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.gitattributes

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AMLR.txt

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+Disclaimer: IMPORTANT: This Apple Machine Learning Research Model is
+specifically developed and released by Apple Inc. ("Apple") for the sole purpose
+of scientific research of artificial intelligence and machine-learning
+technology. “Apple Machine Learning Research Model” means the model, including
+but not limited to algorithms, formulas, trained model weights, parameters,
+configurations, checkpoints, and any related materials (including
+documentation).
+
+This Apple Machine Learning Research Model is provided to You by
+Apple in consideration of your agreement to the following terms, and your use,
+modification, creation of Model Derivatives, and or redistribution of the Apple
+Machine Learning Research Model constitutes acceptance of this Agreement. If You
+do not agree with these terms, please do not use, modify, create Model
+Derivatives of, or distribute this Apple Machine Learning Research Model or
+Model Derivatives.
+
+* License Scope: In consideration of your agreement to abide by the following
+  terms, and subject to these terms, Apple hereby grants you a personal,
+  non-exclusive, worldwide, non-transferable, royalty-free, revocable, and
+  limited license, to use, copy, modify, distribute, and create Model
+  Derivatives (defined below) of the Apple Machine Learning Research Model
+  exclusively for Research Purposes. You agree that any Model Derivatives You
+  may create or that may be created for You will be limited to Research Purposes
+  as well. “Research Purposes” means non-commercial scientific research and
+  academic development activities, such as experimentation, analysis, testing
+  conducted by You with the sole intent to advance scientific knowledge and
+  research. “Research Purposes” does not include any commercial exploitation,
+  product development or use in any commercial product or service.
+
+* Distribution of Apple Machine Learning Research Model and Model Derivatives:
+  If you choose to redistribute Apple Machine Learning Research Model or its
+  Model Derivatives, you must provide a copy of this Agreement to such third
+  party, and ensure that the following attribution notice be provided: “Apple
+  Machine Learning Research Model is licensed under the Apple Machine Learning
+  Research Model License Agreement.” Additionally, all Model Derivatives must
+  clearly be identified as such, including disclosure of modifications and
+  changes made to the Apple Machine Learning Research Model. The name,
+  trademarks, service marks or logos of Apple may not be used to endorse or
+  promote Model Derivatives or the relationship between You and Apple. “Model
+  Derivatives” means any models or any other artifacts created by modifications,
+  improvements, adaptations, alterations to the architecture, algorithm or
+  training processes of the Apple Machine Learning Research Model, or by any
+  retraining, fine-tuning of the Apple Machine Learning Research Model.
+
+* No Other License: Except as expressly stated in this notice, no other rights
+  or licenses, express or implied, are granted by Apple herein, including but
+  not limited to any patent, trademark, and similar intellectual property rights
+  worldwide that may be infringed by the Apple Machine Learning Research Model,
+  the Model Derivatives or by other works in which the Apple Machine Learning
+  Research Model may be incorporated.
+
+* Compliance with Laws: Your use of Apple Machine Learning Research Model must
+  be in compliance with all applicable laws and regulations.
+
+* Term and Termination: The term of this Agreement will begin upon your
+  acceptance of this Agreement or use of the Apple Machine Learning Research
+  Model and will continue until terminated in accordance with the following
+  terms. Apple may terminate this Agreement at any time if You are in breach of
+  any term or condition of this Agreement. Upon termination of this Agreement,
+  You must cease to use all Apple Machine Learning Research Models and Model
+  Derivatives and permanently delete any copy thereof. Sections 3, 6 and 7 will
+  survive termination.
+
+* Disclaimer and Limitation of Liability: This Apple Machine Learning Research
+  Model and any outputs generated by the Apple Machine Learning Research Model
+  are provided on an “AS IS” basis. APPLE MAKES NO WARRANTIES, EXPRESS OR
+  IMPLIED, INCLUDING WITHOUT LIMITATION THE IMPLIED WARRANTIES OF
+  NON-INFRINGEMENT, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE,
+  REGARDING THE APPLE MACHINE LEARNING RESEARCH MODEL OR OUTPUTS GENERATED BY
+  THE APPLE MACHINE LEARNING RESEARCH MODEL. You are solely responsible for
+  determining the appropriateness of using or redistributing the Apple Machine
+  Learning Research Model and any outputs of the Apple Machine Learning Research
+  Model and assume any risks associated with Your use of the Apple Machine
+  Learning Research Model and any output and results. IN NO EVENT SHALL APPLE BE
+  LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
+  IN ANY WAY OUT OF THE USE, REPRODUCTION, MODIFICATION AND/OR DISTRIBUTION OF
+  THE APPLE MACHINE LEARNING RESEARCH MODEL AND ANY OUTPUTS OF THE APPLE MACHINE
+  LEARNING RESEARCH MODEL, HOWEVER CAUSED AND WHETHER UNDER THEORY OF CONTRACT,
+  TORT (INCLUDING NEGLIGENCE), STRICT LIABILITY OR OTHERWISE, EVEN IF APPLE HAS
+  BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+* Governing Law: This Agreement will be governed by and construed under the laws
+  of the State of California without regard to its choice of law principles. The
+  Convention on Contracts for the International Sale of Goods shall not apply to
+  the Agreement except that the arbitration clause and any arbitration hereunder
+  shall be governed by the Federal Arbitration Act, Chapters 1 and 2. 
+
+Copyright (C) 2025 Apple Inc. All Rights Reserved.

