Delete Ovis2.5-2B/.ipynb_checkpoints

Ovis2.5-2B/.ipynb_checkpoints/added_tokens-checkpoint.json

@@ -1,28 +0,0 @@
-{
-  "</think>": 151668,
-  "</tool_call>": 151658,
-  "</tool_response>": 151666,
-  "<think>": 151667,
-  "<tool_call>": 151657,
-  "<tool_response>": 151665,
-  "<|box_end|>": 151649,
-  "<|box_start|>": 151648,
-  "<|endoftext|>": 151643,
-  "<|file_sep|>": 151664,
-  "<|fim_middle|>": 151660,
-  "<|fim_pad|>": 151662,
-  "<|fim_prefix|>": 151659,
-  "<|fim_suffix|>": 151661,
-  "<|im_end|>": 151645,
-  "<|im_start|>": 151644,
-  "<|image_pad|>": 151655,
-  "<|object_ref_end|>": 151647,
-  "<|object_ref_start|>": 151646,
-  "<|quad_end|>": 151651,
-  "<|quad_start|>": 151650,
-  "<|repo_name|>": 151663,
-  "<|video_pad|>": 151656,
-  "<|vision_end|>": 151653,
-  "<|vision_pad|>": 151654,
-  "<|vision_start|>": 151652
-}

Ovis2.5-2B/.ipynb_checkpoints/chat_template-checkpoint.json

@@ -1,3 +0,0 @@
-{
-  "chat_template": "{%- for message in messages %}{{- '<|im_start|>' + message.role + '\n'}}{%- if message.role == 'system' or message.role == 'user' %}{%- if message.content is string %}{{- message.content | replace('<image>', '') | replace('<video>', '') }}{%- else %}{%- for item in message.content %}{%- if item.type == 'text' and 'text' in item %}{{- item.text | replace('<image>', '') | replace('<video>', '') }}{%- elif item.type == 'image' %}{{- '<image>'}}{%- elif item.type == 'video' %}{{- '<video>'}}{%- else %}{{- raise_exception('Invalid content type. Supported types for system and user are text, image, video.')}}{%- endif %}{%- if not loop.last %}{{- '\n'}}{%- endif %}{%- endfor %}{%- endif %}{%- elif message.role == 'assistant' %}{%- set content = '' %}{%- if message.content is string %}{%- set content = message.content | replace('<image>', '') | replace('<video>', '') %}{%- else %}{%- for item in message.content %}{%- if item.type == 'text' and 'text' in item %}{%- set content = content ~ (item.text | replace('<image>', '') | replace('<video>', '')) %}{%- else %}{{- raise_exception('Invalid content type. Supported type for assistant is text.')}}{%- endif %}{%- endfor %}{%- endif %}{%- set content = content.split('</think>')[-1].lstrip('\n') %}{{- content }}{%- else %}{{- raise_exception('Invalid role. Supported roles are system, user, assistant.')}}{%- endif %}{{- '<|im_end|>\n'}}{%- endfor %}{%- if add_generation_prompt %}{{- '<|im_start|>assistant\n' }}{%- if enable_thinking is defined and enable_thinking is false %}{{- '<think>\n\n</think>\n\n' }}{%- endif %}{%- endif %}"
-}

Ovis2.5-2B/.ipynb_checkpoints/config-checkpoint.json

@@ -1,73 +0,0 @@
-{
-  "architectures": [
-    "Ovis2_5"
-  ],
-  "auto_map": {
-    "AutoConfig": "configuration_ovis2_5.Ovis2_5_Config",
-    "AutoModelForCausalLM": "modeling_ovis2_5.Ovis2_5"
-  },
-  "conversation_formatter_class": "Qwen3ConversationFormatter",
-  "hidden_size": 2048,
-  "vocab_size": 151936,
-  "num_attention_heads": 32,
-  "max_position_embeddings": 40960,
-  "llm_config": {
-    "_attn_implementation_autoset": true,
-    "_name_or_path": "Qwen/Qwen3-1.7B",
-    "architectures": [
-      "Qwen3ForCausalLM"
-    ],
-    "attention_bias": false,
-    "attention_dropout": 0.0,
-    "bos_token_id": 151643,
-    "eos_token_id": 151645,
-    "head_dim": 128,
-    "hidden_act": "silu",
-    "hidden_size": 2048,
-    "initializer_range": 0.02,
-    "intermediate_size": 6144,
-    "max_position_embeddings": 40960,
-    "max_window_layers": 28,
-    "model_type": "qwen3",
-    "num_attention_heads": 16,
-    "num_hidden_layers": 28,
-    "num_key_value_heads": 8,
-    "rms_norm_eps": 1e-06,
-    "rope_scaling": null,
-    "rope_theta": 1000000,
-    "sliding_window": null,
-    "tie_word_embeddings": true,
-    "torch_dtype": "bfloat16",
-    "use_cache": true,
-    "use_sliding_window": false,
-    "vocab_size": 151936
-  },
-  "model_type": "ovis2_5",
-  "torch_dtype": "bfloat16",
-  "transformers_version": "4.51.3",
-  "use_cache": true,
-  "visual_vocab_size": 65536,
-  "vit_config": {
-    "_attn_implementation_autoset": true,
-    "_name_or_path": "google/siglip2-so400m-patch16-512",
-    "attention_dropout": 0.0,
-    "fullatt_block_indexes": null,
-    "hidden_act": "gelu_pytorch_tanh",
-    "hidden_size": 1152,
-    "hidden_stride": 2,
-    "image_size": 512,
-    "intermediate_size": 4304,
-    "layer_norm_eps": 1e-06,
-    "model_type": "siglip2_navit",
-    "num_attention_heads": 16,
-    "num_channels": 3,
-    "num_hidden_layers": 27,
-    "num_patches": -1,
-    "patch_size": 16,
-    "preserve_original_pe": true,
-    "temporal_patch_size": 1,
-    "torch_dtype": "bfloat16",
-    "use_rope": true,
-    "window_size": 112
-  }
-}

