_id": 128295,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 8192,
+  "model_type": "vbert",
+  "neftune_noise_alpha": 0.0,
+  "output_attentions": false,
+  "pixel_shuffle_factor": 4,
+  "qk_layer_norms": false,
+  "scale_factor": 4,
+  "text_config": {
+    "hidden_size": 768,
+    "intermediate_size": 3072,
+    "mlp_bias": false,
+    "model_type": "vbert",
+    "num_hidden_layers": 12,
+    "text_model_name": "SmolVEncoder/encoder-210m-30",
+    "vocab_size": 128256
+  },
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": null,
+  "use_cache": true,
+  "use_resampler": false,
+  "vision_config": {
+    "embed_dim": 768,
+    "image_size": 512,
+    "intermediate_size": 3072,
+    "model_type": "vbert",
+    "num_hidden_layers": 12,
+    "patch_size": 16,
+    "vision_model_name": "google/siglip2-base-patch16-512"
+  },
+  "vocab_size": 128256
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-26000__merged/configuration_vbert.py

@@ -0,0 +1,233 @@
+import copy
+import os
+
+from typing import Union, Any, Dict
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+from transformers import CONFIG_MAPPING, AutoConfig
+
+logger = logging.get_logger(__name__)
+
+def collect_arg_in_candidates(config, candidates, default = None) -> Any:
+    """ Gets the argument in a config given a list of candidates """
+    for c in candidates:
+        if hasattr(config, c):
+            return getattr(config, c)
+        elif c in config:
+            return config[c]
+    if default is not None:
+        return default
+    raise ValueError("No matching arguments found in candidates. Candidates: {}, Config: {}".format(candidates, config))
+
+class VBertTextConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaMA-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        embed_dim (`int`, *optional*, defaults to 1152):
+            Dimensionality of the encoder layers and the pooler layer. (elsewhere referred to as `embed_dim`)
+        image_size (`int`, *optional*, defaults to 384):
+            The size (resolution) of each image.
+    """
+    model_type = "vbert"
+
+    def __init__(
+        self,
+        # Case for when vllama3 is from the hub with no vision_model_name
+        text_model_name="EuroBERT/EuroBERT-210m",
+        **kwargs,
+    ):
+        self.text_model_name = text_model_name
+        text_config = AutoConfig.from_pretrained(text_model_name, trust_remote_code=True)
+        if hasattr(text_config, "text_config"):
+            text_config = text_config.text_config
+        
+        self.hidden_size = collect_arg_in_candidates(text_config, ["hidden_size", "embed_dim"])
+        self.num_hidden_layers = collect_arg_in_candidates(text_config, ["num_hidden_layers", "num_hidden_blocks"])
+        self.intermediate_size = collect_arg_in_candidates(text_config, ["intermediate_size", "mlp_dim"])
+        self.mlp_bias = collect_arg_in_candidates(text_config, ["mlp_bias", "mlp_hidden_bias"], default = False)
+        self.vocab_size = collect_arg_in_candidates(text_config, ["vocab_size"])
+    
+        super().__init__(text_model_name=text_model_name, **kwargs)
+
+class VBertVisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaMA-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        embed_dim (`int`, *optional*, defaults to 1152):
+            Dimensionality of the encoder layers and the pooler layer. (elsewhere referred to as `embed_dim`)
+        image_size (`int`, *optional*, defaults to 384):
+            The size (resolution) of each image.
+    """
+    model_type = "vbert"
+    attribute_map = {
+        "hidden_size": "embed_dim",
+    }
+
+    def __init__(
+        self,
+        # Case for when vllama3 is from the hub with no vision_model_name
+        vision_model_name="google/siglip2-base-patch16-512",
+        **kwargs,
+    ):
+        self.vision_model_name = vision_model_name
+        vision_config = AutoConfig.from_pretrained(vision_model_name, trust_remote_code=True)
+        if hasattr(vision_config, "vision_config"):
+            vision_config = vision_config.vision_config
+
+        self.embed_dim = collect_arg_in_candidates(vision_config, ["embed_dim", "hidden_size"])
+        self.image_size = collect_arg_in_candidates(vision_config, ["image_size", "img_size"])
+        self.patch_size = collect_arg_in_candidates(vision_config, ["patch_size"])
+        self.num_hidden_layers = collect_arg_in_candidates(vision_config, ["num_hidden_layers", "num_hidden_blocks"])
+        self.intermediate_size = collect_arg_in_candidates(vision_config, ["intermediate_size", "mlp_dim"])
+
+        super().__init__(vision_model_name=vision_model_name, **kwargs)
+
+class VBertConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`SmolVLMModel`]. It is used to instantiate a
+    SmolVLM model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the model of the SmolVLM
+    [HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should cache the key/value pairs of the attention mechanism. Only
+            relevant if `config.is_decoder=True`.
+        image_token_id (`int`, *optional*, defaults to 128257):
+            The id of the "image" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether or not to tie the word embeddings with the token embeddings.
+        vision_config (`IdeficsVisionConfig` or `dict`, *optional*, defaults to `IdeficsVisionConfig`):
+            Custom vision config or dict for the vision tower
+        text_config (`PretrainedConfig` or `dict`, *optional*, defaults to `LlamaConfig`):
+            Custom text config or dict for the text model
+        scale_factor (`int`, *optional*, defaults to 2):
+            The scale factor for the image encoder.
+        pad_token_id (`int`, *optional*, defaults to 128002):
+            The id of the padding token.
+
+    Example:
+    ```python
+    >>> from transformers import SmolVLMModel, SmolVLMConfig
+    >>> # Initializing configuration
+    >>> configuration = SmolVLMConfig()
+    >>> # Initializing a model from the configuration
+    >>> model = SmolVLMModel(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "vbert"
+    is_composition = True
+    # sub_configs = {"text_config": VBertTextConfig, "vision_config": VBertVisionConfig}
+
+    DEFAULT_TEXT_MODEL_NAME = "EuroBERT/EuroBERT-210m"
+    DEFAULT_VISION_MODEL_NAME = "google/siglip2-base-patch16-512"
+
+    def __init__(
+        self,
+        text_config: Union[PretrainedConfig, Dict[str, Any]] = None,
+        vision_config: Union[PretrainedConfig, Dict[str, Any]] = None,
+        image_token_id: int = 128_257,
+        vocab_size=128_256,
+        use_cache = True,
+        tie_word_embeddings = False,
+        freeze_config = None,
+        pad_token_id = None,
+        initializer_range = 0.02,
+        pixel_shuffle_factor = 4,
+        use_resampler = False,
+        additional_vocab_size = 0,
+        neftune_noise_alpha = 0.0,
+        **kwargs,
+    ):        
+        self.image_token_id = image_token_id
+        self.use_cache = use_cache
+        self.tie_word_embeddings = tie_word_embeddings
+        self.scale_factor = pixel_shuffle_factor
+        self.additional_vocab_size = additional_vocab_size
+
+        if text_config is None:
+            text_config = AutoConfig.from_pretrained(self.DEFAULT_TEXT_MODEL_NAME, trust_remote_code=True)
+        elif isinstance(text_config, dict):
+            text_config = VBertTextConfig(text_config["text_model_name"])
+        self.text_config = text_config
+        
+        if vision_config is None:
+            vision_config = AutoConfig.from_pretrained(self.DEFAULT_VISION_MODEL_NAME, trust_remote_code=True)
+        elif isinstance(vision_config, dict):
+            vision_config = VBertVisionConfig(vision_config["vision_model_name"])
+        self.vision_config = vision_config
+
+        self.freeze_config = freeze_config
+
+        # Pixel shuffle factor
+        self.pixel_shuffle_factor = pixel_shuffle_factor
+        self.use_resampler = use_resampler
+
+        self.neftune_noise_alpha = neftune_noise_alpha
+
+        self.initializer_range = initializer_range
+
+        hidden_size = kwargs.pop("hidden_size", self.text_config.hidden_size)
+
+        super().__init__(
+            **kwargs, 
+            pad_token_id=pad_token_id, 
+            tie_word_embeddings=tie_word_embeddings,
+            vocab_size=vocab_size,
+            hidden_size=hidden_size,
+        )
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
+        Returns:
+            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        output = copy.deepcopy(self.__dict__)
+
+        output["model_type"] = self.__class__.model_type
+        output["vision_config"] = self.vision_config.to_dict()
+        output["text_config"] = self.text_config.to_dict()
+        # output["freeze_config"] = self.freeze_config.to_dict()
+
+        return output
+    
+    # @classmethod
+    # def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+    #     outputs = super(VBertConfig, cls).from_pretrained(pretrained_model_name_or_path, **kwargs)
+    #     return outputs
+
+    @classmethod
+    def from_pretrained_models(
+        cls, 
+        text_model_name: Union[str, os.PathLike],
+        vision_model_name: Union[str, os.PathLike],
+        **kwargs
+    ) -> "PretrainedConfig":
+        # text_model_config = AutoConfig.from_pretrained(text_model_name, trust_remote_code=True)
+        # vision_model_config = AutoConfig.from_pretrained(vision_model_name, trust_remote_code=True)
+        text_model_config = VBertTextConfig(text_model_name)
+        vision_model_config = VBertVisionConfig(vision_model_name)
+        return cls(
+            text_config=text_model_config,
+            vision_config=vision_model_config,
+            **kwargs
+        )

vbert-siglip2-slbert-30_210__im2048/opt_step-26000__merged/modeling_vbert.py

@@ -0,0 +1,630 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import CrossEntropyLoss
+from typing import Optional, Tuple, Union, List
+
+from transformers.cache_utils import DynamicCache
+
+from .configuration_vbert import VBertConfig
+
+from transformers import AutoModel, AutoConfig, AutoModelForMaskedLM, PreTrainedModel
+from transformers.modeling_outputs import BaseModelOutput
+from transformers.models.bert.modeling_bert import BaseModelOutputWithPoolingAndCrossAttentions, MaskedLMOutput
+
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+
+from dataclasses import dataclass
+
+from transformers import logging 
+
+logger = logging.get_logger(__name__)
+
+
+class DecoupledEmbedding(nn.Embedding):
+    # Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/sparse.html#Embedding
+    """
+    Implements a decoupling of parameters to allow freezing (or not) a subset of the embeddings.
+    In practise, the regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `num_additional_embeddings` > 0, then it will create `num_additional_embeddings` additional parameters that are always trained.
+    If `num_additional_embeddings=0`, then the module defaults back to the regular behavior of `nn.Embedding`.
+    """
+
+    def __init__(
+        self,
+        num_embeddings,
+        num_additional_embeddings,
+        embedding_dim,
+        partially_freeze=False,
+        device=None,
+        dtype=None,
+        padding_idx=None,
+        **kwargs,
+    ) -> None:
+        """
+        num_additional_embeddings: int. Number of additional embeddings. Only useful when you `partially_freeze=True`.
+        partially_freeze: bool. If True, the regular `weight` will be frozen. `additional_weight` is never frozen.
+
+        Note: there are a lot of other parameters to initialize a standard `nn.Embedding` such as `padding_idx`, `max_norm` or `norm_type`. We are not supporting these.
+        """
+        if padding_idx is not None and padding_idx > num_embeddings:
+            raise ValueError(f"padding_idx must be within num_embeddings. Got {padding_idx} and {num_embeddings}")
+        super().__init__(
+            num_embeddings=num_embeddings,
+            embedding_dim=embedding_dim,
+            device=device,
+            dtype=dtype,
+            padding_idx=padding_idx,
+            **kwargs,
+        )
+        self.num_embeddings = num_embeddings
+        self.padding_idx = padding_idx
+        self.num_additional_embeddings = num_additional_embeddings
+        self.partially_freeze = partially_freeze
+
+        if partially_freeze:
+            self.weight.requires_grad_(False)
+
+        if self.num_additional_embeddings > 0:
+            self.additional_embedding = nn.Embedding(
+                num_embeddings=self.num_additional_embeddings,
+                embedding_dim=embedding_dim,
+                device=device,
+                dtype=dtype,
+            )
+
+    def forward(self, input_ids):
+        """
+        we have 2 embeddings, with different indices - one pretrained self.weight and another
+        self.additional_embedding.weight that is being trained.
+
+        in order to make a lookup of the input ids, we:
+        1. find out the indices of the entries belonging to the 2nd embedding
+        2. extract those values while subtracting the size of the first embedding (num_embeddings),
+           since the 2nd embedding starts from 0 and not num_embeddings
+        3. perform the 2nd embedding lookup
+        4. now we handle the 1st embedding, we overwrite indices belonging to the 2nd embedding with a padding index
+        5. perform the 1st embedding lookup
+        6. now we overwrite the values in the 1st embedding lookup with the values of the 2nd embedding lookup
+
+        note: for the 1st embedding lookup we could have looked up only the low indices and not do
+        the padding, but then we have to create a new tensor and populate it with 2 tensors that are
+        spread out across various indices - i.e. not a simple concat - I haven't benchmarked the
+        complex case if it's any faster, given that seqlens are usually relatively short it's
+        probably not faster or if faster not by much - but might be a good idea to measure.
+
+        """
+        if self.num_additional_embeddings == 0:
+            return self.additional_embedding(input_ids)
+
+        # Clone so that we don't modify the original input_ids later on
+        input_ids = input_ids.clone()
+        additional_vocab_indices = torch.where(input_ids >= self.num_embeddings)
+        input_ids_additional_vocab = input_ids[additional_vocab_indices]
+        additional_embeddings = self.additional_embedding(input_ids_additional_vocab - self.num_embeddings)
+
+        # for successful lookup replace input_ids with 0, the results of these will be discarded anyway
+        input_ids[additional_vocab_indices] = 0
+        full_vector = F.embedding(input_ids, self.weight)
+
+        # overwrite the records with high indices
+        full_vector[additional_vocab_indices] = additional_embeddings
+
+        return full_vector
+
+    def extra_repr(self) -> str:
+        return "num_embeddings={}, num_additional_embeddings={}, embedding_dim={}, partially_freeze={}".format(
+            self.num_embeddings,
+            self.num_additional_embeddings,
+            self.embedding_dim,
+            self.partially_freeze,
+        )
+    
+@dataclass
+class VBertBaseModelOutput(BaseModelOutput):
+    """
+    Base class for SmolVLM model's outputs that may also contain a past key/values (to speed up sequential decoding).
+    Args:
+        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+            If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
+            hidden_size)` is output.
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+            `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+            encoder_sequence_length, embed_size_per_head)`.
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
+            `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
+            input) to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
+            Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
+            sequence_length, hidden_size)`.
+            image_hidden_states of the model produced by the vision encoder
+    """
+
+    last_hidden_state: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+
+@dataclass
+class VBertMaskedLMOutput(MaskedLMOutput):
+    """
+    Base class for SmolVLM model's outputs that may also contain a past key/values (to speed up sequential decoding).
