added smollama base model - 1B parameter

src/transformers/models/smollama/__init__.py

@@ -0,0 +1,34 @@
+# coding=utf-8
+# Copyright 2025 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.
+
+from .configuration_smollama import SmollamaConfig
+from .modeling_smollama import (
+    SmollamaForCausalLM,
+    SmollamaForQuestionAnswering,
+    SmollamaForSequenceClassification,
+    SmollamaForTokenClassification,
+    SmollamaModel,
+    SmollamaPreTrainedModel,
+)
+
+__all__ = [
+    "SmollamaConfig",
+    "SmollamaPreTrainedModel",
+    "SmollamaModel",
+    "SmollamaForCausalLM",
+    "SmollamaForSequenceClassification",
+    "SmollamaForTokenClassification",
+    "SmollamaForQuestionAnswering",
+] 

src/transformers/models/smollama/configuration_smollama.py

@@ -0,0 +1,251 @@
+# coding=utf-8
+# Copyright 2025 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.
+
+from ...configuration_utils import PretrainedConfig, layer_type_validation
+from ...modeling_rope_utils import rope_config_validation
+
+
+class SmollamaConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`SmollamaModel`]. It is used to instantiate a
+    Smollama model according to the specified arguments, defining the model architecture. Smollama combines the
+    Llama 3.2 1B architecture with SmolLM3's NoPE pattern for better 128K context handling.
+
+    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 128256):
+            Vocabulary size of the Smollama model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`SmollamaModel`]
+        hidden_size (`int`, *optional*, defaults to 2048):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 5632):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 22):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 4):
+            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 `4`.
+        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 131072):
+            The maximum sequence length that this model might ever be used with. Set to 128K for long context.
+        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-06):
+            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`.
+        pad_token_id (`int`, *optional*, defaults to 128004):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 128000):
+            The id of the beginning of sentence token.
+        eos_token_id (`int`, *optional*, defaults to 128001):
+            The id of the end of sentence token.
+        rope_theta (`float`, *optional*, defaults to 2000000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Configured for 128K context.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+        use_sliding_window (`bool`, *optional*, defaults to `False`):
+            Whether to use sliding window attention.
+        sliding_window (`int`, *optional*):
+            Sliding window attention (SWA) window size. If not specified, will default to `None`.
+        no_rope_layers (`List[int]`, *optional*):
+            List with at least the same length as the number of layers in the model.
+            A `1` at an index position indicates that the corresponding layer will use RoPE,
+            while a `0` indicates that it's a NoPE layer.
+        no_rope_layer_interval (`int`, *optional*, defaults to 4):
+            If `no_rope_layers` is `None`, it will be created using a NoPE layer every
+            `no_rope_layer_interval` layers.
+        layer_types (`list`, *optional*):
+            Attention pattern for each layer. Automatically computed based on sliding window and NoPE settings.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
+
+    ```python
+    >>> from transformers import SmollamaModel, SmollamaConfig
+
+    >>> # Initializing a Smollama style configuration
+    >>> configuration = SmollamaConfig()
+
+    >>> # Initializing a model from the Smollama style configuration
+    >>> model = SmollamaModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "smollama"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size=128256,
+        hidden_size=2048,
+        intermediate_size=5632,
+        num_hidden_layers=22,
+        num_attention_heads=16,
+        num_key_value_heads=4,
+        hidden_act="silu",
+        max_position_embeddings=131072,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=128004,
+        bos_token_id=128000,
+        eos_token_id=128001,
+        rope_theta=2000000.0,
+        rope_scaling=None,
+        use_sliding_window=False,
+        sliding_window=None,
+        no_rope_layers=None,
+        no_rope_layer_interval=4,
+        layer_types=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        mlp_bias=False,
+        **kwargs,
+    ):
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.mlp_bias = mlp_bias
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = 4
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        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.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        # Set up NoPE pattern - every 4th layer is NoPE (no RoPE)
+        if no_rope_layers is None:
+            self.no_rope_layers = [
+                int((layer_idx + 1) % no_rope_layer_interval != 0) for layer_idx in range(num_hidden_layers)
+            ]
+        else:
+            self.no_rope_layers = no_rope_layers
+
+        self.no_rope_layer_interval = no_rope_layer_interval
+
+        # Update layer_types based on sliding window and NoPE pattern
+        if layer_types is None:
+            layer_types = []
+            for layer_idx in range(num_hidden_layers):
+                has_rope = self.no_rope_layers[layer_idx]
+                if use_sliding_window and sliding_window is not None and not has_rope:
+                    layer_types.append("sliding_attention")
+                else:
+                    layer_types.append("full_attention")
+
+        self.layer_types = layer_types
+        layer_type_validation(self.layer_types)
+
+        # Set up rope scaling for 128K context if not provided
+        if self.rope_scaling is None:
+            self.rope_scaling = {
+                "rope_type": "llama3",
+                "factor": 64.0,  # 131072 / 2048 = 64
+                "original_max_position_embeddings": 2048,
+                "low_freq_factor": 1.0,
+                "high_freq_factor": 1.1,  # Must be > low_freq_factor
+            }
+
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)
+
+
+__all__ = ["SmollamaConfig"]