Added Solution 2

added_tokens.json

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+}

config.json

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+  "architectures": [
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+  ],
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+  "attention_dropout": 0.0,
+  "auto_map": {
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+  },  
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+  "eos_token_id": [
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+    128008,
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+  ],
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+  "hidden_size": 4096,
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+  "image_size": [
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+  "mm_projector_type": "spp",
+  "model_type": "lamed_llama",
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+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "patch_size": [
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+  "proj_layer_type": "mlp",
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+  "proj_pooling_type": "spatial",
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+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
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+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.43.4",
+  "use_cache": true,
+  "vision_select_feature": "patch",
+  "vision_select_layer": -1,
+  "vision_tower": "vit3d",
+  "vocab_size": 128257
+}

configuration_m3d_lamed.py

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+from transformers import LlamaConfig
+
+
+class LamedConfig(LlamaConfig):
+    model_type = "lamed_llama"

generation_config.json

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+  "do_sample": true,
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+    128008,
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+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.43.4"
+}

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+  }
+}

modeling_m3d_lamed.py

@@ -0,0 +1,1188 @@
+from __future__ import annotations
+from typing import Union
+from transformers import LlamaConfig, LlamaModel, LlamaForCausalLM
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.generation.utils import GenerateOutput
+from .configuration_m3d_lamed import LamedConfig
+from abc import ABC, abstractmethod
+from torch import Tensor
+import math
+from typing import Any, Dict, List
+import torch
+import torch.nn as nn
+from typing import Optional, Tuple, Type
+from monai.networks.blocks import PatchEmbed
+import numpy as np
+import torch.nn.functional as F
+
+from einops import rearrange
+from einops.layers.torch import Rearrange
+from collections.abc import Sequence
+from monai.networks.blocks.patchembedding import PatchEmbeddingBlock
+from monai.networks.blocks.transformerblock import TransformerBlock
+from monai.networks.nets import ViT
+
+
+
+
+
+class LayerNorm2d(nn.Module):
+    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / torch.sqrt(s + self.eps)
+        x = self.weight[:, None, None] * x + self.bias[:, None, None]
+        return x
+
+
+class MLPBlock(nn.Module):
+    def __init__(
+            self,
+            embedding_dim: int,
+            mlp_dim: int,
+            act: Type[nn.Module] = nn.GELU,
+    ) -> None:
+        super().__init__()
+        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
+        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
+        self.act = act()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.lin2(self.act(self.lin1(x)))
+
+
+class TwoWayTransformer(nn.Module):
+    def __init__(
+            self,
+            depth: int,
+            embedding_dim: int,
+            num_heads: int,
+            mlp_dim: int,
+            activation: Type[nn.Module] = nn.ReLU,
+            attention_downsample_rate: int = 2,
+    ) -> None:
+        """
+        A transformer decoder that attends to an input image using
+        queries whose positional embedding is supplied.
+
+        Args:
+          depth (int): number of layers in the transformer
+          embedding_dim (int): the channel dimension for the input embeddings
+          num_heads (int): the number of heads for multihead attention. Must
+            divide embedding_dim
+          mlp_dim (int): the channel dimension internal to the MLP block
+          activation (nn.Module): the activation to use in the MLP block
+        """
+        super().__init__()
+        self.depth = depth
+        self.embedding_dim = embedding_dim
+        self.num_heads = num_heads
+        self.mlp_dim = mlp_dim
+        self.layers = nn.ModuleList()
+
+        for i in range(depth):
+            self.layers.append(
+                TwoWayAttentionBlock(
+                    embedding_dim=embedding_dim,
+                    num_heads=num_heads,
+                    mlp_dim=mlp_dim,
+                    activation=activation,
+                    attention_downsample_rate=attention_downsample_rate,
+                    skip_first_layer_pe=(i == 0),
+                )
+            )
+
+        self.final_attn_token_to_image = Attention(
+            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+        )
+        self.norm_final_attn = nn.LayerNorm(embedding_dim)
+
+    def forward(
+            self,
+            image_embedding: Tensor,
+            image_pe: Tensor,
+            point_embedding: Tensor,