Ovis2.5-2B/.ipynb_checkpoints/configuration_ovis2_5-checkpoint.py

@@ -1,96 +0,0 @@
-from typing import Any, Optional, List, Union
-
-from transformers import Qwen3Config
-from transformers.configuration_utils import PretrainedConfig
-
-__all__ = ["Siglip2NavitConfig", "Ovis2_5_Config"]
-
-
-class Siglip2NavitConfig(PretrainedConfig):
-    """This is the configuration class to store the configuration of an [`AIMv2Model`].
-
-    Instantiating a configuration with the defaults will yield a similar configuration
-    to that of the [apple/aimv2-large-patch14-224](https://huggingface.co/apple/aimv2-large-patch14-224).
-
-    Args:
-        hidden_size: Dimension of the hidden representations.
-        intermediate_size: Dimension of the SwiGLU representations.
-        num_hidden_layers: Number of hidden layers in the Transformer.
-        num_attention_heads: Number of attention heads for each attention layer
-            in the Transformer.
-        num_channels: Number of input channels.
-        image_size: Image size.
-        patch_size: Patch size.
-        rms_norm_eps: Epsilon value used for the RMS normalization layer.
-        attention_dropout: Dropout ratio for attention probabilities.
-        projection_dropout: Dropout ratio for the projection layer after the attention.
-        qkv_bias: Whether to add a bias to the queries, keys and values.
-        use_bias: Whether to add a bias in the feed-forward and projection layers.
-        kwargs: Keyword arguments for the [`PretrainedConfig`].
-    """
-
-    model_type: str = "siglip2_navit"
-
-    def __init__(
-        self,
-        hidden_size: int = 1024,
-        intermediate_size: int = 4096,
-        num_hidden_layers: int = 24,
-        num_attention_heads: int = 16,
-        num_channels: int = 3,
-        num_patches: int = -1,
-        image_size: int = 512,
-        patch_size: int = 16,
-        hidden_act: str="gelu_pytorch_tanh",
-        layer_norm_eps: float = 1e-6,
-        attention_dropout: float = 0.0,
-        hidden_stride: int = 2,
-        window_size: int = 112,
-        fullatt_block_indexes: Optional[list] = None,
-        temporal_patch_size: int = 1,
-        preserve_original_pe: bool = True,
-        use_rope: bool = True,
-        **kwargs: Any,
-    ):
-        super().__init__(**kwargs)
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.num_channels = num_channels
-        self.num_patches = num_patches
-        self.patch_size = patch_size
-        self.image_size = image_size
-        self.hidden_act = hidden_act
-        self.attention_dropout = attention_dropout
-        self.layer_norm_eps = layer_norm_eps
-        self.hidden_stride = hidden_stride
-        self.window_size = window_size
-        self.fullatt_block_indexes = fullatt_block_indexes
-        self.temporal_patch_size = temporal_patch_size
-        self.preserve_original_pe = preserve_original_pe
-        self.use_rope = use_rope
-
-class Ovis2_5_Config(PretrainedConfig):
-    model_type = "ovis2_5"
-    sub_configs = dict(llm_config=Qwen3Config, vit_config=Siglip2NavitConfig)
-
-    def __init__(self,
-        llm_config: Optional[Union[Qwen3Config, dict]] = None,
-        vit_config: Optional[Union[Siglip2NavitConfig, dict]] = None,
-        visual_vocab_size=65536,
-        hidden_size=None,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        if isinstance(llm_config, dict):
-            llm_config = Qwen3Config(**llm_config)
-        self.llm_config = llm_config
-        if isinstance(vit_config, dict):
-            vit_config = Siglip2NavitConfig(**vit_config)
-        self.vit_config = vit_config
-        self.visual_vocab_size = visual_vocab_size
-        self.hidden_size = hidden_size
-        if kwargs.get('attn_implementation'):
-            self.llm_config._attn_implementation = kwargs['attn_implementation']
-            self.vit_config._attn_implementation = kwargs['attn_implementation']

Ovis2.5-2B/.ipynb_checkpoints/generation_config-checkpoint.json

@@ -1,15 +0,0 @@
-{
-  "bos_token_id": 151643,
-  "do_sample": true,
-  "eos_token_id": [
-    151645,
-    151643
-  ],
-  "multimodal_max_length": 8192,
-  "pad_token_id": 151643,
-  "repetition_penalty": 1.05,
-  "temperature": 0.6,
-  "top_k": 20,
-  "top_p": 0.95,
-  "transformers_version": "4.51.3"
-}

