diff --git a/docker/common/uv-pytorch.lock b/docker/common/uv-pytorch.lock
index d847c4758..3beebfa05 100644
--- a/docker/common/uv-pytorch.lock
+++ b/docker/common/uv-pytorch.lock
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 [[package]]
 name = "alembic"
 version = "1.17.1"
@@ -2828,6 +2861,7 @@ dependencies = [
 
 [package.optional-dependencies]
 all = [
+    { name = "albumentations" },
     { name = "backoff" },
     { name = "flash-linear-attention" },
     { name = "mistral-common", extra = ["opencv"] },
@@ -2851,6 +2885,7 @@ extra = [
     { name = "sentencepiece" },
 ]
 vlm = [
+    { name = "albumentations" },
     { name = "backoff" },
     { name = "mistral-common", extra = ["opencv"] },
     { name = "numba" },
@@ -2893,6 +2928,7 @@ test = [
 
 [package.metadata]
 requires-dist = [
+    { name = "albumentations", marker = "extra == 'vlm'" },
     { name = "backoff", marker = "extra == 'vlm'" },
     { name = "datasets", specifier = ">=4.0.0" },
     { name = "diffusers" },
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 name = "sympy"
 version = "1.14.0"
diff --git a/examples/vlm_finetune/nemotron/nemotron_parse_v1_1.yaml b/examples/vlm_finetune/nemotron/nemotron_parse_v1_1.yaml
new file mode 100644
index 000000000..c5bd83c08
--- /dev/null
+++ b/examples/vlm_finetune/nemotron/nemotron_parse_v1_1.yaml
@@ -0,0 +1,96 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+step_scheduler:
+  global_batch_size: 16
+  local_batch_size: 2
+  ckpt_every_steps: 100
+  val_every_steps: 100  # will run every x number of gradient steps
+  max_steps: 1000
+
+dist_env:
+  backend: nccl
+  timeout_minutes: 10
+
+rng:
+  _target_: nemo_automodel.components.training.rng.StatefulRNG
+  seed: 42
+  ranked: true
+
+model:
+  _target_: nemo_automodel.NeMoAutoModelForImageTextToText.from_pretrained
+  pretrained_model_name_or_path: /lustre/fsw/portfolios/coreai/projects/coreai_dlalgo_llm/users/huiyingl/bulb/Automodel-bulb/my/NVIDIA-Nemotron-Parse-v1.1
+  torch_dtype: torch.bfloat16
+  use_liger_kernel: false
+  attn_implementation: eager
+  trust_remote_code: True
+  local_files_only: True
+
+processor:
+  _target_: transformers.AutoProcessor.from_pretrained
+  pretrained_model_name_or_path: nvidia/NVIDIA-Nemotron-Parse-v1.1
+  trust_remote_code: True
+
+checkpoint:
+  enabled: true
+  checkpoint_dir: vlm_checkpoints/
+  model_save_format: safetensors
+  save_consolidated: true
+
+distributed:
+  _target_: nemo_automodel.components.distributed.fsdp2.FSDP2Manager
+  dp_size: none
+  tp_size: 1
+  cp_size: 1
+  sequence_parallel: false
+
+loss_fn:
+  _target_: nemo_automodel.components.loss.masked_ce.MaskedCrossEntropy
+
+dataset:
+  _target_: nemo_automodel.components.datasets.vlm.datasets.make_cord_v2_dataset
+  path_or_dataset: naver-clova-ix/cord-v2
+  split: train
+
+dataloader:
+  _target_: torchdata.stateful_dataloader.StatefulDataLoader
+  num_workers: 0
+  pin_memory: true
+  collate_fn:
+    _target_: nemo_automodel.components.datasets.vlm.collate_fns.nemotron_parse_collate_fn
+
+validation_dataset:
+  _target_: nemo_automodel.components.datasets.vlm.datasets.make_cord_v2_dataset
+  path_or_dataset: naver-clova-ix/cord-v2
+  split: validation
+
+validation_dataloader:
+  _target_: torchdata.stateful_dataloader.StatefulDataLoader
+  
+optimizer:
+  _target_: torch.optim.AdamW
+  lr: 1e-5
+  weight_decay: 0.01
+  betas: [0.9, 0.95]
+
+freeze_config:
+  freeze_embeddings: true
+  freeze_vision_tower: true
+  freeze_language_model: false
+  
+# wandb:
+#   project: <your_project_name>  
+#   entity: <your_entity_name>
+#   name: <your_experiment_name>
diff --git a/nemo_automodel/components/datasets/vlm/collate_fns.py b/nemo_automodel/components/datasets/vlm/collate_fns.py
index 958252070..65a245448 100644
--- a/nemo_automodel/components/datasets/vlm/collate_fns.py
+++ b/nemo_automodel/components/datasets/vlm/collate_fns.py
@@ -253,6 +253,88 @@ def has_data(modality_list):
     return batch
 
 
+def nemotron_parse_collate_fn(
+    examples: Sequence[Dict[str, Any]],
+    processor,
+    task_prompt: str = "</s><s><predict_bbox><predict_classes><output_markdown>",
+) -> Dict[str, torch.Tensor]:
+    """
+    Collate function for NVIDIA Nemotron-Parse models.
