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from transformers import PretrainedConfig
from typing import List
class KeeperConfig(PretrainedConfig):
model_type = "keeper"
def __init__(
self,
retriever_config = {
"_name_or_path": "AdrienB134/ColBERTv1.0-bert-based-spanish-mmarcoES",
"architectures": [
"HF_ColBERT"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": None,
"gradient_checkpointing": False,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"output_past": True,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.35.2",
"type_vocab_size": 2,
"use_cache": True,
"vocab_size": 31002
},
model_config = {
"_name_or_path": "google/gemma-2b-it",
"architectures": [
"GemmaForCausalLM"
],
"attention_bias": False,
"attention_dropout": 0.0,
"bos_token_id": 2,
"eos_token_id": 1,
"head_dim": 256,
"hidden_act": "gelu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 16384,
"max_position_embeddings": 8192,
"model_type": "gemma",
"num_attention_heads": 8,
"num_hidden_layers": 18,
"num_key_value_heads": 1,
"pad_token_id": 0,
"rms_norm_eps": 1e-06,
"rope_scaling": None,
"rope_theta": 10000.0,
"torch_dtype": "bfloat16",
"transformers_version": "4.38.0.dev0",
"use_cache": True,
"vocab_size": 256000
},
auto_map = {
"AutoConfig": "configuration_keeper.KeeperConfig",
"AutoModel": "tokenizer_keeper.KeeperTokenizer",
"AutoModelForCausalLM": "model_keeper.KeeperModelForCausalLM",
},
**kwargs,
):
self.retriever_config = retriever_config
self.model_config = model_config
self.device_map = 'auto'
self.auto_map = auto_map
super().__init__(**kwargs)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
model_config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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