fix(config): Removes auto_map since it is not used anymore.

- Adding `safetensors` variant of this model (7e19e91fd0b2aaa2ec50981d2459604b2af7ac89)


Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>

LICENSE

@@ -1,4 +1,4 @@
-PhyAGI.
+Microsoft.
 Copyright (c) Microsoft Corporation.
 
 MIT License

README.md

@@ -1,5 +1,4 @@
 ---
-inference: false
 license: mit
 license_link: https://huggingface.co/microsoft/phi-1_5/resolve/main/LICENSE
 language:
@@ -21,13 +20,7 @@ Phi-1.5 can write poems, draft emails, create stories, summarize texts, write Py
 
 ## How to Use
 
-Phi-1.5 has been integrated in the development version (4.37.0.dev) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following:
+Phi-1.5 has been integrated in the `transformers` version 4.37.0, please ensure that you are using a version equal or higher than it.
-
-* When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function.
-
-* Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source.
-
-The current `transformers` version can be verified with: `pip list | grep transformers`.
 
 ## Intended Uses
 
@@ -94,8 +87,6 @@ where the model generates the text after the comments.
 
 * Phi-1.5 has not been tested to ensure that it performs adequately for any production-level application. Please refer to the limitation sections of this document for more details.
 
-* If you are using `transformers<4.37.0`, always load the model with `trust_remote_code=True` to prevent side-effects.
-
 ## Sample Code
 
 ```python
@@ -104,8 +95,8 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 
 torch.set_default_device("cuda")
 
-model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", torch_dtype="auto", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", torch_dtype="auto")
-tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5")
 
 inputs = tokenizer('''def print_prime(n):
    """

config.json

@@ -3,10 +3,6 @@
   "architectures": [
     "PhiForCausalLM"
   ],
-  "auto_map": {
-    "AutoConfig": "configuration_phi.PhiConfig",
-    "AutoModelForCausalLM": "modeling_phi.PhiForCausalLM"
-  },
   "attention_dropout": 0.0,
   "bos_token_id": null,
   "embd_pdrop": 0.0,
@@ -28,7 +24,7 @@
   "rope_theta": 10000.0,
   "tie_word_embeddings": false,
   "torch_dtype": "float16",
-  "transformers_version": "4.37.0.dev0",
+  "transformers_version": "4.37.0",
   "use_cache": true,
   "vocab_size": 51200
 }

configuration_phi.py

@@ -1,193 +0,0 @@
-# coding=utf-8
-# Copyright 2023 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 model configuration"""
-
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-PHI_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "microsoft/phi-1_5": "https://huggingface.co/microsoft/phi-1_5/resolve/main/config.json",
-}
-
-
-class PhiConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi
-    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 Phi
-    [microsoft/phi-1](https://huggingface.co/microsoft/phi-1).
-
-    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 51200):
-            Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`PhiModel`].
-        hidden_size (`int`, *optional*, defaults to 2048):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 8192):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 24):
-            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 `"gelu_new"`):
-            The non-linear activation function (function or string) in the decoder.
-        max_position_embeddings (`int`, *optional*, defaults to 2048):
-            The maximum sequence length that this model might ever be used with. Phi-1 and Phi-1.5 supports up to 2048
-            tokens.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
-            The epsilon used by the rms normalization layers.
-        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*):
-            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
-            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
-            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
-            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
-            these scaling strategies behave:
-            https://www.reddit.com/r/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
-            is an experimental feature, subject to breaking API changes in future versions.
-        partial_rotary_factor (`float`, *optional*, defaults to 0.5):
-            Percentage of the query and keys which will have rotary embedding.
-        qk_layernorm (`bool`, *optional*, defaults to `False`):
-            Whether or not to normalize the Queries and Keys after projecting the hidden states.
-        bos_token_id (`int`, *optional*, defaults to 1):
-            Denotes beginning of sequences token id.
-        eos_token_id (`int`, *optional*, defaults to 2):
-            Denotes end of sequences token id.
-
-    Example:
-
-    ```python
-    >>> from transformers import PhiModel, PhiConfig
-
-    >>> # Initializing a Phi-1 style configuration
-    >>> configuration = PhiConfig.from_pretrained("microsoft/phi-1")
-
-    >>> # Initializing a model from the configuration
-    >>> model = PhiModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "phi"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=51200,
-        hidden_size=2048,
-        intermediate_size=8192,
-        num_hidden_layers=24,
-        num_attention_heads=32,
-        num_key_value_heads=None,
-        resid_pdrop=0.0,
-        embd_pdrop=0.0,
-        attention_dropout=0.0,
-        hidden_act="gelu_new",
-        max_position_embeddings=2048,
-        initializer_range=0.02,
-        layer_norm_eps=1e-5,
-        use_cache=True,
-        tie_word_embeddings=False,
-        rope_theta=10000.0,
-        rope_scaling=None,
-        partial_rotary_factor=0.5,
-        qk_layernorm=False,
-        bos_token_id=1,
-        eos_token_id=2,
-        **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.initializer_range = initializer_range
-        self.layer_norm_eps = layer_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.qk_layernorm = qk_layernorm
-        self._rope_scaling_validation()
-
-        super().__init__(
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-
-    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
-    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) != 2:
-            raise ValueError(
-                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
-                f"got {self.rope_scaling}"
-            )
-        rope_scaling_type = self.rope_scaling.get("type", None)
-        rope_scaling_factor = self.rope_scaling.get("factor", None)
-        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
-            raise ValueError(
-                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
-            )
-        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
-            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")