Add model

chat_template.json

@@ -0,0 +1,3 @@
+{
+  "chat_template": "{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}"
+}

configuration_phi3_v.py

@@ -0,0 +1,218 @@
+# coding=utf-8
+# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""" Phi-3-V model configuration"""
+
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+PHI3V_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "microsoft/Phi-3-vision-128k-instruct": "https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/config.json",
+    "microsoft/Phi-3.5-vision-instruct": "https://huggingface.co/microsoft/Phi-3.5-vision-instruct/resolve/main/config.json",
+}
+
+
+class Phi3VConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Phi3VModel`]. It is used to instantiate a Phi-3
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the
+    [microsoft/Phi-3-vision-128k-instruct](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32064):
+            Vocabulary size of the Phi-3-V model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Phi3VModel`].
+        hidden_size (`int`, *optional*, defaults to 3072):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 8192):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        resid_pdrop (`float`, *optional*, defaults to 0.0):
+            Dropout probability for mlp outputs.
+        embd_pdrop (`int`, *optional*, defaults to 0.0):
+            The dropout ratio for the embeddings.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio after computing the attention scores.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used with.
+        original_max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model was trained with. This is used to determine the size of the
+            original RoPE embeddings when using long scaling.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon value used for the RMSNorm.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`dict`, *optional*):
+            The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
+            contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be either `su` or `yarn` and
+            the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
+            divided by the number of attention heads divided by 2.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 32000):
+            The id of the "end-of-sequence" token.
+        pad_token_id (`int`, *optional*, defaults to 32000):
+            The id of the padding token.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If `None`, no sliding window is applied.
+        embd_layer (`str`, *optional*, defaults to `"default"`):
+            The embedding layer to use. Can be either `"default"` or `"image"`. "default" uses the standard embedding for text. 
+
+    Example:
+
+    ```python
+    >>> from transformers import Phi3VModel, Phi3VConfig
+
+    >>> # Initializing a Phi-3-V style configuration
+    >>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-vision-128k-instruct")
+
+    >>> # Initializing a model from the configuration
+    >>> model = Phi3VModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "phi3_v"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32064,
+        hidden_size=3072,
+        intermediate_size=8192,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        resid_pdrop=0.0,
+        embd_pdrop=0.0,
+        attention_dropout=0.0,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        original_max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        bos_token_id=1,
+        eos_token_id=32000,
+        pad_token_id=32000,
+        sliding_window=None,
+        embd_layer: str = "default",
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attention_dropout = attention_dropout
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.original_max_position_embeddings = original_max_position_embeddings
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.sliding_window = sliding_window
+        self.embd_layer = embd_layer
+
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
+        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["su", "yarn"]:
+            raise ValueError(f"`rope_scaling`'s type field must be one of ['su', 'yarn'], got {rope_scaling_type}")
+        if not (
+            isinstance(rope_scaling_short_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
+            )
+        if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
+            )
+        if not (
+            isinstance(rope_scaling_long_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
+            )
+        if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
+            )

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 32000,
+  "transformers_version": "4.45.0"
+}

openvino_config.json

@@ -0,0 +1,28 @@
+{
+  "compression": null,
+  "dtype": "int4",
+  "input_info": null,
+  "optimum_version": "1.24.0.dev0",
+  "quantization_config": {
+    "all_layers": null,
+    "backup_precision": null,
+    "bits": 4,
+    "dataset": "contextual",
+    "gptq": null,
+    "group_size": 128,
+    "ignored_scope": null,
+    "lora_correction": null,
+    "num_samples": null,
+    "processor": null,
+    "quant_method": "awq",
+    "ratio": 1.0,
+    "scale_estimation": null,
+    "sensitivity_metric": null,
+    "sym": false,
+    "tokenizer": null,
+    "trust_remote_code": true,
+    "weight_format": "int4"
+  },
+  "save_onnx_model": false,
+  "transformers_version": "4.45.0"
+}

openvino_detokenizer.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a60c90df70041d2a3db95701b3bc410f557a9d2568b2055ce20b3003c778c3f9
+size 340120

openvino_detokenizer.xml

@@ -0,0 +1,416 @@
+<?xml version="1.0"?>
+<net name="detokenizer" version="11">
+	<layers>
+		<layer id="0" name="Parameter_122" type="Parameter" version="opset1">
+			<data shape="?,?" element_type="i64" />
+			<output>
+				<port id="0" precision="I64" names="Parameter_122">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="1" name="Convert_149" type="Convert" version="opset1">
+			<data destination_type="i32" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="2" name="Constant_89" type="Const" version="opset1">
+			<data element_type="u8" shape="339905" offset="0" size="339905" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>339905</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="3" name="StringTensorUnpack_90" type="StringTensorUnpack" version="extension">
+			<data mode="begins_ends" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>339905</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="3" precision="U8">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="4" name="Constant_126" type="Const" version="opset1">
+			<data element_type="i32" shape="47" offset="339905" size="188" />
+			<output>
+				<port id="0" precision="I32">
+					<dim>47</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="5" name="Constant_124" type="Const" version="opset1">
+			<data element_type="i32" shape="1" offset="340093" size="4" />
+			<output>
+				<port id="0" precision="I32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="6" name="Constant_123" type="Const" version="opset1">
+			<data element_type="i32" shape="1" offset="340097" size="4" />
+			<output>
+				<port id="0" precision="I32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="7" name="Constant_125" type="Const" version="opset1">
+			<data element_type="i32" shape="1" offset="340101" size="4" />
+			<output>
+				<port id="0" precision="I32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="8" name="Constant_128" type="Const" version="opset1">
+			<data element_type="i64" shape="1" offset="340105" size="8" />
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="9" name="Slice_127" type="Slice" version="opset8">
+			<input>
+				<port id="0" precision="I32">
+					<dim>47</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>1</dim>
+				</port>
+				<port id="2" precision="I32">
+					<dim>1</dim>
+				</port>
+				<port id="3" precision="I32">
+					<dim>1</dim>
+				</port>
+				<port id="4" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="5" precision="I32">
+					<dim>47</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="10" name="VocabDecoder_129" type="VocabDecoder" version="extension">
+			<data skip_tokens="" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="3" precision="U8">
+					<dim>-1</dim>
+				</port>
+				<port id="4" precision="I32">
+					<dim>47</dim>
+				</port>
+			</input>
+			<output>
+				<port id="5" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="6" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="7" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="8" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="9" precision="U8">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="11" name="Constant_131" type="Const" version="opset1">