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        Args:
+          image_embedding (torch.Tensor): image to attend to. Should be shape
+            B x embedding_dim x h x w for any h and w.
+          image_pe (torch.Tensor): the positional encoding to add to the image. Must
+            have the same shape as image_embedding.
+          point_embedding (torch.Tensor): the embedding to add to the query points.
+            Must have shape B x N_points x embedding_dim for any N_points.
+
+        Returns:
+          torch.Tensor: the processed point_embedding
+          torch.Tensor: the processed image_embedding
+        """
+        # BxCxHxW -> BxHWxC == B x N_image_tokens x C
+        bs, c, h, w, d = image_embedding.shape
+        image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
+        image_pe = image_pe.flatten(2).permute(0, 2, 1)
+
+        # Prepare queries
+        queries = point_embedding
+        keys = image_embedding
+
+        # Apply transformer blocks and final layernorm
+        for layer in self.layers:
+            queries, keys = layer(
+                queries=queries,
+                keys=keys,
+                query_pe=point_embedding,
+                key_pe=image_pe,
+            )
+
+        # Apply the final attention layer from the points to the image
+        q = queries + point_embedding
+        k = keys + image_pe
+        attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
+        queries = queries + attn_out
+        queries = self.norm_final_attn(queries)
+
+        return queries, keys
+
+
+class TwoWayAttentionBlock(nn.Module):
+    def __init__(
+            self,
+            embedding_dim: int,
+            num_heads: int,
+            mlp_dim: int = 2048,
+            activation: Type[nn.Module] = nn.ReLU,
+            attention_downsample_rate: int = 2,
+            skip_first_layer_pe: bool = False,
+    ) -> None:
+        """
+        A transformer block with four layers: (1) self-attention of sparse
+        inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
+        block on sparse inputs, and (4) cross attention of dense inputs to sparse
+        inputs.
+
+        Arguments:
+          embedding_dim (int): the channel dimension of the embeddings
+          num_heads (int): the number of heads in the attention layers
+          mlp_dim (int): the hidden dimension of the mlp block
+          activation (nn.Module): the activation of the mlp block
+          skip_first_layer_pe (bool): skip the PE on the first layer
+        """
+        super().__init__()
+        self.self_attn = Attention(embedding_dim, num_heads)
+        self.norm1 = nn.LayerNorm(embedding_dim)
+
+        self.cross_attn_token_to_image = Attention(
+            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+        )
+        self.norm2 = nn.LayerNorm(embedding_dim)
+
+        self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
+        self.norm3 = nn.LayerNorm(embedding_dim)
+
+        self.norm4 = nn.LayerNorm(embedding_dim)
+        self.cross_attn_image_to_token = Attention(
+            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+        )
+
+        self.skip_first_layer_pe = skip_first_layer_pe
+
+    def forward(
+            self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
+    ) -> Tuple[Tensor, Tensor]:
+        # Self attention block
+        if self.skip_first_layer_pe:
+            queries = self.self_attn(q=queries, k=queries, v=queries)
+        else:
+            q = queries + query_pe
+            attn_out = self.self_attn(q=q, k=q, v=queries)
+            queries = queries + attn_out
+        queries = self.norm1(queries)
+
+        # Cross attention block, tokens attending to image embedding
+        q = queries + query_pe
+        k = keys + key_pe
+        attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
+        queries = queries + attn_out
+        queries = self.norm2(queries)
+
+        # MLP block
+        mlp_out = self.mlp(queries)
+        queries = queries + mlp_out
+        queries = self.norm3(queries)
+
+        # Cross attention block, image embedding attending to tokens
+        q = queries + query_pe
+        k = keys + key_pe
+        attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
+        keys = keys + attn_out
+        keys = self.norm4(keys)
+
+        return queries, keys
+
+
+class Attention(nn.Module):
+    """
+    An attention layer that allows for downscaling the size of the embedding
+    after projection to queries, keys, and values.
+    """
+
+    def __init__(
+            self,
+            embedding_dim: int,
+            num_heads: int,
+            downsample_rate: int = 1,
+    ) -> None:
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.internal_dim = embedding_dim // downsample_rate
+        self.num_heads = num_heads
+        assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."
+
+        self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.k_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.v_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
+
+    def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
+        b, n, c = x.shape
+        x = x.reshape(b, n, num_heads, c // num_heads)
+        return x.transpose(1, 2)  # B x N_heads x N_tokens x C_per_head
+
+    def _recombine_heads(self, x: Tensor) -> Tensor:
+        b, n_heads, n_tokens, c_per_head = x.shape
+        x = x.transpose(1, 2)
+        return x.reshape(b, n_tokens, n_heads * c_per_head)  # B x N_tokens x C
