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vllm.transformers_utils.configs.olmo3

Olmo3Config

Bases: PretrainedConfig

Source code in vllm/transformers_utils/configs/olmo3.py
class Olmo3Config(PretrainedConfig):

    model_type = "olmo3"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=50304,
        hidden_size=4096,
        intermediate_size=11008,
        num_hidden_layers=32,
        num_attention_heads=32,
        num_key_value_heads=None,
        hidden_act="silu",
        max_position_embeddings=2048,
        initializer_range=0.02,
        use_cache=True,
        pad_token_id=1,
        bos_token_id=None,
        eos_token_id=50279,
        tie_word_embeddings=False,
        rope_theta=10000.0,
        rope_scaling=None,
        attention_bias=False,
        attention_dropout=0.0,
        rms_norm_eps=1e-5,
        sliding_window=4096,
        layer_types=None,
        **kwargs,
    ):
        # This model uses Olmo3ForCausalLM in transformers but Olmo2ForCausalLM
        # in vLLM.
        if "architectures" not in kwargs:
            kwargs["architectures"] = ["Olmo2ForCausalLM"]
        elif "Olmo3ForCausalLM" in kwargs["architectures"]:
            kwargs["architectures"].remove("Olmo3ForCausalLM")
            kwargs["architectures"].append("Olmo2ForCausalLM")

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads

        # for backward compatibility
        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads

        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        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

        self.rms_norm_eps = rms_norm_eps

        self.sliding_window = sliding_window
        self.layer_types = layer_types
        if self.layer_types is None:
            self.layer_types = [
                "sliding_attention" if (i + 1) % 4 != 0 else "full_attention"
                for i in range(self.num_hidden_layers)
            ]

attention_bias instance-attribute

attention_bias = attention_bias

attention_dropout instance-attribute

attention_dropout = attention_dropout

hidden_act instance-attribute

hidden_act = hidden_act

hidden_size instance-attribute

hidden_size = hidden_size

initializer_range instance-attribute

initializer_range = initializer_range

intermediate_size instance-attribute

intermediate_size = intermediate_size

keys_to_ignore_at_inference class-attribute instance-attribute

keys_to_ignore_at_inference = ['past_key_values']

layer_types instance-attribute

layer_types = layer_types

max_position_embeddings instance-attribute

max_position_embeddings = max_position_embeddings

model_type class-attribute instance-attribute

model_type = 'olmo3'

num_attention_heads instance-attribute

num_attention_heads = num_attention_heads

num_hidden_layers instance-attribute

num_hidden_layers = num_hidden_layers

num_key_value_heads instance-attribute

num_key_value_heads = num_key_value_heads

rms_norm_eps instance-attribute

rms_norm_eps = rms_norm_eps

rope_scaling instance-attribute

rope_scaling = rope_scaling

rope_theta instance-attribute

rope_theta = rope_theta

sliding_window instance-attribute

sliding_window = sliding_window

use_cache instance-attribute

use_cache = use_cache

vocab_size instance-attribute

vocab_size = vocab_size

__init__

__init__(
    vocab_size=50304,
    hidden_size=4096,
    intermediate_size=11008,
    num_hidden_layers=32,
    num_attention_heads=32,
    num_key_value_heads=None,
    hidden_act="silu",
    max_position_embeddings=2048,
    initializer_range=0.02,
    use_cache=True,
    pad_token_id=1,
    bos_token_id=None,
    eos_token_id=50279,
    tie_word_embeddings=False,
    rope_theta=10000.0,
    rope_scaling=None,
    attention_bias=False,
    attention_dropout=0.0,
    rms_norm_eps=1e-05,
    sliding_window=4096,
    layer_types=None,
    **kwargs,
)
Source code in vllm/transformers_utils/configs/olmo3.py
def __init__(
    self,
    vocab_size=50304,
    hidden_size=4096,
    intermediate_size=11008,
    num_hidden_layers=32,
    num_attention_heads=32,
    num_key_value_heads=None,
    hidden_act="silu",
    max_position_embeddings=2048,
    initializer_range=0.02,
    use_cache=True,
    pad_token_id=1,
    bos_token_id=None,
    eos_token_id=50279,
    tie_word_embeddings=False,
    rope_theta=10000.0,
    rope_scaling=None,
    attention_bias=False,
    attention_dropout=0.0,
    rms_norm_eps=1e-5,
    sliding_window=4096,
    layer_types=None,
    **kwargs,
):
    # This model uses Olmo3ForCausalLM in transformers but Olmo2ForCausalLM
    # in vLLM.
    if "architectures" not in kwargs:
        kwargs["architectures"] = ["Olmo2ForCausalLM"]
    elif "Olmo3ForCausalLM" in kwargs["architectures"]:
        kwargs["architectures"].remove("Olmo3ForCausalLM")
        kwargs["architectures"].append("Olmo2ForCausalLM")

    super().__init__(
        pad_token_id=pad_token_id,
        bos_token_id=bos_token_id,
        eos_token_id=eos_token_id,
        tie_word_embeddings=tie_word_embeddings,
        **kwargs,
    )
    self.vocab_size = vocab_size
    self.max_position_embeddings = max_position_embeddings
    self.hidden_size = hidden_size
    self.intermediate_size = intermediate_size
    self.num_hidden_layers = num_hidden_layers
    self.num_attention_heads = num_attention_heads

    # for backward compatibility
    if num_key_value_heads is None:
        num_key_value_heads = num_attention_heads

    self.num_key_value_heads = num_key_value_heads
    self.hidden_act = hidden_act
    self.initializer_range = initializer_range
    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

    self.rms_norm_eps = rms_norm_eps

    self.sliding_window = sliding_window
    self.layer_types = layer_types
    if self.layer_types is None:
        self.layer_types = [
            "sliding_attention" if (i + 1) % 4 != 0 else "full_attention"
            for i in range(self.num_hidden_layers)
        ]