+			<data element_type="u8" shape="3" offset="340113" size="3" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="12" name="Constant_133" type="Const" version="opset1">
+			<data element_type="u8" shape="1" offset="340116" size="1" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="13" name="RegexNormalization_134" type="RegexNormalization" version="extension">
+			<data global_replace="true" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="U8">
+					<dim>-1</dim>
+				</port>
+				<port id="3" precision="U8">
+					<dim>3</dim>
+				</port>
+				<port id="4" precision="U8">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="5" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="6" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="7" precision="U8">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="14" name="ByteFallback_135" type="ByteFallback" version="extension">
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="U8">
+					<dim>-1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="3" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="4" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="5" precision="U8">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="15" name="FuzeRagged_136" type="FuzeRagged" version="extension">
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="3" precision="I32">
+					<dim>-1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="4" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="5" precision="I32">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="16" name="Constant_138" type="Const" version="opset1">
+			<data element_type="u8" shape="2" offset="340117" size="2" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="17" name="Constant_140" type="Const" version="opset1">
+			<data element_type="u8" shape="0" offset="340119" size="1" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>0</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="18" name="RegexNormalization_141" type="RegexNormalization" version="extension">
+			<data global_replace="true" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="U8">
+					<dim>-1</dim>
+				</port>
+				<port id="3" precision="U8">
+					<dim>2</dim>
+				</port>
+				<port id="4" precision="U8">
+					<dim>0</dim>
+				</port>
+			</input>
+			<output>
+				<port id="5" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="6" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="7" precision="U8">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="19" name="UTF8Validate_142" type="UTF8Validate" version="extension">
+			<data replace_mode="true" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="U8">
+					<dim>-1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="3" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="4" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="5" precision="U8">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="20" name="StringTensorPack_143" type="StringTensorPack" version="extension">
+			<data mode="begins_ends" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="U8">
+					<dim>-1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="3" precision="STRING" names="string_output">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="21" name="Result_144" type="Result" version="opset1">
+			<input>
+				<port id="0" precision="STRING">
+					<dim>-1</dim>
+				</port>
+			</input>
+		</layer>
+	</layers>
+	<edges>
+		<edge from-layer="0" from-port="0" to-layer="1" to-port="0" />
+		<edge from-layer="1" from-port="1" to-layer="10" to-port="0" />
+		<edge from-layer="2" from-port="0" to-layer="3" to-port="0" />
+		<edge from-layer="3" from-port="3" to-layer="10" to-port="3" />
+		<edge from-layer="3" from-port="2" to-layer="10" to-port="2" />
+		<edge from-layer="3" from-port="1" to-layer="10" to-port="1" />
+		<edge from-layer="4" from-port="0" to-layer="9" to-port="0" />
+		<edge from-layer="5" from-port="0" to-layer="9" to-port="1" />
+		<edge from-layer="6" from-port="0" to-layer="9" to-port="2" />
+		<edge from-layer="7" from-port="0" to-layer="9" to-port="3" />
+		<edge from-layer="8" from-port="0" to-layer="9" to-port="4" />
+		<edge from-layer="9" from-port="5" to-layer="10" to-port="4" />
+		<edge from-layer="10" from-port="7" to-layer="13" to-port="0" />
+		<edge from-layer="10" from-port="8" to-layer="13" to-port="1" />
+		<edge from-layer="10" from-port="9" to-layer="13" to-port="2" />
+		<edge from-layer="10" from-port="6" to-layer="15" to-port="1" />
+		<edge from-layer="10" from-port="5" to-layer="15" to-port="0" />
+		<edge from-layer="11" from-port="0" to-layer="13" to-port="3" />
+		<edge from-layer="12" from-port="0" to-layer="13" to-port="4" />
+		<edge from-layer="13" from-port="6" to-layer="14" to-port="1" />
+		<edge from-layer="13" from-port="7" to-layer="14" to-port="2" />
+		<edge from-layer="13" from-port="5" to-layer="14" to-port="0" />
+		<edge from-layer="14" from-port="3" to-layer="15" to-port="2" />
+		<edge from-layer="14" from-port="4" to-layer="15" to-port="3" />
+		<edge from-layer="14" from-port="5" to-layer="18" to-port="2" />
+		<edge from-layer="15" from-port="4" to-layer="18" to-port="0" />
+		<edge from-layer="15" from-port="5" to-layer="18" to-port="1" />
+		<edge from-layer="16" from-port="0" to-layer="18" to-port="3" />
+		<edge from-layer="17" from-port="0" to-layer="18" to-port="4" />
+		<edge from-layer="18" from-port="5" to-layer="19" to-port="0" />
+		<edge from-layer="18" from-port="6" to-layer="19" to-port="1" />
+		<edge from-layer="18" from-port="7" to-layer="19" to-port="2" />
+		<edge from-layer="19" from-port="3" to-layer="20" to-port="0" />
+		<edge from-layer="19" from-port="4" to-layer="20" to-port="1" />
+		<edge from-layer="19" from-port="5" to-layer="20" to-port="2" />
+		<edge from-layer="20" from-port="3" to-layer="21" to-port="0" />
+	</edges>
+	<rt_info>
+		<add_attention_mask value="True" />
+		<add_prefix_space />
+		<add_special_tokens value="True" />
+		<bos_token_id value="1" />
+		<chat_template value="{% for message in messages %}{{'&lt;|' + message['role'] + '|>' + '&#10;' + message['content'] + '&lt;|end|>&#10;' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '&lt;|assistant|>&#10;' -}}{% endif %}" />
+		<clean_up_tokenization_spaces />
+		<detokenizer_input_type value="i64" />
+		<eos_token_id value="32000" />
+		<handle_special_tokens_with_re value="False" />
+		<number_of_inputs value="1" />
+		<openvino_tokenizers_version value="2025.0.0.0rc2" />
+		<openvino_version value="2025.0.0rc2" />
+		<original_tokenizer_class value="&lt;class 'transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast'>" />
+		<pad_token_id value="32000" />
+		<sentencepiece_version value="0.2.0" />
+		<skip_special_tokens value="True" />
+		<streaming_detokenizer value="False" />
+		<tiktoken_version value="0.8.0" />
+		<tokenizer_output_type value="i64" />
+		<tokenizers_version value="0.20.1" />
+		<transformers_version value="4.45.0" />
+		<use_max_padding value="False" />
+		<use_sentencepiece_backend value="True" />
+		<utf8_replace_mode value="replace" />
+		<with_detokenizer value="True" />
+	</rt_info>
+</net>

openvino_language_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3a9b850352619797981d9c5b022de81ef6b37dbf5bf1efa149af7e8980e360cb
+size 1983162812

openvino_text_embeddings_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b8295cb8c1b0a2ab714d0d309b47015967e11c2b5205e038abff0d6f229b3bc
+size 98564740

openvino_text_embeddings_model.xml

@@ -0,0 +1,173 @@
+<?xml version="1.0"?>
+<net name="Model9" version="11">
+	<layers>
+		<layer id="0" name="input" type="Parameter" version="opset1">
+			<data shape="?,?" element_type="i64" />
+			<output>
+				<port id="0" precision="I64" names="input">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="1" name="self.weight" type="Const" version="opset1">
+			<data element_type="i8" shape="32064, 3072" offset="0" size="98500608" />
+			<output>
+				<port id="0" precision="I8">
+					<dim>32064</dim>
+					<dim>3072</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="2" name="Convert_240039" type="Convert" version="opset1">
+			<data destination_type="f16" />
+			<input>
+				<port id="0" precision="I8">
+					<dim>32064</dim>
+					<dim>3072</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP16">
+					<dim>32064</dim>
+					<dim>3072</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="3" name="self.weight/scale" type="Const" version="opset1">
+			<data element_type="f16" shape="32064, 1" offset="98500608" size="64128" />
+			<output>
+				<port id="0" precision="FP16">
+					<dim>32064</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="4" name="self.weight/fq_weights_0" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP16">