README.md

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+---
+license: apple-amlr
+---
+This is a randomly initialized model from a modified config from [apple/OpenELM-270M](https://huggingface.co/apple/OpenELM-270M). It is for debugging or testing only. Use Apple's definitions for interfacing with this model. The configuration class was modified to remove an assertation that would prevent making this model due to its modified size.
+
+Apple Machine Learning Research Model is licensed under the Apple Machine Learning Research Model License Agreement.

config.json

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+{
+  "activation_fn_name": "swish",
+  "architectures": [
+    "OpenELMModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_openelm.OpenELMConfig",
+    "AutoModelForCausalLM": "modeling_openelm.OpenELMForCausalLM"
+  },
+  "bos_token_id": 1,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "ffn_dim_divisor": 256,
+  "ffn_multipliers": [
+    0.5,
+    0.73,
+    0.97,
+    1.2,
+    1.43,
+    1.67,
+    1.9,
+    2.13,
+    2.37,
+    2.6,
+    2.83,
+    3.07,
+    3.3,
+    3.53,
+    3.77,
+    4.0
+  ],
+  "ffn_with_glu": true,
+  "head_dim": 32,
+  "initializer_range": 0.02,
+  "max_context_length": 2048,
+  "model_dim": 640,
+  "model_type": "openelm",
+  "normalization_layer_name": "rms_norm",
+  "normalize_qk_projections": true,
+  "num_gqa_groups": 4,
+  "num_kv_heads": [
+    3,
+    3,
+    3,
+    3,
+    3,
+    4,
+    4,
+    4,
+    4,
+    4,
+    4,
+    4,
+    5,
+    5,
+    5,
+    5
+  ],
+  "num_query_heads": [
+    12,
+    16,
+    16,
+    20
+  ],
+  "num_transformer_layers": 4,
+  "qkv_multipliers": [
+    0.5,
+    1.0
+  ],
+  "rope_freq_constant": 10000,
+  "rope_max_length": 4096,
+  "share_input_output_layers": true,
+  "transformers_version": "4.57.3",
+  "use_cache": true,
+  "vocab_size": 16000
+}