+
+    The Nemotron-Parse processor does not expose a chat template, so we build the
+    prompt + answer string manually, mask the prompt tokens, and keep the
+    image preprocessing handled by the processor.
+    """
+
+    conversations = [example["conversation"] for example in examples]
+
+    images: List[Any] = []
+    targets: List[str] = []
+    for conversation in conversations:
+        image = None
+        assistant_text = ""
+
+        for message in conversation:
+            role = message.get("role")
+            content = message.get("content")
+
+            if role == "user":
+                if isinstance(content, list):
+                    for item in content:
+                        if isinstance(item, dict) and item.get("type") == "image":
+                            image = item.get("image")
+                            break
+            elif role == "assistant" and not assistant_text:
+                assistant_text = _extract_assistant_text(message)
+
+            if image is not None and assistant_text:
+                break
+
+        images.append(image)
+        targets.append(assistant_text)
+
+    texts = [f"{task_prompt}{target}" for target in targets]
+
+    batch = processor(images=images, text=texts, padding=True, return_tensors="pt")
+
+    if "pixel_values" in batch:
+        batch["pixel_values"] = batch["pixel_values"].to(torch.bfloat16)
+
+    labels = build_labels(
+        batch["input_ids"],
+        conversations,
+        processor,
+    )
+
+    batch["labels"] = labels[:, 1:]
+
+    tokenizer = getattr(processor, "tokenizer", processor)
+    pad_token_id = getattr(tokenizer, "pad_token_id", None)
+    decoder_start_token_id = getattr(tokenizer, "decoder_start_token_id", None) or getattr(
+        tokenizer, "bos_token_id", None
+    )
+    if decoder_start_token_id is None:
+        decoder_start_token_id = getattr(tokenizer, "eos_token_id", None)
+    if pad_token_id is None or decoder_start_token_id is None:
+        raise ValueError("Nemotron-Parse collate_fn requires pad_token_id and decoder_start_token_id.")
+
+    decoder_input_ids = batch["input_ids"].clone()
+    decoder_input_ids[:, 0] = decoder_start_token_id
+    decoder_input_ids[:, 1:] = batch["input_ids"][:, :-1]
+
+    decoder_attention_mask = (decoder_input_ids != pad_token_id).long()
+
+    batch["decoder_input_ids"] = decoder_input_ids[:, 1:]
+    batch["decoder_attention_mask"] = decoder_attention_mask[:, 1:]
+
+    input_shape = batch["input_ids"].shape
+    for key, value in list(batch.items()):
+        if isinstance(value, torch.Tensor) and value.shape == input_shape:
+            batch[key] = value[:, :-1]
+
+    return batch
+
+
 def default_collate_fn(
     examples: Sequence[Dict[str, Any]],
     processor,
@@ -297,5 +379,6 @@ def default_collate_fn(
 COLLATE_FNS = {
     "Qwen2_5_VLProcessor": qwen2_5_collate_fn,
     "Qwen3OmniMoeProcessor": qwen3_omni_collate_fn,
+    "NemotronParseProcessor": nemotron_parse_collate_fn,
     "default": default_collate_fn,
 }
diff --git a/nemo_automodel/components/models/nemotron_parse/model.py b/nemo_automodel/components/models/nemotron_parse/model.py
new file mode 100644
index 000000000..67561f246
--- /dev/null
+++ b/nemo_automodel/components/models/nemotron_parse/model.py
@@ -0,0 +1,592 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+from torch.nn import CrossEntropyLoss
+from transformers import AutoConfig, AutoModel, GenerationMixin, PreTrainedModel
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_attn_mask_utils import (
+    _prepare_4d_attention_mask,
+    _prepare_4d_attention_mask_for_sdpa,
+    _prepare_4d_causal_attention_mask,
+    _prepare_4d_causal_attention_mask_for_sdpa,
+)
+from transformers.modeling_outputs import BaseModelOutput, Seq2SeqLMOutput
+from transformers.models.donut.modeling_donut_swin import DonutSwinModelOutput
+from transformers.models.encoder_decoder.modeling_encoder_decoder import shift_tokens_right
+from transformers.models.mbart.modeling_mbart import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    MBartConfig,
+    MBartDecoderLayer,
+    MBartPreTrainedModel,
+    MBartScaledWordEmbedding,
+)
+
+# -----------------------------------------------------------------------------
+# NemotronParse configuration
+# -----------------------------------------------------------------------------
+
+
+class NemotronParseTextConfig(PretrainedConfig):
+    """Configuration class for NemotronParse text decoder (mBART-based)."""