+    Args:
+        loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided):
+            Masked language modeling (MLM) loss.
+        logits (`torch.FloatTensor`): 
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
+            Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
+            sequence_length, hidden_size)`.
+            image_hidden_states of the model produced by the vision encoder
+    """
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
+    image_hidden_states: Optional[torch.FloatTensor] = None
+
+class VBertSimpleMLP(nn.Module):
+    def __init__(self, input_size, output_size):
+        super().__init__()
+        self.proj = nn.Linear(input_size, output_size, bias=False)
+
+    def forward(self, x):
+        return self.proj(x)
+
+class VBertConnector(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.scale_factor = config.pixel_shuffle_factor
+        self.modality_projection = VBertSimpleMLP(
+            input_size=config.vision_config.hidden_size * (config.scale_factor**2),
+            output_size=config.text_config.hidden_size
+        )
+
+    def pixel_shuffle(self, x, scale_factor):
+        bsz, seq, embed_dim = x.size()
+        height = width = int(seq**0.5)
+        x = x.view(bsz, height, width, embed_dim)
+        x = x.view(bsz, height, int(width / scale_factor), embed_dim * scale_factor)
+        x = x.permute(0, 2, 1, 3)
+        x = x.reshape(bsz, int(width / scale_factor), int(height / scale_factor), embed_dim * (scale_factor**2))
+        x = x.permute(0, 2, 1, 3)
+        x = x.reshape(bsz, int(seq / (scale_factor**2)), embed_dim * (scale_factor**2))
+        return x
+
+    def forward(self, image_hidden_states):
+        image_hidden_states = self.pixel_shuffle(image_hidden_states, self.scale_factor)
+        image_hidden_states = self.modality_projection(image_hidden_states)
+        return image_hidden_states
+
+class VBertPreTrainedModel(PreTrainedModel):
+    config_class = VBertConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["VBertDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        """Initialize the weights."""
+
+        std = (
+            self.config.initializer_range
+            if hasattr(self.config, "initializer_range")
+            else self.config.text_config.initializer_range
+        )
+
+        if hasattr(module, "class_embedding"):
+            module.class_embedding.data.normal_(mean=0.0, std=std)
+
+        if isinstance(module, (nn.Linear, nn.Conv2d)):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+class VBertModel(VBertPreTrainedModel):
+    """
+    A subclass of Idefics3Model. We do *not* remove or block the call to inputs_merger
+    in forward. Instead, we override inputs_merger here with custom logic.
+    """
+
+    def __init__(self, config: VBertConfig, **kwargs):
+        super().__init__(config)
+
+        self.vision_model = VBertModel.init_vision_model(config, **kwargs)
+        self.connector = VBertConnector(config)
+        self.text_model = VBertModel.init_language_model(config, **kwargs)
+
+        self.image_seq_len = int(
+            ((config.vision_config.image_size // config.vision_config.patch_size) ** 2) / (config.scale_factor**2)
+        )
+        self.image_token_id = self.config.image_token_id
+
+        self.post_init()
+
+    @staticmethod
+    def init_vision_model(config: VBertConfig, **kwargs):
+        vision_model_config = AutoConfig.from_pretrained(
+            config.vision_config.vision_model_name,
+            trust_remote_code=True,
+            **kwargs,
+        )
+
+        vision_model = AutoModel.from_config(vision_model_config, trust_remote_code=True, **kwargs)
+
+        if hasattr(vision_model, "vision_model"):
+            # If the model has a vision_model attribute, it means it's a wrapper around another model
+            vision_model = vision_model.vision_model
+
+        return vision_model
+
+    @staticmethod
+    def init_language_model(config: VBertConfig, **kwargs):
+        text_model_config = AutoConfig.from_pretrained(
+            config.text_config.text_model_name,
+            trust_remote_code=True,
+            **kwargs,
+        )
+
+        text_model = AutoModel.from_config(text_model_config, trust_remote_code=True, **kwargs)
+        # extractor = regex_lookup(language_model_name, language_model_name2model)
+
+        embed_layer = DecoupledEmbedding(
+            num_embeddings=text_model_config.vocab_size,
+            num_additional_embeddings=config.additional_vocab_size,
+            embedding_dim=config.hidden_size,
+            partially_freeze=config.freeze_config["freeze_text_layers"],
+            padding_idx=config.pad_token_id,
+        )
+
+        text_model.set_input_embeddings(embed_layer)
+
+        return text_model
+
+    def enable_input_require_grads(self):
+        """
+        Enables the gradients for the input embeddings.
+
+        This is useful for lora when using gradient checkpointing.
+        c.f. https://github.com/huggingface/peft/issues/1402#issuecomment-1913675032
+
+        Override to set output.requires_grad = True for both the decoder's and vision model's embeddings.
+        """
+
+        def get_lowest_module(module):
+            if len(list(module.children())) == 0:
+                # If the module has no children, it is a leaf module (e.g., Linear, Conv2d, etc.)
+                return module
+            else:
+                # Recursively call the function on each child module
+                return get_lowest_module(list(module.children())[0])
+
+        def make_inputs_require_grads(module, input, output):
+            output.requires_grad_(True)
+
+        self._text_require_grads_hook = self.get_input_embeddings().register_forward_hook(make_inputs_require_grads)
+        self._vision_require_grads_hook = get_lowest_module(self.vision_model).register_forward_hook(
+            make_inputs_require_grads
+        )
+
+    def disable_input_require_grads(self):
+        self._text_require_grads_hook.remove()
+        self._vision_require_grads_hook.remove()
+
+    def get_input_embeddings(self):
+        return self.text_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.text_model.set_input_embeddings(value)
+
+    def inputs_merger(
+        self, input_ids: torch.LongTensor, inputs_embeds: torch.Tensor, image_hidden_states: torch.Tensor
+    ):
+        """
+        This method aims at merging the token embeddings with the image hidden states into one single sequence of vectors that are fed to the transformer LM.
+        The merging happens as follows:
+        - The text token sequence is: `tok_1 tok_2 tok_3 <fake_token_around_image> <image> <image> ... <image> <fake_token_around_image> tok_4`.
+        - We get the image hidden states for the image through the vision encoder and that hidden state, after a pixel shuffle operation, is then projected into the text embedding space.
+        We thus have a sequence of image hidden states of size (1, image_seq_len, hidden_dim), where 1 is for batch_size of 1 image and hidden_dim is the hidden_dim of the LM transformer.
+        - The merging happens so that we obtain the following sequence: `vector_tok_1 vector_tok_2 vector_tok_3 vector_fake_tok_around_image {sequence of image_seq_len image hidden states} vector_fake_toke_around_image vector_tok_4`. That sequence is fed to the LM.
+        - To fit the format of that sequence, `input_ids`, `input_embeds`, `attention_mask` are all 3 adapted to insert the image hidden states.
+        """
+        _, patch_size, _ = image_hidden_states.shape
+
+        image_mask = input_ids == self.image_token_id
+        num_image_tokens = image_mask.sum(dim=1)
+        if not torch.all(num_image_tokens % patch_size == 0):
+            raise ValueError("At least one sample has <image> tokens not divisible by patch_size.")
+
+        blocks_per_sample = num_image_tokens // patch_size
+
+        offsets = torch.nn.functional.pad(blocks_per_sample.cumsum(dim=0), (1, 0), value=0)
+        block_offset = offsets[:-1]
+        row_cum = image_mask.cumsum(dim=-1)
+        chunk_idx = (row_cum - 1) // patch_size
+        local_idx = (row_cum - 1) % patch_size
+        block_idx = block_offset.unsqueeze(1) + chunk_idx
+
+        image_embeds = torch.zeros_like(inputs_embeds)
+        image_embeds[image_mask] = image_hidden_states[block_idx[image_mask], local_idx[image_mask], :]
+
+        merged_embeds = torch.where(image_mask.unsqueeze(-1), image_embeds, inputs_embeds)
+        return merged_embeds
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        pixel_attention_mask: Optional[torch.BoolTensor] = None,
+        image_hidden_states: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPoolingAndCrossAttentions]:    
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if self.training and self.text_model.gradient_checkpointing and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+            )
+            use_cache = False
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        past_seen_tokens = 0
+        if use_cache:
+            if past_key_values is None:
+                past_key_values = DynamicCache()
+            past_seen_tokens = past_key_values.get_seq_length()
+
+        if inputs_embeds is not None and input_ids is None and past_seen_tokens == 0:
+            raise ValueError("When first calling the model, if input_embeds are passed, input_ids should not be None.")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.text_model.get_input_embeddings()(input_ids).to(input_ids.device)
+
+        # START VISUAL INPUTS INTEGRATION
+        if pixel_values is not None and image_hidden_states is not None:
+            raise ValueError("You cannot specify both pixel_values and image_hidden_states at the same time")
+        elif pixel_values is not None:
+            batch_size, num_images, num_channels, height, width = pixel_values.shape
+            pixel_values = pixel_values
+            pixel_values = pixel_values.view(batch_size * num_images, *pixel_values.shape[2:])
+
+            # Remove padding images - padding images are full 0.
+            nb_values_per_image = pixel_values.shape[1:].numel()
+            real_images_inds = (pixel_values == 0.0).sum(dim=(-1, -2, -3)) != nb_values_per_image
+
+            if not any(real_images_inds):
+                # no images, leave one empty image.
+                real_images_inds[0] = True
+
+            pixel_values = pixel_values[real_images_inds].contiguous()
+
+            # Handle the vision attention mask
+            if pixel_attention_mask is None:
+                pixel_attention_mask = torch.ones(
+                    size=[pixel_values.shape[i] for i in (0, 2, 3)],
+                    dtype=torch.bool,
+                    device=pixel_values.device,
+                )
+            else:
+                # Remove padding images from the mask
+                pixel_attention_mask = pixel_attention_mask.view(
+                    batch_size * num_images, *pixel_attention_mask.shape[2:]
+                )
+                pixel_attention_mask = pixel_attention_mask[real_images_inds].contiguous()
+
+            # patch_size = self.config.vision_config.patch_size
+            # patches_subgrid = pixel_attention_mask.unfold(dimension=1, size=patch_size, step=patch_size)
+            # patches_subgrid = patches_subgrid.unfold(dimension=2, size=patch_size, step=patch_size)
+            # patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()
+
+            # Get sequence from the vision encoder
+            image_hidden_states = self.vision_model(
+                pixel_values=pixel_values,
+                # patch_attention_mask=patch_attention_mask,
+            ).last_hidden_state
+
+            # Modality projection & resampling
+            image_hidden_states = self.connector(image_hidden_states)
+
+        elif image_hidden_states is not None:
+            image_hidden_states = image_hidden_states.to(dtype=self.dtype, device=input_ids.device)
+
+        if inputs_embeds is not None and image_hidden_states is not None:
+            # When we embed, we don't want to replace the potential image_token_id that we generated by images
+            # that simply don't exist
+            inputs_embeds = self.inputs_merger(
+                input_ids=input_ids,
+                inputs_embeds=inputs_embeds,
+                image_hidden_states=image_hidden_states,
+            )
+
+        outputs = self.text_model(
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            # past_key_values=past_key_values,
+            # use_cache=use_cache,
+            # cache_position=cache_position,
+        )
+
+        if not return_dict:
+            return tuple(v for v in [*outputs, image_hidden_states] if v is not None)
+
+        return VBertBaseModelOutput(
+            last_hidden_state=outputs.last_hidden_state,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=image_hidden_states,
+        )
+
+class VBertForMaskedLM(VBertPreTrainedModel):
+    # _tied_weights_keys = ["predictions.decoder.bias", "cls.predictions.decoder.weight"]
+
+    def __init__(self, config, **kwargs):
+        super().__init__(config)
+
+        self.image_token_id = config.image_token_id
+        self.in_features = config.hidden_size
+        self.out_additional_features = config.additional_vocab_size
+        self.vocab_size = config.vocab_size
+
+        if config.is_decoder:
+            logger.warning(
+                "If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for "
+                "bi-directional self-attention."
+            )
+
+        self.model = VBertModel(config, **kwargs)
+        self.lm_head = VBertForMaskedLM.init_lm_head(config, **kwargs)
+        if self.out_additional_features > 0:
+            self.additional_fc = nn.Linear(
+                in_features=self.in_features,
+                out_features=self.out_additional_features,
+                bias=False,
+            )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @staticmethod
+    def init_lm_head(config, **kwargs):
+        # Get the pretrained model config
+        text_model_config = AutoConfig.from_pretrained(
+            config.text_config.text_model_name,
+            trust_remote_code=True,
+            **kwargs,
+        )
+        model = AutoModelForMaskedLM.from_config(text_model_config, trust_remote_code=True, **kwargs)
+        # Get the lm head
+        lm_head = model.lm_head if hasattr(model, "lm_head") else model.decoder if hasattr(model, "decoder") else None
+        if lm_head is None:
+            logger.warning(f"No lm head was found for {config.text_config.text_model_name}, initializing a new one.")
+            lm_head = nn.Linear(config.hidden_size, config.vocab_size, False)
+        return lm_head
+
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[torch.FloatTensor]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            pixel_values: Optional[torch.FloatTensor] = None,
+            pixel_attention_mask: Optional[torch.BoolTensor] = None,
+            image_hidden_states: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, VBertMaskedLMOutput]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or `model.image_token_id` (where `model` is your instance of `Idefics3ForConditionalGeneration`).
+                Tokens with indices set to `model.image_token_id` are ignored (masked), the loss is only
+                computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        ```"""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+        # Pass the inputs to VBertModel
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            pixel_values=pixel_values,
+            pixel_attention_mask=pixel_attention_mask,
+            image_hidden_states=image_hidden_states,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        # Pass the outputs to the MLM head
+        hidden_states = outputs[0]
+
+        logits = self.lm_head(hidden_states)
+        if self.out_additional_features > 0:
+            additional_features = self.additional_fc(hidden_states)
+            logits = torch.cat((logits, additional_features), -1)
+        logits = logits.float()
+
+        masked_lm_loss = None
+        if labels is not None:
+            # print the ratio of not ignored tokens
+            loss_fct = CrossEntropyLoss()