+
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
+        # Input projections
+        q = self.q_proj(q)
+        k = self.k_proj(k)
+        v = self.v_proj(v)
+
+        # Separate into heads
+        q = self._separate_heads(q, self.num_heads)
+        k = self._separate_heads(k, self.num_heads)
+        v = self._separate_heads(v, self.num_heads)
+
+        # Attention
+        _, _, _, c_per_head = q.shape
+        attn = q @ k.permute(0, 1, 3, 2)  # B x N_heads x N_tokens x N_tokens
+        attn = attn / math.sqrt(c_per_head)
+        attn = torch.softmax(attn, dim=-1)
+
+        # Get output
+        out = attn @ v
+        out = self._recombine_heads(out)
+        out = self.out_proj(out)
+
+        return out
+
+
+
+# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
+class ImageEncoderViT(nn.Module):
+    def __init__(
+            self,
+            img_size: int = 1024,
+            patch_size: int = 16,
+            in_chans: int = 1,
+            embed_dim: int = 768,
+            depth: int = 12,
+            num_heads: int = 12,
+            mlp_ratio: float = 4.0,
+            out_chans: int = 256,
+            qkv_bias: bool = True,
+            norm_layer: Type[nn.Module] = nn.LayerNorm,
+            act_layer: Type[nn.Module] = nn.GELU,
+            use_abs_pos: bool = True,
+            use_rel_pos: bool = False,
+            rel_pos_zero_init: bool = True,
+            window_size: int = 0,
+            global_attn_indexes: Tuple[int, ...] = (),
+    ) -> None:
+        """
+        Args:
+            img_size (int): Input image size.
+            patch_size (int): Patch size.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+            depth (int): Depth of ViT.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_abs_pos (bool): If True, use absolute positional embeddings.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks.
+            global_attn_indexes (list): Indexes for blocks using global attention.
+        """
+        super().__init__()
+        self.img_size = img_size
+
+        # self.patch_embed = PatchEmbed(
+        #     kernel_size=(patch_size, patch_size),
+        #     stride=(patch_size, patch_size),
+        #     in_chans=in_chans,
+        #     embed_dim=embed_dim,
+        # )
+
+        self.patch_embed = PatchEmbed(
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            spatial_dims=3,
+        )
+
+        self.pos_embed: Optional[nn.Parameter] = None
+        if use_abs_pos:
+            # Initialize absolute positional embedding with pretrain image size.
+            self.pos_embed = nn.Parameter(
+                torch.zeros(1, img_size // patch_size, img_size // patch_size, img_size // patch_size, embed_dim)
+            )
+
+        self.blocks = nn.ModuleList()
+        for i in range(depth):
+            block = Block(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                use_rel_pos=use_rel_pos,
+                rel_pos_zero_init=rel_pos_zero_init,
+                window_size=window_size if i not in global_attn_indexes else 0,
+                input_size=(img_size // patch_size, img_size // patch_size),
+            )
+            self.blocks.append(block)
+
+        self.neck = nn.Sequential(
+            nn.Conv2d(
+                embed_dim,
+                out_chans,
+                kernel_size=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+            nn.Conv2d(
+                out_chans,
+                out_chans,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.patch_embed(x)
+        print('patch embedded shape: ', x.shape)  # embedded: [8, 768, 6, 6, 6]
+        if self.pos_embed is not None:
+            x = x + self.pos_embed
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x = self.neck(x.permute(0, 3, 1, 2))
+
+        return x
+
+
+class Block(nn.Module):
+    """Transformer blocks with support of window attention and residual propagation blocks"""
+
+    def __init__(
+            self,
+            dim: int,
+            num_heads: int,
+            mlp_ratio: float = 4.0,
+            qkv_bias: bool = True,
+            norm_layer: Type[nn.Module] = nn.LayerNorm,
+            act_layer: Type[nn.Module] = nn.GELU,
+            use_rel_pos: bool = False,
+            rel_pos_zero_init: bool = True,
+            window_size: int = 0,
+            input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks. If it equals 0, then
+                use global attention.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
+        """
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention2(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            use_rel_pos=use_rel_pos,
+            rel_pos_zero_init=rel_pos_zero_init,
+            input_size=input_size if window_size == 0 else (window_size, window_size),
+        )
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
+
+        self.window_size = window_size
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shortcut = x
+        x = self.norm1(x)
+        # Window partition
+        if self.window_size > 0:
+            H, W = x.shape[1], x.shape[2]
+            x, pad_hw = window_partition(x, self.window_size)
+
+        x = self.attn(x)
+        # Reverse window partition
+        if self.window_size > 0:
+            x = window_unpartition(x, self.window_size, pad_hw, (H, W))
+
+        x = shortcut + x
+        x = x + self.mlp(self.norm2(x))
+
+        return x
+
+
+class Attention2(nn.Module):