+
+    model_type = "nemotron_parse_text"
+
+    def __init__(
+        self,
+        vocab_size: int = 250027,
+        d_model: int = 1024,
+        encoder_layers: int = 12,
+        decoder_layers: int = 12,
+        encoder_attention_heads: int = 16,
+        decoder_attention_heads: int = 16,
+        decoder_ffn_dim: int = 4096,
+        encoder_ffn_dim: int = 4096,
+        activation_function: str = "gelu",
+        dropout: float = 0.1,
+        attention_dropout: float = 0.0,
+        activation_dropout: float = 0.0,
+        classifier_dropout: float = 0.0,
+        init_std: float = 0.02,
+        encoder_layerdrop: float = 0.0,
+        decoder_layerdrop: float = 0.0,
+        scale_embedding: bool = False,
+        use_cache: bool = True,
+        num_labels: int = 3,
+        forced_eos_token_id: int = 2,
+        add_cross_attention: bool = True,
+        is_decoder: bool = True,
+        max_sequence_length: int = 9000,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.d_model = d_model
+        self.encoder_layers = encoder_layers
+        self.decoder_layers = decoder_layers
+        self.encoder_attention_heads = encoder_attention_heads
+        self.decoder_attention_heads = decoder_attention_heads
+        self.decoder_ffn_dim = decoder_ffn_dim
+        self.encoder_ffn_dim = encoder_ffn_dim
+        self.activation_function = activation_function
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.activation_dropout = activation_dropout
+        self.classifier_dropout = classifier_dropout
+        self.init_std = init_std
+        self.encoder_layerdrop = encoder_layerdrop
+        self.decoder_layerdrop = decoder_layerdrop
+        self.scale_embedding = scale_embedding
+        self.use_cache = use_cache
+        self.num_labels = num_labels
+        self.add_cross_attention = add_cross_attention
+        self.is_decoder = is_decoder
+        self.hidden_size = self.d_model
+        self.forced_eos_token_id = forced_eos_token_id
+        self.num_attention_heads = self.encoder_attention_heads
+        self.max_sequence_length = max_sequence_length
+
+
+class NemotronParseEncoderConfig(PretrainedConfig):
+    """Configuration class for NemotronParse vision encoder (RADIO-based)."""
+
+    model_type = "nemotron_parse_encoder"
+
+    def __init__(
+        self,
+        patch_size: int = 16,
+        max_resolution: int = 2048,
+        preferred_resolution: List[int] = None,
+        torch_dtype: str = "bfloat16",
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.patch_size = patch_size
+        self.max_resolution = max_resolution
+        self.preferred_resolution = preferred_resolution or [768, 768]
+        self.torch_dtype = torch_dtype
+        # Store any additional args from the original RADIO config
+        for key, value in kwargs.items():
+            if not hasattr(self, key):
+                setattr(self, key, value)
+
+
+class NemotronParseConfig(PretrainedConfig):
+    """Configuration class for NemotronParse model."""
+
+    model_type = "nemotron_parse"
+    is_composition = True
+
+    def __init__(
+        self,
+        encoder: Optional[dict] = None,
+        decoder: Optional[dict] = None,
+        tie_word_embeddings: bool = False,
+        decoder_start_token_id: int = 2,
+        pad_token_id: int = 1,
+        eos_token_id: int = 2,
+        bos_token_id: int = 0,
+        image_size: List[int] = None,
+        is_encoder_decoder: bool = True,
+        max_sequence_length: int = 9000,
+        **kwargs,
+    ):
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            decoder_start_token_id=decoder_start_token_id,
+            pad_token_id=pad_token_id,
+            eos_token_id=eos_token_id,
+            bos_token_id=bos_token_id,
+            max_sequence_length=max_sequence_length,
+            **kwargs,
+        )
+
+        if decoder is None:
+            decoder = {}
+        if encoder is None:
+            encoder = {}
+
+        if encoder:
+            radio_model_path = encoder.get("_name_or_path", "nvidia/C-RADIOv2-H")
+            self.encoder = AutoConfig.from_pretrained(radio_model_path, trust_remote_code=True)
+            # Update with any overrides from encoder dict
+            for key, value in encoder.items():
+                if hasattr(self.encoder, key):
+                    setattr(self.encoder, key, value)
+        else:
+            self.encoder = PretrainedConfig()
+
+        decoder["max_sequence_length"] = max_sequence_length
+        self.decoder = NemotronParseTextConfig(**decoder)
+        self.image_size = image_size or [2048, 1648]
+        self.vocab_size = self.decoder.vocab_size
+        self.is_encoder_decoder = is_encoder_decoder
+        self.max_sequence_length = max_sequence_length
+
+    def to_dict(self):
+        output = super().to_dict()
+        output["encoder"] = self.encoder.to_dict()
+        output["decoder"] = self.decoder.to_dict()
+        output["model_type"] = self.model_type
+        output["is_encoder_decoder"] = self.is_encoder_decoder
+        return output
+
+
+# -----------------------------------------------------------------------------
+# NemotronParse modeling
+# -----------------------------------------------------------------------------
+
+
+class NemotronParseDecoder(MBartPreTrainedModel):
+    """Transformer decoder consisting of *config.decoder_layers* layers."""