Ovis2.5-2B/.ipynb_checkpoints/modeling_ovis2_5-checkpoint.py

@@ -1,949 +0,0 @@
-import math
-from typing import Dict, List, Optional, Tuple, Union
-
-import PIL.Image
-import numpy as np
-import torch
-from flash_attn import flash_attn_varlen_func
-from flash_attn.layers.rotary import apply_rotary_emb
-from torch import Tensor, nn
-from torch.nn import functional as F
-from transformers import (
-    AutoConfig,
-    AutoImageProcessor,
-    AutoModel,
-    AutoModelForCausalLM,
-    AutoTokenizer,
-)
-from transformers.activations import ACT2FN
-from transformers.generation.utils import GenerateOutput
-from transformers.modeling_outputs import BaseModelOutputWithNoAttention
-from transformers.modeling_utils import PreTrainedModel
-
-from .configuration_ovis2_5 import Siglip2NavitConfig, Ovis2_5_Config
-
-IMAGE_PLACEHOLDER = "<image>"
-IMAGE_PLACEHOLDER_ID = -200
-VIDEO_PLACEHOLDER = "<video>"
-VIDEO_PLACEHOLDER_ID = -201
-
-VISUAL_ATOM_ID = -300
-INDICATOR_IDS = [-301, -302, -303, -304]
-
-# copied from qwen2.5-vl
-class VisionRotaryEmbedding(nn.Module):
-    def __init__(self, dim: int, theta: float = 10000.0) -> None:
-        super().__init__()
-        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-
-    def forward(self, seqlen: int) -> torch.Tensor:
-        seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
-        freqs = torch.outer(seq, self.inv_freq)
-        return freqs
-
-
-class Siglip2VisionEmbeddings(nn.Module):
-    def __init__(self, config: Siglip2NavitConfig):
-        super().__init__()
-        self.config = config
-        self.embed_dim = config.hidden_size
-        self.patch_size = config.patch_size
-        self.image_size = config.image_size
-        self.num_patches = config.num_patches
-        self.preserve_original_pe = config.preserve_original_pe
-        self.hidden_stride = config.hidden_stride
-
-
-        # siglip2 naflex
-        if self.num_patches > 0:
-            self.patch_embedding = nn.Linear(
-                    in_features=config.num_channels * self.patch_size * self.patch_size,
-                    out_features=self.embed_dim,
-                )
-            if self.preserve_original_pe:
-                self.position_embedding_size = int(self.num_patches**0.5)
-                self.position_embedding = nn.Embedding(self.num_patches, self.embed_dim)
-
-        else:
-            self.patch_embedding = nn.Conv2d(
-                    in_channels=config.num_channels,
-                    out_channels=self.embed_dim,
-                    kernel_size=self.patch_size,
-                    stride=self.patch_size,
-                    padding="valid",
-                )
-            if self.preserve_original_pe:
-                self.num_patches = (self.image_size // self.patch_size) ** 2
-                self.position_embedding_size = self.image_size // self.patch_size
-                self.position_embedding = nn.Embedding(self.num_patches, self.embed_dim)
-        
-    @staticmethod
-    def resize_positional_embeddings(
-        positional_embeddings: torch.Tensor,
-        spatial_shapes: torch.LongTensor,
-        max_length: int,
-    ) -> torch.Tensor:
-        """
-        Resize positional embeddings to image-specific size and pad to a fixed size.
-
-        Args:
-            positional_embeddings (`torch.Tensor`):
-                Position embeddings of shape (height, width, embed_dim)
-            spatial_shapes (`torch.LongTensor`):
-                Spatial shapes of shape (batch_size, 2) to resize the positional embeddings to
-            max_length (`int`):
-                Maximum length of the positional embeddings to pad resized positional embeddings to
-
-        Returns:
-            `torch.Tensor`: Embeddings of shape (batch_size, max_length, embed_dim)
-        """
-        batch_size = spatial_shapes.shape[0]
-        embed_dim = positional_embeddings.shape[-1]
-        source_dtype = positional_embeddings.dtype
-
-        resulted_positional_embeddings = torch.empty(
-            (batch_size, max_length, embed_dim),
-            device=positional_embeddings.device,
-            dtype=source_dtype,
-        )
-
-        # (height, width, embed_dim) -> (1, embed_dim, height, width) for interpolation
-        positional_embeddings = positional_embeddings.permute(2, 0, 1).unsqueeze(0)
-
-        # Upcast to float32 on CPU because antialias is not supported for bfloat16/float16 on CPU
-        if positional_embeddings.device.type == "cpu":
-            positional_embeddings = positional_embeddings.to(torch.float32)
-
-        for i in range(batch_size):
-            # (1, dim, height, width) -> (1, dim, target_height, target_width)
-            height, width = spatial_shapes[i]
-            resized_embeddings = F.interpolate(
-                positional_embeddings,
-                size=(height, width),
-                mode="bilinear",
-                align_corners=False,
-                antialias=True,
-            )
-
-            # (1, dim, target_height, target_width) -> (target_height * target_width, dim)
-            resized_embeddings = resized_embeddings.reshape(embed_dim, height * width).transpose(0, 1)
-
-            # Cast to original dtype
-            resized_embeddings = resized_embeddings.to(source_dtype)
-
-            resulted_positional_embeddings[i, : height * width] = resized_embeddings
-            resulted_positional_embeddings[i, height * width :] = resized_embeddings[0]
-
-        return resulted_positional_embeddings
-
-    def forward(self, pixel_values: torch.FloatTensor, 
-                grid_thws: Optional[torch.LongTensor] = None) -> torch.Tensor:
-        """
-        Args:
-            pixel_values (`torch.FloatTensor`):
-                Pixel values of shape (num_patches, num_channels * temporal_patch_size * patch_size * patch_size)
-            grid_thws: (`torch.LongTensor`):
-                grid shape (num_patches, 3)
-        """
-
-        # Apply patch embeddings to already patchified pixel values
-        target_dtype = self.patch_embedding.weight.dtype
-        if isinstance(self.patch_embedding, nn.Linear):
-            patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))
-        elif isinstance(self.patch_embedding, nn.Conv2d):
-            pixel_values = pixel_values.view(-1, self.config.num_channels * self.config.temporal_patch_size, self.patch_size,
-                   self.patch_size)
-            patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))
-            patch_embeds = patch_embeds.reshape(-1, self.embed_dim)
-
-
-        if self.preserve_original_pe:
-            assert grid_thws is not None
-            pos_embed_new = torch.zeros_like(patch_embeds)
-            ori_h = ori_w = self.position_embedding_size
-            positional_embeddings = self.position_embedding.weight.reshape(
-                                self.position_embedding_size, self.position_embedding_size, -1
-                            ).unsqueeze(0).permute(0,3,1,2)
-            # pos_embed = self.pos_embed.reshape(1, ori_h, ori_w, -1).permute(0, 3, 1, 2)
-            cnt = 0
-            for t, h, w in grid_thws:
-                thw = t * h * w
-                pe = F.interpolate(positional_embeddings, size=(h, w), mode='bicubic', align_corners=False)
-                pe = pe.permute(0, 2, 3, 1).reshape(1, h * w, -1)
-                pe = pe[0].repeat(t, 1)
-                pe = pe.reshape(t, h // self.hidden_stride, self.hidden_stride, w // self.hidden_stride,
-                                self.hidden_stride, -1)
-                pe = pe.permute(0, 1, 3, 2, 4, 5).reshape(thw, -1)
-                pos_embed_new[cnt:cnt + thw] = pe
-                cnt += thw
-            patch_embeds = patch_embeds + pos_embed_new
-
-        return patch_embeds
-
-
-# copied from qwen2.5-vl
-def apply_rotary_pos_emb_flashatt(
-    q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    cos = cos.chunk(2, dim=-1)[0].contiguous()
