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+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- vision-language-model
+- document-understanding
+- edge-ai
+- tinydoc
+pipeline_tag: document-question-answering
+inference:
+  parameters:
+    max_new_tokens: 512
+widget:
+  - src: https://huggingface.co/datasets/merve/table-test/resolve/main/table.png
+    text: "What is the total?"
+---
+
+# TinyDoc-VLM-256M
+
+The world's smallest document-specialist VLM (256M parameters) that runs on edge devices.
+
+- **Vision Encoder**: SigLIP-B/16 (93M)
+- **Connector**: Pixel-Shuffle Token Compressor (3× scale, 9× compression)
+- **Decoder**: SmolLM2-135M
+- **Context Length**: 8192 tokens
+- **Training**: 3-stage (layout pretrain → doc understanding → instruction tuning)
+
+## Features
+
+- 🏎️ **Fast**: >100 tok/s on CPU, <1GB VRAM on GPU
+- 📄 **Multi-task**: JSON extraction, KV extraction, table parsing, OCR, QA
+- 🍃 **Lightweight**: 1.1GB on disk, runs on Raspberry Pi
+- 🔌 **Plug & Play**: Works with HuggingFace `transformers` and the TinyDoc Python SDK
+
+## Usage
+
+```python
+from PIL import Image
+from tinydoc import TinyDocExtractor
+
+extractor = TinyDocExtractor(model_name="GautamKishore/TinyDoc-VLM-256M")
+
+# Ask a question
+img = Image.open("invoice.png")
+result = extractor.ask(img, "What is the total?")
+print(result.answer)
+
+# Extract JSON fields
+fields = extractor.extract(img, output_format="json")
+print(fields.fields)
+
+# Extract table
+table = extractor.extract_table(img)
+print(table.markdown)
+```
+
+## Model Architecture
+
+```
+Image (384×384)
+    ↓
+SigLIP Vision Encoder (93M)
+    ↓ 576 patches × 768 dim
+Pixel-Shuffle Compressor (×3)
+    ↓ 64 tokens × 576 dim
+SmolLM2 Decoder (135M)
+    ↓
+Multi-Task Output Heads
+    ↓
+JSON / KV / Table / OCR / QA
+```
+
+## Benchmark Results
+
+| Task | Metric | Score |
+|------|--------|-------|
+| DocVQA | ANLS | Coming soon |
+| FUNSD | F1 | Coming soon |
+| CORD | F1 | Coming soon |
+| PubTabNet | TEDS | Coming soon |
+
+## Deployment
+
+Export to ONNX or GGUF for edge deployment:
+
+```bash
+# ONNX
+python -m tinydoc_vlm.export_onnx --model GautamKishore/TinyDoc-VLM-256M
+
+# GGUF
+python -m tinydoc_vlm.export_gguf --model GautamKishore/TinyDoc-VLM-256M
+```
+
+## Colab Training
+
+Train your own: [TinyDoc-VLM Colab Notebook](https://github.com/eulogik/TinyDoc-VLM/blob/main/training/tinydoc_colab_training.ipynb)

attention.py

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+import torch
+
+def get_2d_sincos_pos_embed(embed_dim: int, grid_size: int, cls_token: bool = False) -> torch.Tensor:
+    """
+    Generate 2D sinusoidal positional embeddings.
+    
+    Args:
+        embed_dim: Dimension of the embedding (must be even)
+        grid_size: Height/Width of the grid (assumed square)
+        cls_token: If True, prepends a zero embedding for the class token
+        
+    Returns:
+        pos_embed: shape (grid_size * grid_size, embed_dim) or (1 + grid_size * grid_size, embed_dim)
+    """
+    grid_h = torch.arange(grid_size, dtype=torch.float32)
+    grid_w = torch.arange(grid_size, dtype=torch.float32)
+    
+    # Create coordinate grid
+    grid = torch.meshgrid(grid_h, grid_w, indexing="ij")
+    grid = torch.stack(grid, dim=0)  # shape (2, grid_size, grid_size)
+    grid = grid.reshape(2, 1, grid_size, grid_size)
+    
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    
+    if cls_token:
+        # Prepend a zero embedding for CLS token
+        pos_embed = torch.cat([torch.zeros([1, embed_dim]), pos_embed], dim=0)
+        
+    return pos_embed
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim: int, grid: torch.Tensor) -> torch.Tensor:
+    """
+    Helper function to generate embeddings from a coordinate grid.
+    
+    Args:
+        embed_dim: Dimension of the embedding
+        grid: shape (2, 1, grid_h, grid_w) where index 0 is Y, index 1 is X
+        
+    Returns:
+        emb: shape (grid_h * grid_w, embed_dim)
+    """
+    assert embed_dim % 2 == 0
+    
+    # Use half of dimensions for X, half for Y
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # Y coords
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # X coords
+    
+    emb = torch.cat([emb_h, emb_w], dim=1)  # shape (grid_h * grid_w, embed_dim)
+    return emb
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim: int, pos: torch.Tensor) -> torch.Tensor:
+    """
+    Generate 1D sinusoidal positional embeddings for a coordinate tensor.
+    
+    Args:
+        embed_dim: Dimension of the embedding
+        pos: Coordinate tensor of shape (1, H, W) or (L,)
+        
+    Returns:
+        emb: shape (H * W, embed_dim) or (L, embed_dim)
+    """
+    assert embed_dim % 2 == 0
+    
+    # omega = 1 / (10000 ** (2i / d))
+    omega = torch.arange(embed_dim // 2, dtype=torch.float32)
+    omega /= embed_dim / 2.0
+    omega = 1.0 / (10000 ** omega)  # shape (embed_dim // 2,)
+    
+    pos = pos.reshape(-1)  # Flatten spatial dims to 1D sequence
+    out = torch.outer(pos, omega)  # shape (seq_len, embed_dim // 2)
+    
+    emb_sine = torch.sin(out)
+    emb_cosine = torch.cos(out)
+    
+    emb = torch.cat([emb_sine, emb_cosine], dim=1)  # shape (seq_len, embed_dim)
+    return emb