+    """Multi-head Attention block with relative position embeddings."""
+
+    def __init__(
+            self,
+            dim: int,
+            num_heads: int = 8,
+            qkv_bias: bool = True,
+            use_rel_pos: bool = False,
+            rel_pos_zero_init: bool = True,
+            input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads.
+            qkv_bias (bool):  If True, add a learnable bias to query, key, value.
+            rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
+        """
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.proj = nn.Linear(dim, dim)
+
+        self.use_rel_pos = use_rel_pos
+        if self.use_rel_pos:
+            assert (
+                    input_size is not None
+            ), "Input size must be provided if using relative positional encoding."
+            # initialize relative positional embeddings
+            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
+            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, H, W, _ = x.shape
+        # qkv with shape (3, B, nHead, H * W, C)
+        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        # q, k, v with shape (B * nHead, H * W, C)
+        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
+
+        attn = (q * self.scale) @ k.transpose(-2, -1)
+
+        if self.use_rel_pos:
+            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
+
+        attn = attn.softmax(dim=-1)
+        x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
+        x = self.proj(x)
+
+        return x
+
+
+def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
+    """
+    Partition into non-overlapping windows with padding if needed.
+    Args:
+        x (tensor): input tokens with [B, H, W, C].
+        window_size (int): window size.
+
+    Returns:
+        windows: windows after partition with [B * num_windows, window_size, window_size, C].
+        (Hp, Wp): padded height and width before partition
+    """
+    B, H, W, C = x.shape
+
+    pad_h = (window_size - H % window_size) % window_size
+    pad_w = (window_size - W % window_size) % window_size
+    if pad_h > 0 or pad_w > 0:
+        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
+    Hp, Wp = H + pad_h, W + pad_w
+
+    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows, (Hp, Wp)
+
+
+def window_unpartition(windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]) -> torch.Tensor:
+    """
+    Window unpartition into original sequences and removing padding.
+    Args:
+        windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
+        window_size (int): window size.
+        pad_hw (Tuple): padded height and width (Hp, Wp).
+        hw (Tuple): original height and width (H, W) before padding.
+
+    Returns:
+        x: unpartitioned sequences with [B, H, W, C].
+    """
+    Hp, Wp = pad_hw
+    H, W = hw
+    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
+    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
+
+    if Hp > H or Wp > W:
+        x = x[:, :H, :W, :].contiguous()
+    return x
+
+
+def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
+    """
+    Get relative positional embeddings according to the relative positions of
+        query and key sizes.
+    Args:
+        q_size (int): size of query q.
+        k_size (int): size of key k.
+        rel_pos (Tensor): relative position embeddings (L, C).
+
+    Returns:
+        Extracted positional embeddings according to relative positions.
+    """
+    max_rel_dist = int(2 * max(q_size, k_size) - 1)
+    # Interpolate rel pos if needed.
+    if rel_pos.shape[0] != max_rel_dist:
+        # Interpolate rel pos.
+        rel_pos_resized = F.interpolate(
+            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
+            size=max_rel_dist,
+            mode="linear",
+        )
+        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
+    else:
+        rel_pos_resized = rel_pos
+
+    # Scale the coords with short length if shapes for q and k are different.
+    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
+    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
+    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
+
+    return rel_pos_resized[relative_coords.long()]
+
+
+def add_decomposed_rel_pos(
+        attn: torch.Tensor,
+        q: torch.Tensor,
+        rel_pos_h: torch.Tensor,
+        rel_pos_w: torch.Tensor,
+        q_size: Tuple[int, int],
+        k_size: Tuple[int, int],
+) -> torch.Tensor:
+    """
+    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
+    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950
+    Args:
+        attn (Tensor): attention map.
+        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
+        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
+        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
+        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
+        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
+
+    Returns:
+        attn (Tensor): attention map with added relative positional embeddings.
+    """
+    q_h, q_w = q_size
+    k_h, k_w = k_size
+    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
+    Rw = get_rel_pos(q_w, k_w, rel_pos_w)
+
+    B, _, dim = q.shape
+    r_q = q.reshape(B, q_h, q_w, dim)
+    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
+    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
+
+    attn = (
