| 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() |