Delete nf4-g64-awq-se-ov

nf4-g64-awq-se-ov/added_tokens.json

@@ -1,12 +0,0 @@
-{
-  "<|/tool_call|>": 200026,
-  "<|/tool|>": 200024,
-  "<|assistant|>": 200019,
-  "<|end|>": 200020,
-  "<|system|>": 200022,
-  "<|tag|>": 200028,
-  "<|tool_call|>": 200025,
-  "<|tool_response|>": 200027,
-  "<|tool|>": 200023,
-  "<|user|>": 200021
-}

nf4-g64-awq-se-ov/config.json

@@ -1,143 +0,0 @@
-{
-  "architectures": [
-    "Phi3ForCausalLM"
-  ],
-  "attention_bias": false,
-  "attention_dropout": 0.0,
-  "auto_map": {
-    "AutoConfig": "configuration_phi3.Phi3Config",
-    "AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM",
-    "AutoTokenizer": "Xenova/gpt-4o"
-  },
-  "bos_token_id": 199999,
-  "embd_pdrop": 0.0,
-  "eos_token_id": 199999,
-  "full_attn_mod": 1,
-  "hidden_act": "silu",
-  "hidden_size": 3072,
-  "initializer_range": 0.02,
-  "intermediate_size": 8192,
-  "interpolate_factor": 1,
-  "lm_head_bias": false,
-  "max_position_embeddings": 4096,
-  "mlp_bias": false,
-  "model_type": "phi3",
-  "num_attention_heads": 24,
-  "num_hidden_layers": 32,
-  "num_key_value_heads": 8,
-  "original_max_position_embeddings": 4096,
-  "pad_token_id": 199999,
-  "partial_rotary_factor": 0.75,
-  "resid_pdrop": 0.0,
-  "rms_norm_eps": 1e-05,
-  "rope_scaling": {
-    "long_factor": [
-      1,
-      1.118320672,
-      1.250641126,
-      1.398617824,
-      1.564103225,
-      1.74916897,
-      1.956131817,
-      2.187582649,
-      2.446418898,
-      2.735880826,
-      3.059592084,
-      3.421605075,
-      3.826451687,
-      4.279200023,
-      4.785517845,
-      5.351743533,
-      5.984965424,
-      6.693110555,
-      7.485043894,
-      8.370679318,
-      9.36110372,
-      10.4687158,
-      11.70738129,
-      13.09260651,
-      14.64173252,
-      16.37415215,
-      18.31155283,
-      20.47818807,
-      22.90118105,
-      25.61086418,
-      28.64115884,
-      32.03,
-      32.1,
-      32.13,
-      32.23,
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-      32.61,
-      32.64,
-      32.66,
-      32.7,
-      32.71,
-      32.93,
-      32.97,
-      33.28,
-      33.49,
-      33.5,
-      44.16,
-      47.77
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-    "short_factor": [
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
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-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
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-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0,
-      1.0
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-    "type": "longrope"
-  },
-  "rope_theta": 10000.0,
-  "sliding_window": 262144,
-  "tie_word_embeddings": true,
-  "torch_dtype": "bfloat16",
-  "transformers_version": "4.51.3",
-  "use_cache": true,
-  "vocab_size": 200064
-}