+            attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
+    ).view(B, q_h * q_w, k_h * k_w)
+
+    return attn
+
+
+
+
+
+
+
+
+
+
+
+class IdentityMap(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, *args, **kwargs):
+        return x
+
+    @property
+    def config(self):
+        return {"mm_projector_type": 'identity'}
+
+
+class SpatialPoolingProjector(nn.Module):
+    def __init__(self, image_size, patch_size, in_dim, out_dim, layer_type, layer_num, pooling_type='spatial', pooling_size=2):
+        super().__init__()
+        self.in_dim = in_dim
+        self.pooling_size = pooling_size
+
+        self.num_patches_pre = [img // pch for img, pch in zip(image_size, patch_size)]
+        self.num_patches_post = [num // pooling_size for num in self.num_patches_pre]
+
+        if layer_type == 'linear':
+            depth = int(layer_num)
+            modules = [nn.Linear(in_dim, out_dim)]
+            for _ in range(1, depth):
+                modules.append(nn.Linear(out_dim, out_dim))
+            self.projector = nn.Sequential(*modules)
+        elif layer_type == 'mlp':
+            depth = int(layer_num)
+            modules = [nn.Linear(in_dim, out_dim)]
+            for _ in range(1, depth):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(out_dim, out_dim))
+            self.projector = nn.Sequential(*modules)
+        else:
+            print("Projector error!")
+
+        self.pooling_type = pooling_type
+
+    def forward(self, x):
+        B = x.shape[0] # B*N*D
+
+        if self.pooling_type == 'spatial':
+            to_3d = Rearrange("b (p1 p2 p3) d -> b d p1 p2 p3", b=B, d=self.in_dim, p1=self.num_patches_pre[0], p2=self.num_patches_pre[1], p3=self.num_patches_pre[2])
+            x = to_3d(x)
+            x = F.avg_pool3d(x, kernel_size=self.pooling_size, stride=self.pooling_size)
+            to_seq = Rearrange("b d p1 p2 p3 -> b (p1 p2 p3) d", b=B, d=self.in_dim, p1=self.num_patches_post[0], p2=self.num_patches_post[1], p3=self.num_patches_post[2])
+            x = to_seq(x)
+        elif self.pooling_type == 'sequence':
+            x = x.permute(0, 2, 1) #b d n
+            x = F.avg_pool1d(x, kernel_size=self.pooling_size**3, stride=self.pooling_size**3)
+            x = x.permute(0, 2, 1) #b n d
+
+        x = rearrange(x, "b n d -> (b n) d")
+        x = self.projector(x)
+        x = rearrange(x, "(b n) d -> b n d", b=B)
+
+        return x
+
+    @property
+    def proj_out_num(self):
+        num = 1
+        for n in self.num_patches_post:
+            num *= n
+        return num
+
+
+class Minigpt(nn.Module):
+    def __init__(self, config=None):
+        super(Minigpt, self).__init__()
+        # c*4 is the input size, and c is the output size for the linear layer
+        inc, ouc = config.mm_hidden_size, config.hidden_size
+        self.linear = nn.Linear(inc * 4, ouc)
+
+    def forward(self, x):
+        # x is the input tensor with shape [b, num_tokens, c]
+        b, num_tokens, c = x.shape
+
+        # Check if num_tokens is divisible by 4
+        if num_tokens % 4 != 0:
+            raise ValueError("num_tokens must be divisible by 4")
+
+        # Reshape x to [b, num_tokens/4, c*4]
+        x = x.view(b, num_tokens // 4, c * 4)
+
+        # Apply the linear transformation
+        x = self.linear(x)
+        return x
+
+
+class Vanilla(nn.Module):
+    def __init__(self, config=None):
+        super(Vanilla, self).__init__()
+        # c*4 is the input size, and c is the output size for the linear layer
+        inc, ouc = config.mm_hidden_size, config.hidden_size
+        self.linear = nn.Linear(inc * 4, ouc)
+
+    def forward(self, x):
+        b, num_tokens, c = x.shape
+
+        # Check if num_tokens is divisible by 4
+        if num_tokens % 4 != 0:
+            raise ValueError("num_tokens must be divisible by 4")
+
+        # First, reshape to [b, num_tokens//4, 4, c]
+        x = x.view(b, num_tokens // 4, 4, c)
+
+        # Then, permute to interleave the tokens
+        x = x.permute(0, 1, 3, 2).contiguous()
+
+        # Finally, reshape to [b, num_tokens//4, c*4] to interleave features of 4 tokens
+        x = x.view(b, num_tokens // 4, c * 4)
+
+        # Apply the linear transformation
+        x = self.linear(x)
+        return x
+
+
+def build_mm_projector(config, delay_load=False, **kwargs):
+    projector_type = getattr(config, 'mm_projector_type')
+
+    if projector_type == 'linear':
+        return nn.Linear(config.mm_hidden_size, config.hidden_size)
+
+
+    elif projector_type == 'spp':
+        return SpatialPoolingProjector(image_size=config.image_size,
+                                        patch_size=config.patch_size,
+                                        in_dim=config.mm_hidden_size,
+                                        out_dim=config.hidden_size,
+                                        layer_type=config.proj_layer_type,
+                                        layer_num=config.proj_layer_num,
+                                        pooling_type=config.proj_pooling_type,
+                                        pooling_size=config.proj_pooling_size)
+
+
+    elif projector_type == 'identity':
+        return IdentityMap()
+    else:
+        raise ValueError(f'Unknown projector type: {projector_type}')
+
+
+class myViT(nn.Module):
+    """
+    Vision Transformer (ViT), based on: "Dosovitskiy et al.,
+    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>"
+