-    sin = sin.chunk(2, dim=-1)[0].contiguous()
-    q_embed = apply_rotary_emb(q.float(), cos.float(), sin.float()).type_as(q)
-    k_embed = apply_rotary_emb(k.float(), cos.float(), sin.float()).type_as(k)
-    return q_embed, k_embed
-
-
-class Siglip2Attention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.embed_dim = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.embed_dim // self.num_heads
-        if self.head_dim * self.num_heads != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
-                f" {self.num_heads})."
-            )
-        self.scale = self.head_dim**-0.5
-        self.dropout = config.attention_dropout
-        self.is_causal = False
-
-        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
-        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
-        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
-        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
-
-        self.use_rope = config.use_rope
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        cu_seqlens: torch.Tensor,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
-        """Input shape: Batch x Time x Channel"""
-
-        seq_length, embed_dim = hidden_states.shape
-
-        queries = self.q_proj(hidden_states)
-        keys = self.k_proj(hidden_states)
-        values = self.v_proj(hidden_states)
-
-        queries = queries.view(seq_length, self.num_heads, self.head_dim)
-        keys = keys.view(seq_length, self.num_heads, self.head_dim)
-        values = values.view(seq_length, self.num_heads, self.head_dim)
-
-        if self.use_rope:
-            cos, sin = position_embeddings
-            queries, keys = apply_rotary_pos_emb_flashatt(queries.unsqueeze(0), keys.unsqueeze(0), cos, sin)
-            queries = queries.squeeze(0)
-            keys = keys.squeeze(0)
-
-        max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
-        attn_output = flash_attn_varlen_func(queries, keys, values, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen).reshape(
-                                            seq_length, -1
-                                        )
-        attn_output = self.out_proj(attn_output)
-        return attn_output
-
-class Siglip2MLP(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.activation_fn = ACT2FN[config.hidden_act]
-        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
-        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.fc1(hidden_states)
-        hidden_states = self.activation_fn(hidden_states)
-        hidden_states = self.fc2(hidden_states)
-        return hidden_states
-
-
-class Siglip2EncoderLayer(nn.Module):
-    def __init__(self, config: Siglip2NavitConfig):
-        super().__init__()
-        self.embed_dim = config.hidden_size
-        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
-        self.self_attn = Siglip2Attention(config)
-        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
-        self.mlp = Siglip2MLP(config)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        cu_seqlens: torch.Tensor, 
-        position_embeddings: torch.Tensor
-    ) -> tuple[torch.FloatTensor]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`):
-                Input to the layer of shape `(batch, seq_len, embed_dim)`.
-            attention_mask (`torch.FloatTensor`):
-                Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
-            output_attentions (`bool`, *optional*, defaults to `False`):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        residual = hidden_states
-
-        hidden_states = self.layer_norm1(hidden_states)
-        hidden_states = self.self_attn(
-            hidden_states=hidden_states,
-            cu_seqlens=cu_seqlens, 
-            position_embeddings=position_embeddings
-        )
-        hidden_states = residual + hidden_states
-
-        residual = hidden_states
-        hidden_states = self.layer_norm2(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-
-        return hidden_states
-
-class Siglip2Encoder(nn.Module):
-    """
-    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
-    [`Siglip2EncoderLayer`].
-
-    Args:
-        config: Siglip2NavitConfig
-    """
-
-    def __init__(self, config: Siglip2NavitConfig):
-        super().__init__()
-        self.config = config
-        self.layers = nn.ModuleList([Siglip2EncoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
-
-        self.rotary_pos_emb = VisionRotaryEmbedding(config.hidden_size // config.num_attention_heads // 2)
-        self.patch_size = config.patch_size
-        self.hidden_stride = config.hidden_stride
-        self.window_size = config.window_size
-        self.spatial_merge_unit = config.hidden_stride * config.hidden_stride
-        self.fullatt_block_indexes = None if config.fullatt_block_indexes is None else [int(i) for i in config.fullatt_block_indexes.split('|')]
-        
-
-    # copied from qwen2.5_vl
-    def rot_pos_emb(self, grid_thw):
-        pos_ids = []
-        for t, h, w in grid_thw:
-            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
-            hpos_ids = hpos_ids.reshape(
-                h // self.hidden_stride,
-                self.hidden_stride,
-                w // self.hidden_stride,
-                self.hidden_stride,
-            )
-            hpos_ids = hpos_ids.permute(0, 2, 1, 3)
-            hpos_ids = hpos_ids.flatten()
-
-            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
-            wpos_ids = wpos_ids.reshape(
-                h // self.hidden_stride,
-                self.hidden_stride,
-                w // self.hidden_stride,
-                self.hidden_stride,
-            )
-            wpos_ids = wpos_ids.permute(0, 2, 1, 3)
-            wpos_ids = wpos_ids.flatten()
-            pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
-        pos_ids = torch.cat(pos_ids, dim=0)
-        max_grid_size = grid_thw[:, 1:].max()
-        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
-        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
-        return rotary_pos_emb
-
-    def get_window_index(self, grid_thw):
-        window_index: list = []
-        cu_window_seqlens: list = [0]
-        window_index_id = 0
-        vit_merger_window_size = self.window_size // self.hidden_stride // self.patch_size  # patch (after merge) number in each window
-
-        for grid_t, grid_h, grid_w in grid_thw:
-            llm_grid_h, llm_grid_w = (
-                grid_h // self.hidden_stride,  # number of patch after merge
-                grid_w // self.hidden_stride,
-            )
-            index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w)
-            pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size
-            pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size
-            num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size
-            num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size
-            index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100)
-            index_padded = index_padded.reshape(
-                grid_t,
-                num_windows_h,
-                vit_merger_window_size,
-                num_windows_w,
-                vit_merger_window_size,
-            )
-            index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape(
-                grid_t,
-                num_windows_h * num_windows_w,
-                vit_merger_window_size,
-                vit_merger_window_size,
-            )
-            seqlens = (index_padded != -100).sum([2, 3]).reshape(-1)
-            index_padded = index_padded.reshape(-1)
-            index_new = index_padded[index_padded != -100]
-            window_index.append(index_new + window_index_id)
-            cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1]
-            cu_window_seqlens.extend(cu_seqlens_tmp.tolist())
-            window_index_id += (grid_t * llm_grid_h * llm_grid_w).item()