+            masked_lm_loss = loss_fct(logits.view(-1, self.vocab_size + self.out_additional_features), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+        return VBertMaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=outputs.image_hidden_states,
+        )

vbert-siglip2-slbert-30_210__im2048/opt_step-26000__merged/preprocessor_config.json

@@ -0,0 +1,28 @@
+{
+    "do_convert_rgb": true,
+    "do_image_splitting": true,
+    "do_normalize": true,
+    "do_pad": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "image_processor_type": "Idefics3ImageProcessor",
+    "image_std": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "max_image_size": {
+        "longest_edge": 512
+    },
+    "processor_class": "Idefics3Processor",
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vbert-siglip2-slbert-30_210__im2048/opt_step-26000__merged/processor_config.json

@@ -0,0 +1,4 @@
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vbert-siglip2-slbert-30_210__im2048/opt_step-26000__merged/special_tokens_map.json

@@ -0,0 +1,72 @@
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vbert-siglip2-slbert-30_210__im2048/opt_step-26000__merged/tokenizer_config.json

@@ -0,0 +1,2429 @@
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128289": {
+      "content": "<row_6_col_3>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128290": {
+      "content": "<row_6_col_4>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128291": {
+      "content": "<row_6_col_5>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128292": {
+      "content": "<row_6_col_6>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128293": {
+      "content": "<end_of_utterance>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128294": {
+      "content": "<fake_token_around_image>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128295": {
+      "content": "<image>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<global-img>",
+    "<row_1_col_1>",
+    "<row_1_col_2>",
+    "<row_1_col_3>",
+    "<row_1_col_4>",
+    "<row_1_col_5>",
+    "<row_1_col_6>",
+    "<row_2_col_1>",
+    "<row_2_col_2>",
+    "<row_2_col_3>",
+    "<row_2_col_4>",
+    "<row_2_col_5>",
+    "<row_2_col_6>",
+    "<row_3_col_1>",
+    "<row_3_col_2>",
+    "<row_3_col_3>",
+    "<row_3_col_4>",
+    "<row_3_col_5>",
+    "<row_3_col_6>",
+    "<row_4_col_1>",
+    "<row_4_col_2>",
+    "<row_4_col_3>",
+    "<row_4_col_4>",
+    "<row_4_col_5>",
+    "<row_4_col_6>",
+    "<row_5_col_1>",
+    "<row_5_col_2>",
+    "<row_5_col_3>",
+    "<row_5_col_4>",
+    "<row_5_col_5>",
+    "<row_5_col_6>",
+    "<row_6_col_1>",
+    "<row_6_col_2>",
+    "<row_6_col_3>",
+    "<row_6_col_4>",
+    "<row_6_col_5>",
+    "<row_6_col_6>",
+    "<end_of_utterance>",
+    "<fake_token_around_image>",
+    "<image>"
+  ],
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|end_of_text|>",
+  "extra_special_tokens": {},
+  "legacy": false,
+  "mask_token": "<|reserved_special_token_0|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask",
+    "pixel_values",
+    "pixel_attention_mask"
+  ],
+  "model_max_length": 8192,
+  "pad_token": "<|end_of_text|>",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-28000/resume_run_infos.json

@@ -0,0 +1,91 @@
+{
+  "train_logs": {
+    "lr": 1.8350341907227396e-05,
+    "num_opt_steps": 28000,
+    "num_epochs": 0,
+    "per_token_loss": {
+      "sft": 0.7181523889303207,
+      "all": 0.7181523889303207
+    },
+    "z_loss": {
+      "sft": 0.0,
+      "all": 0.0
+    },
+    "watt/s": {
+      "sft": 415.80285607069436,
+      "all": 415.80285607069436
+    },
+    "tflops": {
+      "sft": 12.985541543582652,
+      "all": 12.985541543582652
+    },
+    "tflop_counter": {
+      "sft": 32687350.4992218,
+      "all": 32687350.4992218
+    },
+    "fwd_bwd_time": {
+      "sft": 2742255.1830587387,
+      "all": 2742255.1830587387
+    },
+    "tflops_acc": {
+      "sft": 11.919879193285727,
+      "all": 11.919879193285727
+    },
+    "num_per_device_batches": {
+      "sft": 1792000,
+      "all": 1792000
+    },
+    "num_images": {
+      "sft": 137620237,
+      "all": 137620237
+    },
+    "num_image_tokens": {
+      "sft": 8807695168,
+      "all": 8807695168
+    },
+    "num_tokens": {
+      "sft": 3422216030,
+      "all": 3422216030
+    },
+    "image_to_text_ratio": {
+      "sft": 0.09514428675174713,
+      "all": 0.09514428675174713
+    },
+    "pixel_values_sum": {
+      "sft": 35937884795160.0,
+      "all": 35937884795160.0
+    },
+    "num_padding": {
+      "sft": 1917068016,
+      "all": 1917068016
+    },
+    "num_per_device_batches_in_curr_epoch": {
+      "sft": 1792000,
+      "all": 1792000
+    },
+    "num_batches": {
+      "all": 1792000
+    },
+    "num_batches_in_curr_epoch": {
+      "all": 1792000
+    },
+    "per_token_loss_acc": {},
+    "z_loss_acc": {},
+    "num_batches_since_training_logged": {},
+    "num_per_device_batches_since_training_logged": {},
+    "tflop_counter_since_training_logged": {},
+    "total_energy_delta_since_training_logged": {},
+    "fwd_bwd_time_since_training_logged": {},
+    "global_batch_size_current": 256
+  },
+  "wandb_run_id": "eyif7ogo",
+  "seed": 42,
+  "resume_opt_step": 28000,
+  "resume_epoch": 0,
+  "gbs_running": {
+    "global_seen_samples": 7168000,
+    "global_batch_size_current": 256,
+    "next_goal_samples": 0,
+    "grad_acc_size_current": 4
+  }
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-28000__merged/chat_template.jinja

@@ -0,0 +1,2 @@
+<|begin_of_text|>{% for message in messages %}{{message['role'] | capitalize}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{line['text']}}{% elif line['type'] == 'image' %}{{ '<image>' }}{% endif %}{% endfor %}<end_of_utterance>
+{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}

vbert-siglip2-slbert-30_210__im2048/opt_step-28000__merged/chat_template.json

@@ -0,0 +1,3 @@
+{
+    "chat_template": "<|begin_of_text|>{% for message in messages %}{{message['role'] | capitalize}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{line['text']}}{% elif line['type'] == 'image' %}{{ '<image>' }}{% endif %}{% endfor %}<end_of_utterance>\n{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}"
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-28000__merged/config.json

@@ -0,0 +1,50 @@
+{
+  "additional_vocab_size": 40,
+  "architectures": [
+    "VBertForMaskedLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_vbert.VBertConfig",
+    "AutoModel": "modeling_vbert.VBertModel",
+    "AutoModelForMaskedLM": "modeling_vbert.VBertForMaskedLM"
+  },
+  "freeze_config": {
+    "freeze_lm_head": true,
+    "freeze_text_layers": true,
+    "freeze_vision_layers": true
+  },
+  "hidden_size": 768,
+  "image_token_id": 128295,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 8192,
+  "model_type": "vbert",
+  "neftune_noise_alpha": 0.0,
+  "output_attentions": false,
+  "pixel_shuffle_factor": 4,
+  "qk_layer_norms": false,
+  "scale_factor": 4,
+  "text_config": {
+    "hidden_size": 768,
+    "intermediate_size": 3072,
+    "mlp_bias": false,
+    "model_type": "vbert",
+    "num_hidden_layers": 12,
+    "text_model_name": "SmolVEncoder/encoder-210m-30",
+    "vocab_size": 128256
+  },
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": null,
+  "use_cache": true,
+  "use_resampler": false,
+  "vision_config": {
+    "embed_dim": 768,
+    "image_size": 512,
+    "intermediate_size": 3072,
+    "model_type": "vbert",
+    "num_hidden_layers": 12,
+    "patch_size": 16,
+    "vision_model_name": "google/siglip2-base-patch16-512"
+  },
+  "vocab_size": 128256
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-28000__merged/configuration_vbert.py

@@ -0,0 +1,233 @@
+import copy
+import os
+
+from typing import Union, Any, Dict
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+from transformers import CONFIG_MAPPING, AutoConfig
+
+logger = logging.get_logger(__name__)
+
+def collect_arg_in_candidates(config, candidates, default = None) -> Any:
+    """ Gets the argument in a config given a list of candidates """
+    for c in candidates:
+        if hasattr(config, c):
+            return getattr(config, c)
+        elif c in config:
+            return config[c]
+    if default is not None:
+        return default
+    raise ValueError("No matching arguments found in candidates. Candidates: {}, Config: {}".format(candidates, config))
+
+class VBertTextConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaMA-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        embed_dim (`int`, *optional*, defaults to 1152):
+            Dimensionality of the encoder layers and the pooler layer. (elsewhere referred to as `embed_dim`)
+        image_size (`int`, *optional*, defaults to 384):
+            The size (resolution) of each image.
+    """
+    model_type = "vbert"
+
+    def __init__(
+        self,
+        # Case for when vllama3 is from the hub with no vision_model_name
+        text_model_name="EuroBERT/EuroBERT-210m",
+        **kwargs,
+    ):
+        self.text_model_name = text_model_name
+        text_config = AutoConfig.from_pretrained(text_model_name, trust_remote_code=True)
+        if hasattr(text_config, "text_config"):
+            text_config = text_config.text_config
+        
+        self.hidden_size = collect_arg_in_candidates(text_config, ["hidden_size", "embed_dim"])
+        self.num_hidden_layers = collect_arg_in_candidates(text_config, ["num_hidden_layers", "num_hidden_blocks"])
+        self.intermediate_size = collect_arg_in_candidates(text_config, ["intermediate_size", "mlp_dim"])
+        self.mlp_bias = collect_arg_in_candidates(text_config, ["mlp_bias", "mlp_hidden_bias"], default = False)
+        self.vocab_size = collect_arg_in_candidates(text_config, ["vocab_size"])
+    
+        super().__init__(text_model_name=text_model_name, **kwargs)
+
+class VBertVisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaMA-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        embed_dim (`int`, *optional*, defaults to 1152):
+            Dimensionality of the encoder layers and the pooler layer. (elsewhere referred to as `embed_dim`)
+        image_size (`int`, *optional*, defaults to 384):
+            The size (resolution) of each image.
+    """
+    model_type = "vbert"
+    attribute_map = {
+        "hidden_size": "embed_dim",
+    }
+
+    def __init__(
+        self,
+        # Case for when vllama3 is from the hub with no vision_model_name
+        vision_model_name="google/siglip2-base-patch16-512",
+        **kwargs,
+    ):
+        self.vision_model_name = vision_model_name
+        vision_config = AutoConfig.from_pretrained(vision_model_name, trust_remote_code=True)
+        if hasattr(vision_config, "vision_config"):
+            vision_config = vision_config.vision_config
+
+        self.embed_dim = collect_arg_in_candidates(vision_config, ["embed_dim", "hidden_size"])
+        self.image_size = collect_arg_in_candidates(vision_config, ["image_size", "img_size"])
+        self.patch_size = collect_arg_in_candidates(vision_config, ["patch_size"])
+        self.num_hidden_layers = collect_arg_in_candidates(vision_config, ["num_hidden_layers", "num_hidden_blocks"])
+        self.intermediate_size = collect_arg_in_candidates(vision_config, ["intermediate_size", "mlp_dim"])
+
+        super().__init__(vision_model_name=vision_model_name, **kwargs)
+
+class VBertConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`SmolVLMModel`]. It is used to instantiate a
+    SmolVLM model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the model of the SmolVLM
+    [HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should cache the key/value pairs of the attention mechanism. Only
+            relevant if `config.is_decoder=True`.
+        image_token_id (`int`, *optional*, defaults to 128257):
+            The id of the "image" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether or not to tie the word embeddings with the token embeddings.
+        vision_config (`IdeficsVisionConfig` or `dict`, *optional*, defaults to `IdeficsVisionConfig`):
+            Custom vision config or dict for the vision tower
+        text_config (`PretrainedConfig` or `dict`, *optional*, defaults to `LlamaConfig`):
+            Custom text config or dict for the text model
+        scale_factor (`int`, *optional*, defaults to 2):
+            The scale factor for the image encoder.
+        pad_token_id (`int`, *optional*, defaults to 128002):
+            The id of the padding token.
+
+    Example:
+    ```python
+    >>> from transformers import SmolVLMModel, SmolVLMConfig
+    >>> # Initializing configuration
+    >>> configuration = SmolVLMConfig()
+    >>> # Initializing a model from the configuration
+    >>> model = SmolVLMModel(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "vbert"
+    is_composition = True
+    # sub_configs = {"text_config": VBertTextConfig, "vision_config": VBertVisionConfig}
+
+    DEFAULT_TEXT_MODEL_NAME = "EuroBERT/EuroBERT-210m"
+    DEFAULT_VISION_MODEL_NAME = "google/siglip2-base-patch16-512"
+
+    def __init__(
+        self,
+        text_config: Union[PretrainedConfig, Dict[str, Any]] = None,
+        vision_config: Union[PretrainedConfig, Dict[str, Any]] = None,
+        image_token_id: int = 128_257,
+        vocab_size=128_256,
+        use_cache = True,
+        tie_word_embeddings = False,
+        freeze_config = None,
+        pad_token_id = None,
+        initializer_range = 0.02,
+        pixel_shuffle_factor = 4,
+        use_resampler = False,
+        additional_vocab_size = 0,
+        neftune_noise_alpha = 0.0,
+        **kwargs,
+    ):        
+        self.image_token_id = image_token_id
+        self.use_cache = use_cache
+        self.tie_word_embeddings = tie_word_embeddings
+        self.scale_factor = pixel_shuffle_factor
+        self.additional_vocab_size = additional_vocab_size
+
+        if text_config is None:
+            text_config = AutoConfig.from_pretrained(self.DEFAULT_TEXT_MODEL_NAME, trust_remote_code=True)
+        elif isinstance(text_config, dict):
+            text_config = VBertTextConfig(text_config["text_model_name"])
+        self.text_config = text_config
+        
+        if vision_config is None:
+            vision_config = AutoConfig.from_pretrained(self.DEFAULT_VISION_MODEL_NAME, trust_remote_code=True)
+        elif isinstance(vision_config, dict):
+            vision_config = VBertVisionConfig(vision_config["vision_model_name"])
+        self.vision_config = vision_config
+
+        self.freeze_config = freeze_config
+
+        # Pixel shuffle factor
+        self.pixel_shuffle_factor = pixel_shuffle_factor
+        self.use_resampler = use_resampler
+
+        self.neftune_noise_alpha = neftune_noise_alpha
+
+        self.initializer_range = initializer_range
+
+        hidden_size = kwargs.pop("hidden_size", self.text_config.hidden_size)
+
+        super().__init__(
+            **kwargs, 
+            pad_token_id=pad_token_id, 
+            tie_word_embeddings=tie_word_embeddings,
+            vocab_size=vocab_size,
+            hidden_size=hidden_size,
+        )
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
+        Returns:
+            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        output = copy.deepcopy(self.__dict__)
+
+        output["model_type"] = self.__class__.model_type
+        output["vision_config"] = self.vision_config.to_dict()
+        output["text_config"] = self.text_config.to_dict()
+        # output["freeze_config"] = self.freeze_config.to_dict()
+
+        return output
+    
+    # @classmethod
+    # def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+    #     outputs = super(VBertConfig, cls).from_pretrained(pretrained_model_name_or_path, **kwargs)
+    #     return outputs
+
+    @classmethod
+    def from_pretrained_models(
+        cls, 
+        text_model_name: Union[str, os.PathLike],
+        vision_model_name: Union[str, os.PathLike],
+        **kwargs
+    ) -> "PretrainedConfig":
+        # text_model_config = AutoConfig.from_pretrained(text_model_name, trust_remote_code=True)
+        # vision_model_config = AutoConfig.from_pretrained(vision_model_name, trust_remote_code=True)
+        text_model_config = VBertTextConfig(text_model_name)
+        vision_model_config = VBertVisionConfig(vision_model_name)
+        return cls(
+            text_config=text_model_config,
+            vision_config=vision_model_config,
+            **kwargs
+        )