+					<dim>32064</dim>
+					<dim>3072</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>32064</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>32064</dim>
+					<dim>3072</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="5" name="self.weight/fq_weights_0/convert" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="FP16">
+					<dim>32064</dim>
+					<dim>3072</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>32064</dim>
+					<dim>3072</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="6" name="aten::embedding/Convert" type="Convert" version="opset1">
+			<data destination_type="i32" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="7" name="aten::embedding/Constant" type="Const" version="opset1">
+			<data element_type="i32" shape="" offset="98564736" size="4" />
+			<output>
+				<port id="0" precision="I32" />
+			</output>
+		</layer>
+		<layer id="8" name="aten::embedding/Gather" type="Gather" version="opset8">
+			<data batch_dims="0" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>32064</dim>
+					<dim>3072</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="I32" />
+			</input>
+			<output>
+				<port id="3" precision="FP32" names="inputs_embeds">
+					<dim>-1</dim>
+					<dim>-1</dim>
+					<dim>3072</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="9" name="Result_129532" type="Result" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+					<dim>3072</dim>
+				</port>
+			</input>
+		</layer>
+	</layers>
+	<edges>
+		<edge from-layer="0" from-port="0" to-layer="6" to-port="0" />
+		<edge from-layer="1" from-port="0" to-layer="2" to-port="0" />
+		<edge from-layer="2" from-port="1" to-layer="4" to-port="0" />
+		<edge from-layer="3" from-port="0" to-layer="4" to-port="1" />
+		<edge from-layer="4" from-port="2" to-layer="5" to-port="0" />
+		<edge from-layer="5" from-port="1" to-layer="8" to-port="0" />
+		<edge from-layer="6" from-port="1" to-layer="8" to-port="1" />
+		<edge from-layer="7" from-port="0" to-layer="8" to-port="2" />
+		<edge from-layer="8" from-port="3" to-layer="9" to-port="0" />
+	</edges>
+	<rt_info>
+		<Runtime_version value="2025.0.0-17933-815af98acd8-releases/2025/0" />
+		<conversion_parameters>
+			<framework value="pytorch" />
+			<is_python_object value="True" />
+		</conversion_parameters>
+		<nncf>
+			<friendly_names_were_updated value="True" />
+			<weight_compression>
+				<advanced_parameters value="{'statistics_path': None, 'awq_params': {'subset_size': 32, 'percent_to_apply': 0.002, 'alpha_min': 0.0, 'alpha_max': 1.0, 'steps': 100}, 'scale_estimation_params': {'subset_size': 64, 'initial_steps': 5, 'scale_steps': 5, 'weight_penalty': -1.0}, 'gptq_params': {'damp_percent': 0.1, 'block_size': 128, 'subset_size': 128}, 'lora_correction_params': {'adapter_rank': 8, 'num_iterations': 3, 'apply_regularization': True, 'subset_size': 128, 'use_int8_adapters': True}}" />
+				<all_layers value="False" />
+				<awq value="False" />
+				<backup_mode value="int8_asym" />
+				<gptq value="False" />
+				<group_size value="-1" />
+				<ignored_scope value="[]" />
+				<lora_correction value="False" />
+				<mode value="int8_sym" />
+				<ratio value="1.0" />
+				<scale_estimation value="False" />
+				<sensitivity_metric value="weight_quantization_error" />
+			</weight_compression>
+		</nncf>
+		<optimum>
+			<optimum_intel_version value="1.22.0.dev0+753f84d" />
+			<optimum_version value="1.24.0.dev0" />
+			<pytorch_version value="2.5.0+cpu" />
+			<transformers_version value="4.45.0" />
+		</optimum>
+	</rt_info>
+</net>

openvino_tokenizer.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6c1510cca0328e390459199fa46fde85bc7a72c42a3156d44a9c5b3975daca20
+size 1300299

openvino_tokenizer.xml

@@ -0,0 +1,835 @@
+<?xml version="1.0"?>
+<net name="tokenizer" version="11">
+	<layers>
+		<layer id="0" name="Parameter_1" type="Parameter" version="opset1">
+			<data shape="?" element_type="string" />
+			<output>
+				<port id="0" precision="STRING" names="Parameter_1">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="1" name="Constant_106" type="Const" version="opset1">
+			<data element_type="i32" shape="" offset="0" size="4" />
+			<output>
+				<port id="0" precision="I32" />
+			</output>
+		</layer>
+		<layer id="2" name="Constant_107" type="Const" version="opset1">
+			<data element_type="i32" shape="" offset="4" size="4" />
+			<output>
+				<port id="0" precision="I32" />
+			</output>
+		</layer>
+		<layer id="3" name="Constant_108" type="Const" version="opset1">
+			<data element_type="i32" shape="1" offset="4" size="4" />
+			<output>
+				<port id="0" precision="I32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="4" name="Constant_7" type="Const" version="opset1">
+			<data element_type="i64" shape="" offset="8" size="8" />
+			<output>
+				<port id="0" precision="I64" />
+			</output>
+		</layer>
+		<layer id="5" name="StringTensorUnpack_2" type="StringTensorUnpack" version="extension">
+			<data mode="begins_ends" />
+			<input>
+				<port id="0" precision="STRING">
+					<dim>-1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="3" precision="U8">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="6" name="ShapeOf_3" type="ShapeOf" version="opset3">
+			<data output_type="i64" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="7" name="Constant_4" type="Const" version="opset1">
+			<data element_type="i64" shape="" offset="8" size="8" />
+			<output>
+				<port id="0" precision="I64" />
+			</output>
+		</layer>
+		<layer id="8" name="Constant_5" type="Const" version="opset1">
+			<data element_type="i64" shape="" offset="8" size="8" />
+			<output>
+				<port id="0" precision="I64" />
+			</output>
+		</layer>
+		<layer id="9" name="Gather_6" type="Gather" version="opset8">
+			<data batch_dims="0" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64" />
+				<port id="2" precision="I64" />
+			</input>
+			<output>
+				<port id="3" precision="I64" />
+			</output>
+		</layer>
+		<layer id="10" name="Constant_8" type="Const" version="opset1">
+			<data element_type="i64" shape="" offset="16" size="8" />
+			<output>
+				<port id="0" precision="I64" />
+			</output>
+		</layer>
+		<layer id="11" name="Range_9" type="Range" version="opset4">
+			<data output_type="i32" />
+			<input>
+				<port id="0" precision="I64" />
+				<port id="1" precision="I64" />
+				<port id="2" precision="I64" />
+			</input>
+			<output>
+				<port id="3" precision="I32">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="12" name="Constant_10" type="Const" version="opset1">
+			<data element_type="i64" shape="" offset="16" size="8" />
+			<output>
+				<port id="0" precision="I64" />
+			</output>
+		</layer>
+		<layer id="13" name="Constant_11" type="Const" version="opset1">
+			<data element_type="i64" shape="" offset="16" size="8" />
+			<output>
+				<port id="0" precision="I64" />
+			</output>
+		</layer>
+		<layer id="14" name="Add_12" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="I64" />
+				<port id="1" precision="I64" />
+			</input>
+			<output>
+				<port id="2" precision="I64" />
+			</output>
+		</layer>
+		<layer id="15" name="Constant_13" type="Const" version="opset1">
+			<data element_type="i64" shape="" offset="16" size="8" />
+			<output>
+				<port id="0" precision="I64" />
+			</output>
+		</layer>
+		<layer id="16" name="Range_14" type="Range" version="opset4">
+			<data output_type="i32" />
+			<input>
+				<port id="0" precision="I64" />
+				<port id="1" precision="I64" />
+				<port id="2" precision="I64" />
+			</input>
+			<output>
+				<port id="3" precision="I32">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="17" name="Constant_76" type="Const" version="opset1">
+			<data element_type="u8" shape="1350" offset="24" size="1350" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>1350</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="18" name="SpecialTokensSplit_77" type="SpecialTokensSplit" version="extension">
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="3" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="4" precision="U8">
+					<dim>-1</dim>
+				</port>
+				<port id="5" precision="U8">
+					<dim>1350</dim>
+				</port>
+			</input>
+			<output>
+				<port id="6" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="7" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="8" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="9" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="10" precision="U8">
+					<dim>-1</dim>
+				</port>
+				<port id="11" precision="BOOL">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="19" name="Constant_79" type="Const" version="opset1">