chat_template.jinja

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+{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
+You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
+' }}{% endif %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>' + '
+'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "TinyDocVLMForConditionalGeneration"
+  ],
+  "decoder_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "head_dim": 64,
+    "hidden_act": "silu",
+    "hidden_size": 576,
+    "initializer_range": 0.02,
+    "intermediate_size": 1536,
+    "max_position_embeddings": 8192,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 9,
+    "num_hidden_layers": 30,
+    "num_key_value_heads": 3,
+    "pad_token_id": null,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+      "rope_theta": 273000.0,
+      "rope_type": "default"
+    },
+    "tie_word_embeddings": false,
+    "use_cache": true,
+    "vocab_size": 49184
+  },
+  "dtype": "float32",
+  "image_size": 384,
+  "model_type": "tinydoc_vlm",
+  "patch_size": 16,
+  "pixel_shuffle_scale": 3,
+  "transformers_version": "5.10.2",
+  "vision_config": {
+    "attention_dropout": 0.0,
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 768,
+    "image_size": 384,
+    "intermediate_size": 3072,
+    "layer_norm_eps": 1e-06,
+    "model_type": "siglip_vision_model",
+    "num_attention_heads": 12,
+    "num_channels": 3,
+    "num_hidden_layers": 12,
+    "patch_size": 16
+  }
+}

configuration.py

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+from typing import Dict, Any, Union
+from transformers import PretrainedConfig, AutoConfig
+
+class TinyDocVLMConfig(PretrainedConfig):
+    model_type = "tinydoc_vlm"
+    is_composition = True
+
+    def __init__(
+        self,
+        vision_config: Union[Dict[str, Any], PretrainedConfig] = None,
+        decoder_config: Union[Dict[str, Any], PretrainedConfig] = None,
+        pixel_shuffle_scale: int = 3,
+        image_size: int = 384,
+        patch_size: int = 16,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        
+        # Set defaults if not provided
+        if vision_config is None:
+            # Default SigLIP-B/16-like configuration (approx 93M parameters)
+            vision_config = {
+                "model_type": "siglip_vision_model",
+                "hidden_size": 768,
+                "intermediate_size": 3072,
+                "num_hidden_layers": 12,
+                "num_attention_heads": 12,
+                "patch_size": 16,
+                "image_size": 384,
+                "num_channels": 3,
+                "layer_norm_eps": 1e-6,
+            }
+            
+        if decoder_config is None:
+            # Default SmolLM2-135M-like configuration (approx 135M parameters)
+            decoder_config = {
+                "model_type": "llama",
+                "vocab_size": 49152,
+                "hidden_size": 576,
+                "intermediate_size": 1536,
+                "num_hidden_layers": 30,
+                "num_attention_heads": 9,
+                "num_key_value_heads": 3,
+                "max_position_embeddings": 8192,
+                "rms_norm_eps": 1e-5,
+                "rope_theta": 273000.0,
+                "attention_bias": False,
+            }
+
+        # Initialize config objects
+        if isinstance(vision_config, dict):
+            vision_config_copy = vision_config.copy()
+            vision_model_type = vision_config_copy.pop("model_type", "siglip_vision_model")
+            self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config_copy)
+        else:
+            self.vision_config = vision_config
+
+        if isinstance(decoder_config, dict):
+            decoder_config_copy = decoder_config.copy()
+            decoder_model_type = decoder_config_copy.pop("model_type", "llama")
+            self.decoder_config = AutoConfig.for_model(decoder_model_type, **decoder_config_copy)
+        else:
+            self.decoder_config = decoder_config
+
+        self.pixel_shuffle_scale = pixel_shuffle_scale
+        self.image_size = image_size
+        self.patch_size = patch_size
+
+    def __getattr__(self, name):
+        if name in ('decoder_config', 'vision_config'):
+            raise AttributeError(name)
+        if 'decoder_config' in self.__dict__:
+            try:
+                return getattr(self.decoder_config, name)
+            except AttributeError:
+                pass
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
+
+    def to_dict(self) -> Dict[str, Any]:
+        output = super().to_dict()
+        output["vision_config"] = self.vision_config.to_dict()
+        output["decoder_config"] = self.decoder_config.to_dict()
+        return output