vbert-siglip2-slbert-30_210__im2048/opt_step-28000__merged/modeling_vbert.py

@@ -0,0 +1,630 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import CrossEntropyLoss
+from typing import Optional, Tuple, Union, List
+
+from transformers.cache_utils import DynamicCache
+
+from .configuration_vbert import VBertConfig
+
+from transformers import AutoModel, AutoConfig, AutoModelForMaskedLM, PreTrainedModel
+from transformers.modeling_outputs import BaseModelOutput
+from transformers.models.bert.modeling_bert import BaseModelOutputWithPoolingAndCrossAttentions, MaskedLMOutput
+
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+
+from dataclasses import dataclass
+
+from transformers import logging 
+
+logger = logging.get_logger(__name__)
+
+
+class DecoupledEmbedding(nn.Embedding):
+    # Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/sparse.html#Embedding
+    """
+    Implements a decoupling of parameters to allow freezing (or not) a subset of the embeddings.
+    In practise, the regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `num_additional_embeddings` > 0, then it will create `num_additional_embeddings` additional parameters that are always trained.
+    If `num_additional_embeddings=0`, then the module defaults back to the regular behavior of `nn.Embedding`.
+    """
+
+    def __init__(
+        self,
+        num_embeddings,
+        num_additional_embeddings,
+        embedding_dim,
+        partially_freeze=False,
+        device=None,
+        dtype=None,
+        padding_idx=None,
+        **kwargs,
+    ) -> None:
+        """
+        num_additional_embeddings: int. Number of additional embeddings. Only useful when you `partially_freeze=True`.
+        partially_freeze: bool. If True, the regular `weight` will be frozen. `additional_weight` is never frozen.
+
+        Note: there are a lot of other parameters to initialize a standard `nn.Embedding` such as `padding_idx`, `max_norm` or `norm_type`. We are not supporting these.
+        """
+        if padding_idx is not None and padding_idx > num_embeddings:
+            raise ValueError(f"padding_idx must be within num_embeddings. Got {padding_idx} and {num_embeddings}")
+        super().__init__(
+            num_embeddings=num_embeddings,
+            embedding_dim=embedding_dim,
+            device=device,
+            dtype=dtype,
+            padding_idx=padding_idx,
+            **kwargs,
+        )
+        self.num_embeddings = num_embeddings
+        self.padding_idx = padding_idx
+        self.num_additional_embeddings = num_additional_embeddings
+        self.partially_freeze = partially_freeze
+
+        if partially_freeze:
+            self.weight.requires_grad_(False)
+
+        if self.num_additional_embeddings > 0:
+            self.additional_embedding = nn.Embedding(
+                num_embeddings=self.num_additional_embeddings,
+                embedding_dim=embedding_dim,
+                device=device,
+                dtype=dtype,
+            )
+
+    def forward(self, input_ids):
+        """
+        we have 2 embeddings, with different indices - one pretrained self.weight and another
+        self.additional_embedding.weight that is being trained.
+
+        in order to make a lookup of the input ids, we:
+        1. find out the indices of the entries belonging to the 2nd embedding
+        2. extract those values while subtracting the size of the first embedding (num_embeddings),
+           since the 2nd embedding starts from 0 and not num_embeddings
+        3. perform the 2nd embedding lookup
+        4. now we handle the 1st embedding, we overwrite indices belonging to the 2nd embedding with a padding index
+        5. perform the 1st embedding lookup
+        6. now we overwrite the values in the 1st embedding lookup with the values of the 2nd embedding lookup
+
+        note: for the 1st embedding lookup we could have looked up only the low indices and not do
+        the padding, but then we have to create a new tensor and populate it with 2 tensors that are
+        spread out across various indices - i.e. not a simple concat - I haven't benchmarked the
+        complex case if it's any faster, given that seqlens are usually relatively short it's
+        probably not faster or if faster not by much - but might be a good idea to measure.
+
+        """
+        if self.num_additional_embeddings == 0:
+            return self.additional_embedding(input_ids)
+
+        # Clone so that we don't modify the original input_ids later on
+        input_ids = input_ids.clone()
+        additional_vocab_indices = torch.where(input_ids >= self.num_embeddings)
+        input_ids_additional_vocab = input_ids[additional_vocab_indices]
+        additional_embeddings = self.additional_embedding(input_ids_additional_vocab - self.num_embeddings)
+
+        # for successful lookup replace input_ids with 0, the results of these will be discarded anyway
+        input_ids[additional_vocab_indices] = 0
+        full_vector = F.embedding(input_ids, self.weight)
+
+        # overwrite the records with high indices
+        full_vector[additional_vocab_indices] = additional_embeddings
+
+        return full_vector
+
+    def extra_repr(self) -> str:
+        return "num_embeddings={}, num_additional_embeddings={}, embedding_dim={}, partially_freeze={}".format(
+            self.num_embeddings,
+            self.num_additional_embeddings,
+            self.embedding_dim,
+            self.partially_freeze,
+        )
+    
+@dataclass
+class VBertBaseModelOutput(BaseModelOutput):
+    """
+    Base class for SmolVLM model's outputs that may also contain a past key/values (to speed up sequential decoding).
+    Args:
+        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+            If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
+            hidden_size)` is output.
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+            `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+            encoder_sequence_length, embed_size_per_head)`.
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
+            `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
+            input) to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
+            Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
+            sequence_length, hidden_size)`.
+            image_hidden_states of the model produced by the vision encoder
+    """
+
+    last_hidden_state: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+
+@dataclass
+class VBertMaskedLMOutput(MaskedLMOutput):
+    """
+    Base class for SmolVLM model's outputs that may also contain a past key/values (to speed up sequential decoding).
+    Args:
+        loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided):
+            Masked language modeling (MLM) loss.
+        logits (`torch.FloatTensor`): 
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
+            Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
+            sequence_length, hidden_size)`.
+            image_hidden_states of the model produced by the vision encoder
+    """
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
+    image_hidden_states: Optional[torch.FloatTensor] = None
+
+class VBertSimpleMLP(nn.Module):
+    def __init__(self, input_size, output_size):
+        super().__init__()
+        self.proj = nn.Linear(input_size, output_size, bias=False)
+
+    def forward(self, x):
+        return self.proj(x)
+
+class VBertConnector(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.scale_factor = config.pixel_shuffle_factor
+        self.modality_projection = VBertSimpleMLP(
+            input_size=config.vision_config.hidden_size * (config.scale_factor**2),
+            output_size=config.text_config.hidden_size
+        )
+
+    def pixel_shuffle(self, x, scale_factor):
+        bsz, seq, embed_dim = x.size()
+        height = width = int(seq**0.5)
+        x = x.view(bsz, height, width, embed_dim)
+        x = x.view(bsz, height, int(width / scale_factor), embed_dim * scale_factor)
+        x = x.permute(0, 2, 1, 3)
+        x = x.reshape(bsz, int(width / scale_factor), int(height / scale_factor), embed_dim * (scale_factor**2))
+        x = x.permute(0, 2, 1, 3)
+        x = x.reshape(bsz, int(seq / (scale_factor**2)), embed_dim * (scale_factor**2))
+        return x
+
+    def forward(self, image_hidden_states):
+        image_hidden_states = self.pixel_shuffle(image_hidden_states, self.scale_factor)
+        image_hidden_states = self.modality_projection(image_hidden_states)
+        return image_hidden_states
+
+class VBertPreTrainedModel(PreTrainedModel):
+    config_class = VBertConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["VBertDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        """Initialize the weights."""
+
+        std = (
+            self.config.initializer_range
+            if hasattr(self.config, "initializer_range")
+            else self.config.text_config.initializer_range
+        )
+
+        if hasattr(module, "class_embedding"):
+            module.class_embedding.data.normal_(mean=0.0, std=std)
+
+        if isinstance(module, (nn.Linear, nn.Conv2d)):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+class VBertModel(VBertPreTrainedModel):
+    """
+    A subclass of Idefics3Model. We do *not* remove or block the call to inputs_merger
+    in forward. Instead, we override inputs_merger here with custom logic.
+    """
+
+    def __init__(self, config: VBertConfig, **kwargs):
+        super().__init__(config)
+
+        self.vision_model = VBertModel.init_vision_model(config, **kwargs)
+        self.connector = VBertConnector(config)
+        self.text_model = VBertModel.init_language_model(config, **kwargs)
+
+        self.image_seq_len = int(
+            ((config.vision_config.image_size // config.vision_config.patch_size) ** 2) / (config.scale_factor**2)
+        )
+        self.image_token_id = self.config.image_token_id
+
+        self.post_init()
+
+    @staticmethod
+    def init_vision_model(config: VBertConfig, **kwargs):
+        vision_model_config = AutoConfig.from_pretrained(
+            config.vision_config.vision_model_name,
+            trust_remote_code=True,
+            **kwargs,
+        )
+
+        vision_model = AutoModel.from_config(vision_model_config, trust_remote_code=True, **kwargs)
+
+        if hasattr(vision_model, "vision_model"):
+            # If the model has a vision_model attribute, it means it's a wrapper around another model
+            vision_model = vision_model.vision_model
+
+        return vision_model
+
+    @staticmethod
+    def init_language_model(config: VBertConfig, **kwargs):
+        text_model_config = AutoConfig.from_pretrained(
+            config.text_config.text_model_name,
+            trust_remote_code=True,
+            **kwargs,
+        )
+
+        text_model = AutoModel.from_config(text_model_config, trust_remote_code=True, **kwargs)
+        # extractor = regex_lookup(language_model_name, language_model_name2model)
+
+        embed_layer = DecoupledEmbedding(
+            num_embeddings=text_model_config.vocab_size,
+            num_additional_embeddings=config.additional_vocab_size,
+            embedding_dim=config.hidden_size,
+            partially_freeze=config.freeze_config["freeze_text_layers"],
+            padding_idx=config.pad_token_id,
+        )
+
+        text_model.set_input_embeddings(embed_layer)
+
+        return text_model
+
+    def enable_input_require_grads(self):
+        """
+        Enables the gradients for the input embeddings.
+
+        This is useful for lora when using gradient checkpointing.
+        c.f. https://github.com/huggingface/peft/issues/1402#issuecomment-1913675032
+
+        Override to set output.requires_grad = True for both the decoder's and vision model's embeddings.
+        """
+
+        def get_lowest_module(module):
+            if len(list(module.children())) == 0:
+                # If the module has no children, it is a leaf module (e.g., Linear, Conv2d, etc.)
+                return module