+    ViT supports Torchscript but only works for Pytorch after 1.8.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        img_size: Sequence[int] | int,
+        patch_size: Sequence[int] | int,
+        hidden_size: int = 768,
+        mlp_dim: int = 3072,
+        num_layers: int = 12,
+        num_heads: int = 12,
+        pos_embed: str = "conv",
+        classification: bool = False,
+        num_classes: int = 2,
+        dropout_rate: float = 0.0,
+        spatial_dims: int = 3,
+        post_activation="Tanh",
+        qkv_bias: bool = False,
+        save_attn: bool = False,
+    ) -> None:
+        """
+        Args:
+            in_channels (int): dimension of input channels.
+            img_size (Union[Sequence[int], int]): dimension of input image.
+            patch_size (Union[Sequence[int], int]): dimension of patch size.
+            hidden_size (int, optional): dimension of hidden layer. Defaults to 768.
+            mlp_dim (int, optional): dimension of feedforward layer. Defaults to 3072.
+            num_layers (int, optional): number of transformer blocks. Defaults to 12.
+            num_heads (int, optional): number of attention heads. Defaults to 12.
+            pos_embed (str, optional): position embedding layer type. Defaults to "conv".
+            classification (bool, optional): bool argument to determine if classification is used. Defaults to False.
+            num_classes (int, optional): number of classes if classification is used. Defaults to 2.
+            dropout_rate (float, optional): faction of the input units to drop. Defaults to 0.0.
+            spatial_dims (int, optional): number of spatial dimensions. Defaults to 3.
+            post_activation (str, optional): add a final acivation function to the classification head
+                when `classification` is True. Default to "Tanh" for `nn.Tanh()`.
+                Set to other values to remove this function.
+            qkv_bias (bool, optional): apply bias to the qkv linear layer in self attention block. Defaults to False.
+            save_attn (bool, optional): to make accessible the attention in self attention block. Defaults to False.
+
+        Examples::
+
+            # for single channel input with image size of (96,96,96), conv position embedding and segmentation backbone
+            >>> net = ViT(in_channels=1, img_size=(96,96,96), pos_embed='conv')
+
+            # for 3-channel with image size of (128,128,128), 24 layers and classification backbone
+            >>> net = ViT(in_channels=3, img_size=(128,128,128), pos_embed='conv', classification=True)
+
+            # for 3-channel with image size of (224,224), 12 layers and classification backbone
+            >>> net = ViT(in_channels=3, img_size=(224,224), pos_embed='conv', classification=True, spatial_dims=2)
+
+        """
+
+        super().__init__()
+
+        if not (0 <= dropout_rate <= 1):
+            raise ValueError("dropout_rate should be between 0 and 1.")
+
+        if hidden_size % num_heads != 0:
+            raise ValueError("hidden_size should be divisible by num_heads.")
+        self.hidden_size = hidden_size
+        self.classification = classification
+        self.patch_embedding = PatchEmbeddingBlock(
+            in_channels=in_channels,
+            img_size=img_size,
+            patch_size=patch_size,
+            hidden_size=hidden_size,
+            num_heads=num_heads,
+            pos_embed=pos_embed,
+            dropout_rate=dropout_rate,
+            spatial_dims=spatial_dims,
+        )
+        self.blocks = nn.ModuleList(
+            [
+                TransformerBlock(hidden_size, mlp_dim, num_heads, dropout_rate, qkv_bias, save_attn)
+                for i in range(num_layers)
+            ]
+        )
+        self.norm = nn.LayerNorm(hidden_size)
+        if self.classification:
+            self.cls_token = nn.Parameter(torch.zeros(1, 1, hidden_size))
+            # if post_activation == "Tanh":
+            #     self.classification_head = nn.Sequential(nn.Linear(hidden_size, num_classes), nn.Tanh())
+            # else:
+            #     self.classification_head = nn.Linear(hidden_size, num_classes)  # type: ignore
+
+    def forward(self, x):
+        x = self.patch_embedding(x)
+        if hasattr(self, "cls_token"):
+            cls_token = self.cls_token.expand(x.shape[0], -1, -1)
+            x = torch.cat((cls_token, x), dim=1)
+        hidden_states_out = []
+        for blk in self.blocks:
+            x = blk(x)
+            hidden_states_out.append(x)
+        x = self.norm(x)
+        # if hasattr(self, "classification_head"):
+        #     x = self.classification_head(x[:, 0])
+        return x, hidden_states_out
+
+
+class ViT3DTower(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.select_layer = config.vision_select_layer
+        self.select_feature = config.vision_select_feature
+
+        self.vision_tower = myViT(
+            in_channels=self.config.image_channel,
+            img_size=self.config.image_size,
+            patch_size=self.config.patch_size,
+            pos_embed="perceptron",
+            spatial_dims=len(self.config.patch_size),
+            classification=True,
+        )
+
+    def forward(self, images):
+        last_feature, hidden_states = self.vision_tower(images)
+        if self.select_layer == -1:
+            image_features = last_feature
+        elif self.select_layer < -1:
+            image_features = hidden_states[self.select_feature]
+        else:
+            raise ValueError(f'Unexpected select layer: {self.select_layer}')
+
+        if self.select_feature == 'patch':
+            image_features = image_features[:, 1:]
+        elif self.select_feature == 'cls_patch':
+            image_features = image_features
+        else:
+            raise ValueError(f'Unexpected select feature: {self.select_feature}')
+
+        return image_features
+
+    @property
+    def dtype(self):
+        return self.vision_tower.dtype
+
+    @property
+    def device(self):
+        return self.vision_tower.device
+
+    @property
+    def hidden_size(self):
+        return self.vision_tower.hidden_size
+
+
+def build_vision_tower(config, **kwargs):
+    vision_tower = getattr(config, 'vision_tower', None)
+    if 'vit3d' in vision_tower.lower():
+        return ViT3DTower(config, **kwargs)
+    else:
+        raise ValueError(f'Unknown vision tower: {vision_tower}')
+
+
+class LamedMetaModel:
+    def __init__(self, config):
+        super(LamedMetaModel, self).__init__(config)
+
+        self.config = config
+
+        if hasattr(config, "vision_tower"):
+            self.vision_tower = build_vision_tower(config)
+            self.mm_projector = build_mm_projector(config)
+
+    def get_vision_tower(self):
+        vision_tower = getattr(self, 'vision_tower', None)
+        return vision_tower
+
+    def initialize_vision_modules(self, model_args):
+        self.config.image_channel = model_args.image_channel
+        self.config.image_size = model_args.image_size
+        self.config.patch_size = model_args.patch_size
+
+        self.config.vision_tower = model_args.vision_tower
+        self.config.vision_select_layer = model_args.vision_select_layer
+        self.config.vision_select_feature = model_args.vision_select_feature
+
+        self.config.mm_projector_type = model_args.mm_projector_type
+        self.config.proj_layer_type = model_args.proj_layer_type
+        self.config.proj_layer_num = model_args.proj_layer_num
+        self.config.proj_pooling_type = model_args.proj_pooling_type
+        self.config.proj_pooling_size = model_args.proj_pooling_size
+
+        # vision tower
+        if self.get_vision_tower() is None:
+            self.vision_tower = build_vision_tower(self.config)
+            # If you have a more robust vision encoder, try freezing the vision tower by requires_grad_(False)
+
+
+        if model_args.pretrain_vision_model is not None:
+            vision_model_weights = torch.load(model_args.pretrain_vision_model, map_location='cpu')
+            self.vision_tower.vision_tower.load_state_dict(vision_model_weights, strict=True)
+
+        self.config.mm_hidden_size = self.vision_tower.hidden_size
+
+        # mm_projector
+        if getattr(self, 'mm_projector', None) is None:
+            self.mm_projector = build_mm_projector(self.config)
+
+        if model_args.pretrain_mm_mlp_adapter is not None:
+            mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
+            def get_w(weights, keyword):
+                return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
+            self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'), strict=True)
+
+
+class LamedMetaForCausalLM(ABC):
+    @abstractmethod
+    def get_model(self):
+        pass
+
+    def get_vision_tower(self):
+        return self.get_model().get_vision_tower()
+
+    def encode_images(self, images):
+        image_features = self.get_model().get_vision_tower()(images)
+        image_features = self.get_model().mm_projector(image_features)
+        return image_features
+
+    def prepare_inputs_for_multimodal(self, input_ids, position_ids, attention_mask, past_key_values, labels, images):
+        vision_tower = self.get_vision_tower()
+        if vision_tower is None or images is None or input_ids.shape[1] == 1:
+            return input_ids, position_ids, attention_mask, past_key_values, None, labels
+        else:
+            image_features = self.encode_images(images)
+            inputs_embeds = self.get_model().embed_tokens(input_ids)
+            inputs_embeds = torch.cat((inputs_embeds[:, :1, :], image_features, inputs_embeds[:, (image_features.shape[1] + 1):, :]), dim=1)
+        return None, position_ids, attention_mask, past_key_values, inputs_embeds, labels
+
+    def initialize_vision_tokenizer(self, model_args, tokenizer):
+        num_new_tokens = model_args.num_new_tokens
+
+        self.resize_token_embeddings(len(tokenizer))
+
+        if num_new_tokens > 0:
+            input_embeddings = self.get_input_embeddings().weight.data
+            output_embeddings = self.get_output_embeddings().weight.data
+
+            input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
+                dim=0, keepdim=True)
+            output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
+                dim=0, keepdim=True)
+
+            input_embeddings[-num_new_tokens:] = input_embeddings_avg
+            output_embeddings[-num_new_tokens:] = output_embeddings_avg
+
+            if model_args.tune_mm_mlp_adapter:
+                for p in self.get_input_embeddings().parameters():
+                    p.requires_grad = True
+                for p in self.get_output_embeddings().parameters():
+                    p.requires_grad = False
+            else:
+                # we add 4 new tokens
+                # if new tokens need input, please train input_embeddings
+                for p in self.get_input_embeddings().parameters():