+
+    def __init__(self, config: MBartConfig, embed_tokens: Optional[nn.Embedding] = None):
+        super().__init__(config)
+        self.dropout = config.dropout
+        self.layerdrop = config.decoder_layerdrop
+        self.padding_idx = config.pad_token_id
+        embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
+
+        self.embed_tokens = MBartScaledWordEmbedding(
+            config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale
+        )
+
+        if embed_tokens is not None:
+            self.embed_tokens.weight = embed_tokens.weight
+
+        self.layers = nn.ModuleList([MBartDecoderLayer(config) for _ in range(config.decoder_layers)])
+        self.config = config
+
+        self.layernorm_embedding = nn.LayerNorm(config.d_model)
+        self.layer_norm = nn.LayerNorm(config.d_model)
+
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        cross_attn_head_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        inputs_embeds: 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,
+    ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
+        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
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            input = input_ids
+            input_shape = input.size()
+            input_ids = input_ids.view(-1, input_shape[-1])
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+            input = inputs_embeds[:, :, -1]
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        past_key_values_length = 0
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if self.config._attn_implementation == "flash_attention_2":
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        elif self.config._attn_implementation == "sdpa" and not output_attentions and cross_attn_head_mask is None:
+            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+                attention_mask, input_shape, inputs_embeds, past_key_values_length
+            )
+        else:
+            attention_mask = _prepare_4d_causal_attention_mask(
+                attention_mask, input_shape, inputs_embeds, past_key_values_length
+            )
+
+        if encoder_hidden_states is not None and encoder_attention_mask is not None:
+            if self.config._attn_implementation == "flash_attention_2":
+                encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
+            elif self.config._attn_implementation == "sdpa" and cross_attn_head_mask is None and not output_attentions:
+                encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa(
+                    encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+                )
+            else:
+                encoder_attention_mask = _prepare_4d_attention_mask(
+                    encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+                )
+
+        hidden_states = self.layernorm_embedding(inputs_embeds)
+        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
+
+        for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
+            if attn_mask is not None and attn_mask.size()[0] != len(self.layers):
+                raise ValueError(
+                    f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for {attn_mask.size()[0]}."
+                )
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+            if self.training:
+                dropout_probability = torch.rand([])
+                if dropout_probability < self.layerdrop:
+                    continue
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    head_mask[idx] if head_mask is not None else None,
+                    cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
+                    None,
+                    output_attentions,
+                    False,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=encoder_attention_mask,
+                    layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+                    cross_attn_layer_head_mask=(
+                        cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
+                    ),
+                    past_key_value=None,
+                    output_attentions=output_attentions,
+                    use_cache=False,
+                )
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+                if encoder_hidden_states is not None:
+                    all_cross_attentions += (layer_outputs[2],)
+
+        hidden_states = self.layer_norm(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, None, all_hidden_states, all_self_attns, all_cross_attentions]
+                if v is not None
+            )
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=None,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+class RadioWithNeck(nn.Module):
+    """Vision encoder using RADIO model with custom neck."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+
+        # Create RADIO encoder from config (which is now actual RADIOConfig)
+        self.model_encoder = AutoModel.from_config(config, trust_remote_code=True)
+
+        # Neck components (dtype conversion done later via .to())
+        last_hidden_state = 1024
+        self.conv1 = nn.Conv1d(1280, last_hidden_state, 1)
+        self.layer_norm1 = nn.LayerNorm(last_hidden_state, eps=1e-06, elementwise_affine=True)
+        self.conv2 = nn.Conv2d(
+            last_hidden_state, last_hidden_state, kernel_size=(1, 4), stride=(1, 4), padding=0, bias=False
+        )
+        self.layer_norm2 = nn.LayerNorm(last_hidden_state, eps=1e-06, elementwise_affine=True)
+        self.sum_proj = nn.Linear(3840, last_hidden_state)
+        self.layer_norm3 = nn.LayerNorm(last_hidden_state, eps=1e-06, elementwise_affine=True)
+
+    def forward(self, pixel_values, output_attentions=False, output_hidden_states=False, return_dict=False, **kwargs):
+        radio_output = self.model_encoder(pixel_values)
+        summary, feature = radio_output
+
+        output = self.conv1(feature.permute(0, 2, 1)).permute(0, 2, 1)
+        output = self.layer_norm1(output)
+
+        patch_size = self.config.patch_size
+        output = rearrange(
+            output,
+            "b (h w) d -> b d h w",
+            h=pixel_values.shape[-2] // patch_size,
+            w=pixel_values.shape[-1] // patch_size,
+        )
+        output = self.conv2(output)
+        output = rearrange(output, "b d h w -> b (h w) d")
+        output = self.layer_norm2(output)
+        summary = self.layer_norm3(self.sum_proj(summary))
+        output = torch.cat((output, summary.unsqueeze(1)), dim=1)
+
+        return DonutSwinModelOutput(last_hidden_state=output)
+
+
+class NemotronParsePreTrainedModel(PreTrainedModel):
+    """Abstract class to handle weights initialization."""