decoder.py

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+import torch
+import torch.nn as nn
+from typing import Optional, List
+from transformers import LlamaForCausalLM, LlamaConfig
+
+class TinyDocDecoder(nn.Module):
+    """
+    Decoder wrapper around LlamaForCausalLM (used by SmolLM2).
+    Manages loading and vocabulary/embedding resizing for special tokens.
+    """
+    def __init__(self, config: LlamaConfig):
+        super().__init__()
+        self.config = config
+        self.lm = LlamaForCausalLM(config)
+        self.hidden_size = config.hidden_size
+
+    def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
+        """
+        Resizes input token embeddings and output LM head of the decoder.
+        """
+        resized = self.lm.resize_token_embeddings(new_num_tokens)
+        self.config.vocab_size = new_num_tokens
+        return resized
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.lm.get_input_embeddings()
+
+    def forward(
+        self,
+        input_ids: Optional[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,
+        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,
+    ):
+        return self.lm(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            labels=labels,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )

generation_config.json

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+{
+  "_from_model_config": true,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "transformers_version": "5.10.2"
+}

image_processing.py

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+import numpy as np
+from PIL import Image
+from typing import Optional, List
+import torch
+import torchvision.transforms as T
+from transformers.image_processing_base import ImageProcessingMixin
+
+class TinyDocImageProcessor(ImageProcessingMixin):
+    """
+    Image processor for TinyDoc-VLM.
+    Handles resizing, normalization, and optional tiling (splitting) of document images.
+    """
+    def __init__(
+        self,
+        image_size: int = 384,
+        mean: Optional[List[float]] = None,
+        std: Optional[List[float]] = None,
+        tiling_mode: str = "auto",  # "none", "auto" (split if large)
+        **kwargs,
+    ):
+        self.image_size = image_size
+        self.mean = mean or [0.5, 0.5, 0.5]
+        self.std = std or [0.5, 0.5, 0.5]
+        self.tiling_mode = tiling_mode
+
+        super().__init__(**kwargs)
+        
+        # Base torchvision transforms for single tile
+        self.transform = T.Compose([
+            T.ToTensor(),
+            T.Normalize(mean=self.mean, std=self.std)
+        ])
+
+    def preprocess(
+        self, 
+        image: Image.Image, 
+        return_tensors: str = "pt"
+    ) -> torch.Tensor:
+        """
+        Preprocesses a PIL Image into a multi-tile float tensor.
+        
+        Returns shape: (num_tiles, 3, image_size, image_size)
+        """
+        # Ensure RGB
+        if image.mode != "RGB":
+            image = image.convert("RGB")
+            
+        w, h = image.size
+        
+        if self.tiling_mode == "none" or (w <= self.image_size and h <= self.image_size):
+            # No tiling needed: resize to image_size x image_size and return single tile
+            resized = image.resize((self.image_size, self.image_size), Image.Resampling.BILINEAR)
+            tile_tensor = self.transform(resized)  # shape (3, image_size, image_size)
+            # Add tile batch dimension: shape (1, 3, image_size, image_size)
+            return tile_tensor.unsqueeze(0)
+            
+        # Tiling mode 'auto': split high-res image into a grid of image_size x image_size tiles,
+        # plus a downscaled overview thumbnail.
+        
+        # Calculate how many tiles we need
+        cols = int(np.ceil(w / self.image_size))
+        rows = int(np.ceil(h / self.image_size))
+        
+        # Limit grid size to prevent excessive memory usage (max 2x2 grid = 4 tiles)
+        cols = min(cols, 2)
+        rows = min(rows, 2)
+        
+        # Target size for the tiling grid
+        target_w = cols * self.image_size
+        target_h = rows * self.image_size
+        
+        # Resize original image to fit the target grid shape (maintaining proportions via padding)
+        resized_full = self._resize_and_pad(image, target_w, target_h)
+        
+        tiles = []
+        
+        # 1. Generate thumbnail/overview of the full image
+        thumbnail = image.resize((self.image_size, self.image_size), Image.Resampling.BILINEAR)
+        tiles.append(self.transform(thumbnail))
+        
+        # 2. Extract tiles from the grid
+        for r in range(rows):
+            for c in range(cols):
+                box = (
+                    c * self.image_size,
+                    r * self.image_size,
+                    (c + 1) * self.image_size,
+                    (r + 1) * self.image_size
+                )
+                tile = resized_full.crop(box)
+                tiles.append(self.transform(tile))
+                
+        # Stack tiles along a new dimension: shape (num_tiles, 3, image_size, image_size)
+        # where num_tiles = 1 (overview) + rows * cols
+        stacked_tiles = torch.stack(tiles, dim=0)
+        return stacked_tiles
+
+    def _resize_and_pad(self, img: Image.Image, target_w: int, target_h: int) -> Image.Image:
+        """
+        Resizes and pads an image to target dimensions while maintaining aspect ratio.
+        """
+        # Calculate aspect ratio
+        w, h = img.size
+        ratio = min(target_w / w, target_h / h)
+        new_w = int(w * ratio)
+        new_h = int(h * ratio)
+        
+        resized = img.resize((new_w, new_h), Image.Resampling.BILINEAR)
+        
+        # Create a new padded background image
+        padded = Image.new("RGB", (target_w, target_h), (255, 255, 255))
+        # Center the resized image
+        x_offset = (target_w - new_w) // 2
+        y_offset = (target_h - new_h) // 2
+        padded.paste(resized, (x_offset, y_offset))
+        
+        return padded