-        window_index = torch.cat(window_index, dim=0)
-
-        return window_index, cu_window_seqlens
-
-    # Ignore copy
-    def forward(
-        self,
-        inputs_embeds,
-        grid_thws: torch.Tensor,
-        output_hidden_states: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, ...]]]:
-        r"""
-        Args:
-            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
-                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
-                than the model's internal embedding lookup matrix.
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-        """
-
-        rotary_pos_emb = self.rot_pos_emb(grid_thws)
-        window_index, cu_window_seqlens = self.get_window_index(grid_thws)
-        cu_window_seqlens = torch.tensor(
-            cu_window_seqlens,
-            device=inputs_embeds.device,
-            dtype=grid_thws.dtype if torch.jit.is_tracing() else torch.int32,
-        )
-        cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens)
-
-        seq_len, _ = inputs_embeds.size()
-        inputs_embeds = inputs_embeds.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
-        inputs_embeds = inputs_embeds[window_index, :, :]
-        inputs_embeds = inputs_embeds.reshape(seq_len, -1)
-        rotary_pos_emb = rotary_pos_emb.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
-        rotary_pos_emb = rotary_pos_emb[window_index, :, :]
-        rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
-        emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
-        position_embeddings = (emb.cos(), emb.sin())
-
-        cu_seqlens = torch.repeat_interleave(grid_thws[:, 1] * grid_thws[:, 2], grid_thws[:, 0]).cumsum(
-            dim=0,
-            # Select dtype based on the following factors:
-            #  - FA2 requires that cu_seqlens_q must have dtype int32
-            #  - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw
-            # See https://github.com/huggingface/transformers/pull/34852 for more information
-            dtype=grid_thws.dtype if torch.jit.is_tracing() else torch.int32,
-        )
-        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
-
-        reverse_indices = torch.argsort(window_index)
-        encoder_states = () if output_hidden_states else None
-
-        hidden_states = inputs_embeds
-        for index, block in enumerate(self.layers):
-            if self.fullatt_block_indexes is None or index in self.fullatt_block_indexes:
-                cu_seqlens_tmp = cu_seqlens
-            else:
-                cu_seqlens_tmp = cu_window_seqlens
-            if self.gradient_checkpointing and self.training:
-                hidden_states = self._gradient_checkpointing_func(block.__call__, hidden_states, cu_seqlens_tmp, position_embeddings)
-            else:
-                hidden_states = block(hidden_states, cu_seqlens_tmp, position_embeddings)
-            if output_hidden_states:
-                hidden_states_ = hidden_states.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
-                encoder_states += (hidden_states_[reverse_indices, :].reshape(seq_len, -1),)
-        # tokens = self.post_trunk_norm(tokens)
-        hidden_states = hidden_states.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
-        hidden_states = hidden_states[reverse_indices, :].reshape(seq_len, -1)
-
-        return hidden_states, encoder_states
-
-class Siglip2VisionTransformer(nn.Module):
-    def __init__(self, config: Siglip2NavitConfig):
-        super().__init__()
-        self.config = config
-        embed_dim = config.hidden_size
-
-        self.embeddings = Siglip2VisionEmbeddings(config)
-        self.encoder = Siglip2Encoder(config)
-        self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
-        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
-
-    def forward(
-        self,
-        pixel_values: torch.FloatTensor,
-        grid_thws: torch.LongTensor,
-        output_hidden_states: Optional[bool] = True,
-        return_dict: Optional[bool] = True,
-    ) -> Union[
-        Tuple[torch.Tensor],
-        Tuple[torch.Tensor, Tuple[torch.Tensor, ...]],
-        BaseModelOutputWithNoAttention,
-    ]:
-        r"""
-        spatial_shapes (`torch.LongTensor` of shape `(batch_size, 2)`):
-            Tensor containing the spatial dimensions (height, width) of the input images.
-        """
-        # 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
-        # )
-
-        hidden_states = self.embeddings(pixel_values, grid_thws)
-
-        last_hidden_state, hidden_states = self.encoder(hidden_states, grid_thws, output_hidden_states)
-        last_hidden_state = self.post_layernorm(last_hidden_state)
-
-        if not return_dict:
-            output = (last_hidden_state,)
-            output += (hidden_states,) if output_hidden_states else ()
-            return output
-        
-        return BaseModelOutputWithNoAttention(
-                last_hidden_state=last_hidden_state, 
-                hidden_states=hidden_states
-            )
-       
-class Siglip2PreTrainedModel(PreTrainedModel):
-    config_class = Siglip2NavitConfig
-    base_model_prefix = "siglip2_navit"
-    supports_gradient_checkpointing = True
-
-    _no_split_modules = [
-        "Siglip2VisionEmbeddings",
-        "Siglip2EncoderLayer",
-    ]
-    _supports_flash_attn_2 = True
-    _supports_sdpa = False
-    _supports_flex_attn = False
-    _supports_attention_backend = True
-
-
-class Siglip2NavitModel(Siglip2PreTrainedModel):
-    config_class = Siglip2NavitConfig
-    main_input_name = "pixel_values"
-
-    def __init__(self, config: Siglip2NavitConfig):
-        super().__init__(config)
-
-        self.vision_model = Siglip2VisionTransformer(config)
-
-    def get_input_embeddings(self) -> nn.Module:
-        return self.vision_model.embeddings.patch_embedding
-
-    def forward(
-        self,
-        pixel_values: torch.FloatTensor,
-        grid_thws: torch.LongTensor,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[
-        Tuple[torch.Tensor],
-        Tuple[torch.Tensor, Tuple[torch.Tensor, ...]],
-        BaseModelOutputWithNoAttention,
-    ]:
-        
-        if output_hidden_states is None:
-            output_hidden_states = self.config.output_hidden_states
-        if return_dict is None:
-            return_dict = self.config.use_return_dict
-
-        return self.vision_model(
-            pixel_values=pixel_values,
-            grid_thws=grid_thws,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-class VisualEmbedding(torch.nn.Embedding):
-    """
-    A visual embedding layer that can handle both discrete token IDs (long) and continuous
-    soft-token probabilities (float).
-    """
-
-    def forward(self, visual_tokens: Tensor) -> Tensor:
-        if visual_tokens.dtype in [torch.int8, torch.int16, torch.int32, torch.int64, torch.long]:
-            return super().forward(visual_tokens)
-        # Handle soft tokens (probabilities) by matrix multiplication with the embedding weight
-        return torch.matmul(visual_tokens, self.weight)
-
-
-class VisualTokenizer(torch.nn.Module):
-    """
-    Tokenizes images or videos into a sequence of continuous visual tokens.
-    """
-
-    def __init__(self, vit, visual_vocab_size, image_processor_name_or_path, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.vit = vit
-        self.image_processor = AutoImageProcessor.from_pretrained(image_processor_name_or_path, do_center_crop=False)
-        head_dim = visual_vocab_size - len(INDICATOR_IDS)
-        self.head = torch.nn.Sequential(
-            torch.nn.Linear(self.vit.config.hidden_size * self.vit.config.hidden_stride ** 2, head_dim, bias=False),
-            torch.nn.LayerNorm(head_dim)
-        )
-
-    def _encode(self, pixel_values, grid_thws):
-        output = self.vit(pixel_values, grid_thws, output_hidden_states=True, return_dict=True)
-        features = output.hidden_states[-1]