+
+    config_class = NemotronParseConfig
+    base_model_prefix = "vision_encoder_decoder"
+    main_input_name = "pixel_values"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["RadioWithNeck", "MBartDecoder"]
+    _skip_keys_device_placement = "past_key_values"
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=self.config.decoder.init_std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.decoder.init_std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+class NemotronParseForConditionalGeneration(NemotronParsePreTrainedModel, GenerationMixin):
+    """NemotronParse model for conditional generation tasks."""
+
+    def __init__(self, config: NemotronParseConfig, **kwargs):
+        super().__init__(config)
+
+        self.encoder = RadioWithNeck(config.encoder)
+        self.encoder.main_input_name = "pixel_values"
+        self.encoder = self.encoder.to(torch.bfloat16)
+
+        self.decoder = NemotronParseDecoder(config.decoder)
+        self.decoder = self.decoder.to(torch.bfloat16)
+
+        self.lm_head = nn.Linear(config.decoder.d_model, config.decoder.vocab_size, bias=False, dtype=torch.bfloat16)
+
+        num_extra_heads = getattr(config, "num_extra_heads", 0)
+        self.decoder.extra_heads = nn.ModuleList(
+            [
+                nn.Linear(config.decoder.d_model, config.decoder.d_model, dtype=torch.bfloat16)
+                for _ in range(num_extra_heads)
+            ]
+        )
+        self.decoder.extra_proj = nn.ModuleList(
+            [
+                nn.Linear(config.decoder.d_model, config.decoder.d_model, dtype=torch.bfloat16)
+                for _ in range(num_extra_heads)
+            ]
+        )
+
+        self.class_token_indx_start = getattr(config, "class_token_start_idx", 50000)
+        self.post_init()
+
+    def get_encoder(self):
+        return self.encoder
+
+    def get_decoder(self):
+        return self.decoder
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def get_input_embeddings(self):
+        return self.decoder.get_input_embeddings()
+
+    def forward(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        decoder_input_ids: Optional[torch.LongTensor] = None,
+        decoder_attention_mask: Optional[torch.BoolTensor] = None,
+        encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        decoder_inputs_embeds: 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,
+        **kwargs,
+    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        kwargs_encoder = {k: v for k, v in kwargs.items() if not k.startswith("decoder_")}
+        kwargs_decoder = {k[len("decoder_") :]: v for k, v in kwargs.items() if k.startswith("decoder_")}
+
+        if encoder_outputs is None:
+            if pixel_values is None:
+                raise ValueError("You have to specify pixel_values")
+            encoder_outputs = self.encoder(
+                pixel_values,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+                **kwargs_encoder,
+            )
+        elif isinstance(encoder_outputs, tuple):
+            encoder_outputs = BaseModelOutput(*encoder_outputs)
+
+        encoder_hidden_states = encoder_outputs[0]
+        encoder_attention_mask = None
+
+        if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
+            decoder_input_ids = shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
+
+        output_hidden_states = True
+
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            inputs_embeds=decoder_inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            use_cache=use_cache,
+            past_key_values=past_key_values,
+            return_dict=return_dict,
+            **kwargs_decoder,
+        )
+
+        loss = None
+        if labels is not None:
+            main_logits = self.lm_head(decoder_outputs.last_hidden_state)
+            logits = [main_logits]
+            decoder_inputs_embeds = decoder_outputs.inputs_embeds
+            for iii, head in enumerate(self.decoder.extra_heads):
+                decoder_input_embeds_shift = self.decoder.extra_proj[iii](
+                    torch.cat(
+                        (
+                            decoder_inputs_embeds[:, 1:, :],
+                            torch.zeros_like(decoder_inputs_embeds[:, 0, :].unsqueeze(1)),
+                        ),
+                        axis=1,
+                    )
+                )
+                hidden = head(decoder_outputs["hidden_states"][-1] + decoder_input_embeds_shift)
+                logits.append(self.lm_head(hidden))
+
+            logits = torch.stack(logits, dim=-2)
+            loss_fct = CrossEntropyLoss(reduction="none")
+
+            losses_per_head = []
+            tokens_per_head = []
+            for head_num in range(len(self.decoder.extra_heads) + 1):
+                logits_head = logits[:, :, head_num, :]
+                labels_head = torch.cat((labels[:, head_num:], torch.full_like(labels[:, :head_num], -100)), 1)
+                loss_full = loss_fct(logits_head.permute(0, 2, 1), labels_head)
+                loss_full[labels_head >= self.class_token_indx_start] *= 10
+                losses_per_head.append(loss_full.sum(1))
+                tokens_per_head.append((labels_head != -100).sum(1))
+
+            losses_per_sample = torch.stack(losses_per_head, dim=1).sum(1)