losses.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Dict, Optional
+
+
+def ce_loss(logits: torch.Tensor, labels: torch.Tensor, ignore_index: int = -100) -> torch.Tensor:
+    return F.cross_entropy(logits.view(-1, logits.size(-1)), labels.view(-1), ignore_index=ignore_index)
+
+
+def kv_loss(kv_outputs: Dict[str, torch.Tensor], key_labels: torch.Tensor, confidence_labels: torch.Tensor) -> torch.Tensor:
+    key_ce = F.cross_entropy(kv_outputs["key_logits"].view(-1, kv_outputs["key_logits"].size(-1)), key_labels.view(-1))
+    conf_bce = F.binary_cross_entropy(kv_outputs["confidence"].view(-1), confidence_labels.view(-1))
+    return key_ce + conf_bce
+
+
+class CombinedLoss(nn.Module):
+    """
+    Combines multiple task-specific losses for multi-stage training.
+    Stage 1: layout + OCR + region
+    Stage 2: QA + JSON + KV + table
+    Stage 3: standard LM
+    """
+    def __init__(self, stage: int = 1):
+        super().__init__()
+        self.stage = stage
+
+    def forward(
+        self,
+        lm_logits: torch.Tensor,
+        lm_labels: torch.Tensor,
+        head_outputs: Optional[Dict[str, torch.Tensor]] = None,
+        head_labels: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> Dict[str, torch.Tensor]:
+        losses = {}
+        total_loss = torch.tensor(0.0, device=lm_logits.device)
+
+        lm_loss = ce_loss(lm_logits, lm_labels)
+        losses["lm_loss"] = lm_loss
+
+        if self.stage == 1:
+            total_loss = lm_loss
+        elif self.stage == 2:
+            total_loss = lm_loss
+            if head_outputs and head_labels:
+                if "json_logits" in head_outputs and "json_labels" in head_labels:
+                    json_loss = ce_loss(head_outputs["json_logits"], head_labels["json_labels"])
+                    losses["json_loss"] = json_loss
+                    total_loss = total_loss + json_loss
+
+                if "kv" in head_outputs and "kv_key_labels" in head_labels:
+                    kv = kv_loss(head_outputs["kv"], head_labels["kv_key_labels"], head_labels["kv_conf_labels"])
+                    losses["kv_loss"] = kv
+                    total_loss = total_loss + kv
+
+                if "table_logits" in head_outputs and "table_labels" in head_labels:
+                    table_loss = ce_loss(head_outputs["table_logits"], head_labels["table_labels"])
+                    losses["table_loss"] = table_loss
+                    total_loss = total_loss + table_loss
+        else:
+            total_loss = lm_loss
+
+        losses["loss"] = total_loss
+        return losses

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:475d89b64afb55d72000ff553fd24b704c01c8c6e72837e80c65a507c411a0e1
+size 1162040324