-        seq_len, _ = features.shape
-        features = features.reshape(seq_len // (self.vit.config.hidden_stride ** 2), -1)
-        return features
-
-    # Adapted from qwen2_vl
-    @staticmethod
-    def smart_resize(
-        height: int, width: int, factor: int = 28, min_pixels: int = 448 * 448, max_pixels: int = 1344 * 1792
-    ):
-        """Rescales the image so that the following conditions are met:
-        1. Both dimensions are divisible by 'factor'.
-        2. The total number of pixels is within ['min_pixels', 'max_pixels'].
-        3. The aspect ratio is maintained as closely as possible.
-        """
-        if height < factor or width < factor:
-            if height < width:
-                width = round(factor / height * width)
-                height = factor
-            else:
-                height = round(factor / width * height)
-                width = factor
-
-        elif max(height, width) / min(height, width) > 200:
-            if height > width:
-                height = 200 * width
-            else:
-                width = 200 * height
-
-        h_bar = round(height / factor) * factor
-        w_bar = round(width / factor) * factor
-        if h_bar * w_bar > max_pixels:
-            beta = math.sqrt((height * width) / max_pixels)
-            h_bar = math.floor(height / beta / factor) * factor
-            w_bar = math.floor(width / beta / factor) * factor
-        elif h_bar * w_bar < min_pixels:
-            beta = math.sqrt(min_pixels / (height * width))
-            h_bar = math.ceil(height * beta / factor) * factor
-            w_bar = math.ceil(width * beta / factor) * factor
-        return h_bar, w_bar
-
-    def preprocess(
-        self,
-        image: Optional[PIL.Image.Image] = None,
-        video: Optional[List[PIL.Image.Image]] = None,
-        min_pixels: Optional[int] = None,
-        max_pixels: Optional[int] = None
-    ):
-        patch_size = self.vit.config.patch_size
-        temporal_patch_size = self.vit.config.temporal_patch_size
-        hidden_stride = self.vit.config.hidden_stride
-        assert (image is None) ^ (video is None), "Invalid input: expect either image or video"
-        if image is not None:
-            images = [image]
-        else:
-            images = video
-        images = [image.convert("RGB") if image.mode != 'RGB' else image for image in images]
-        width, height = images[0].size
-        processed_images = []
-        for image in images:
-            resized_height, resized_width = self.smart_resize(
-                height,
-                width,
-                factor=patch_size * hidden_stride,
-                min_pixels=min_pixels,
-                max_pixels=max_pixels,
-            )
-            new_size = dict(height=resized_height, width=resized_width)
-            new_image = self.image_processor.preprocess(image, size=new_size, return_tensors="np")['pixel_values'][0]
-            processed_images.append(new_image)
-
-        patches = np.array(processed_images)
-        if patches.shape[0] % temporal_patch_size != 0:
-            repeats = np.repeat(patches[-1][np.newaxis], temporal_patch_size - 1, axis=0)
-            patches = np.concatenate([patches, repeats], axis=0)
-        channel = patches.shape[1]
-        grid_t = patches.shape[0] // temporal_patch_size
-        grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
-        grid_thw = torch.tensor([[grid_t, grid_h, grid_w]])
-
-        patches = patches.reshape(
-            grid_t, temporal_patch_size, channel,
-            grid_h // hidden_stride, hidden_stride, patch_size,
-            grid_w // hidden_stride, hidden_stride, patch_size,
-        )
-        patches = patches.transpose(0, 3, 6, 4, 7, 2, 1, 5, 8)
-        flatten_patches = patches.reshape(
-            grid_t * grid_h * grid_w, channel * temporal_patch_size * patch_size * patch_size
-        )
-        flatten_patches = torch.tensor(flatten_patches)
-
-        return flatten_patches, grid_thw
-
-    def forward(
-        self, pixel_values, grid_thws
-    ) -> torch.Tensor:  # [BatchSize, ImageShape] -> [BatchSize, #Token, VocabSize]
-        features = self._encode(pixel_values, grid_thws)
-        logits = self.head(features)
-        tokens = torch.softmax(logits, dim=-1, dtype=torch.float32).to(logits.dtype)
-
-        token_len, _ = tokens.shape
-        padding_tensor = torch.zeros(size=(token_len, len(INDICATOR_IDS)),
-                                     dtype=tokens.dtype,
-                                     device=tokens.device,
-                                     layout=tokens.layout,
-                                     requires_grad=False)
-        tokens = torch.cat((tokens, padding_tensor), dim=1)
-        return tokens
-
-
-class OvisPreTrainedModel(PreTrainedModel):
-    config_class = Ovis2_5_Config
-    base_model_prefix = "ovis2_5"
-
-
-class Ovis2_5(OvisPreTrainedModel):
-    _supports_flash_attn_2 = True
-
-    def __init__(self, config: Ovis2_5_Config, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
-
-        self.llm = AutoModelForCausalLM.from_config(self.config.llm_config)
-        assert self.config.hidden_size == self.llm.config.hidden_size, "hidden size mismatch"
-        self.text_tokenizer = AutoTokenizer.from_pretrained(self.config.name_or_path)
-        self.visual_tokenizer = VisualTokenizer(vit=AutoModel.from_config(self.config.vit_config),
-                                                visual_vocab_size=self.config.visual_vocab_size,
-                                                image_processor_name_or_path=self.config.name_or_path)
-
-        self.vte = VisualEmbedding(self.config.visual_vocab_size, self.config.hidden_size,
-                                   device=self.visual_tokenizer.vit.device, dtype=self.visual_tokenizer.vit.dtype)
-        indicator_token_indices = torch.arange(
-            self.config.visual_vocab_size - len(INDICATOR_IDS),
-            self.config.visual_vocab_size,
-            dtype=torch.long
-        )
-        self.register_buffer("indicator_token_indices", indicator_token_indices, persistent=False)
-
-        def _merge_modules(modules_list: tuple):
-            merged_modules = []
-            for modules in modules_list:
-                merged_modules.extend(modules if modules else [])
-            return merged_modules
-
-        # Standard model configurations for parallelism and device placement
-        self._no_split_modules = _merge_modules(
-            (self.llm._no_split_modules, self.visual_tokenizer.vit._no_split_modules))
-        self._skip_keys_device_placement = self.llm._skip_keys_device_placement
-        self._keep_in_fp32_modules = _merge_modules(
-            (self.llm._keep_in_fp32_modules, self.visual_tokenizer.vit._keep_in_fp32_modules))
-        self.is_parallelizable = all((self.llm.is_parallelizable, self.visual_tokenizer.vit.is_parallelizable))
-        self.supports_gradient_checkpointing = True
-
-    def tie_weights(self):
-        self.llm.tie_weights()
-
-    def get_wte(self):
-        return self.llm.get_input_embeddings()
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        attention_mask: torch.Tensor,
-        pixel_values: Optional[torch.Tensor],
-        grid_thws: Optional[torch.Tensor],
-        labels: Optional[torch.Tensor] = None,
-        **kwargs
-    ):
-        inputs_embeds = self.merge_multimodal(
-            input_ids=input_ids,
-            pixel_values=pixel_values,
-            grid_thws=grid_thws,
-        )
-        return self.llm(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels, **kwargs)
-
-    def merge_multimodal(
-        self,
-        input_ids: torch.Tensor,
-        pixel_values: Optional[torch.Tensor],
-        grid_thws: Optional[torch.Tensor],
-    ):
-        placeholder_token_mask = torch.lt(input_ids, 0)
-        multimodal_embeds = self.get_wte()(torch.masked_fill(input_ids, placeholder_token_mask, 0))
-
-        if pixel_values is not None:
-            visual_indicator_embeds = self.vte(self.indicator_token_indices).to(
-                dtype=multimodal_embeds.dtype, device=multimodal_embeds.device
-            )
-            visual_tokens = self.visual_tokenizer(pixel_values, grid_thws)
-            visual_embeds = self.vte(visual_tokens).to(dtype=multimodal_embeds.dtype, device=multimodal_embeds.device)
-
-            for i, indicator_id in enumerate(INDICATOR_IDS):
-                multimodal_embeds[input_ids == indicator_id] = visual_indicator_embeds[i]
-            multimodal_embeds[input_ids == VISUAL_ATOM_ID] = visual_embeds
-
-        return multimodal_embeds
-
-    def _merge_inputs(
-        self, raw_input_ids, placeholder_id, grid_thws, indicator_begin_id, indicator_end_id
-    ):
-        input_ids = []
-        prev_index = 0
-        placeholder_indexes = [i for i, v in enumerate(raw_input_ids) if v == placeholder_id]
-        for placeholder_index, grid_thw in zip(placeholder_indexes, grid_thws):
-            input_ids.extend(raw_input_ids[prev_index:placeholder_index])
-            num_image_atoms = grid_thw.prod().item()
-            num_image_atoms //= self.visual_tokenizer.vit.config.hidden_stride ** 2
-            num_image_atoms //= self.visual_tokenizer.vit.config.temporal_patch_size
-            input_ids.extend([indicator_begin_id] + [VISUAL_ATOM_ID] * num_image_atoms + [indicator_end_id])
-            prev_index = placeholder_index + 1
-        input_ids.extend(raw_input_ids[prev_index:])
-        return input_ids
-
-    def _tokenize_with_visual_placeholder(self, text):
-        placeholder = VIDEO_PLACEHOLDER if VIDEO_PLACEHOLDER in text else IMAGE_PLACEHOLDER
-        placeholder_id = VIDEO_PLACEHOLDER_ID if VIDEO_PLACEHOLDER in text else IMAGE_PLACEHOLDER_ID
-        chunks = [self.text_tokenizer(chunk, add_special_tokens=False).input_ids for chunk in text.split(placeholder)]
-        input_ids = chunks[0]
-        for chunk in chunks[1:]:
-            input_ids.append(placeholder_id)
-            input_ids.extend(chunk)
-        return input_ids
-
-    def preprocess_inputs(
-        self,
-        messages: List[Union[str, Dict]],
-        min_pixels=448 * 448,
-        max_pixels=1344 * 1792,
-        add_generation_prompt=True,
-        enable_thinking=False
-    ):
-        text = self.text_tokenizer.apply_chat_template(
-            messages,
-            tokenize=False,
-            add_generation_prompt=add_generation_prompt,
-            enable_thinking=enable_thinking
-        )
-        input_ids = self._tokenize_with_visual_placeholder(text)
-        images = []
-        videos = []
-        for message in messages:
-            content = message["content"]
-            if isinstance(content, list):
-                images.extend([item["image"] for item in content if item.get("image") is not None])
-                videos.extend([item["video"] for item in content if item.get("video") is not None])
-        if images and videos:
-            raise ValueError(
-                "Multiple visual input data types detected (both image and video provided). "
-                "This model supports only one type of visual input data at a time. "
-                "Please provide either image or video, but not both."
-            )
-
-        pixel_values, grid_thws = None, None
-        if images:
-            pixel_values, grid_thws = zip(
-                *(self.visual_tokenizer.preprocess(image=image, min_pixels=min_pixels, max_pixels=max_pixels)
-                  for image in images)
-            )
-            input_ids = self._merge_inputs(
-                input_ids, IMAGE_PLACEHOLDER_ID, grid_thws, INDICATOR_IDS[0], INDICATOR_IDS[1]
-            )
-            pixel_values = torch.cat(pixel_values, dim=0)
-            grid_thws = torch.cat(grid_thws, dim=0)
-        elif videos:
-            assert len(videos) == 1, "only support single video"
-            pixel_values, grid_thws = self.visual_tokenizer.preprocess(
-                video=videos[0], min_pixels=min_pixels, max_pixels=max_pixels
-            )
-            input_ids = self._merge_inputs(
-                input_ids, VIDEO_PLACEHOLDER_ID, grid_thws, INDICATOR_IDS[2], INDICATOR_IDS[3]
-            )
-
-        input_ids = torch.tensor(input_ids, dtype=torch.long).unsqueeze(0)
-
-        return input_ids, pixel_values, grid_thws
-
-    def generate(
-        self,
-        inputs: Optional[torch.Tensor] = None,
-        **kwargs,
-    ) -> Union[GenerateOutput, torch.LongTensor]:
-        attention_mask = torch.ne(inputs, self.text_tokenizer.pad_token_id).to(device=inputs.device)
-        inputs_embeds = self.merge_multimodal(
-            input_ids=inputs,
-            pixel_values=kwargs.pop('pixel_values', None),
-            grid_thws=kwargs.pop('grid_thws', None)
-        )
-        enable_thinking = kwargs.pop('enable_thinking', False)
-        enable_thinking_budget = kwargs.pop('enable_thinking_budget', False)
-        thinking_budget = kwargs.pop('thinking_budget', 1024)
-        
-        if enable_thinking and enable_thinking_budget:
-            actual_max_new_tokens = kwargs['max_new_tokens']
-            kwargs['max_new_tokens'] = thinking_budget
-            generated_ids = self.llm.generate(inputs=None, inputs_embeds=inputs_embeds, attention_mask=attention_mask, **kwargs)
-            output_ids = generated_ids
-            output_ids_list = generated_ids[0]
-            
-            # check if the generation has already finished (151645 is <|im_end|>)
-            if 151645 not in output_ids_list:
-                # check if the thinking process has finished (151668 is </think>)
-                # and prepare the second model input
-                if 151668 not in output_ids_list:
-                    early_stopping_text = "\n\nConsidering the limited time by the user, I have to give the solution based on the thinking directly now.\n</think>\n\n"
-                    early_stopping_ids = self.text_tokenizer(early_stopping_text, return_tensors="pt", return_attention_mask=False).input_ids.to(inputs.device)
-                    input_ids_appendent = torch.cat([output_ids, early_stopping_ids], dim=-1)
-                    kwargs['streamer'].put(early_stopping_ids) if 'streamer' in kwargs else None
-                else:
-                    input_ids_appendent = output_ids
-                
-
-                # second generation
-                new_inputs = torch.cat([inputs, input_ids_appendent], dim=-1)
-                attention_mask = torch.ne(new_inputs, self.text_tokenizer.pad_token_id).to(device=inputs.device)
-                inputs_embeds_appendent = self.merge_multimodal(
-                    input_ids=input_ids_appendent,
-                    pixel_values=None,
-                    grid_thws=None
-                )
-                new_inputs_embeds = torch.cat([inputs_embeds, inputs_embeds_appendent], dim=-2)
-                
-                kwargs['max_new_tokens'] = inputs_embeds.size(-2) + actual_max_new_tokens - new_inputs_embeds.size(-2)
-                generated_ids2 = self.llm.generate(inputs=None, inputs_embeds=new_inputs_embeds, attention_mask=attention_mask, **kwargs)
-                kwargs['streamer'].manual_end() if 'streamer' in kwargs else None
-                return torch.cat([input_ids_appendent, generated_ids2], dim=-1)
-            
-            else:
-                kwargs['streamer'].manual_end() if 'streamer' in kwargs else None
-                return generated_ids
-            
-        else:
-            generated_ids = self.llm.generate(inputs=None, inputs_embeds=inputs_embeds, attention_mask=attention_mask, **kwargs)
-            kwargs['streamer'].manual_end() if 'streamer' in kwargs else None
-            return generated_ids
-
-
-AutoConfig.register('siglip2_navit', Siglip2NavitConfig)
-AutoModel.register(Siglip2NavitConfig, Siglip2NavitModel)
-AutoConfig.register("ovis2_5", Ovis2_5_Config)
-AutoModelForCausalLM.register(Ovis2_5_Config, Ovis2_5)