+			<data element_type="u8" shape="13" offset="1374" size="13" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>13</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="20" name="Constant_81" type="Const" version="opset1">
+			<data element_type="u8" shape="5" offset="1387" size="5" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>5</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="21" name="RegexNormalization_82" type="RegexNormalization" version="extension">
+			<data global_replace="true" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="U8">
+					<dim>-1</dim>
+				</port>
+				<port id="3" precision="BOOL">
+					<dim>-1</dim>
+				</port>
+				<port id="4" precision="U8">
+					<dim>13</dim>
+				</port>
+				<port id="5" precision="U8">
+					<dim>5</dim>
+				</port>
+			</input>
+			<output>
+				<port id="6" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="7" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="8" precision="U8">
+					<dim>-1</dim>
+				</port>
+				<port id="9" precision="BOOL">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="22" name="Constant_84" type="Const" version="opset1">
+			<data element_type="u8" shape="1" offset="1392" size="1" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="23" name="Constant_86" type="Const" version="opset1">
+			<data element_type="u8" shape="3" offset="1393" size="3" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="24" name="RegexNormalization_87" type="RegexNormalization" version="extension">
+			<data global_replace="true" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="U8">
+					<dim>-1</dim>
+				</port>
+				<port id="3" precision="BOOL">
+					<dim>-1</dim>
+				</port>
+				<port id="4" precision="U8">
+					<dim>1</dim>
+				</port>
+				<port id="5" precision="U8">
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="6" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="7" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="8" precision="U8">
+					<dim>-1</dim>
+				</port>
+				<port id="9" precision="BOOL">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="25" name="Constant_89" type="Const" version="opset1">
+			<data element_type="u8" shape="339905" offset="1396" size="339905" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>339905</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="26" name="StringTensorUnpack_90" type="StringTensorUnpack" version="extension">
+			<data mode="begins_ends" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>339905</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="3" precision="U8">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="27" name="Constant_95" type="Const" version="opset1">
+			<data element_type="u8" shape="499127" offset="341301" size="499127" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>499127</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="28" name="StringTensorUnpack_96" type="StringTensorUnpack" version="extension">
+			<data mode="begins_ends" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>499127</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="3" precision="U8">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="29" name="Constant_98" type="Const" version="opset1">
+			<data element_type="u8" shape="412810" offset="840428" size="412810" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>412810</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="30" name="StringTensorUnpack_99" type="StringTensorUnpack" version="extension">
+			<data mode="begins_ends" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>412810</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="3" precision="U8">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="31" name="Constant_92" type="Const" version="opset1">
+			<data element_type="u8" shape="39809" offset="1253238" size="39809" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>39809</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="32" name="StringTensorUnpack_93" type="StringTensorUnpack" version="extension">
+			<data mode="begins_ends" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>39809</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="3" precision="U8">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="33" name="Constant_100" type="Const" version="opset1">
+			<data element_type="i32" shape="1811" offset="1293047" size="7244" />
+			<output>
+				<port id="0" precision="I32">
+					<dim>1811</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="34" name="BPETokenizer_101" type="BPETokenizer" version="extension">
+			<data unk_token="&lt;unk>" fuse_unk="true" suffix_indicator="" end_suffix="" byte_fallback="true" cache_capacity="20000" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="3" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="4" precision="U8">
+					<dim>-1</dim>
+				</port>
+				<port id="5" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="6" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="7" precision="U8">
+					<dim>-1</dim>
+				</port>
+				<port id="8" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="9" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="10" precision="U8">
+					<dim>-1</dim>
+				</port>
+				<port id="11" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="12" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="13" precision="U8">
+					<dim>-1</dim>
+				</port>
+				<port id="14" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="15" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="16" precision="U8">
+					<dim>-1</dim>
+				</port>
+				<port id="17" precision="I32">
+					<dim>1811</dim>
+				</port>
+			</input>
+			<output>
+				<port id="18" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="19" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="20" precision="I32">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="35" name="Subtract_102" type="Subtract" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I32">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="36" name="Constant_103" type="Const" version="opset1">
+			<data element_type="i32" shape="" offset="1300291" size="4" />
+			<output>
+				<port id="0" precision="I32" />
+			</output>
+		</layer>
+		<layer id="37" name="Minimum_104" type="Minimum" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32" />
+			</input>
+			<output>
+				<port id="2" precision="I32">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="38" name="Add_105" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I32">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="39" name="Constant_109" type="Const" version="opset1">
+			<data element_type="i32" shape="2" offset="8" size="8" />
+			<output>
+				<port id="0" precision="I32">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="40" name="CombineSegments_110" type="CombineSegments" version="extension">
+			<input>
+				<port id="0" precision="I32" />
+				<port id="1" precision="I32" />
+				<port id="2" precision="I32">
+					<dim>1</dim>
+				</port>
+				<port id="3" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="4" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="5" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="6" precision="I32">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="7" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="8" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="9" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="10" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="11" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="12" precision="I32">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="41" name="Subtract_111" type="Subtract" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I32">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="42" name="Constant_112" type="Const" version="opset1">
+			<data element_type="i32" shape="" offset="0" size="4" />
+			<output>
+				<port id="0" precision="I32" />
+			</output>
+		</layer>
+		<layer id="43" name="ReduceMax_113" type="ReduceMax" version="opset1">
+			<data keep_dims="false" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32" />
+			</input>
+			<output>
+				<port id="2" precision="I32" />
+			</output>
+		</layer>
+		<layer id="44" name="Constant_114" type="Const" version="opset1">
+			<data element_type="i32" shape="" offset="1300295" size="4" />
+			<output>
+				<port id="0" precision="I32" />
+			</output>
+		</layer>
+		<layer id="45" name="RaggedToDense_115" type="RaggedToDense" version="extension">
+			<data pad_right="true" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="2" precision="I32">
+					<dim>-1</dim>
+				</port>
+				<port id="3" precision="I32" />
+				<port id="4" precision="I32" />
+			</input>
+			<output>