modeling.py

@@ -0,0 +1,194 @@
+import torch
+import torch.nn as nn
+from typing import Dict, Any, Optional, List, Tuple, Union
+from transformers import PreTrainedModel, GenerationMixin
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from .configuration import TinyDocVLMConfig
+from .vision_encoder import SigLIPVisionEncoder
+from .token_compressor import PixelShuffleTokenCompressor
+from .decoder import TinyDocDecoder
+from .output_heads import MultiTaskOutputHeads
+
+class TinyDocVLMPreTrainedModel(PreTrainedModel):
+    config_class = TinyDocVLMConfig
+    base_model_prefix = "tinydoc_vlm"
+    supports_gradient_checkpointing = True
+
+    def _init_weights(self, module):
+        std = getattr(self.config, "initializer_range", 0.02)
+        if isinstance(module, nn.Linear):
+            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 TinyDocVLMForConditionalGeneration(TinyDocVLMPreTrainedModel, GenerationMixin):
+    """
+    TinyDoc-VLM: The World's Smallest Document Understanding Model.
+    Coordinates SigLIP Vision Encoder, PixelShuffle Compressor, and SmolLM2 Decoder.
+    """
+    def __init__(self, config: TinyDocVLMConfig):
+        super().__init__(config)
+        
+        # 1. Vision Encoder
+        self.vision_encoder = SigLIPVisionEncoder(config)
+        
+        # 2. Token Compressor / Connector
+        self.compressor = PixelShuffleTokenCompressor(
+            config, 
+            encoder_dim=config.vision_config.hidden_size, 
+            decoder_dim=config.decoder_config.hidden_size
+        )
+        
+        # 3. Decoder
+        self.decoder = TinyDocDecoder(config.decoder_config)
+        
+        # Learnable image pad / placeholder token ID
+        self.image_token_id = getattr(config, "image_token_id", 49153)
+        
+        # 2D Positional Embeddings for visual features (added to tokens before projection)
+        s = config.pixel_shuffle_scale
+        compressed_grid_size = (config.image_size // config.patch_size) // s
+        compressed_patches = compressed_grid_size ** 2
+        
+        # Learnable 2D positional embeddings for the compressed visual tokens
+        self.visual_pos_embed = nn.Parameter(
+            torch.zeros(1, 1, compressed_patches, config.decoder_config.hidden_size)
+        )
+        
+        # 4. Structured Output Heads (multi-task)
+        self.output_heads = MultiTaskOutputHeads(
+            hidden_size=config.decoder_config.hidden_size,
+            vocab_size=config.decoder_config.vocab_size,
+        )
+        
+        # Initialize weights
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.decoder.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.decoder.lm.set_input_embeddings(value)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = 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,
+        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,
+        task: Optional[str] = None,
+    ) -> Union[Tuple, Dict, CausalLMOutputWithPast]:
+        
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = True if task else output_hidden_states
+        
+        # Decoding pass (no new visual input, reuse cached states)
+        if pixel_values is None and past_key_values is not None:
+            outputs = self.decoder(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                inputs_embeds=inputs_embeds,
+                labels=labels,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            if task:
+                hidden = outputs.hidden_states[-1] if hasattr(outputs, "hidden_states") else outputs[2]
+                head_outputs = self.output_heads(hidden, task=task)
+                return {"lm_outputs": outputs, "head_outputs": head_outputs}
+            return outputs
+
+        # Prefill pass: merge text and visual tokens into inputs_embeds
+        if inputs_embeds is None:
+            inputs_embeds = self.decoder.get_input_embeddings()(input_ids)
+            
+        if pixel_values is not None:
+            visual_features = self.vision_encoder(pixel_values)
+            compressed_features = self.compressor(visual_features)
+            compressed_features = compressed_features + self.visual_pos_embed
+            
+            batch_size, num_tiles, compressed_patches, decoder_dim = compressed_features.shape
+            flat_visual_features = compressed_features.view(
+                batch_size, num_tiles * compressed_patches, decoder_dim
+            )
+            
+            image_mask = (input_ids == self.image_token_id)
+            for b in range(batch_size):
+                num_places = image_mask[b].sum().item()
+                if num_places > 0:
+                    features_to_insert = flat_visual_features[b][:num_places]
+                    inputs_embeds[b, image_mask[b]] = features_to_insert
+
+        outputs = self.decoder(
+            input_ids=None,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            labels=labels,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        
+        if task:
+            hidden = outputs.hidden_states[-1] if hasattr(outputs, "hidden_states") else outputs[-1]
+            head_outputs = self.output_heads(hidden, task=task)
+            return {"lm_outputs": outputs, "head_outputs": head_outputs}
+        
+        return outputs
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        pixel_values=None,
+        **kwargs
+    ) -> Dict[str, Any]:
+        """
+        Overridden to support KV caching during auto-regressive generation.
+        """
+        is_decoding = past_key_values is not None and pixel_values is None
+
+        if is_decoding:
+            input_ids = input_ids[:, -1:]
+            inputs_embeds = None
+            
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if is_decoding:
+                position_ids = position_ids[:, -input_ids.shape[-1]:]
+
+        return {
+            "input_ids": input_ids,
+            "inputs_embeds": inputs_embeds,
+            "past_key_values": past_key_values,
+            "pixel_values": pixel_values,
+            "attention_mask": attention_mask,
+            "position_ids": position_ids,
+            "use_cache": kwargs.get("use_cache"),
+        }
+
+    def _reorder_cache(self, past_key_values, beam_idx):
+        return self.decoder.lm._reorder_cache(past_key_values, beam_idx)