+            tokens_per_sample = torch.stack(tokens_per_head, dim=1).sum(1)
+            loss = losses_per_sample.sum() / (tokens_per_sample.sum() + 1e-6)
+
+        if not return_dict:
+            return (
+                (loss,) + decoder_outputs + encoder_outputs if loss is not None else decoder_outputs + encoder_outputs
+            )
+
+        output_logits = self.lm_head(decoder_outputs.last_hidden_state)
+        return Seq2SeqLMOutput(
+            loss=loss,
+            logits=output_logits,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=getattr(encoder_outputs, "hidden_states", None),
+            encoder_attentions=getattr(encoder_outputs, "attentions", None),
+        )
+
+    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
+        return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
+
+    def _reorder_cache(self, past_key_values, beam_idx):
+        return self.decoder._reorder_cache(past_key_values, beam_idx)
+
+
+AutoConfig.register("nemotron_parse", NemotronParseConfig)
+
+ModelClass = NemotronParseForConditionalGeneration
diff --git a/pyproject.toml b/pyproject.toml
index 482ec0f29..baa2fabae 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -104,6 +104,7 @@ vlm = [
     "numba",
     "torchcodec; (platform_machine == 'x86_64' and platform_system != 'Darwin')",
     "mistral_common[opencv]",
+    "albumentations"
 ]
 all = [
     "nemo_automodel[extra]",
diff --git a/tests/unit_tests/datasets/vlm/test_collate_fns.py b/tests/unit_tests/datasets/vlm/test_collate_fns.py
index 4b82a0aad..8d4c16971 100644
--- a/tests/unit_tests/datasets/vlm/test_collate_fns.py
+++ b/tests/unit_tests/datasets/vlm/test_collate_fns.py
@@ -137,6 +137,24 @@ def __call__(
         return {"input_ids": base, "attention_mask": attention_mask, "extra": extra}
 
 
+class DummyNemotronParseProcessor:
+    def __init__(self):
+        self.tokenizer = types.SimpleNamespace(
+            pad_token_id=0,
+            decoder_start_token_id=5,
+            bos_token_id=6,
+            eos_token_id=7,
+        )
+
+    def __call__(self, *, images, text, padding, return_tensors):
+        assert padding is True and return_tensors == "pt"
+        batch_size = len(text)
+        input_ids = torch.tensor([[10, 11, 12, 13]], dtype=torch.long).repeat(batch_size, 1)
+        attention_mask = torch.ones_like(input_ids)
+        pixel_values = torch.ones(batch_size, 3, 2, 2, dtype=torch.float32)
+        return {"input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values}
+
+
 def test_build_labels_includes_stop_token(collate_mod, monkeypatch):
     """
     Ensure `build_labels` copies the trailing stop token when it matches the configured set.
@@ -288,6 +306,48 @@ def test_qwen3_omni_collate_shapes(collate_mod, fake_qwen_utils, monkeypatch):
     assert batch["labels"].shape == (3, 4)
 
 
+def test_nemotron_parse_collate_shifts_and_casts(collate_mod, monkeypatch):
+    processor = DummyNemotronParseProcessor()
+
+    # Return deterministic labels to bypass tokenizer-heavy logic.
+    labels_stub = torch.tensor([[20, 21, 22, 23]], dtype=torch.long)
+
+    def fake_build_labels(input_ids, conversations, processor_arg):
+        assert processor_arg is processor
+        assert input_ids.shape == (1, 4)
+        return labels_stub
+
+    monkeypatch.setattr(collate_mod, "build_labels", fake_build_labels, raising=True)
+
+    examples = [
+        {
+            "conversation": [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": "please parse"},
+                        {"type": "image", "image": "dummy.png"},
+                    ],
+                },
+                {"role": "assistant", "content": "ok"},
+            ]
+        }
+    ]
+
+    batch = collate_mod.nemotron_parse_collate_fn(
+        examples,
+        processor=processor,
+        task_prompt="</s><s><predict_bbox>",
+    )
+
+    assert batch["pixel_values"].dtype == torch.bfloat16
+    assert torch.equal(batch["input_ids"], torch.tensor([[10, 11, 12]]))
+    assert torch.equal(batch["attention_mask"], torch.tensor([[1, 1, 1]]))
+    assert torch.equal(batch["labels"], torch.tensor([[21, 22, 23]]))
+    assert torch.equal(batch["decoder_input_ids"], torch.tensor([[10, 11, 12]]))
+    assert torch.equal(batch["decoder_attention_mask"], torch.tensor([[1, 1, 1]]))
+
+
 @pytest.mark.parametrize("fn_name", ["qwen2_5_collate_fn", "default_collate_fn", "qwen3_omni_collate_fn"])
 def test_import_error_when_qwen_utils_missing(collate_mod, fn_name, monkeypatch):
     monkeypatch.setattr(collate_mod, "HAVE_QWEN_VL_UTILS", False, raising=True)
diff --git a/tests/unit_tests/models/nemotron_parse/test_nemotron_parse_model.py b/tests/unit_tests/models/nemotron_parse/test_nemotron_parse_model.py
new file mode 100644
index 000000000..2a15e4086
--- /dev/null
+++ b/tests/unit_tests/models/nemotron_parse/test_nemotron_parse_model.py
@@ -0,0 +1,99 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import types
+
+import torch
+from transformers.models.donut.modeling_donut_swin import DonutSwinModelOutput
+
+from nemo_automodel.components.models.nemotron_parse import model as np_model
+
+
+def test_nemotron_parse_forward_with_stub_encoder(monkeypatch):
+    """
+    Smoke-test NemotronParseForConditionalGeneration by stubbing the vision encoder
+    to avoid heavy RADIO dependencies. Ensures forward + loss computation works.