configuration_openelm.py

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+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+#
+
+"""Implements HF OpenELMConfig based on PretrainedConfig"""
+from numbers import Number
+from typing import List, Optional, Union
+
+import numpy as np
+from transformers import PretrainedConfig
+
+
+def make_divisible(
+    v: Union[float, int],
+    divisor: Optional[int] = 8,
+    min_value: Optional[Union[float, int]] = None,
+) -> Union[float, int]:
+    """
+    This function is taken from the original tf repo.
+    It ensures that all layers have a channel number that is divisible by the divisor
+    It can be seen at:
+    https://github.com/tensorflow/models/blob/2cfc99eff5e5eb729c6793d2f3d03aa1c9be2b15/research/slim/nets/mobilenet/mobilenet.py#L62
+
+    Args:
+        v: input value
+        divisor: default to 8
+        min_value: minimum divisor value
+    Returns:
+        new_v: new divisible value
+    """
+    if min_value is None:
+        min_value = divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than 10%.
+    if new_v < 0.9 * v:
+        new_v += divisor
+    return new_v
+
+
+def compute_heads(model_dim: int, head_dim: int) -> int:
+    """Compute the number of heads.
+
+    Args:
+        model_dim: Model dimension.
+        head_dim: Head dimension.
+
+    Returns:
+        An integer denoting number of heads in multi-head attention is returned.
+
+    Raises:
+        ValueError: if model dimension is not divisible by head dimension.
+    """
+    if model_dim % head_dim == 0:
+        return model_dim // head_dim
+    else:
+        raise ValueError(
+            f"Model dimension should be divisible by head dimension. Got: {model_dim} and {head_dim}."
+        )
+
+
+OpenELM_CONFIGS = {
+    "OpenELM-270M": dict(
+        num_transformer_layers=16,
+        model_dim=1280,
+        head_dim=64,
+        num_gqa_groups=4,
+        normalize_qk_projections=True,
+        share_input_output_layers=True,
+        # Vary the FFN and QKV multipliers to create variable FFN and attention layers respectively.
+        ffn_multipliers=(0.5, 4.0),
+        qkv_multipliers=(0.5, 1.0),
+    ),
+    "OpenELM-450M": dict(
+        num_transformer_layers=20,
+        model_dim=1536,
+        head_dim=64,
+        num_gqa_groups=4,
+        normalize_qk_projections=True,
+        share_input_output_layers=True,
+        # Vary the FFN and QKV multipliers to create variable FFN and attention layers respectively.
+        ffn_multipliers=(0.5, 4.0),
+        qkv_multipliers=(0.5, 1.0),
+    ),
+    "OpenELM-1_1B": dict(
+        num_transformer_layers=28,
+        model_dim=2048,
+        head_dim=64,
+        num_gqa_groups=4,
+        normalize_qk_projections=True,
+        share_input_output_layers=True,
+        # Vary the FFN and QKV multipliers to create variable FFN and attention layers respectively.
+        ffn_multipliers=(0.5, 4.0),
+        qkv_multipliers=(0.5, 1.0),
+    ),
+    "OpenELM-3B": dict(
+        num_transformer_layers=36,
+        model_dim=3072,
+        head_dim=128,
+        num_gqa_groups=4,
+        normalize_qk_projections=True,
+        share_input_output_layers=True,
+        # Vary the FFN and QKV multipliers to create variable FFN and attention layers respectively.
+        ffn_multipliers=(0.5, 4.0),
+        qkv_multipliers=(0.5, 1.0),
+    ),
+}
+
+
+class OpenELMConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`OpenELMModel`]. It is used to instantiate an OpenELM model according to the specified arguments, defining the model architecture.
+
+    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 32000):
+            Vocabulary size of the OpenELM model.
+        max_context_length (`int`, *optional*, defaults to 2048):
+            Maximum number of input tokens.
+        num_transformer_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer decoder.
+        model_dim (`int`, *optional*, defaults to 2048):
+            Dimension of the hidden representations.
+        head_dim (`int`, *optional*, defaults to 128):
+            The attention head dimension.
+        qkv_multipliers (`Union[Number, List[Number]]`, *optional*, defaults to 1.0):
+            If the qkv_multipliers is a Number, then all attention layers have the same latent dimensions,
+            resulting in uniform allocation of parameters.
+            If the qkv_multipliers is a List of Number, then each attention layer have different latent dimensions
+            assuming qkv_multipliers[0] != qkv_multipliers[1]. This results in variable allocation of parameters in attention layer.