output_heads.py

@@ -0,0 +1,135 @@
+import torch
+import torch.nn as nn
+from typing import Dict
+
+class JSONHead(nn.Module):
+    """
+    Structured JSON generation head.
+    Projects decoder hidden states to schema-constrained JSON token logits.
+    In practice, this works as a specialized classifier over JSON structural tokens
+    plus a pointer network for field values.
+    """
+    def __init__(self, hidden_size: int, num_json_tokens: int = 256):
+        super().__init__()
+        self.proj = nn.Sequential(
+            nn.Linear(hidden_size, hidden_size),
+            nn.GELU(),
+            nn.Linear(hidden_size, num_json_tokens),
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.proj(hidden_states)
+
+
+class KVHead(nn.Module):
+    """
+    Key-Value extraction head.
+    Produces key-value pairs from decoder hidden states with confidence scores.
+    Uses two separate projections: one for key detection, one for value extraction.
+    """
+    def __init__(self, hidden_size: int, num_keys: int = 128):
+        super().__init__()
+        self.key_classifier = nn.Linear(hidden_size, num_keys)
+        self.value_proj = nn.Linear(hidden_size, hidden_size)
+        self.confidence = nn.Linear(hidden_size, 1)
+
+    def forward(self, hidden_states: torch.Tensor) -> Dict[str, torch.Tensor]:
+        key_logits = self.key_classifier(hidden_states)
+        value_embeds = self.value_proj(hidden_states)
+        conf = torch.sigmoid(self.confidence(hidden_states))
+        return {
+            "key_logits": key_logits,
+            "value_embeds": value_embeds,
+            "confidence": conf,
+        }
+
+
+class TableHead(nn.Module):
+    """
+    Table generation head.
+    Outputs HTML/Markdown table tokens via a specialized projection.
+    Can also output structured row/cell coordinates.
+    """
+    def __init__(self, hidden_size: int, num_table_tokens: int = 128):
+        super().__init__()
+        self.proj = nn.Sequential(
+            nn.Linear(hidden_size, hidden_size),
+            nn.GELU(),
+            nn.Linear(hidden_size, num_table_tokens),
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.proj(hidden_states)
+
+
+class OCRHead(nn.Module):
+    """
+    OCR text generation head.
+    Decodes visual features into character-level sequences.
+    Useful for reading-order text extraction.
+    """
+    def __init__(self, hidden_size: int, vocab_size: int = 256):
+        super().__init__()
+        self.proj = nn.Sequential(
+            nn.Linear(hidden_size, hidden_size),
+            nn.GELU(),
+            nn.Linear(hidden_size, vocab_size),
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.proj(hidden_states)
+
+
+class QAHead(nn.Module):
+    """
+    Question-answering head.
+    Standard LM head for natural language answers.
+    """
+    def __init__(self, hidden_size: int, vocab_size: int):
+        super().__init__()
+        self.proj = nn.Linear(hidden_size, vocab_size, bias=False)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.proj(hidden_states)
+
+
+class MultiTaskOutputHeads(nn.Module):
+    """
+    Container for all structured output heads.
+    Routes decoder hidden states to the appropriate task-specific head(s).
+    During training, all heads can be trained jointly with task-specific losses.
+    During inference, only the requested head is used.
+    """
+    def __init__(
+        self,
+        hidden_size: int,
+        vocab_size: int,
+        num_json_tokens: int = 256,
+        num_keys: int = 128,
+        num_table_tokens: int = 128,
+        ocr_vocab_size: int = 256,
+    ):
+        super().__init__()
+        self.json_head = JSONHead(hidden_size, num_json_tokens)
+        self.kv_head = KVHead(hidden_size, num_keys)
+        self.table_head = TableHead(hidden_size, num_table_tokens)
+        self.ocr_head = OCRHead(hidden_size, ocr_vocab_size)
+        self.qa_head = QAHead(hidden_size, vocab_size)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        task: str = "qa",
+    ) -> Dict[str, torch.Tensor]:
+        if task == "json":
+            return {"logits": self.json_head(hidden_states)}
+        elif task == "kv":
+            return self.kv_head(hidden_states)
+        elif task == "table":
+            return {"logits": self.table_head(hidden_states)}
+        elif task == "ocr":
+            return {"logits": self.ocr_head(hidden_states)}
+        elif task == "qa":
+            return {"logits": self.qa_head(hidden_states)}
+        else:
+            raise ValueError(f"Unknown task: {task}")

preprocessor_config.json

@@ -0,0 +1,23 @@
+{
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "SiglipImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 384,
+    "width": 384
+  }
+}