+				<port id="5" precision="I32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+				<port id="6" precision="BOOL">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="46" name="Convert_116" type="Convert" version="opset1">
+			<data destination_type="i32" />
+			<input>
+				<port id="0" precision="BOOL">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="47" name="Convert_116.0" type="Convert" version="opset1">
+			<data destination_type="i64" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64" names="attention_mask">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="49" name="RaggedToDense_115.0" type="Convert" version="opset1">
+			<data destination_type="i64" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64" names="input_ids">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="50" name="Result_119" type="Result" version="opset1">
+			<input>
+				<port id="0" precision="I64">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</input>
+		</layer>
+		<layer id="48" name="Result_121" type="Result" version="opset1">
+			<input>
+				<port id="0" precision="I64">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</input>
+		</layer>
+	</layers>
+	<edges>
+		<edge from-layer="0" from-port="0" to-layer="5" to-port="0" />
+		<edge from-layer="1" from-port="0" to-layer="40" to-port="0" />
+		<edge from-layer="2" from-port="0" to-layer="40" to-port="1" />
+		<edge from-layer="3" from-port="0" to-layer="40" to-port="2" />
+		<edge from-layer="4" from-port="0" to-layer="11" to-port="0" />
+		<edge from-layer="5" from-port="1" to-layer="6" to-port="0" />
+		<edge from-layer="5" from-port="3" to-layer="18" to-port="4" />
+		<edge from-layer="5" from-port="2" to-layer="18" to-port="3" />
+		<edge from-layer="5" from-port="1" to-layer="18" to-port="2" />
+		<edge from-layer="6" from-port="1" to-layer="9" to-port="0" />
+		<edge from-layer="7" from-port="0" to-layer="9" to-port="1" />
+		<edge from-layer="8" from-port="0" to-layer="9" to-port="2" />
+		<edge from-layer="9" from-port="3" to-layer="14" to-port="0" />
+		<edge from-layer="9" from-port="3" to-layer="11" to-port="1" />
+		<edge from-layer="10" from-port="0" to-layer="11" to-port="2" />
+		<edge from-layer="11" from-port="3" to-layer="18" to-port="0" />
+		<edge from-layer="12" from-port="0" to-layer="16" to-port="0" />
+		<edge from-layer="13" from-port="0" to-layer="14" to-port="1" />
+		<edge from-layer="14" from-port="2" to-layer="16" to-port="1" />
+		<edge from-layer="15" from-port="0" to-layer="16" to-port="2" />
+		<edge from-layer="16" from-port="3" to-layer="18" to-port="1" />
+		<edge from-layer="17" from-port="0" to-layer="18" to-port="5" />
+		<edge from-layer="18" from-port="7" to-layer="34" to-port="1" />
+		<edge from-layer="18" from-port="6" to-layer="34" to-port="0" />
+		<edge from-layer="18" from-port="10" to-layer="21" to-port="2" />
+		<edge from-layer="18" from-port="8" to-layer="21" to-port="0" />
+		<edge from-layer="18" from-port="9" to-layer="21" to-port="1" />
+		<edge from-layer="18" from-port="11" to-layer="21" to-port="3" />
+		<edge from-layer="19" from-port="0" to-layer="21" to-port="4" />
+		<edge from-layer="20" from-port="0" to-layer="21" to-port="5" />
+		<edge from-layer="21" from-port="7" to-layer="24" to-port="1" />
+		<edge from-layer="21" from-port="9" to-layer="24" to-port="3" />
+		<edge from-layer="21" from-port="8" to-layer="24" to-port="2" />
+		<edge from-layer="21" from-port="6" to-layer="24" to-port="0" />
+		<edge from-layer="22" from-port="0" to-layer="24" to-port="4" />
+		<edge from-layer="23" from-port="0" to-layer="24" to-port="5" />
+		<edge from-layer="24" from-port="6" to-layer="34" to-port="2" />
+		<edge from-layer="24" from-port="7" to-layer="34" to-port="3" />
+		<edge from-layer="24" from-port="8" to-layer="34" to-port="4" />
+		<edge from-layer="25" from-port="0" to-layer="26" to-port="0" />
+		<edge from-layer="26" from-port="1" to-layer="34" to-port="5" />
+		<edge from-layer="26" from-port="3" to-layer="34" to-port="7" />
+		<edge from-layer="26" from-port="2" to-layer="34" to-port="6" />
+		<edge from-layer="27" from-port="0" to-layer="28" to-port="0" />
+		<edge from-layer="28" from-port="1" to-layer="34" to-port="8" />
+		<edge from-layer="28" from-port="3" to-layer="34" to-port="10" />
+		<edge from-layer="28" from-port="2" to-layer="34" to-port="9" />
+		<edge from-layer="29" from-port="0" to-layer="30" to-port="0" />
+		<edge from-layer="30" from-port="1" to-layer="34" to-port="11" />
+		<edge from-layer="30" from-port="2" to-layer="34" to-port="12" />
+		<edge from-layer="30" from-port="3" to-layer="34" to-port="13" />
+		<edge from-layer="31" from-port="0" to-layer="32" to-port="0" />
+		<edge from-layer="32" from-port="1" to-layer="34" to-port="14" />
+		<edge from-layer="32" from-port="2" to-layer="34" to-port="15" />
+		<edge from-layer="32" from-port="3" to-layer="34" to-port="16" />
+		<edge from-layer="33" from-port="0" to-layer="34" to-port="17" />
+		<edge from-layer="34" from-port="18" to-layer="40" to-port="3" />
+		<edge from-layer="34" from-port="20" to-layer="40" to-port="5" />
+		<edge from-layer="34" from-port="18" to-layer="38" to-port="0" />
+		<edge from-layer="34" from-port="18" to-layer="35" to-port="1" />
+		<edge from-layer="34" from-port="19" to-layer="35" to-port="0" />
+		<edge from-layer="35" from-port="2" to-layer="37" to-port="0" />
+		<edge from-layer="36" from-port="0" to-layer="37" to-port="1" />
+		<edge from-layer="37" from-port="2" to-layer="38" to-port="1" />
+		<edge from-layer="38" from-port="2" to-layer="40" to-port="4" />
+		<edge from-layer="39" from-port="0" to-layer="40" to-port="6" />
+		<edge from-layer="40" from-port="8" to-layer="41" to-port="0" />
+		<edge from-layer="40" from-port="7" to-layer="41" to-port="1" />
+		<edge from-layer="40" from-port="7" to-layer="45" to-port="0" />
+		<edge from-layer="40" from-port="8" to-layer="45" to-port="1" />
+		<edge from-layer="40" from-port="9" to-layer="45" to-port="2" />
+		<edge from-layer="41" from-port="2" to-layer="43" to-port="0" />
+		<edge from-layer="42" from-port="0" to-layer="43" to-port="1" />
+		<edge from-layer="43" from-port="2" to-layer="45" to-port="3" />
+		<edge from-layer="44" from-port="0" to-layer="45" to-port="4" />
+		<edge from-layer="45" from-port="6" to-layer="46" to-port="0" />
+		<edge from-layer="45" from-port="5" to-layer="49" to-port="0" />
+		<edge from-layer="46" from-port="1" to-layer="47" to-port="0" />
+		<edge from-layer="47" from-port="1" to-layer="48" to-port="0" />
+		<edge from-layer="49" from-port="1" to-layer="50" to-port="0" />
+	</edges>
+	<rt_info>
+		<add_attention_mask value="True" />
+		<add_prefix_space />
+		<add_special_tokens value="True" />
+		<bos_token_id value="1" />
+		<chat_template value="{% for message in messages %}{{'&lt;|' + message['role'] + '|>' + '&#10;' + message['content'] + '&lt;|end|>&#10;' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '&lt;|assistant|>&#10;' -}}{% endif %}" />
+		<clean_up_tokenization_spaces />
+		<detokenizer_input_type value="i64" />
+		<eos_token_id value="32000" />
+		<handle_special_tokens_with_re value="False" />
+		<number_of_inputs value="1" />
+		<openvino_tokenizers_version value="2025.0.0.0rc2" />
+		<openvino_version value="2025.0.0rc2" />
+		<original_tokenizer_class value="&lt;class 'transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast'>" />
+		<pad_token_id value="32000" />
+		<sentencepiece_version value="0.2.0" />
+		<skip_special_tokens value="True" />
+		<streaming_detokenizer value="False" />
+		<tiktoken_version value="0.8.0" />
+		<tokenizer_output_type value="i64" />
+		<tokenizers_version value="0.20.1" />
+		<transformers_version value="4.45.0" />
+		<use_max_padding value="False" />
+		<use_sentencepiece_backend value="True" />
+		<utf8_replace_mode value="replace" />
+		<with_detokenizer value="True" />
+	</rt_info>
+</net>

openvino_vision_embeddings_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb63340ca7e80ba5789d291b7c1d01888e1a773dd4c77fc9d1507df7ef0ff3d9
+size 294035604

openvino_vision_projection_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:70e6b5f4c4ce54dde0bdd193e7346ef5641c12fcf8370167f14f26ad85f8f60d
+size 22056960

openvino_vision_projection_model.xml

@@ -0,0 +1,331 @@
+<?xml version="1.0"?>
+<net name="Model3" version="11">
+	<layers>
+		<layer id="0" name="input" type="Parameter" version="opset1">
+			<data shape="?,?,4096" element_type="f32" />
+			<output>
+				<port id="0" precision="FP32" names="input">
+					<dim>-1</dim>
+					<dim>-1</dim>
+					<dim>4096</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="1" name="self.0.weight" type="Const" version="opset1">
+			<data element_type="i8" shape="3072, 4096" offset="0" size="12582912" />
+			<output>
+				<port id="0" precision="I8">