+            This scaling is known as layer-wise or block-wise scaling: https://arxiv.org/abs/2008.00623
+        num_query_heads (`Union[int, None]`, *optional*, defaults to None):
+            The number of query heads, computed from `compute_heads(model_dim=model_dim, head_dim=head_dim)`.
+        num_gqa_groups (`int`, *optional*, defaults to 1):
+            This variable allows to switch between multi-head attention, group query attention, and multi-query attention.
+            When num_gqa_groups == 1, then it is multi-head attention.
+            When 1 < num_gqa_groups < num_heads and num_heads is divisible by num_gqa_groups, then it is group query attention
+            When num_gqa_groups == num_heads, then it is multi-query attention
+        ffn_multipliers (`Union[Number, List[Number]]`, *optional*, defaults to 4.0):
+            Feed-forward network (FFN) multipliers.
+            If the ffn_multipliers is a Number, then all FFN layers have the same latent dimensions,
+            resulting in uniform allocation of parameters.
+            If the ffn_multipliers is a List of Number, then each FFN layer have different latent dimensions
+            assuming ffn_multipliers[0] != ffn_multipliers[1]. This results in variable allocation of parameters in FFN layer.
+            This scaling is known as layer-wise or block-wise scaling: https://arxiv.org/abs/2008.00623
+        ffn_with_glu (`bool`, *optional*, defaults to True):
+            Whether to use FFN with Gated Linear Unit (GLU)
+        ffn_dim_divisor (`int`, *optional*, defaults to 256):
+            The ffn layer dimension divisor.
+        activation_fn_name (`str` or `function`, *optional*, defaults to `"swish"`):
+            The non-linear activation function (function or string) in the decoder.
+        normalization_layer_name (`str` or `function`, *optional*, defaults to `"rms_norm"`):
+            Type of normalization layer.
+        normalize_qk_projections (`bool`, *optional*, defaults to False):
+            Whether to normalize queries and keys after projections
+        share_input_output_layers (`bool`, *optional*, defaults to False):
+            Whether to share the embedding between input and output linear layer
+        rope_freq_constant (`int`, *optional*, defaults to 10000):
+            The base period of the RoPE embeddings.
+        rope_max_length (`int`, *optional*, defaults to 4096):
+            That rope_max_length is set to twice of max_context_length.
+            This allows flexibility in token lengths during training or fine-tuning.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        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`.
+        bos_token_id (`int`, *optional*, defaults to 2):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 1):
+            End of stream token id.
+    """
+
+    model_type = "openelm"
+
+    def __init__(
+        self,
+        vocab_size: int = 32000,
+        max_context_length: int = 2048,
+        num_transformer_layers: int = 12,
+        model_dim: int = 2048,
+        head_dim: int = 128,
+        qkv_multipliers: Union[Number, List[Number]] = 1.0,
+        num_query_heads: Union[int, None] = None,
+        num_gqa_groups: int = 1,
+        ffn_multipliers: Union[Number, List[Number]] = 4.0,
+        ffn_with_glu: bool = True,
+        ffn_dim_divisor: int = 256,
+        activation_fn_name: str = "swish",
+        normalization_layer_name: str = "rms_norm",
+        normalize_qk_projections: bool = False,
+        share_input_output_layers: bool = False,
+        rope_freq_constant: int = 10000,
+        rope_max_length: int = 4096,
+        initializer_range: float = 0.02,
+        use_cache: bool = True,
+        bos_token_id: int = 1,
+        eos_token_id: int = 2,
+        **kwargs,
+    ) -> None:
+        self.vocab_size = vocab_size
+        self.max_context_length = max_context_length
+        self.num_transformer_layers = num_transformer_layers
+        self.model_dim = model_dim
+        self.head_dim = head_dim
+        self.qkv_multipliers = qkv_multipliers
+        self.num_query_heads = num_query_heads
+        self.num_gqa_groups = num_gqa_groups
+        self.ffn_multipliers = ffn_multipliers
+        self.ffn_with_glu = ffn_with_glu
+        self.ffn_dim_divisor = ffn_dim_divisor
+        self.activation_fn_name = activation_fn_name
+        self.normalization_layer_name = normalization_layer_name
+        self.normalize_qk_projections = normalize_qk_projections
+        self.share_input_output_layers = share_input_output_layers
