|
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) |
|
|