+					<dim>3072</dim>
+					<dim>4096</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="2" name="Convert_240046" type="Convert" version="opset1">
+			<data destination_type="f16" />
+			<input>
+				<port id="0" precision="I8">
+					<dim>3072</dim>
+					<dim>4096</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP16">
+					<dim>3072</dim>
+					<dim>4096</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="3" name="self.0.weight/scale" type="Const" version="opset1">
+			<data element_type="f16" shape="3072, 1" offset="12582912" size="6144" />
+			<output>
+				<port id="0" precision="FP16">
+					<dim>3072</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="4" name="self.0.weight/fq_weights_1" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP16">
+					<dim>3072</dim>
+					<dim>4096</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>3072</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>3072</dim>
+					<dim>4096</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="5" name="self.0.weight/fq_weights_1/convert" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="FP16">
+					<dim>3072</dim>
+					<dim>4096</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>3072</dim>
+					<dim>4096</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="6" name="__module.0/aten::linear/MatMul" type="MatMul" version="opset1">
+			<data transpose_a="false" transpose_b="true" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+					<dim>4096</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>3072</dim>
+					<dim>4096</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+					<dim>3072</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="7" name="Constant_110946" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 1, 3072" offset="12589056" size="12288" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3072</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="8" name="__module.0/aten::linear/Add" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+					<dim>3072</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3072</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="11">
+					<dim>-1</dim>
+					<dim>-1</dim>
+					<dim>3072</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="9" name="__module.1/aten::gelu/Gelu" type="Gelu" version="opset7">
+			<data approximation_mode="ERF" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+					<dim>3072</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="13">
+					<dim>-1</dim>
+					<dim>-1</dim>
+					<dim>3072</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="10" name="self.2.weight" type="Const" version="opset1">
+			<data element_type="i8" shape="3072, 3072" offset="12601344" size="9437184" />
+			<output>
+				<port id="0" precision="I8">
+					<dim>3072</dim>
+					<dim>3072</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="11" name="Convert_240053" type="Convert" version="opset1">
+			<data destination_type="f16" />
+			<input>
+				<port id="0" precision="I8">
+					<dim>3072</dim>
+					<dim>3072</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP16">
+					<dim>3072</dim>
+					<dim>3072</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="12" name="self.2.weight/scale" type="Const" version="opset1">
+			<data element_type="f16" shape="3072, 1" offset="22038528" size="6144" />
+			<output>
+				<port id="0" precision="FP16">
+					<dim>3072</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="13" name="self.2.weight/fq_weights_1" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP16">
+					<dim>3072</dim>
+					<dim>3072</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>3072</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>3072</dim>
+					<dim>3072</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="14" name="self.2.weight/fq_weights_1/convert" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="FP16">
+					<dim>3072</dim>
+					<dim>3072</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>3072</dim>
+					<dim>3072</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="15" name="__module.2/aten::linear/MatMul" type="MatMul" version="opset1">
+			<data transpose_a="false" transpose_b="true" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+					<dim>3072</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>3072</dim>
+					<dim>3072</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+					<dim>3072</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="16" name="Constant_110947" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 1, 3072" offset="22044672" size="12288" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3072</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="17" name="__module.2/aten::linear/Add" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+					<dim>3072</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3072</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="last_hidden_state">
+					<dim>-1</dim>
+					<dim>-1</dim>
+					<dim>3072</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="18" name="Result_109306" type="Result" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+					<dim>3072</dim>
+				</port>
+			</input>
+		</layer>
+	</layers>
+	<edges>
+		<edge from-layer="0" from-port="0" to-layer="6" to-port="0" />
+		<edge from-layer="1" from-port="0" to-layer="2" to-port="0" />
+		<edge from-layer="2" from-port="1" to-layer="4" to-port="0" />
+		<edge from-layer="3" from-port="0" to-layer="4" to-port="1" />
+		<edge from-layer="4" from-port="2" to-layer="5" to-port="0" />
+		<edge from-layer="5" from-port="1" to-layer="6" to-port="1" />
+		<edge from-layer="6" from-port="2" to-layer="8" to-port="0" />
+		<edge from-layer="7" from-port="0" to-layer="8" to-port="1" />
+		<edge from-layer="8" from-port="2" to-layer="9" to-port="0" />
+		<edge from-layer="9" from-port="1" to-layer="15" to-port="0" />
+		<edge from-layer="10" from-port="0" to-layer="11" to-port="0" />
+		<edge from-layer="11" from-port="1" to-layer="13" to-port="0" />
+		<edge from-layer="12" from-port="0" to-layer="13" to-port="1" />
+		<edge from-layer="13" from-port="2" to-layer="14" to-port="0" />
+		<edge from-layer="14" from-port="1" to-layer="15" to-port="1" />
+		<edge from-layer="15" from-port="2" to-layer="17" to-port="0" />
+		<edge from-layer="16" from-port="0" to-layer="17" to-port="1" />
+		<edge from-layer="17" from-port="2" to-layer="18" to-port="0" />
+	</edges>
+	<rt_info>
+		<Runtime_version value="2025.0.0-17933-815af98acd8-releases/2025/0" />
+		<conversion_parameters>
+			<framework value="pytorch" />
+			<is_python_object value="True" />
+		</conversion_parameters>
+		<nncf>
+			<friendly_names_were_updated value="True" />
+			<weight_compression>
+				<advanced_parameters value="{'statistics_path': None, 'awq_params': {'subset_size': 32, 'percent_to_apply': 0.002, 'alpha_min': 0.0, 'alpha_max': 1.0, 'steps': 100}, 'scale_estimation_params': {'subset_size': 64, 'initial_steps': 5, 'scale_steps': 5, 'weight_penalty': -1.0}, 'gptq_params': {'damp_percent': 0.1, 'block_size': 128, 'subset_size': 128}, 'lora_correction_params': {'adapter_rank': 8, 'num_iterations': 3, 'apply_regularization': True, 'subset_size': 128, 'use_int8_adapters': True}}" />
+				<all_layers value="False" />
+				<awq value="False" />
+				<backup_mode value="int8_asym" />
+				<gptq value="False" />
+				<group_size value="-1" />
+				<ignored_scope value="[]" />
+				<lora_correction value="False" />
+				<mode value="int8_sym" />
+				<ratio value="1.0" />
+				<scale_estimation value="False" />
+				<sensitivity_metric value="weight_quantization_error" />
+			</weight_compression>
+		</nncf>
+		<optimum>
+			<optimum_intel_version value="1.22.0.dev0+753f84d" />
+			<optimum_version value="1.24.0.dev0" />
+			<pytorch_version value="2.5.0+cpu" />
+			<transformers_version value="4.45.0" />
+		</optimum>
+	</rt_info>
+</net>

preprocessor_config.json

@@ -0,0 +1,21 @@
+{
+  "auto_map": {
+    "AutoImageProcessor": "microsoft/Phi-3.5-vision-instruct--processing_phi3_v.Phi3VImageProcessor",
+    "AutoProcessor": "processing_phi3_v.Phi3VProcessor"
+  },
+  "do_convert_rgb": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "Phi3VImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "num_crops": 4,
+  "num_img_tokens": 144,
+  "processor_class": "Phi3VProcessor"
+}

processing_phi3_v.py

@@ -0,0 +1,478 @@
+# coding=utf-8
+# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Processor class for Phi3-V.
+"""
+import re
+from typing import List, Optional, Union
+
+import torch
+
+import transformers
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils_base import PaddingStrategy, TextInput, TruncationStrategy
+from transformers.utils import TensorType
+
+
+"""Image processor class for Phi3-V."""