+        self.rope_freq_constant = rope_freq_constant
+        self.rope_max_length = rope_max_length
+        self.num_query_heads = (
+            compute_heads(model_dim=model_dim, head_dim=head_dim)
+            if num_query_heads is None
+            else num_query_heads
+        )
+        self.initializer_range = initializer_range
+
+        self.__post_init__()
+        super().__init__(
+            use_cache=use_cache,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+
+    def __post_init__(self) -> None:
+        if self.num_gqa_groups is not None:
+            head_multiple_of = self.num_gqa_groups
+        else:
+            head_multiple_of = 2
+
+        if isinstance(self.qkv_multipliers, Number):
+            # All attention layers have the same latent dimensions, resulting in uniform allocation of parameters.
+            qkv_dim = make_divisible(
+                self.model_dim * self.qkv_multipliers,
+                divisor=self.head_dim * head_multiple_of,
+            )
+            query_dims = [int(qkv_dim)] * self.num_transformer_layers
+
+        elif (
+            isinstance(self.qkv_multipliers, (tuple, list))
+            and len(self.qkv_multipliers) == 2
+        ):
+            # Each attention layer have different latent dimensions assuming qkv_multipliers[0] != qkv_multipliers[1].
+            # This results in variable allocation of parameters in attention layer.
+            # This scaling is known as layer-wise or block-wise scaling: https://arxiv.org/abs/2008.00623
+            qkv_multipliers = [
+                round(v, 2)
+                for v in np.linspace(
+                    self.qkv_multipliers[0],
+                    self.qkv_multipliers[1],
+                    num=self.num_transformer_layers,
+                    dtype=float,
+                )
+            ]
+            # Make sure that scaled model dimension is divisible by scaled head dimension.
+            query_dims = [
+                int(
+                    make_divisible(
+                        self.model_dim * m, divisor=self.head_dim * head_multiple_of
+                    )
+                )
+                for m in qkv_multipliers
+            ]
+        else:
+            raise NotImplementedError(
+                f"QKV multipliers should be a single number or a list containing exactly two numbers. Got: {qkv_multipliers}."
+            )
+
+        # compute the number of query, key, and value heads
+        # For multi-head and multi-query attention, the number of heads for query, key, and value are the same.
+        # For group query attention, the number of key and value heads are the same.
+        self.num_query_heads = [
+            int(compute_heads(q_dim, self.head_dim)) for q_dim in query_dims
+        ]
+        self.num_kv_heads = [
+            q_heads // self.num_gqa_groups for q_heads in self.num_query_heads
+        ]
+
+        # Feed-forward network (FFN) multipliers
+        if isinstance(self.ffn_multipliers, Number):
+            # All FFN layers have the same latent dimensions, resulting in uniform allocation of parameters.
+            self.ffn_multipliers = [self.ffn_multipliers] * self.num_transformer_layers
+        elif isinstance(self.ffn_multipliers, (tuple, list)):
+            # Each FFN layer have different latent dimensions assuming ffn_multipliers[0] != ffn_multipliers[1].
+            # This results in variable allocation of parameters in FFN layer.
+            # This scaling is known as layer-wise or block-wise scaling: https://arxiv.org/abs/2008.00623
+            if len(self.ffn_multipliers) == 2:
+                self.ffn_multipliers = [
+                    round(v, 2)
+                    for v in np.linspace(
+                        self.ffn_multipliers[0],
+                        self.ffn_multipliers[1],
+                        num=self.num_transformer_layers,
+                        dtype=float,
+                    )
+                ]
+            else:
+                pass
+        else:
+            raise NotImplementedError(
+                f"FFN multipliers should be a single number or a list containing exactly two numbers. Got: {qkv_multipliers}."
+            )
+
+        # check num_query_heads divisible by num_kv_heads for every layer
+        for layer_idx in range(len(query_dims)):
+            assert self.num_query_heads[layer_idx] % self.num_kv_heads[layer_idx] == 0

model.safetensors

@@ -0,0 +1,3 @@
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+oid sha256:263c29e306efb75d845e6c89119f5a46e79586900ffc1c16a6ab9579a1b4345b
+size 73099968