+    """
+
+    decoder_dim = 32
+    vocab_size = 50
+
+    # Stub the underlying RADIO encoder creation to return a lightweight module.
+    class DummyEncoder(torch.nn.Module):
+        def forward(self, pixel_values, *args, **kwargs):
+            batch = pixel_values.shape[0]
+            # Return shapes compatible with RadioWithNeck but cheap to compute.
+            summary = torch.zeros(batch, 3840)
+            feature = torch.zeros(batch, 16, 1280)
+            return summary, feature
+
+    class DummyEncoderConfig:
+        def __init__(self, patch_size=16, max_resolution=64):
+            self.patch_size = patch_size
+            self.max_resolution = max_resolution
+
+        def to_dict(self):
+            return {"patch_size": self.patch_size, "max_resolution": self.max_resolution}
+
+    # Avoid downloading RADIO config (which requires open_clip) by returning a stub.
+    dummy_encoder_config = DummyEncoderConfig()
+    monkeypatch.setattr(np_model.AutoConfig, "from_pretrained", lambda *args, **kwargs: dummy_encoder_config)
+    monkeypatch.setattr(np_model.AutoModel, "from_config", lambda config, trust_remote_code=True: DummyEncoder())
+
+    # Ensure decoder outputs carry inputs_embeds (not present in BaseModelOutput).
+    original_decoder_forward = np_model.NemotronParseDecoder.forward
+
+    def decoder_forward_with_inputs(self, *args, **kwargs):
+        outputs = original_decoder_forward(self, *args, **kwargs)
+        # Prefer passed embeds; otherwise derive from input_ids for the test.
+        inputs_embeds = kwargs.get("inputs_embeds")
+        if inputs_embeds is None and kwargs.get("input_ids") is not None:
+            inputs_embeds = self.embed_tokens(kwargs["input_ids"])
+        outputs.inputs_embeds = inputs_embeds
+        return outputs
+
+    monkeypatch.setattr(np_model.NemotronParseDecoder, "forward", decoder_forward_with_inputs, raising=True)
+
+    # Bypass RadioWithNeck heavy convs by returning a small hidden state directly.
+    def fake_forward(self, pixel_values, *args, **kwargs):
+        batch = pixel_values.shape[0]
+        hidden = torch.zeros(batch, 2, decoder_dim, dtype=torch.bfloat16)
+        return DonutSwinModelOutput(last_hidden_state=hidden)
+
+    monkeypatch.setattr(np_model.RadioWithNeck, "forward", fake_forward, raising=True)
+
+    config = np_model.NemotronParseConfig(
+        encoder={"patch_size": 16, "max_resolution": 64},
+        decoder={
+            "vocab_size": vocab_size,
+            "d_model": decoder_dim,
+            "encoder_attention_heads": 4,
+            "decoder_attention_heads": 4,
+            "decoder_ffn_dim": 64,
+            "encoder_ffn_dim": 64,
+        },
+        max_sequence_length=32,
+    )
+
+    model = np_model.NemotronParseForConditionalGeneration(config)
+
+    pixel_values = torch.zeros(1, 3, 4, 4, dtype=torch.bfloat16)
+    labels = torch.tensor([[1, 2]])
+
+    outputs = model(pixel_values=pixel_values, labels=labels, return_dict=True)
+
+    assert outputs.logits.shape == (1, labels.shape[1], vocab_size)
+    assert outputs.loss is not None
+    assert torch.isfinite(outputs.loss)
+
diff --git a/uv.lock b/uv.lock
index 3bab63d2d..99dc5169d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -205,6 +205,39 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl", hash = "sha256:fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b", size = 13929, upload-time = "2024-07-26T18:15:02.05Z" },
 ]
 
+[[package]]
+name = "albucore"
+version = "0.0.24"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "opencv-python-headless" },
+    { name = "simsimd" },
+    { name = "stringzilla" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/13/69/d4cbcf2a5768bf91cd14ffef783520458431e5d2b22fbc08418d3ba09a88/albucore-0.0.24.tar.gz", hash = "sha256:f2cab5431fadf94abf87fd0c89d9f59046e49fe5de34afea8f89bc8390253746", size = 16981, upload-time = "2025-03-09T18:46:51.409Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0a/e2/91f145e1f32428e9e1f21f46a7022ffe63d11f549ee55c3b9265ff5207fc/albucore-0.0.24-py3-none-any.whl", hash = "sha256:adef6e434e50e22c2ee127b7a3e71f2e35fa088bcf54431e18970b62d97d0005", size = 15372, upload-time = "2025-03-09T18:46:50.177Z" },
+]
+
+[[package]]
+name = "albumentations"
+version = "2.0.8"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "albucore" },
+    { name = "numpy" },