+
+from typing import List, Optional, Union
+
+import numpy as np
+
+from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
+from transformers.image_transforms import (
+    convert_to_rgb,
+)
+from transformers.image_utils import (
+    OPENAI_CLIP_MEAN,
+    OPENAI_CLIP_STD,
+    ImageInput,
+    make_list_of_images,
+    valid_images,
+)
+from transformers.utils import TensorType, is_vision_available, logging
+
+from transformers import AutoImageProcessor
+
+logger = logging.get_logger(__name__)
+
+
+if is_vision_available():
+    from PIL import Image
+
+import torch
+import torchvision
+
+def padding_336(b):
+    width, height = b.size
+    tar = int(np.ceil(height / 336) * 336)
+    top_padding = int((tar - height)/2)
+    bottom_padding = tar - height - top_padding
+    left_padding = 0
+    right_padding = 0
+    b = torchvision.transforms.functional.pad(b, [left_padding, top_padding, right_padding, bottom_padding], fill=[255,255,255])
+
+    return b
+
+def calc_padded_size(width, height, padding_unit=336):  
+    target_height = int(np.ceil(height / padding_unit) * padding_unit)  
+    top_padding = int((target_height - height) / 2)  
+    bottom_padding = target_height - height - top_padding  
+    left_padding = 0  
+    right_padding = 0  
+    padded_width = width + left_padding + right_padding  
+    padded_height = height + top_padding + bottom_padding  
+    return padded_width, padded_height  
+
+def HD_transform(img, hd_num=16):
+    width, height = img.size
+    trans = False
+    if width < height:
+        img = img.transpose(Image.TRANSPOSE)
+        trans = True
+        width, height = img.size
+    ratio = (width/ height)
+    scale = 1
+    while scale*np.ceil(scale/ratio) <= hd_num:
+        scale += 1
+    scale -= 1
+    new_w = int(scale * 336)
+    new_h = int(new_w / ratio)
+
+    img = torchvision.transforms.functional.resize(img, [new_h, new_w],)
+    img = padding_336(img)
+    width, height = img.size
+    if trans:
+        img = img.transpose(Image.TRANSPOSE)
+
+    return img
+
+def calc_hd_transform_size(width, height, hd_num=16):  
+    transposed = False  
+    if width < height:  
+        width, height = height, width  
+        transposed = True  
+  
+    ratio = width / height  
+    scale = 1  
+    while scale * np.ceil(scale / ratio) <= hd_num:  
+        scale += 1  
+    scale -= 1  
+  
+    new_width = int(scale * 336)  
+    new_height = int(new_width / ratio)  
+  
+    padded_width, padded_height = calc_padded_size(new_width, new_height)  
+      
+    if transposed:  
+        padded_width, padded_height = padded_height, padded_width  
+  
+    return padded_width, padded_height  
+
+def pad_to_max_num_crops_tensor(images, max_crops=5):
+    """
+    images: B x 3 x H x W, B<=max_crops
+    """
+    B, _, H, W = images.shape
+    if B < max_crops:
+        pad = torch.zeros(max_crops - B, 3, H, W, dtype=images.dtype, device=images.device)
+        images = torch.cat([images, pad], dim=0)
+    return images
+
+
+class Phi3VImageProcessor(BaseImageProcessor):
+    r"""
+    Constructs a Phi3 image processor. Based on [`CLIPImageProcessor`] with incorporation of additional techniques
+    for processing high resolution images as explained in the [InternLM-XComposer2-4KHD](https://arxiv.org/pdf/2404.06512)
+
+    Args:
+        image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
+            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+        image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
+            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+            Can be overridden by the `image_std` parameter in the `preprocess` method.
+        do_convert_rgb (`bool`, *optional*, defaults to `True`):
+            Whether to convert the image to RGB.
+    """
+
+    model_input_names = ["pixel_values"]
+
+    def __init__(
+        self,
+        num_crops: int = 1,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_convert_rgb: bool = True,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.num_crops = num_crops
+        self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
+        self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
+        self.do_convert_rgb = do_convert_rgb
+    
+    def calc_num_image_tokens(
+            self, 
+            images: ImageInput 
+    ):
+        """ Calculate the number of image tokens for each image.
+        Args:
+            images (`ImageInput`):
+                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+        """
+        images = make_list_of_images(images)
+
+        if not valid_images(images):
+            raise ValueError(
+                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                "torch.Tensor, tf.Tensor or jax.ndarray."
+            )
+
+        images = [image.convert('RGB') for image in images]
+        # (H, W, C)
+        elems = [HD_transform(im, hd_num = self.num_crops) for im in images] 
+        shapes = [[im.size[1], im.size[0]] for im in elems]
+        num_img_tokens = [int((h//336*w//336+1)*144 + 1 + (h//336+1)*12) for h, w in shapes]
+        return num_img_tokens
+
+    def calc_num_image_tokens_from_image_size(self, width, height):
+        """
+        Calculate the number of image tokens for a given image size.
+        Args:
+            width (`int`): Width of the image.
+            height (`int`): Height of the image.
+        """
+        new_width, new_height = calc_hd_transform_size(width, height, hd_num=self.num_crops)  
+        num_img_tokens = int((new_height // 336 * new_width // 336 + 1) * 144 + 1 + (new_height // 336 + 1) * 12)  
+        return num_img_tokens
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_convert_rgb: bool = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+    ):
+        """
+        Args:
+            images (`ImageInput`):
+                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
+                Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
+                Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+                `True`.
+            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+                Whether to convert the image to RGB.
+            return_tensors (`str` or `TensorType`, *optional*):
+                The type of tensors to return. Can be one of:
+                - Unset: Return a list of `np.ndarray`.
+                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+        """
+        image_mean = image_mean if image_mean is not None else self.image_mean
+        image_std = image_std if image_std is not None else self.image_std
+        do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+
+        images = make_list_of_images(images)
+
+        if not valid_images(images):
+            raise ValueError(
+                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                "torch.Tensor, tf.Tensor or jax.ndarray."
+            )
+
+        if do_convert_rgb:
+            images = [convert_to_rgb(image) for image in images]
+
+        image_sizes = []
+        img_processor = torchvision.transforms.Compose([
+            torchvision.transforms.ToTensor(),
+            torchvision.transforms.Normalize(image_mean, image_std)
+        ])
+
+        # PIL images
+        # HD_transform pad images to size of multiiply of 336, 336
+        # convert to RGB first
+        images = [image.convert('RGB') for image in images]
+        elems = [HD_transform(im, hd_num = self.num_crops) for im in images] 
+        # tensor transform and normalize
+        hd_images = [img_processor(im) for im in elems]
+        # create global image 
+        global_image = [torch.nn.functional.interpolate(im.unsqueeze(0).float(), size=(336, 336), mode='bicubic',).to(im.dtype) for im in hd_images]
+
+        # [(3, h, w)], where h, w is multiple of 336
+        shapes = [[im.size(1), im.size(2)] for im in hd_images]
+        num_img_tokens = [int(((h//336)*(w//336)+1)*144 + 1 + (h//336+1)*12) for h, w in shapes]
+        # reshape to channel dimension -> (num_images, num_crops, 3, 336, 336)
+        # (1, 3, h//336, 336, w//336, 336) -> (1, h//336, w//336, 3, 336, 336) -> (h//336*w//336, 3, 336, 336)
+        hd_images_reshape = [im.reshape(1, 3, h//336, 336, w//336, 336).permute(0,2,4,1,3,5).reshape(-1, 3, 336, 336).contiguous() for im, (h, w) in zip(hd_images, shapes)]
+        # concat global image and local image
+        hd_images_reshape = [torch.cat([_global_image] + [_im], dim=0) for _global_image, _im in zip(global_image, hd_images_reshape)]
+
+        # pad to max_num_crops
+        image_transformed = [pad_to_max_num_crops_tensor(im, self.num_crops+1) for im in hd_images_reshape]
+        image_transformed = torch.stack(image_transformed, dim=0)
+        image_sizes = [torch.LongTensor(_shapes) for _shapes in shapes]
+        padded_images = image_transformed
+        image_sizes = shapes
+
+        data = {"pixel_values": padded_images, 
+                "image_sizes": image_sizes,
+                "num_img_tokens": num_img_tokens
+                }
+
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+AutoImageProcessor.register("Phi3VImageProcessor", Phi3VImageProcessor)
+
+transformers.Phi3VImageProcessor = Phi3VImageProcessor 
+
+class Phi3VProcessor(ProcessorMixin):
+    r"""
+    Constructs a Phi3-V processor which wraps a Phi3-V image processor and a LLaMa tokenizer into a single processor.
+
+    [`Phi3VProcessor`] offers all the functionalities of [`Phi3VImageProcessor`] and [`LlamaTokenizerFast`]. See the
+    [`~Phi3VProcessor.__call__`] and [`~Phi3VProcessor.decode`] for more information.