nf4-g64-awq-se-ov/configuration_phi3.py

@@ -1,226 +0,0 @@
-# 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 model configuration"""
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class Phi3Config(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`Phi3Model`]. 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-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-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 model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`Phi3Model`].
-        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 `longrope` 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.
-        partial_rotary_factor (`float`, *optional*, defaults to 1.0):
-            Percentage of the query and keys which will have rotary embedding. Must be between 0.0 and 1.0.
-        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.
-
-    Example:
-
-    ```python
-    >>> from transformers import Phi3Model, Phi3Config
-
-    >>> # Initializing a Phi-3 style configuration
-    >>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
-
-    >>> # Initializing a model from the configuration
-    >>> model = Phi3Model(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "phi3"
-    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,
-        partial_rotary_factor=1.0,
-        bos_token_id=1,
-        eos_token_id=32000,
-        pad_token_id=32000,
-        sliding_window=None,
-        **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.partial_rotary_factor = partial_rotary_factor
-        self._rope_scaling_adjustment()
-        self._rope_scaling_validation()
-        self.sliding_window = sliding_window
-
-        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_adjustment(self):
-        """
-        Adjust the `type` of the `rope_scaling` configuration for backward compatibility.
-        """
-        if self.rope_scaling is None:
-            return
-
-        rope_scaling_type = self.rope_scaling.get("type", None)
-
-        # For backward compatibility if previous version used "su" or "yarn"
-        if rope_scaling_type is not None and rope_scaling_type in ["su", "yarn"]:
-            self.rope_scaling["type"] = "longrope"
-
-    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 ["longrope"]:
-            raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], 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}"
-            )
-        rotary_ndims = int(self.hidden_size // self.num_attention_heads * self.partial_rotary_factor)
-        if not len(rope_scaling_short_factor) == rotary_ndims // 2:
-            raise ValueError(
-                f"`rope_scaling`'s short_factor field must have length {rotary_ndims // 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) == rotary_ndims // 2:
-            raise ValueError(
-                f"`rope_scaling`'s long_factor field must have length {rotary_ndims // 2}, got {len(rope_scaling_long_factor)}"
-            )

nf4-g64-awq-se-ov/generation_config.json

@@ -1,10 +0,0 @@
-{
-  "_from_model_config": true,
-  "bos_token_id": 199999,
-  "eos_token_id": [
-    200020,
-    199999
-  ],
-  "pad_token_id": 199999,
-  "transformers_version": "4.51.3"
-}

nf4-g64-awq-se-ov/openvino_config.json

@@ -1,27 +0,0 @@
-{
-  "dtype": "nf4",
-  "input_info": null,
-  "optimum_version": "1.25.2",
-  "quantization_config": {
-    "all_layers": null,
-    "backup_precision": null,
-    "bits": 4,
-    "dataset": "wikitext2",
-    "dtype": "nf4",
-    "gptq": null,
-    "group_size": 64,
-    "ignored_scope": null,
-    "lora_correction": null,
-    "num_samples": null,
-    "processor": null,
-    "quant_method": "awq",
-    "ratio": 0.8,
-    "scale_estimation": true,
-    "sensitivity_metric": null,
-    "sym": false,
-    "tokenizer": null,
-    "trust_remote_code": true
-  },
-  "save_onnx_model": false,
-  "transformers_version": "4.51.3"
-}

nf4-g64-awq-se-ov/openvino_detokenizer.bin

@@ -1,3 +0,0 @@
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-oid sha256:58ec20da66d1d780b298f3cdcf252ccc0e228636fc7bee219163af81f1837e0a
-size 2998349

nf4-g64-awq-se-ov/openvino_detokenizer.xml

@@ -1,219 +0,0 @@
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-<net name="detokenizer" version="11">
-	<layers>
-		<layer id="0" name="Parameter_791033" type="Parameter" version="opset1">
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-			<data destination_type="i32" />
-			<input>
-				<port id="0" precision="I64">
-					<dim>-1</dim>
-					<dim>-1</dim>
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-			<output>
-				<port id="0" precision="I32">
-					<dim>200029</dim>
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-		<layer id="3" name="Constant_791037" type="Const" version="opset1">
-			<data element_type="i32" shape="200029" offset="800116" size="800116" />
-			<output>
-				<port id="0" precision="I32">
-					<dim>200029</dim>
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-			</output>
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-		<layer id="4" name="Constant_791039" type="Const" version="opset1">
-			<data element_type="u8" shape="1398089" offset="1600232" size="1398089" />
-			<output>
-				<port id="0" precision="U8">
-					<dim>1398089</dim>
-				</port>
-			</output>
-		</layer>
-		<layer id="5" name="Slice_791044" type="Const" version="opset1">
-			<data element_type="i32" shape="7" offset="2998321" size="28" />
-			<output>
-				<port id="0" precision="I32">
-					<dim>7</dim>
-				</port>
-			</output>
-		</layer>
-		<layer id="6" name="VocabDecoder_791046" type="VocabDecoder" version="extension">
-			<data skip_tokens="" />
-			<input>
-				<port id="0" precision="I32">
-					<dim>-1</dim>
-					<dim>-1</dim>
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-				<port id="1" precision="I32">
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-				<port id="4" precision="I32">
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-				<port id="5" precision="I32">
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-				<port id="9" precision="U8">
-					<dim>-1</dim>
-				</port>
-			</output>
-		</layer>
-		<layer id="7" name="FuzeRagged_791047" type="FuzeRagged" version="extension">
-			<input>
-				<port id="0" precision="I32">
-					<dim>-1</dim>
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-				<port id="5" precision="I32">
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-		</layer>
-		<layer id="8" name="UTF8Validate_791048" type="UTF8Validate" version="extension">
-			<data replace_mode="true" />
-			<input>
-				<port id="0" precision="I32">
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-		<layer id="9" name="StringTensorPack_791049" type="StringTensorPack" version="opset15">
-			<input>
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-				<port id="3" precision="STRING" names="Result_791050,string_output">
-					<dim>-1</dim>
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-		<layer id="10" name="Result_791050" type="Result" version="opset1" output_names="Result_791050,string_output">
-			<input>
-				<port id="0" precision="STRING">
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-	</layers>
-	<edges>
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nf4-g64-awq-se-ov/special_tokens_map.json

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-{
-  "bos_token": {
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nf4-g64-awq-se-ov/tokenizer.json

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nf4-g64-awq-se-ov/tokenizer_config.json

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-{
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