+                    p.requires_grad = True
+                # if new tokens need predict, please train output_embeddings
+                for p in self.get_output_embeddings().parameters():
+                    p.requires_grad = True
+
+        if model_args.pretrain_mm_mlp_adapter:
+            mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
+            embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
+
+            if input_embeddings.shape == embed_tokens_weight.shape:
+                input_embeddings = embed_tokens_weight
+            elif embed_tokens_weight.shape[0] == num_new_tokens:
+                input_embeddings[-num_new_tokens:] = embed_tokens_weight
+            else:
+                raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
+
+
+class LamedLlamaModel(LamedMetaModel, LlamaModel):
+    config_class = LamedConfig
+    def __init__(self, config: LlamaConfig):
+        super(LamedLlamaModel, self).__init__(config)
+
+
+class LamedLlamaForCausalLM(LamedMetaForCausalLM, LlamaForCausalLM):
+    config_class = LamedConfig
+
+    def __init__(self, config):
+        super(LlamaForCausalLM, self).__init__(config)
+        self.model = LamedLlamaModel(config)
+        self.pretraining_tp = config.pretraining_tp
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_model(self):
+        return self.model
+
+    def forward(
+            self,
+            images: Optional[torch.FloatTensor] = None,
+            input_ids: torch.LongTensor = None,
+            labels: 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,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+
+        input_ids_pre = input_ids
+
+        if inputs_embeds is None:
+            (
+                input_ids,
+                position_ids,
+                attention_mask,
+                past_key_values,
+                inputs_embeds,
+                labels
+            ) = self.prepare_inputs_for_multimodal(
+                input_ids,
+                position_ids,
+                attention_mask,
+                past_key_values,
+                labels,
+                images,
+            )
+
+        return super().forward(
+            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
+        )
+
+    @torch.no_grad()
+    def generate(
+        self,
+        images: Optional[torch.Tensor] = None,
+        inputs: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> Union[GenerateOutput, torch.LongTensor, Any]:
+        position_ids = kwargs.pop("position_ids", None)
+        attention_mask = kwargs.pop("attention_mask", None)
+        if "inputs_embeds" in kwargs:
+            raise NotImplementedError("`inputs_embeds` is not supported")
+
+        if images is not None:
+            (
+                inputs,
+                position_ids,
+                attention_mask,
+                _,
+                inputs_embeds,
+                _
+            ) = self.prepare_inputs_for_multimodal(
+                inputs,
+                position_ids,
+                attention_mask,
+                None,
+                None,
+                images,
+            )
+        else:
+            inputs_embeds = self.get_model().embed_tokens(inputs)
+
+        return super().generate(
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            **kwargs
+        )
+
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
+        images = kwargs.pop("images", None)
+        inputs = super().prepare_inputs_for_generation(input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs)
+        if images is not None:
+            inputs['images'] = images
+        return inputs

special_tokens_map.json

@@ -0,0 +1,26 @@
+{
+  "additional_special_tokens": [
+    {
+      "content": "<im_patch>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    }
+  ],
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|eot_id|>"
+}

tokenizer_config.json

@@ -0,0 +1,2075 @@
+{
+  "added_tokens_decoder": {
+    "128000": {
+      "content": "<|begin_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128001": {
+      "content": "<|end_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128002": {
+      "content": "<|reserved_special_token_0|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128003": {
+      "content": "<|reserved_special_token_1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128004": {
+      "content": "<|finetune_right_pad_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128005": {
+      "content": "<|reserved_special_token_2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128006": {
+      "content": "<|start_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128007": {
+      "content": "<|end_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128008": {
+      "content": "<|eom_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128009": {
+      "content": "<|eot_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128010": {
+      "content": "<|python_tag|>",
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128011": {
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128012": {
+      "content": "<|reserved_special_token_4|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "128013": {
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+      "special": true
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+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|eot_id|>",
+  "padding_side": "right",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}