+    { name = "opencv-python-headless" },
+    { name = "pydantic" },
+    { name = "pyyaml" },
+    { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "scipy", version = "1.16.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f4/f4/85eb56c3217b53bcfc2d12e840a0b18ca60902086321cafa5a730f9c0470/albumentations-2.0.8.tar.gz", hash = "sha256:4da95e658e490de3c34af8fcdffed09e36aa8a4edd06ca9f9e7e3ea0b0b16856", size = 354460, upload-time = "2025-05-27T21:23:17.415Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8e/64/013409c451a44b61310fb757af4527f3de57fc98a00f40448de28b864290/albumentations-2.0.8-py3-none-any.whl", hash = "sha256:c4c4259aaf04a7386ad85c7fdcb73c6c7146ca3057446b745cc035805acb1017", size = 369423, upload-time = "2025-05-27T21:23:15.609Z" },
+]
+
 [[package]]
 name = "alembic"
 version = "1.17.1"
@@ -2829,6 +2862,7 @@ dependencies = [
 
 [package.optional-dependencies]
 all = [
+    { name = "albumentations" },
     { name = "backoff" },
     { name = "flash-linear-attention" },
     { name = "mistral-common", extra = ["opencv"] },
@@ -2852,6 +2886,7 @@ extra = [
     { name = "sentencepiece" },
 ]
 vlm = [
+    { name = "albumentations" },
     { name = "backoff" },
     { name = "mistral-common", extra = ["opencv"] },
     { name = "numba" },
@@ -2895,6 +2930,7 @@ test = [
 
 [package.metadata]
 requires-dist = [
+    { name = "albumentations", marker = "extra == 'vlm'" },
     { name = "backoff", marker = "extra == 'vlm'" },
     { name = "datasets", specifier = ">=4.0.0" },
     { name = "diffusers" },
@@ -4965,6 +5001,112 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e0/f9/0595336914c5619e5f28a1fb793285925a8cd4b432c9da0a987836c7f822/shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686", size = 9755, upload-time = "2023-10-24T04:13:38.866Z" },
 ]
 
+[[package]]
+name = "simsimd"
+version = "6.5.12"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a4/13/dbcee7d607cbcfdfdf3a0593bec46479ce4e5957b39c5e81333efe540464/simsimd-6.5.12.tar.gz", hash = "sha256:c9b8720c9bc9dcfc36f570c2f96bfd74d1c9e1d0ebeecafc7a130ad3f0affe41", size = 186676, upload-time = "2025-12-21T01:13:38.467Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/48/bd/e74191ef929f0f817a5ea22024a721b12c5dd70f6b2edc830ecd705707e7/simsimd-6.5.12-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:3c0ca16b56471273f9371cc037d3bdaad011d658910dcbe95a155a19225a58ea", size = 106300, upload-time = "2025-12-21T01:10:18.284Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/02/7dc1df5d5418a73c9d9b1e93a3a443c31d19b9764e9c25b38778541e9a13/simsimd-6.5.12-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a9932e3f613bc9b2a168d138dd471f9b200c00c0f17c43ffcb3cff64427e121c", size = 94585, upload-time = "2025-12-21T01:10:19.96Z" },
+    { url = "https://files.pythonhosted.org/packages/f4/e2/2fab39b24f806029782750bd2321b7b5bbee4aaf36d24dc97dea7bdc5371/simsimd-6.5.12-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a323f78514c673b1f7203c5b3ac790548e443e63a8ffa54cc37bd6681e7136e3", size = 384486, upload-time = "2025-12-21T01:10:21.581Z" },
+    { url = "https://files.pythonhosted.org/packages/57/95/2dc78ee7286bb7702e12c181780c66610ddbe9aaa2ae54b8afecdc71be44/simsimd-6.5.12-cp310-cp310-manylinux2014_i686.manylinux_2_17_i686.manylinux_2_28_i686.whl", hash = "sha256:4a4f9b128edab78a093fff1ccfa1b6e2cfa67b5e95df8ebdd769c4c6ff8346dd", size = 273708, upload-time = "2025-12-21T01:10:22.778Z" },
+    { url = "https://files.pythonhosted.org/packages/24/a6/e9f4f5dc76af942d63b6355da9ec02fd69f59a6260c054f1c3cd04eb95b4/simsimd-6.5.12-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:10c322d2c3164f82e41e0bd11883660ac73026b07d63ba6a6c7f5ed09f97c83d", size = 295064, upload-time = "2025-12-21T01:10:24.461Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/b2/0a444636b31227442833e8d1bee761c419f9b55d2e13ea4c59e51edbe124/simsimd-6.5.12-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5705edf0a4fd11399c63c8a94fa6808fcb78903eb2d665ed68f950c1702a8421", size = 285045, upload-time = "2025-12-21T01:10:26.147Z" },
+    { url = "https://files.pythonhosted.org/packages/6d/a0/84e128cc7be66797132c1279fc359a581e54c3b86f71e7e13604e006d8de/simsimd-6.5.12-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:864a16e2ebf45c0ae6ecc89f3a798de871ae56b29fcb8a448754781256b80fd6", size = 582277, upload-time = "2025-12-21T01:10:27.79Z" },
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