+            else:
+                # Recursively call the function on each child module
+                return get_lowest_module(list(module.children())[0])
+
+        def make_inputs_require_grads(module, input, output):
+            output.requires_grad_(True)
+
+        self._text_require_grads_hook = self.get_input_embeddings().register_forward_hook(make_inputs_require_grads)
+        self._vision_require_grads_hook = get_lowest_module(self.vision_model).register_forward_hook(
+            make_inputs_require_grads
+        )
+
+    def disable_input_require_grads(self):
+        self._text_require_grads_hook.remove()
+        self._vision_require_grads_hook.remove()
+
+    def get_input_embeddings(self):
+        return self.text_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.text_model.set_input_embeddings(value)
+
+    def inputs_merger(
+        self, input_ids: torch.LongTensor, inputs_embeds: torch.Tensor, image_hidden_states: torch.Tensor
+    ):
+        """
+        This method aims at merging the token embeddings with the image hidden states into one single sequence of vectors that are fed to the transformer LM.
+        The merging happens as follows:
+        - The text token sequence is: `tok_1 tok_2 tok_3 <fake_token_around_image> <image> <image> ... <image> <fake_token_around_image> tok_4`.
+        - We get the image hidden states for the image through the vision encoder and that hidden state, after a pixel shuffle operation, is then projected into the text embedding space.
+        We thus have a sequence of image hidden states of size (1, image_seq_len, hidden_dim), where 1 is for batch_size of 1 image and hidden_dim is the hidden_dim of the LM transformer.
+        - The merging happens so that we obtain the following sequence: `vector_tok_1 vector_tok_2 vector_tok_3 vector_fake_tok_around_image {sequence of image_seq_len image hidden states} vector_fake_toke_around_image vector_tok_4`. That sequence is fed to the LM.
+        - To fit the format of that sequence, `input_ids`, `input_embeds`, `attention_mask` are all 3 adapted to insert the image hidden states.
+        """
+        _, patch_size, _ = image_hidden_states.shape
+
+        image_mask = input_ids == self.image_token_id
+        num_image_tokens = image_mask.sum(dim=1)
+        if not torch.all(num_image_tokens % patch_size == 0):
+            raise ValueError("At least one sample has <image> tokens not divisible by patch_size.")
+
+        blocks_per_sample = num_image_tokens // patch_size
+
+        offsets = torch.nn.functional.pad(blocks_per_sample.cumsum(dim=0), (1, 0), value=0)
+        block_offset = offsets[:-1]
+        row_cum = image_mask.cumsum(dim=-1)
+        chunk_idx = (row_cum - 1) // patch_size
+        local_idx = (row_cum - 1) % patch_size
+        block_idx = block_offset.unsqueeze(1) + chunk_idx
+
+        image_embeds = torch.zeros_like(inputs_embeds)
+        image_embeds[image_mask] = image_hidden_states[block_idx[image_mask], local_idx[image_mask], :]
+
+        merged_embeds = torch.where(image_mask.unsqueeze(-1), image_embeds, inputs_embeds)
+        return merged_embeds
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        pixel_attention_mask: Optional[torch.BoolTensor] = None,
+        image_hidden_states: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPoolingAndCrossAttentions]:    
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if self.training and self.text_model.gradient_checkpointing and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+            )
+            use_cache = False
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        past_seen_tokens = 0
+        if use_cache:
+            if past_key_values is None:
+                past_key_values = DynamicCache()
+            past_seen_tokens = past_key_values.get_seq_length()
+
+        if inputs_embeds is not None and input_ids is None and past_seen_tokens == 0:
+            raise ValueError("When first calling the model, if input_embeds are passed, input_ids should not be None.")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.text_model.get_input_embeddings()(input_ids).to(input_ids.device)
+
+        # START VISUAL INPUTS INTEGRATION
+        if pixel_values is not None and image_hidden_states is not None:
+            raise ValueError("You cannot specify both pixel_values and image_hidden_states at the same time")
+        elif pixel_values is not None:
+            batch_size, num_images, num_channels, height, width = pixel_values.shape
+            pixel_values = pixel_values
+            pixel_values = pixel_values.view(batch_size * num_images, *pixel_values.shape[2:])
+
+            # Remove padding images - padding images are full 0.
+            nb_values_per_image = pixel_values.shape[1:].numel()
+            real_images_inds = (pixel_values == 0.0).sum(dim=(-1, -2, -3)) != nb_values_per_image
+
+            if not any(real_images_inds):
+                # no images, leave one empty image.
+                real_images_inds[0] = True
+
+            pixel_values = pixel_values[real_images_inds].contiguous()
+
+            # Handle the vision attention mask
+            if pixel_attention_mask is None:
+                pixel_attention_mask = torch.ones(
+                    size=[pixel_values.shape[i] for i in (0, 2, 3)],
+                    dtype=torch.bool,
+                    device=pixel_values.device,
+                )
+            else:
+                # Remove padding images from the mask
+                pixel_attention_mask = pixel_attention_mask.view(
+                    batch_size * num_images, *pixel_attention_mask.shape[2:]
+                )
+                pixel_attention_mask = pixel_attention_mask[real_images_inds].contiguous()
+
+            # patch_size = self.config.vision_config.patch_size
+            # patches_subgrid = pixel_attention_mask.unfold(dimension=1, size=patch_size, step=patch_size)
+            # patches_subgrid = patches_subgrid.unfold(dimension=2, size=patch_size, step=patch_size)
+            # patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()
+
+            # Get sequence from the vision encoder
+            image_hidden_states = self.vision_model(
+                pixel_values=pixel_values,
+                # patch_attention_mask=patch_attention_mask,
+            ).last_hidden_state
+
+            # Modality projection & resampling
+            image_hidden_states = self.connector(image_hidden_states)
+
+        elif image_hidden_states is not None:
+            image_hidden_states = image_hidden_states.to(dtype=self.dtype, device=input_ids.device)
+
+        if inputs_embeds is not None and image_hidden_states is not None:
+            # When we embed, we don't want to replace the potential image_token_id that we generated by images
+            # that simply don't exist
+            inputs_embeds = self.inputs_merger(
+                input_ids=input_ids,
+                inputs_embeds=inputs_embeds,
+                image_hidden_states=image_hidden_states,
+            )
+
+        outputs = self.text_model(
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            # past_key_values=past_key_values,
+            # use_cache=use_cache,
+            # cache_position=cache_position,
+        )
+
+        if not return_dict:
+            return tuple(v for v in [*outputs, image_hidden_states] if v is not None)
+
+        return VBertBaseModelOutput(
+            last_hidden_state=outputs.last_hidden_state,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=image_hidden_states,
+        )
+
+class VBertForMaskedLM(VBertPreTrainedModel):
+    # _tied_weights_keys = ["predictions.decoder.bias", "cls.predictions.decoder.weight"]
+
+    def __init__(self, config, **kwargs):
+        super().__init__(config)
+
+        self.image_token_id = config.image_token_id
+        self.in_features = config.hidden_size
+        self.out_additional_features = config.additional_vocab_size
+        self.vocab_size = config.vocab_size
+
+        if config.is_decoder:
+            logger.warning(
+                "If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for "
+                "bi-directional self-attention."
+            )
+
+        self.model = VBertModel(config, **kwargs)
+        self.lm_head = VBertForMaskedLM.init_lm_head(config, **kwargs)
+        if self.out_additional_features > 0:
+            self.additional_fc = nn.Linear(
+                in_features=self.in_features,
+                out_features=self.out_additional_features,
+                bias=False,
+            )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @staticmethod
+    def init_lm_head(config, **kwargs):
+        # Get the pretrained model config
+        text_model_config = AutoConfig.from_pretrained(
+            config.text_config.text_model_name,
+            trust_remote_code=True,
+            **kwargs,
+        )
+        model = AutoModelForMaskedLM.from_config(text_model_config, trust_remote_code=True, **kwargs)
+        # Get the lm head
+        lm_head = model.lm_head if hasattr(model, "lm_head") else model.decoder if hasattr(model, "decoder") else None
+        if lm_head is None:
+            logger.warning(f"No lm head was found for {config.text_config.text_model_name}, initializing a new one.")
+            lm_head = nn.Linear(config.hidden_size, config.vocab_size, False)
+        return lm_head
+
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[torch.FloatTensor]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            pixel_values: Optional[torch.FloatTensor] = None,
+            pixel_attention_mask: Optional[torch.BoolTensor] = None,
+            image_hidden_states: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, VBertMaskedLMOutput]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or `model.image_token_id` (where `model` is your instance of `Idefics3ForConditionalGeneration`).
+                Tokens with indices set to `model.image_token_id` are ignored (masked), the loss is only
+                computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        ```"""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+        # Pass the inputs to VBertModel
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            pixel_values=pixel_values,
+            pixel_attention_mask=pixel_attention_mask,
+            image_hidden_states=image_hidden_states,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        # Pass the outputs to the MLM head
+        hidden_states = outputs[0]
+
+        logits = self.lm_head(hidden_states)
+        if self.out_additional_features > 0:
+            additional_features = self.additional_fc(hidden_states)
+            logits = torch.cat((logits, additional_features), -1)
+        logits = logits.float()
+
+        masked_lm_loss = None
+        if labels is not None:
+            # print the ratio of not ignored tokens
+            loss_fct = CrossEntropyLoss()
+            masked_lm_loss = loss_fct(logits.view(-1, self.vocab_size + self.out_additional_features), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+        return VBertMaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=outputs.image_hidden_states,
+        )

vbert-siglip2-slbert-30_210__im2048/opt_step-28000__merged/preprocessor_config.json

@@ -0,0 +1,28 @@
+{
+    "do_convert_rgb": true,
+    "do_image_splitting": true,
+    "do_normalize": true,
+    "do_pad": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "image_processor_type": "Idefics3ImageProcessor",
+    "image_std": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "max_image_size": {
+        "longest_edge": 512
+    },
+    "processor_class": "Idefics3Processor",
+    "resample": 1,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
+        "longest_edge": 2048
+    }
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-28000__merged/processor_config.json

@@ -0,0 +1,4 @@
+{
+    "image_seq_len": 64,
+    "processor_class": "Idefics3Processor"
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-28000__merged/special_tokens_map.json