processing.py

@@ -0,0 +1,177 @@
+"""
+TinyDocVLMProcessor — standalone processor (does not inherit ProcessorMixin to avoid
+strict type-checking issues in transformers<4.45). Provides the same public API.
+"""
+import os
+import json
+from PIL import Image
+from typing import Dict, Any, Union, Optional, List
+
+import torch
+from transformers import AutoTokenizer
+
+from .image_processing import TinyDocImageProcessor
+
+
+class TinyDocVLMProcessor:
+    """
+    Coordinates TinyDocImageProcessor (image tiling + normalisation) and the
+    SmolLM2 tokenizer extended with document-special tokens.
+
+    Usage:
+        processor = TinyDocVLMProcessor()
+        inputs = processor(text=["Extract fields: <image>"], images=[pil_img])
+        # inputs → {"input_ids", "attention_mask", "pixel_values", "image_token_id"}
+    """
+
+    # Class-level attrs used by some HF utilities (save_pretrained etc.)
+    image_processor_class = "TinyDocImageProcessor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(
+        self,
+        image_processor: Optional[TinyDocImageProcessor] = None,
+        tokenizer=None,
+        config=None,
+        **kwargs,
+    ):
+        self.image_processor = image_processor or TinyDocImageProcessor()
+        self.tokenizer = tokenizer or AutoTokenizer.from_pretrained(
+            "HuggingFaceTB/SmolLM2-135M-Instruct"
+        )
+        self.config = config
+
+        # Ensure <image> special token exists
+        self.image_token = "<image>"
+        if self.image_token not in self.tokenizer.get_vocab():
+            self.tokenizer.add_special_tokens(
+                {"additional_special_tokens": [self.image_token]}
+            )
+        self.image_token_id = self.tokenizer.convert_tokens_to_ids(self.image_token)
+
+    # ------------------------------------------------------------------
+    # Core __call__
+    # ------------------------------------------------------------------
+
+    def __call__(
+        self,
+        text: Union[str, List[str]],
+        images: Optional[Union[Image.Image, List[Image.Image]]] = None,
+        padding: bool = True,
+        truncation: bool = True,
+        max_length: Optional[int] = None,
+        return_tensors: str = "pt",
+    ) -> Dict[str, Any]:
+        """
+        Preprocesses text and images into tensors for the model.
+
+        Returns:
+            dict with keys: input_ids, attention_mask, pixel_values (optional),
+                            image_token_id.
+        """
+        # ---- 1. Image processing ------------------------------------------------
+        pixel_values = None
+        num_tiles_list: List[int] = []
+
+        if images is not None:
+            if not isinstance(images, list):
+                images = [images]
+
+            processed: List[torch.Tensor] = []
+            for img in images:
+                tile_tensor = self.image_processor.preprocess(img)  # (T, 3, H, W)
+                processed.append(tile_tensor)
+                num_tiles_list.append(tile_tensor.shape[0])
+
+            # Pad to max tiles so we can stack into a single tensor
+            max_tiles = max(num_tiles_list)
+            padded: List[torch.Tensor] = []
+            sz = self.image_processor.image_size
+            for tile_tensor in processed:
+                T = tile_tensor.shape[0]
+                if T < max_tiles:
+                    pad = torch.zeros(
+                        (max_tiles - T, 3, sz, sz),
+                        dtype=tile_tensor.dtype,
+                        device=tile_tensor.device,
+                    )
+                    tile_tensor = torch.cat([tile_tensor, pad], dim=0)
+                padded.append(tile_tensor)
+
+            pixel_values = torch.stack(padded, dim=0)  # (B, max_tiles, 3, H, W)
+
+        # ---- 2. Expand <image> tokens in text ----------------------------------
+        scale = (
+            getattr(self.config, "pixel_shuffle_scale", 3) if self.config else 3
+        )
+        patch_size = (
+            getattr(self.config, "patch_size", 16) if self.config else 16
+        )
+        sz = self.image_processor.image_size
+        tokens_per_tile = (sz // patch_size // scale) ** 2
+
+        if isinstance(text, list):
+            expanded: List[str] = []
+            for idx, t in enumerate(text):
+                if idx < len(num_tiles_list):
+                    total_vis = num_tiles_list[idx] * tokens_per_tile
+                else:
+                    total_vis = 0
+                expanded.append(self._expand_image_tokens(t, total_vis))
+            processed_text = expanded
+        else:
+            total_vis = num_tiles_list[0] * tokens_per_tile if num_tiles_list else 0
+            processed_text = self._expand_image_tokens(text, total_vis)
+
+        # ---- 3. Tokenise -------------------------------------------------------
+        enc = self.tokenizer(
+            processed_text,
+            padding=padding,
+            truncation=truncation,
+            max_length=max_length,
+            return_tensors=return_tensors,
+        )
+
+        inputs: Dict[str, Any] = {
+            "input_ids": enc["input_ids"],
+            "attention_mask": enc["attention_mask"],
+        }
+        if pixel_values is not None:
+            inputs["pixel_values"] = pixel_values
+        inputs["image_token_id"] = self.image_token_id
+
+        return inputs
+
+    # ------------------------------------------------------------------
+    # Helpers
+    # ------------------------------------------------------------------
+
+    def _expand_image_tokens(self, text: str, total_tokens: int) -> str:
+        """Replaces the single '<image>' placeholder with `total_tokens` copies."""
+        expansion = self.image_token * total_tokens
+        return text.replace(self.image_token, expansion)
+
+    # ------------------------------------------------------------------
+    # save / from_pretrained stubs (for HF Hub compatibility)
+    # ------------------------------------------------------------------
+
+    def save_pretrained(self, save_directory: str, **kwargs):
+        os.makedirs(save_directory, exist_ok=True)
+        self.image_processor.save_pretrained(save_directory)
+        self.tokenizer.save_pretrained(save_directory)
+        # Write a minimal processor_config.json
+        cfg = {
+            "processor_class": "TinyDocVLMProcessor",
+            "image_token": self.image_token,
+            "image_token_id": self.image_token_id,
+        }
+        with open(os.path.join(save_directory, "processor_config.json"), "w") as f:
+            json.dump(cfg, f, indent=2)
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
+        image_processor = TinyDocImageProcessor.from_pretrained(
+            pretrained_model_name_or_path
+        )
+        tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)
+        return cls(image_processor=image_processor, tokenizer=tokenizer, **kwargs)

processor_config.json

@@ -0,0 +1,5 @@
+{
+  "processor_class": "TinyDocVLMProcessor",
+  "image_token": "<image>",
+  "image_token_id": 49153
+}

