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from typing import List
from transformers import PretrainedConfig, AutoTokenizer
class MolmoConfig(PretrainedConfig):
model_type = "molmo"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=50304,
embedding_size=50304,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
max_position_embeddings=2048,
initializer_range=0.02,
use_cache=True,
layer_norm_eps: float = 1e-5,
rope_theta=10000.0,
clip_qkv=None,
qkv_bias: bool = False,
weight_tying: bool = False,
use_position_ids: bool=True,
tie_word_embeddings: bool=True,
attention_layer_norm: bool=False,
norm_after: bool = False,
layer_norm_type: str="rms",
**kwargs,
):
self.vocab_size = vocab_size
self.embedding_size = embedding_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
self.layer_norm_eps = layer_norm_eps
self.weight_tying = weight_tying
self.use_position_ids = use_position_ids
self.attention_layer_norm = attention_layer_norm
self.num_key_value_heads = num_key_value_heads
self.initializer_range = initializer_range
self.use_cache = use_cache
self.rope_theta = rope_theta
self.clip_qkv = clip_qkv
self.qkv_bias = qkv_bias
self.norm_after = norm_after
self.tie_word_embeddings = tie_word_embeddings
self.layer_norm_type = layer_norm_type
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
MolmoConfig.register_for_auto_class() |