@@ -0,0 +1,72 @@
+{
+  "additional_special_tokens": [
+    "<global-img>",
+    "<row_1_col_1>",
+    "<row_1_col_2>",
+    "<row_1_col_3>",
+    "<row_1_col_4>",
+    "<row_1_col_5>",
+    "<row_1_col_6>",
+    "<row_2_col_1>",
+    "<row_2_col_2>",
+    "<row_2_col_3>",
+    "<row_2_col_4>",
+    "<row_2_col_5>",
+    "<row_2_col_6>",
+    "<row_3_col_1>",
+    "<row_3_col_2>",
+    "<row_3_col_3>",
+    "<row_3_col_4>",
+    "<row_3_col_5>",
+    "<row_3_col_6>",
+    "<row_4_col_1>",
+    "<row_4_col_2>",
+    "<row_4_col_3>",
+    "<row_4_col_4>",
+    "<row_4_col_5>",
+    "<row_4_col_6>",
+    "<row_5_col_1>",
+    "<row_5_col_2>",
+    "<row_5_col_3>",
+    "<row_5_col_4>",
+    "<row_5_col_5>",
+    "<row_5_col_6>",
+    "<row_6_col_1>",
+    "<row_6_col_2>",
+    "<row_6_col_3>",
+    "<row_6_col_4>",
+    "<row_6_col_5>",
+    "<row_6_col_6>",
+    "<end_of_utterance>",
+    "<fake_token_around_image>",
+    "<image>"
+  ],
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<|reserved_special_token_0|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-28000__merged/tokenizer_config.json

@@ -0,0 +1,2429 @@
+{
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+      "special": true
+    },
+    "128280": {
+      "content": "<row_4_col_6>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128281": {
+      "content": "<row_5_col_1>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128282": {
+      "content": "<row_5_col_2>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128283": {
+      "content": "<row_5_col_3>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128284": {
+      "content": "<row_5_col_4>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128285": {
+      "content": "<row_5_col_5>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128286": {
+      "content": "<row_5_col_6>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128287": {
+      "content": "<row_6_col_1>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128288": {
+      "content": "<row_6_col_2>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128289": {
+      "content": "<row_6_col_3>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128290": {
+      "content": "<row_6_col_4>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128291": {
+      "content": "<row_6_col_5>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128292": {
+      "content": "<row_6_col_6>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128293": {
+      "content": "<end_of_utterance>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128294": {
+      "content": "<fake_token_around_image>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128295": {
+      "content": "<image>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<global-img>",
+    "<row_1_col_1>",
+    "<row_1_col_2>",
+    "<row_1_col_3>",
+    "<row_1_col_4>",
+    "<row_1_col_5>",
+    "<row_1_col_6>",
+    "<row_2_col_1>",
+    "<row_2_col_2>",
+    "<row_2_col_3>",
+    "<row_2_col_4>",
+    "<row_2_col_5>",
+    "<row_2_col_6>",
+    "<row_3_col_1>",
+    "<row_3_col_2>",
+    "<row_3_col_3>",
+    "<row_3_col_4>",
+    "<row_3_col_5>",
+    "<row_3_col_6>",
+    "<row_4_col_1>",
+    "<row_4_col_2>",
+    "<row_4_col_3>",
+    "<row_4_col_4>",
+    "<row_4_col_5>",
+    "<row_4_col_6>",
+    "<row_5_col_1>",
+    "<row_5_col_2>",
+    "<row_5_col_3>",
+    "<row_5_col_4>",
+    "<row_5_col_5>",
+    "<row_5_col_6>",
+    "<row_6_col_1>",
+    "<row_6_col_2>",
+    "<row_6_col_3>",
+    "<row_6_col_4>",
+    "<row_6_col_5>",
+    "<row_6_col_6>",
+    "<end_of_utterance>",
+    "<fake_token_around_image>",
+    "<image>"
+  ],
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|end_of_text|>",
+  "extra_special_tokens": {},
+  "legacy": false,
+  "mask_token": "<|reserved_special_token_0|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask",
+    "pixel_values",
+    "pixel_attention_mask"
+  ],
+  "model_max_length": 8192,
+  "pad_token": "<|end_of_text|>",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-30000/resume_run_infos.json

@@ -0,0 +1,91 @@
+{
+  "train_logs": {
+    "lr": 0.0,
+    "num_opt_steps": 30000,
+    "num_epochs": 0,
+    "per_token_loss": {
+      "sft": 0.7141110450029373,
+      "all": 0.7141110450029373
+    },
+    "z_loss": {
+      "sft": 0.0,
+      "all": 0.0
+    },
+    "watt/s": {
+      "sft": 417.0350536224932,
+      "all": 417.0350536224932
+    },
+    "tflops": {
+      "sft": 13.429037684329803,
+      "all": 13.429037684329803
+    },
+    "tflop_counter": {
+      "sft": 38063184.07423401,
+      "all": 38063184.07423401
+    },
+    "fwd_bwd_time": {
+      "sft": 3140386.4775877,
+      "all": 3140386.4775877
+    },
+    "tflops_acc": {
+      "sft": 12.120541323777573,
+      "all": 12.120541323777573
+    },
+    "num_per_device_batches": {
+      "sft": 1920000,
+      "all": 1920000
+    },
+    "num_images": {
+      "sft": 162098728,
+      "all": 162098728
+    },
+    "num_image_tokens": {
+      "sft": 10374318592,
+      "all": 10374318592
+    },
+    "num_tokens": {
+      "sft": 3721131526,
+      "all": 3721131526
+    },
+    "image_to_text_ratio": {
+      "sft": 0.10010632872581482,
+      "all": 0.10010632872581482
+    },
+    "pixel_values_sum": {
+      "sft": 42389038096024.0,
+      "all": 42389038096024.0
+    },
+    "num_padding": {
+      "sft": 2108321884,
+      "all": 2108321884
+    },
+    "num_per_device_batches_in_curr_epoch": {
+      "sft": 1920000,
+      "all": 1920000
+    },
+    "num_batches": {
+      "all": 1920000
+    },
+    "num_batches_in_curr_epoch": {
+      "all": 1920000
+    },
+    "per_token_loss_acc": {},
+    "z_loss_acc": {},
+    "num_batches_since_training_logged": {},
+    "num_per_device_batches_since_training_logged": {},
+    "tflop_counter_since_training_logged": {},
+    "total_energy_delta_since_training_logged": {},
+    "fwd_bwd_time_since_training_logged": {},
+    "global_batch_size_current": 256
+  },
+  "wandb_run_id": "eyif7ogo",
+  "seed": 42,
+  "resume_opt_step": 30000,
+  "resume_epoch": 0,
+  "gbs_running": {
+    "global_seen_samples": 7680000,
+    "global_batch_size_current": 256,
+    "next_goal_samples": 0,
+    "grad_acc_size_current": 4
+  }
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-30000/unwrapped_adapter/adapter_config.json

@@ -0,0 +1,43 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "None",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "fc1",
+    "fc2",
+    "q_proj",
+    "k_proj",
+    "v_proj",
+    "lm_head",
+    "up_proj",
+    "gate_proj",
+    "o_proj",
+    "down_proj",
+    "self_attn.out_proj"
+  ],
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_dora": true,
+  "use_rslora": false
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-30000__merged/chat_template.jinja

@@ -0,0 +1,2 @@
+<|begin_of_text|>{% for message in messages %}{{message['role'] | capitalize}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{line['text']}}{% elif line['type'] == 'image' %}{{ '<image>' }}{% endif %}{% endfor %}<end_of_utterance>
+{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}

vbert-siglip2-slbert-30_210__im2048/opt_step-30000__merged/chat_template.json

@@ -0,0 +1,3 @@
+{
+    "chat_template": "<|begin_of_text|>{% for message in messages %}{{message['role'] | capitalize}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{line['text']}}{% elif line['type'] == 'image' %}{{ '<image>' }}{% endif %}{% endfor %}<end_of_utterance>\n{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}"
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-30000__merged/config.json

@@ -0,0 +1,50 @@
+{
+  "additional_vocab_size": 40,
+  "architectures": [
+    "VBertForMaskedLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_vbert.VBertConfig",
+    "AutoModel": "modeling_vbert.VBertModel",
+    "AutoModelForMaskedLM": "modeling_vbert.VBertForMaskedLM"
+  },
+  "freeze_config": {
+    "freeze_lm_head": true,
+    "freeze_text_layers": true,
+    "freeze_vision_layers": true
+  },
+  "hidden_size": 768,
+  "image_token_id": 128295,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 8192,
+  "model_type": "vbert",
+  "neftune_noise_alpha": 0.0,
+  "output_attentions": false,
+  "pixel_shuffle_factor": 4,
+  "qk_layer_norms": false,
+  "scale_factor": 4,
+  "text_config": {
+    "hidden_size": 768,
+    "intermediate_size": 3072,
+    "mlp_bias": false,
+    "model_type": "vbert",
+    "num_hidden_layers": 12,
+    "text_model_name": "SmolVEncoder/encoder-210m-30",
+    "vocab_size": 128256
+  },
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": null,
+  "use_cache": true,
+  "use_resampler": false,
+  "vision_config": {
+    "embed_dim": 768,
+    "image_size": 512,
+    "intermediate_size": 3072,
+    "model_type": "vbert",
+    "num_hidden_layers": 12,
+    "patch_size": 16,
+    "vision_model_name": "google/siglip2-base-patch16-512"
+  },
+  "vocab_size": 128256
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-30000__merged/configuration_vbert.py