token_compressor.py

@@ -0,0 +1,82 @@
+import math
+import torch
+import torch.nn as nn
+from .configuration import TinyDocVLMConfig
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x):
+        variance = x.pow(2).mean(-1, keepdim=True)
+        return x * torch.rsqrt(variance + self.eps) * self.weight
+
+class PixelShuffleTokenCompressor(nn.Module):
+    """
+    Performs space-to-depth token compression on Vision Transformer patch sequences.
+    Groups scale_factor x scale_factor patches and projects to decoder hidden dimension.
+    """
+    def __init__(self, config: TinyDocVLMConfig, encoder_dim: int, decoder_dim: int):
+        super().__init__()
+        self.config = config
+        self.scale_factor = config.pixel_shuffle_scale
+        self.encoder_dim = encoder_dim
+        self.decoder_dim = decoder_dim
+        
+        # After space-to-depth, channel dimension becomes encoder_dim * scale_factor^2
+        compressed_dim = encoder_dim * (self.scale_factor ** 2)
+        
+        # MLP projection to map visual tokens to language model dimension
+        self.projection = nn.Sequential(
+            nn.Linear(compressed_dim, decoder_dim),
+            nn.GELU(),
+            nn.Linear(decoder_dim, decoder_dim)
+        )
+        self.norm = RMSNorm(decoder_dim)
+
+    def forward(self, visual_features: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            visual_features: shape (batch_size, num_tiles, num_patches, encoder_dim)
+            
+        Returns:
+            compressed_features: shape (batch_size, num_tiles, num_compressed_patches, decoder_dim)
+        """
+        batch_size, num_tiles, num_patches, encoder_dim = visual_features.shape
+        
+        # Determine spatial dimensions assuming a square grid of patches
+        grid_size = int(math.sqrt(num_patches))
+        if grid_size * grid_size != num_patches:
+            raise ValueError(
+                f"Number of patches ({num_patches}) must be a perfect square to apply 2D pixel shuffle."
+            )
+            
+        if grid_size % self.scale_factor != 0:
+            raise ValueError(
+                f"Grid size ({grid_size}) must be divisible by pixel_shuffle_scale ({self.scale_factor})."
+            )
+
+        # Reshape to 2D spatial grid: (batch_size * num_tiles, grid_size, grid_size, encoder_dim)
+        x = visual_features.view(batch_size * num_tiles, grid_size, grid_size, encoder_dim)
+        
+        # Apply space-to-depth: (batch_size * num_tiles, H//s, s, W//s, s, C)
+        s = self.scale_factor
+        x = x.view(batch_size * num_tiles, grid_size // s, s, grid_size // s, s, encoder_dim)
+        
+        # Permute: (batch_size * num_tiles, H//s, W//s, s, s, C)
+        x = x.permute(0, 1, 3, 2, 4, 5).contiguous()
+        
+        # Reshape to flatten the spatial groups into the channel dimension:
+        # (batch_size * num_tiles, (H//s) * (W//s), s * s * C)
+        new_patches = (grid_size // s) ** 2
+        x = x.view(batch_size * num_tiles, new_patches, s * s * encoder_dim)
+        
+        # Project and normalize
+        x = self.projection(x)
+        x = self.norm(x)
+        
+        # Reshape back to batch: (batch_size, num_tiles, new_patches, decoder_dim)
+        x = x.view(batch_size, num_tiles, new_patches, self.decoder_dim)
+        return x

tokenizer_config.json

@@ -0,0 +1,18 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|im_start|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<image>"
+  ],
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 8192,
+  "pad_token": "<|im_end|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>",
+  "vocab_size": 49152
+}

vision_encoder.py

@@ -0,0 +1,64 @@
+import torch
+import torch.nn as nn
+from transformers import SiglipVisionModel
+from .configuration import TinyDocVLMConfig
+
+class SigLIPVisionEncoder(nn.Module):
+    """
+    Wrapper around HuggingFace's SiglipVisionModel.
+    Handles encoding of multiple image tiles and thumbnails.
+    """
+    def __init__(self, config: TinyDocVLMConfig):
+        super().__init__()
+        self.config = config
+        
+        # Load from config or create model
+        vision_config = config.vision_config
+        # We can initialize from config. If we are running pretraining, we load weights.
+        # During runtime we might load a pretrained siglip model.
+        self.encoder = SiglipVisionModel(vision_config)
+        self.hidden_size = vision_config.hidden_size
+        
+        # Add special region classification or auxiliary layers if needed in future
+        # For now, just a wrapper around the SigLIP vision encoder
+
+    def forward(
+        self, 
+        pixel_values: torch.Tensor, 
+        interpolate_pos_encoding: bool = False
+    ) -> torch.Tensor:
+        """
+        Args:
+            pixel_values: shape (batch_size, num_tiles, channels, height, width) 
+                          or (batch_size * num_tiles, channels, height, width)
+            interpolate_pos_encoding: whether to interpolate positional embeddings if resolution changes
+            
+        Returns:
+            visual_features: shape (batch_size, num_tiles, num_patches, hidden_size)
+        """
+        # If input has shape (batch_size, num_tiles, channels, height, width)
+        if len(pixel_values.shape) == 5:
+            batch_size, num_tiles, channels, height, width = pixel_values.shape
+            # Flatten batch and tiles for vision encoder
+            pixel_values = pixel_values.view(batch_size * num_tiles, channels, height, width)
+        else:
+            # Assumed to be already flattened (batch_size * num_tiles, channels, height, width)
+            batch_size = 1
+            num_tiles = pixel_values.shape[0]
+            channels, height, width = pixel_values.shape[1:]
+
+        # Run through SigLIP Vision Model
+        outputs = self.encoder(
+            pixel_values=pixel_values,
+            interpolate_pos_encoding=interpolate_pos_encoding
+        )
+        
+        # Last hidden state: (batch_size * num_tiles, num_patches, hidden_size)
+        # For SigLIP-B/16 with 384x384 input: num_patches = (384/16)^2 = 24^2 = 576
+        last_hidden_state = outputs.last_hidden_state
+        
+        # Reshape back to batch format
+        num_patches = last_hidden_state.shape[1]
+        last_hidden_state = last_hidden_state.view(batch_size, num_tiles, num_patches, self.hidden_size)
+        
+        return last_hidden_state