+
+    Args:
+        image_processor ([`Phi3VImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`LlamaTokenizerFast`], *optional*):
+            The tokenizer is a required input.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "Phi3VImageProcessor"
+    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
+    special_image_token = "<|image|>"
+
+    def __init__(self, image_processor, tokenizer):
+        self.image_processor = image_processor
+        self.tokenizer = tokenizer
+        self.num_img_tokens = image_processor.num_img_tokens
+        self.img_tokens = [f"<|image_{i+1}|>" for i in range(1000000)]
+
+    def __call__(
+        self,
+        text: Union[TextInput, List[TextInput]],
+        images: ImageInput = None,
+        padding: Union[bool, str, PaddingStrategy] = False,
+        truncation: Union[bool, str, TruncationStrategy] = None,
+        max_length=None,
+        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+        and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
+        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
+        Phi3ImageProcessor's [`~Phi3ImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
+        of the above two methods for more information.
+
+        Args:
+            text (`str`, `List[str]`, `List[List[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. Both channels-first and channels-last formats are supported.
+            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
+                Select a strategy to pad the returned sequences (according to the model's padding side and padding
+                index) among:
+                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+                  sequence if provided).
+                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+                  acceptable input length for the model if that argument is not provided.
+                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+                  lengths).
+            max_length (`int`, *optional*):
+                Maximum length of the returned list and optionally padding length (see above).
+            truncation (`bool`, *optional*):
+                Activates truncation to cut input sequences longer than `max_length` to `max_length`.
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Acceptable values are:
+
+                - `'tf'`: Return TensorFlow `tf.constant` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return NumPy `np.ndarray` objects.
+                - `'jax'`: Return JAX `jnp.ndarray` objects.
+
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+        """
+        if images is not None:
+            image_inputs = self.image_processor(images, return_tensors=return_tensors)
+        else:
+            image_inputs = {}
+        inputs = self._convert_images_texts_to_inputs(image_inputs, text, padding=padding, truncation=truncation, max_length=max_length, return_tensors=return_tensors)
+        return inputs
+
+    def calc_num_image_tokens(self, images: ImageInput):
+        """ Calculate the number of image tokens for each image.
+        Args:
+            images (`ImageInput`):
+                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+        """
+        return self.image_processor.calc_num_image_tokens(images)
+        
+    def calc_num_image_tokens_from_image_size(self, width, height):
+        """ Calculate the number of image token for an image with given width and height.
+        Args:
+            width (`int`):
+                Width of the image.
+            height (`int`):
+                Height of the image.
+        """
+        return self.image_processor.calc_num_image_tokens_from_image_size(width, height)
+    
+    
+    @property 
+    def special_image_token_id(self):
+        return self.tokenizer.convert_tokens_to_ids(self.special_image_token)
+
+    def get_special_image_token_id(self):
+        return self.tokenizer.convert_tokens_to_ids(self.special_image_token)
+    
+    def _convert_images_texts_to_inputs(self, images, texts, padding=False, truncation=None, max_length=None, return_tensors=None):
+
+        if not len(images):
+            model_inputs = self.tokenizer(texts, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length)
+            return BatchFeature(data={**model_inputs})
+
+        pattern = r"<\|image_\d+\|>"
+        prompt_chunks = [self.tokenizer(chunk).input_ids for chunk in re.split(pattern, texts)] 
+
+        if 'num_img_tokens' in images:
+            num_img_tokens = images['num_img_tokens']
+        else:
+            assert 'num_crops' in images, 'num_crops must be provided in images if num_img_tokens is not provided'
+            num_crops = images['num_crops']
+            num_img_tokens = [_num_crops * self.num_img_tokens for _num_crops in num_crops] 
+
+        images, image_sizes = images['pixel_values'], images['image_sizes']
+
+        # image_tags needs to start from 1 to n
+        image_tags = re.findall(pattern, texts) 
+        # image_ids = [int(s.split("|")[1].split("_")[-1]) * -1 for s in image_tags]
+        # image_ids_pad = [[iid]*num_img_tokens[i] for i, iid in enumerate(image_ids)]
+        image_ids = [int(s.split("|")[1].split("_")[-1]) for s in image_tags]
+        unique_image_ids = sorted(list(set(image_ids)))
+        # image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be [1, 4, 5]
+        # check the condition
+        assert unique_image_ids == list(range(1, len(unique_image_ids)+1)), f"image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be {unique_image_ids}"
+        # total images must be the same as the number of image tags
+        assert len(unique_image_ids) == len(images), f"total images must be the same as the number of image tags, got {len(unique_image_ids)} image tags and {len(images)} images"
+
+        image_ids_pad = [[-iid]*num_img_tokens[iid-1] for iid in image_ids]
+
+        def insert_separator(X, sep_list):
+            if len(X) > len(sep_list):
+                sep_list.append([])
+            return [ele for sublist in zip(X, sep_list) for ele in sublist]
+        input_ids = []
+        offset = 0                
+        for x in insert_separator(prompt_chunks, image_ids_pad):
+            input_ids.extend(x[offset:])
+
+        input_ids = torch.tensor(input_ids, dtype=torch.long).unsqueeze(0)
+        attention_mask = (input_ids > -1000000).to(torch.long)
+
+        return BatchFeature(data={"input_ids": input_ids,
+                                  "attention_mask": attention_mask,
+                                  "pixel_values": images, 
+                                  "image_sizes": image_sizes})
+
+
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

processor_config.json

@@ -0,0 +1,6 @@
+{
+  "auto_map": {
+    "AutoProcessor": "processing_phi3_v.Phi3VProcessor"
+  },
+  "processor_class": "Phi3VProcessor"
+}

special_tokens_map.json

@@ -0,0 +1,36 @@
+{
+  "additional_special_tokens": [
+    "<|system|>",
+    "<|end|>",
+    "<|user|>",
+    "<|end|>"
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,413 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
+    },
+    "32000": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32001": {
+      "content": "<|assistant|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32002": {
+      "content": "<|placeholder1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32003": {
+      "content": "<|placeholder2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32004": {
+      "content": "<|placeholder3|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32005": {
+      "content": "<|placeholder4|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32006": {
+      "content": "<|system|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32007": {
+      "content": "<|end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32008": {
+      "content": "<|placeholder5|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32009": {
+      "content": "<|placeholder6|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32010": {
+      "content": "<|user|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32011": {
+      "content": "<|placeholder7|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32012": {
+      "content": "<|placeholder8|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32013": {
+      "content": "<|placeholder9|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32014": {
+      "content": "<|placeholder10|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32015": {
+      "content": "<|placeholder11|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32016": {
+      "content": "<|placeholder12|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32017": {
+      "content": "<|placeholder13|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32018": {
+      "content": "<|placeholder14|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32019": {
+      "content": "<|placeholder15|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32020": {
+      "content": "<|placeholder16|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32021": {
+      "content": "<|placeholder17|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32022": {
+      "content": "<|placeholder18|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32023": {
+      "content": "<|placeholder19|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32024": {
+      "content": "<|placeholder20|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32025": {
+      "content": "<|placeholder21|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32026": {
+      "content": "<|placeholder22|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32027": {
+      "content": "<|placeholder23|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32028": {
+      "content": "<|placeholder24|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32029": {
+      "content": "<|placeholder25|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32030": {
+      "content": "<|placeholder26|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32031": {
+      "content": "<|placeholder27|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32032": {
+      "content": "<|placeholder28|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32033": {
+      "content": "<|placeholder29|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32034": {
+      "content": "<|placeholder30|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32035": {
+      "content": "<|placeholder31|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32036": {
+      "content": "<|placeholder32|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32037": {
+      "content": "<|placeholder33|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32038": {
+      "content": "<|placeholder34|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32039": {
+      "content": "<|placeholder35|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32040": {
+      "content": "<|placeholder36|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32041": {
+      "content": "<|placeholder37|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32042": {
+      "content": "<|placeholder38|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32043": {
+      "content": "<|placeholder39|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32044": {
+      "content": "<|image|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|system|>",
+    "<|end|>",
+    "<|user|>",
+    "<|end|>"
+  ],
+  "auto_map": {
+    "AutoProcessor": "processing_phi3_v.Phi3VProcessor"
+  },
+  "bos_token": "<s>",
+  "chat_template": "{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "legacy": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "right",
+  "processor_class": "Phi3VProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}