@@ -0,0 +1,233 @@
+import copy
+import os
+
+from typing import Union, Any, Dict
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+from transformers import CONFIG_MAPPING, AutoConfig
+
+logger = logging.get_logger(__name__)
+
+def collect_arg_in_candidates(config, candidates, default = None) -> Any:
+    """ Gets the argument in a config given a list of candidates """
+    for c in candidates:
+        if hasattr(config, c):
+            return getattr(config, c)
+        elif c in config:
+            return config[c]
+    if default is not None:
+        return default
+    raise ValueError("No matching arguments found in candidates. Candidates: {}, Config: {}".format(candidates, config))
+
+class VBertTextConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaMA-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        embed_dim (`int`, *optional*, defaults to 1152):
+            Dimensionality of the encoder layers and the pooler layer. (elsewhere referred to as `embed_dim`)
+        image_size (`int`, *optional*, defaults to 384):
+            The size (resolution) of each image.
+    """
+    model_type = "vbert"
+
+    def __init__(
+        self,
+        # Case for when vllama3 is from the hub with no vision_model_name
+        text_model_name="EuroBERT/EuroBERT-210m",
+        **kwargs,
+    ):
+        self.text_model_name = text_model_name
+        text_config = AutoConfig.from_pretrained(text_model_name, trust_remote_code=True)
+        if hasattr(text_config, "text_config"):
+            text_config = text_config.text_config
+        
+        self.hidden_size = collect_arg_in_candidates(text_config, ["hidden_size", "embed_dim"])
+        self.num_hidden_layers = collect_arg_in_candidates(text_config, ["num_hidden_layers", "num_hidden_blocks"])
+        self.intermediate_size = collect_arg_in_candidates(text_config, ["intermediate_size", "mlp_dim"])
+        self.mlp_bias = collect_arg_in_candidates(text_config, ["mlp_bias", "mlp_hidden_bias"], default = False)
+        self.vocab_size = collect_arg_in_candidates(text_config, ["vocab_size"])
+    
+        super().__init__(text_model_name=text_model_name, **kwargs)
+
+class VBertVisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaMA-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        embed_dim (`int`, *optional*, defaults to 1152):
+            Dimensionality of the encoder layers and the pooler layer. (elsewhere referred to as `embed_dim`)
+        image_size (`int`, *optional*, defaults to 384):
+            The size (resolution) of each image.
+    """
+    model_type = "vbert"
+    attribute_map = {
+        "hidden_size": "embed_dim",
+    }
+
+    def __init__(
+        self,
+        # Case for when vllama3 is from the hub with no vision_model_name
+        vision_model_name="google/siglip2-base-patch16-512",
+        **kwargs,
+    ):
+        self.vision_model_name = vision_model_name
+        vision_config = AutoConfig.from_pretrained(vision_model_name, trust_remote_code=True)
+        if hasattr(vision_config, "vision_config"):
+            vision_config = vision_config.vision_config
+
+        self.embed_dim = collect_arg_in_candidates(vision_config, ["embed_dim", "hidden_size"])
+        self.image_size = collect_arg_in_candidates(vision_config, ["image_size", "img_size"])
+        self.patch_size = collect_arg_in_candidates(vision_config, ["patch_size"])
+        self.num_hidden_layers = collect_arg_in_candidates(vision_config, ["num_hidden_layers", "num_hidden_blocks"])
+        self.intermediate_size = collect_arg_in_candidates(vision_config, ["intermediate_size", "mlp_dim"])
+
+        super().__init__(vision_model_name=vision_model_name, **kwargs)
+
+class VBertConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`SmolVLMModel`]. It is used to instantiate a
+    SmolVLM model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the model of the SmolVLM
+    [HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should cache the key/value pairs of the attention mechanism. Only
+            relevant if `config.is_decoder=True`.
+        image_token_id (`int`, *optional*, defaults to 128257):
+            The id of the "image" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether or not to tie the word embeddings with the token embeddings.
+        vision_config (`IdeficsVisionConfig` or `dict`, *optional*, defaults to `IdeficsVisionConfig`):
+            Custom vision config or dict for the vision tower
+        text_config (`PretrainedConfig` or `dict`, *optional*, defaults to `LlamaConfig`):
+            Custom text config or dict for the text model
+        scale_factor (`int`, *optional*, defaults to 2):
+            The scale factor for the image encoder.
+        pad_token_id (`int`, *optional*, defaults to 128002):
+            The id of the padding token.
+
+    Example:
+    ```python
+    >>> from transformers import SmolVLMModel, SmolVLMConfig
+    >>> # Initializing configuration
+    >>> configuration = SmolVLMConfig()
+    >>> # Initializing a model from the configuration
+    >>> model = SmolVLMModel(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "vbert"
+    is_composition = True
+    # sub_configs = {"text_config": VBertTextConfig, "vision_config": VBertVisionConfig}
+
+    DEFAULT_TEXT_MODEL_NAME = "EuroBERT/EuroBERT-210m"
+    DEFAULT_VISION_MODEL_NAME = "google/siglip2-base-patch16-512"
+
+    def __init__(
+        self,
+        text_config: Union[PretrainedConfig, Dict[str, Any]] = None,
+        vision_config: Union[PretrainedConfig, Dict[str, Any]] = None,
+        image_token_id: int = 128_257,
+        vocab_size=128_256,
+        use_cache = True,
+        tie_word_embeddings = False,
+        freeze_config = None,
+        pad_token_id = None,
+        initializer_range = 0.02,
+        pixel_shuffle_factor = 4,
+        use_resampler = False,
+        additional_vocab_size = 0,
+        neftune_noise_alpha = 0.0,
+        **kwargs,
+    ):        
+        self.image_token_id = image_token_id
+        self.use_cache = use_cache
+        self.tie_word_embeddings = tie_word_embeddings
+        self.scale_factor = pixel_shuffle_factor
+        self.additional_vocab_size = additional_vocab_size
+
+        if text_config is None:
+            text_config = AutoConfig.from_pretrained(self.DEFAULT_TEXT_MODEL_NAME, trust_remote_code=True)
+        elif isinstance(text_config, dict):
+            text_config = VBertTextConfig(text_config["text_model_name"])
+        self.text_config = text_config
+        
+        if vision_config is None:
+            vision_config = AutoConfig.from_pretrained(self.DEFAULT_VISION_MODEL_NAME, trust_remote_code=True)
+        elif isinstance(vision_config, dict):
+            vision_config = VBertVisionConfig(vision_config["vision_model_name"])
+        self.vision_config = vision_config
+
+        self.freeze_config = freeze_config
+
+        # Pixel shuffle factor
+        self.pixel_shuffle_factor = pixel_shuffle_factor
+        self.use_resampler = use_resampler
+
+        self.neftune_noise_alpha = neftune_noise_alpha
+
+        self.initializer_range = initializer_range
+
+        hidden_size = kwargs.pop("hidden_size", self.text_config.hidden_size)
+
+        super().__init__(
+            **kwargs, 
+            pad_token_id=pad_token_id, 
+            tie_word_embeddings=tie_word_embeddings,
+            vocab_size=vocab_size,
+            hidden_size=hidden_size,
+        )
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
+        Returns:
+            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        output = copy.deepcopy(self.__dict__)
+
+        output["model_type"] = self.__class__.model_type
+        output["vision_config"] = self.vision_config.to_dict()
+        output["text_config"] = self.text_config.to_dict()
+        # output["freeze_config"] = self.freeze_config.to_dict()
+
+        return output
+    
+    # @classmethod
+    # def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+    #     outputs = super(VBertConfig, cls).from_pretrained(pretrained_model_name_or_path, **kwargs)
+    #     return outputs
+
+    @classmethod
+    def from_pretrained_models(
+        cls, 
+        text_model_name: Union[str, os.PathLike],
+        vision_model_name: Union[str, os.PathLike],
+        **kwargs
+    ) -> "PretrainedConfig":
+        # text_model_config = AutoConfig.from_pretrained(text_model_name, trust_remote_code=True)
+        # vision_model_config = AutoConfig.from_pretrained(vision_model_name, trust_remote_code=True)
+        text_model_config = VBertTextConfig(text_model_name)
+        vision_model_config = VBertVisionConfig(vision_model_name)
+        return cls(
+            text_config=text_model_config,
+            vision_config=vision_model_config,
+            **kwargs
+        )

vbert-siglip2-slbert-30_210__im2048/opt_step-30000__merged/modeling_vbert.py

@@ -0,0 +1,630 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import CrossEntropyLoss
+from typing import Optional, Tuple, Union, List
+
+from transformers.cache_utils import DynamicCache
+
+from .configuration_vbert import VBertConfig
+
+from transformers import AutoModel, AutoConfig, AutoModelForMaskedLM, PreTrainedModel
+from transformers.modeling_outputs import BaseModelOutput
+from transformers.models.bert.modeling_bert import BaseModelOutputWithPoolingAndCrossAttentions, MaskedLMOutput
+
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+
+from dataclasses import dataclass
+
+from transformers import logging 
+
+logger = logging.get_logger(__name__)
+
+
+class DecoupledEmbedding(nn.Embedding):
+    # Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/sparse.html#Embedding
+    """
+    Implements a decoupling of parameters to allow freezing (or not) a subset of the embeddings.
+    In practise, the regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `num_additional_embeddings` > 0, then it will create `num_additional_embeddings` additional parameters that are always trained.
+    If `num_additional_embeddings=0`, then the module defaults back to the regular behavior of `nn.Embedding`.
+    """
+
+    def __init__(
+        self,
+        num_embeddings,
+        num_additional_embeddings,
+        embedding_dim,
+        partially_freeze=False,
+        device=None,
+        dtype=None,
+        padding_idx=None,
+        **kwargs,
+    ) -> None:
+        """
+        num_additional_embeddings: int. Number of additional embeddings. Only useful when you `partially_freeze=True`.
+        partially_freeze: bool. If True, the regular `weight` will be frozen. `additional_weight` is never frozen.
+
+        Note: there are a lot of other parameters to initialize a standard `nn.Embedding` such as `padding_idx`, `max_norm` or `norm_type`. We are not supporting these.
+        """
+        if padding_idx is not None and padding_idx > num_embeddings:
+            raise ValueError(f"padding_idx must be within num_embeddings. Got {padding_idx} and {num_embeddings}")
+        super().__init__(
+            num_embeddings=num_embeddings,
+            embedding_dim=embedding_dim,
+            device=device,
+            dtype=dtype,
+            padding_idx=padding_idx,
+            **kwargs,
+        )
+        self.num_embeddings = num_embeddings
+        self.padding_idx = padding_idx
+        self.num_additional_embeddings = num_additional_embeddings
+        self.partially_freeze = partially_freeze
+
+        if partially_freeze:
+            self.weight.requires_grad_(False)
+
+        if self.num_additional_embeddings > 0:
+            self.additional_embedding = nn.Embedding(
+                num_embeddings=self.num_additional_embeddings,
+                embedding_dim=embedding_dim,
+                device=device,
+                dtype=dtype,
+            )
+
+    def forward(self, input_ids):
+        """
+        we have 2 embeddings, with different indices - one pretrained self.weight and another
+        self.additional_embedding.weight that is being trained.
+
+        in order to make a lookup of the input ids, we:
+        1. find out the indices of the entries belonging to the 2nd embedding
+        2. extract those values while subtracting the size of the first embedding (num_embeddings),
+           since the 2nd embedding starts from 0 and not num_embeddings
+        3. perform the 2nd embedding lookup
+        4. now we handle the 1st embedding, we overwrite indices belonging to the 2nd embedding with a padding index
+        5. perform the 1st embedding lookup
+        6. now we overwrite the values in the 1st embedding lookup with the values of the 2nd embedding lookup
+
+        note: for the 1st embedding lookup we could have looked up only the low indices and not do
+        the padding, but then we have to create a new tensor and populate it with 2 tensors that are
+        spread out across various indices - i.e. not a simple concat - I haven't benchmarked the
+        complex case if it's any faster, given that seqlens are usually relatively short it's
+        probably not faster or if faster not by much - but might be a good idea to measure.
+
+        """
+        if self.num_additional_embeddings == 0:
+            return self.additional_embedding(input_ids)
+
+        # Clone so that we don't modify the original input_ids later on
+        input_ids = input_ids.clone()
+        additional_vocab_indices = torch.where(input_ids >= self.num_embeddings)
+        input_ids_additional_vocab = input_ids[additional_vocab_indices]
+        additional_embeddings = self.additional_embedding(input_ids_additional_vocab - self.num_embeddings)
+
+        # for successful lookup replace input_ids with 0, the results of these will be discarded anyway
+        input_ids[additional_vocab_indices] = 0
+        full_vector = F.embedding(input_ids, self.weight)
+
+        # overwrite the records with high indices
+        full_vector[additional_vocab_indices] = additional_embeddings
+
+        return full_vector
+
+    def extra_repr(self) -> str:
+        return "num_embeddings={}, num_additional_embeddings={}, embedding_dim={}, partially_freeze={}".format(
+            self.num_embeddings,
+            self.num_additional_embeddings,
+            self.embedding_dim,
+            self.partially_freeze,
+        )
+    
+@dataclass
+class VBertBaseModelOutput(BaseModelOutput):
+    """
+    Base class for SmolVLM model's outputs that may also contain a past key/values (to speed up sequential decoding).
+    Args:
+        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+            If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
+            hidden_size)` is output.
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+            `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+            encoder_sequence_length, embed_size_per_head)`.
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
+            `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
+            input) to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
+            Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
+            sequence_length, hidden_size)`.
+            image_hidden_states of the model produced by the vision encoder
+    """
+
+    last_hidden_state: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+
+@dataclass
+class VBertMaskedLMOutput(MaskedLMOutput):
+    """
+    Base class for SmolVLM model's outputs that may also contain a past key/values (to speed up sequential decoding).
+    Args:
+        loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided):
+            Masked language modeling (MLM) loss.
+        logits (`torch.FloatTensor`): 
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
+            Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
+            sequence_length, hidden_size)`.
+            image_hidden_states of the model produced by the vision encoder
+    """
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
+    image_hidden_states: Optional[torch.FloatTensor] = None
+
+class VBertSimpleMLP(nn.Module):
+    def __init__(self, input_size, output_size):
+        super().__init__()
+        self.proj = nn.Linear(input_size, output_size, bias=False)
+
+    def forward(self, x):
+        return self.proj(x)
+
+class VBertConnector(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.scale_factor = config.pixel_shuffle_factor
+        self.modality_projection = VBertSimpleMLP(
+            input_size=config.vision_config.hidden_size * (config.scale_factor**2),
+            output_size=config.text_config.hidden_size
+        )
+
+    def pixel_shuffle(self, x, scale_factor):
+        bsz, seq, embed_dim = x.size()
+        height = width = int(seq**0.5)
+        x = x.view(bsz, height, width, embed_dim)
+        x = x.view(bsz, height, int(width / scale_factor), embed_dim * scale_factor)
+        x = x.permute(0, 2, 1, 3)
+        x = x.reshape(bsz, int(width / scale_factor), int(height / scale_factor), embed_dim * (scale_factor**2))
+        x = x.permute(0, 2, 1, 3)
+        x = x.reshape(bsz, int(seq / (scale_factor**2)), embed_dim * (scale_factor**2))
+        return x
+
+    def forward(self, image_hidden_states):
+        image_hidden_states = self.pixel_shuffle(image_hidden_states, self.scale_factor)
+        image_hidden_states = self.modality_projection(image_hidden_states)