Ovis2.5-2B/.ipynb_checkpoints/preprocessor_config-checkpoint.json

@@ -1,24 +0,0 @@
-{
-  "do_convert_rgb": null,
-  "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
-  ],
-  "processor_class": "SiglipProcessor",
-  "resample": 2,
-  "rescale_factor": 0.00392156862745098,
-  "size": {
-    "height": 512,
-    "width": 512
-  }
-}

Ovis2.5-2B/.ipynb_checkpoints/tokenizer_config-checkpoint.json

@@ -1,240 +0,0 @@
-{
-  "add_bos_token": false,
-  "add_prefix_space": false,
-  "added_tokens_decoder": {
-    "151643": {
-      "content": "<|endoftext|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151644": {
-      "content": "<|im_start|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151645": {
-      "content": "<|im_end|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151646": {
-      "content": "<|object_ref_start|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151647": {
-      "content": "<|object_ref_end|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151648": {
-      "content": "<|box_start|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151649": {
-      "content": "<|box_end|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151650": {
-      "content": "<|quad_start|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151651": {
-      "content": "<|quad_end|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151652": {
-      "content": "<|vision_start|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151653": {
-      "content": "<|vision_end|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151654": {
-      "content": "<|vision_pad|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151655": {
-      "content": "<|image_pad|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151656": {
-      "content": "<|video_pad|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "151657": {
-      "content": "<tool_call>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151658": {
-      "content": "</tool_call>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151659": {
-      "content": "<|fim_prefix|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151660": {
-      "content": "<|fim_middle|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151661": {
-      "content": "<|fim_suffix|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151662": {
-      "content": "<|fim_pad|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151663": {
-      "content": "<|repo_name|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151664": {
-      "content": "<|file_sep|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151665": {
-      "content": "<tool_response>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151666": {
-      "content": "</tool_response>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151667": {
-      "content": "<think>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "151668": {
-      "content": "</think>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    }
-  },
-  "additional_special_tokens": [
-    "<|im_start|>",
-    "<|im_end|>",
-    "<|object_ref_start|>",
-    "<|object_ref_end|>",
-    "<|box_start|>",
-    "<|box_end|>",
-    "<|quad_start|>",
-    "<|quad_end|>",
-    "<|vision_start|>",
-    "<|vision_end|>",
-    "<|vision_pad|>",
-    "<|image_pad|>",
-    "<|video_pad|>"
-  ],
-  "bos_token": null,
-  "chat_template": "{%- for message in messages %}{{- '<|im_start|>' + message.role + '\n'}}{%- if message.role == 'system' or message.role == 'user' %}{%- if message.content is string %}{{- message.content | replace('<image>', '') | replace('<video>', '') }}{%- else %}{%- for item in message.content %}{%- if item.type == 'text' and 'text' in item %}{{- item.text | replace('<image>', '') | replace('<video>', '') }}{%- elif item.type == 'image' %}{{- '<image>'}}{%- elif item.type == 'video' %}{{- '<video>'}}{%- else %}{{- raise_exception('Invalid content type. Supported types for system and user are text, image, video.')}}{%- endif %}{%- if not loop.last %}{{- '\n'}}{%- endif %}{%- endfor %}{%- endif %}{%- elif message.role == 'assistant' %}{%- set content = '' %}{%- if message.content is string %}{%- set content = message.content | replace('<image>', '') | replace('<video>', '') %}{%- else %}{%- for item in message.content %}{%- if item.type == 'text' and 'text' in item %}{%- set content = content ~ (item.text | replace('<image>', '') | replace('<video>', '')) %}{%- else %}{{- raise_exception('Invalid content type. Supported type for assistant is text.')}}{%- endif %}{%- endfor %}{%- endif %}{%- set content = content.split('</think>')[-1].lstrip('\n') %}{{- content }}{%- else %}{{- raise_exception('Invalid role. Supported roles are system, user, assistant.')}}{%- endif %}{{- '<|im_end|>\n'}}{%- endfor %}{%- if add_generation_prompt %}{{- '<|im_start|>assistant\n' }}{%- if enable_thinking is defined and enable_thinking is false %}{{- '<think>\n\n</think>\n\n' }}{%- endif %}{%- endif %}",
-  "clean_up_tokenization_spaces": false,
-  "eos_token": "<|im_end|>",
-  "errors": "replace",
-  "extra_special_tokens": {},
-  "model_max_length": 131072,
-  "pad_token": "<|endoftext|>",
-  "split_special_tokens": false,
-  "tokenizer_class": "Qwen2Tokenizer",
-  "unk_token": null
-}