+        return image_hidden_states
+
+class VBertPreTrainedModel(PreTrainedModel):
+    config_class = VBertConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["VBertDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        """Initialize the weights."""
+
+        std = (
+            self.config.initializer_range
+            if hasattr(self.config, "initializer_range")
+            else self.config.text_config.initializer_range
+        )
+
+        if hasattr(module, "class_embedding"):
+            module.class_embedding.data.normal_(mean=0.0, std=std)
+
+        if isinstance(module, (nn.Linear, nn.Conv2d)):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+class VBertModel(VBertPreTrainedModel):
+    """
+    A subclass of Idefics3Model. We do *not* remove or block the call to inputs_merger
+    in forward. Instead, we override inputs_merger here with custom logic.
+    """
+
+    def __init__(self, config: VBertConfig, **kwargs):
+        super().__init__(config)
+
+        self.vision_model = VBertModel.init_vision_model(config, **kwargs)
+        self.connector = VBertConnector(config)
+        self.text_model = VBertModel.init_language_model(config, **kwargs)
+
+        self.image_seq_len = int(
+            ((config.vision_config.image_size // config.vision_config.patch_size) ** 2) / (config.scale_factor**2)
+        )
+        self.image_token_id = self.config.image_token_id
+
+        self.post_init()
+
+    @staticmethod
+    def init_vision_model(config: VBertConfig, **kwargs):
+        vision_model_config = AutoConfig.from_pretrained(
+            config.vision_config.vision_model_name,
+            trust_remote_code=True,
+            **kwargs,
+        )
+
+        vision_model = AutoModel.from_config(vision_model_config, trust_remote_code=True, **kwargs)
+
+        if hasattr(vision_model, "vision_model"):
+            # If the model has a vision_model attribute, it means it's a wrapper around another model
+            vision_model = vision_model.vision_model
+
+        return vision_model
+
+    @staticmethod
+    def init_language_model(config: VBertConfig, **kwargs):
+        text_model_config = AutoConfig.from_pretrained(
+            config.text_config.text_model_name,
+            trust_remote_code=True,
+            **kwargs,
+        )
+
+        text_model = AutoModel.from_config(text_model_config, trust_remote_code=True, **kwargs)
+        # extractor = regex_lookup(language_model_name, language_model_name2model)
+
+        embed_layer = DecoupledEmbedding(
+            num_embeddings=text_model_config.vocab_size,
+            num_additional_embeddings=config.additional_vocab_size,
+            embedding_dim=config.hidden_size,
+            partially_freeze=config.freeze_config["freeze_text_layers"],
+            padding_idx=config.pad_token_id,
+        )
+
+        text_model.set_input_embeddings(embed_layer)
+
+        return text_model
+
+    def enable_input_require_grads(self):
+        """
+        Enables the gradients for the input embeddings.
+
+        This is useful for lora when using gradient checkpointing.
+        c.f. https://github.com/huggingface/peft/issues/1402#issuecomment-1913675032
+
+        Override to set output.requires_grad = True for both the decoder's and vision model's embeddings.
+        """
+
+        def get_lowest_module(module):
+            if len(list(module.children())) == 0:
+                # If the module has no children, it is a leaf module (e.g., Linear, Conv2d, etc.)
+                return module
+            else:
+                # Recursively call the function on each child module
+                return get_lowest_module(list(module.children())[0])
+
+        def make_inputs_require_grads(module, input, output):
+            output.requires_grad_(True)
+
+        self._text_require_grads_hook = self.get_input_embeddings().register_forward_hook(make_inputs_require_grads)
+        self._vision_require_grads_hook = get_lowest_module(self.vision_model).register_forward_hook(
+            make_inputs_require_grads
+        )
+
+    def disable_input_require_grads(self):
+        self._text_require_grads_hook.remove()
+        self._vision_require_grads_hook.remove()
+
+    def get_input_embeddings(self):
+        return self.text_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.text_model.set_input_embeddings(value)
+
+    def inputs_merger(
+        self, input_ids: torch.LongTensor, inputs_embeds: torch.Tensor, image_hidden_states: torch.Tensor
+    ):
+        """
+        This method aims at merging the token embeddings with the image hidden states into one single sequence of vectors that are fed to the transformer LM.
+        The merging happens as follows:
+        - The text token sequence is: `tok_1 tok_2 tok_3 <fake_token_around_image> <image> <image> ... <image> <fake_token_around_image> tok_4`.
+        - We get the image hidden states for the image through the vision encoder and that hidden state, after a pixel shuffle operation, is then projected into the text embedding space.
+        We thus have a sequence of image hidden states of size (1, image_seq_len, hidden_dim), where 1 is for batch_size of 1 image and hidden_dim is the hidden_dim of the LM transformer.
+        - The merging happens so that we obtain the following sequence: `vector_tok_1 vector_tok_2 vector_tok_3 vector_fake_tok_around_image {sequence of image_seq_len image hidden states} vector_fake_toke_around_image vector_tok_4`. That sequence is fed to the LM.
+        - To fit the format of that sequence, `input_ids`, `input_embeds`, `attention_mask` are all 3 adapted to insert the image hidden states.
+        """
+        _, patch_size, _ = image_hidden_states.shape
+
+        image_mask = input_ids == self.image_token_id
+        num_image_tokens = image_mask.sum(dim=1)
+        if not torch.all(num_image_tokens % patch_size == 0):
+            raise ValueError("At least one sample has <image> tokens not divisible by patch_size.")
+
+        blocks_per_sample = num_image_tokens // patch_size
+
+        offsets = torch.nn.functional.pad(blocks_per_sample.cumsum(dim=0), (1, 0), value=0)
+        block_offset = offsets[:-1]
+        row_cum = image_mask.cumsum(dim=-1)
+        chunk_idx = (row_cum - 1) // patch_size
+        local_idx = (row_cum - 1) % patch_size
+        block_idx = block_offset.unsqueeze(1) + chunk_idx
+
+        image_embeds = torch.zeros_like(inputs_embeds)
+        image_embeds[image_mask] = image_hidden_states[block_idx[image_mask], local_idx[image_mask], :]
+
+        merged_embeds = torch.where(image_mask.unsqueeze(-1), image_embeds, inputs_embeds)
+        return merged_embeds
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        pixel_attention_mask: Optional[torch.BoolTensor] = None,
+        image_hidden_states: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPoolingAndCrossAttentions]:    
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if self.training and self.text_model.gradient_checkpointing and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+            )
+            use_cache = False
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        past_seen_tokens = 0
+        if use_cache:
+            if past_key_values is None:
+                past_key_values = DynamicCache()
+            past_seen_tokens = past_key_values.get_seq_length()
+
+        if inputs_embeds is not None and input_ids is None and past_seen_tokens == 0:
+            raise ValueError("When first calling the model, if input_embeds are passed, input_ids should not be None.")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.text_model.get_input_embeddings()(input_ids).to(input_ids.device)
+
+        # START VISUAL INPUTS INTEGRATION
+        if pixel_values is not None and image_hidden_states is not None:
+            raise ValueError("You cannot specify both pixel_values and image_hidden_states at the same time")
+        elif pixel_values is not None:
+            batch_size, num_images, num_channels, height, width = pixel_values.shape
+            pixel_values = pixel_values
+            pixel_values = pixel_values.view(batch_size * num_images, *pixel_values.shape[2:])
+
+            # Remove padding images - padding images are full 0.
+            nb_values_per_image = pixel_values.shape[1:].numel()
+            real_images_inds = (pixel_values == 0.0).sum(dim=(-1, -2, -3)) != nb_values_per_image
+
+            if not any(real_images_inds):
+                # no images, leave one empty image.
+                real_images_inds[0] = True
+
+            pixel_values = pixel_values[real_images_inds].contiguous()
+
+            # Handle the vision attention mask
+            if pixel_attention_mask is None:
+                pixel_attention_mask = torch.ones(
+                    size=[pixel_values.shape[i] for i in (0, 2, 3)],
+                    dtype=torch.bool,
+                    device=pixel_values.device,
+                )
+            else:
+                # Remove padding images from the mask
+                pixel_attention_mask = pixel_attention_mask.view(
+                    batch_size * num_images, *pixel_attention_mask.shape[2:]
+                )
+                pixel_attention_mask = pixel_attention_mask[real_images_inds].contiguous()
+
+            # patch_size = self.config.vision_config.patch_size
+            # patches_subgrid = pixel_attention_mask.unfold(dimension=1, size=patch_size, step=patch_size)
+            # patches_subgrid = patches_subgrid.unfold(dimension=2, size=patch_size, step=patch_size)
+            # patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()
+
+            # Get sequence from the vision encoder
+            image_hidden_states = self.vision_model(
+                pixel_values=pixel_values,
+                # patch_attention_mask=patch_attention_mask,
+            ).last_hidden_state
+
+            # Modality projection & resampling
+            image_hidden_states = self.connector(image_hidden_states)
+
+        elif image_hidden_states is not None:
+            image_hidden_states = image_hidden_states.to(dtype=self.dtype, device=input_ids.device)
+
+        if inputs_embeds is not None and image_hidden_states is not None:
+            # When we embed, we don't want to replace the potential image_token_id that we generated by images
+            # that simply don't exist
+            inputs_embeds = self.inputs_merger(
+                input_ids=input_ids,
+                inputs_embeds=inputs_embeds,
+                image_hidden_states=image_hidden_states,
+            )
+
+        outputs = self.text_model(
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            # past_key_values=past_key_values,
+            # use_cache=use_cache,
+            # cache_position=cache_position,
+        )
+
+        if not return_dict:
+            return tuple(v for v in [*outputs, image_hidden_states] if v is not None)
+
+        return VBertBaseModelOutput(
+            last_hidden_state=outputs.last_hidden_state,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=image_hidden_states,
+        )
+
+class VBertForMaskedLM(VBertPreTrainedModel):
+    # _tied_weights_keys = ["predictions.decoder.bias", "cls.predictions.decoder.weight"]
+
+    def __init__(self, config, **kwargs):
+        super().__init__(config)
+
+        self.image_token_id = config.image_token_id
+        self.in_features = config.hidden_size
+        self.out_additional_features = config.additional_vocab_size
+        self.vocab_size = config.vocab_size
+
+        if config.is_decoder:
+            logger.warning(
+                "If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for "
+                "bi-directional self-attention."
+            )
+
+        self.model = VBertModel(config, **kwargs)
+        self.lm_head = VBertForMaskedLM.init_lm_head(config, **kwargs)
+        if self.out_additional_features > 0:
+            self.additional_fc = nn.Linear(
+                in_features=self.in_features,
+                out_features=self.out_additional_features,
+                bias=False,
+            )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @staticmethod
+    def init_lm_head(config, **kwargs):
+        # Get the pretrained model config
+        text_model_config = AutoConfig.from_pretrained(
+            config.text_config.text_model_name,
+            trust_remote_code=True,
+            **kwargs,
+        )
+        model = AutoModelForMaskedLM.from_config(text_model_config, trust_remote_code=True, **kwargs)
+        # Get the lm head
+        lm_head = model.lm_head if hasattr(model, "lm_head") else model.decoder if hasattr(model, "decoder") else None
+        if lm_head is None:
+            logger.warning(f"No lm head was found for {config.text_config.text_model_name}, initializing a new one.")
+            lm_head = nn.Linear(config.hidden_size, config.vocab_size, False)
+        return lm_head
+
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[torch.FloatTensor]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            pixel_values: Optional[torch.FloatTensor] = None,
+            pixel_attention_mask: Optional[torch.BoolTensor] = None,
+            image_hidden_states: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, VBertMaskedLMOutput]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or `model.image_token_id` (where `model` is your instance of `Idefics3ForConditionalGeneration`).
+                Tokens with indices set to `model.image_token_id` are ignored (masked), the loss is only
+                computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        ```"""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+        # Pass the inputs to VBertModel
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            pixel_values=pixel_values,
+            pixel_attention_mask=pixel_attention_mask,
+            image_hidden_states=image_hidden_states,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        # Pass the outputs to the MLM head
+        hidden_states = outputs[0]
+
+        logits = self.lm_head(hidden_states)
+        if self.out_additional_features > 0:
+            additional_features = self.additional_fc(hidden_states)
+            logits = torch.cat((logits, additional_features), -1)
+        logits = logits.float()
+
+        masked_lm_loss = None
+        if labels is not None:
+            # print the ratio of not ignored tokens
+            loss_fct = CrossEntropyLoss()
+            masked_lm_loss = loss_fct(logits.view(-1, self.vocab_size + self.out_additional_features), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+        return VBertMaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=outputs.image_hidden_states,
+        )

vbert-siglip2-slbert-30_210__im2048/opt_step-30000__merged/preprocessor_config.json

@@ -0,0 +1,28 @@
+{
+    "do_convert_rgb": true,
+    "do_image_splitting": true,
+    "do_normalize": true,
+    "do_pad": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "image_processor_type": "Idefics3ImageProcessor",
+    "image_std": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "max_image_size": {
+        "longest_edge": 512
+    },
+    "processor_class": "Idefics3Processor",
+    "resample": 1,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
+        "longest_edge": 2048
+    }
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-30000__merged/processor_config.json

@@ -0,0 +1,4 @@
+{
+    "image_seq_len": 64,
+    "processor_class": "Idefics3Processor"
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-30000__merged/special_tokens_map.json

@@ -0,0 +1,72 @@
+{
+  "additional_special_tokens": [
+    "<global-img>",
+    "<row_1_col_1>",
+    "<row_1_col_2>",
+    "<row_1_col_3>",
+    "<row_1_col_4>",
+    "<row_1_col_5>",
+    "<row_1_col_6>",
+    "<row_2_col_1>",
+    "<row_2_col_2>",
+    "<row_2_col_3>",
+    "<row_2_col_4>",
+    "<row_2_col_5>",
+    "<row_2_col_6>",
+    "<row_3_col_1>",
+    "<row_3_col_2>",
+    "<row_3_col_3>",
+    "<row_3_col_4>",
+    "<row_3_col_5>",
+    "<row_3_col_6>",
+    "<row_4_col_1>",
+    "<row_4_col_2>",
+    "<row_4_col_3>",
+    "<row_4_col_4>",
+    "<row_4_col_5>",
+    "<row_4_col_6>",
+    "<row_5_col_1>",
+    "<row_5_col_2>",
+    "<row_5_col_3>",
+    "<row_5_col_4>",
+    "<row_5_col_5>",
+    "<row_5_col_6>",
+    "<row_6_col_1>",
+    "<row_6_col_2>",
+    "<row_6_col_3>",
+    "<row_6_col_4>",
+    "<row_6_col_5>",
+    "<row_6_col_6>",
+    "<end_of_utterance>",
+    "<fake_token_around_image>",
+    "<image>"
+  ],
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<|reserved_special_token_0|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

vbert-siglip2-slbert-30_210__im2048/opt_step-30000__merged/tokenizer_config.json

@@ -0,0 +1,2429 @@
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vbert-siglip2-slbert-30_210__im2048/train_logs.json

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vlm-siglip2-sllm_210/latest_opt_step_dir

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vlm-siglip2-sllm_210/train_logs.json

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vlm-siglip2-sllm_210__clm-20/latest_opt_step_dir

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vlm-siglip2-sllm_210__clm-20/train_logs.json

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