File size: 2,232 Bytes
bd979f8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | from transformers.configuration_utils import PretrainedConfig
from typing import Tuple, List, Optional
class NegativeConfig(PretrainedConfig):
model_type = "negative"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size: int = 2564,
hidden_size: int = 128,
num_hidden_layers: int = 21,
num_attention_heads: int = 4,
num_key_value_heads: int = 2,
intermediate_size: int = 345,
swiglu_interval: int = 3,
num_lanes: int = 4,
use_engram: bool = True,
engram_entries: int = 2400,
engram_ngram_orders: Tuple[int, ...] = (2, 3),
use_xsa: bool = False,
use_per_head_gating: bool = False,
max_position_embeddings: int = 2048,
rope_theta: float = 2500.0,
rms_norm_eps: float = 1e-5,
tie_word_embeddings: bool = True,
use_cache: bool = False,
initializer_range: float = 0.02,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.intermediate_size = intermediate_size
self.swiglu_interval = swiglu_interval
self.num_lanes = num_lanes
self.use_engram = use_engram
self.engram_entries = engram_entries
self.engram_ngram_orders = list(engram_ngram_orders)
self.use_xsa = use_xsa
self.use_per_head_gating = use_per_head_gating
self.max_position_embeddings = max_position_embeddings
self.rope_theta = rope_theta
self.rms_norm_eps = rms_norm_eps
self.initializer_range = initializer_range
self.head_dim = hidden_size // num_attention_heads
self.auto_map = {
"AutoConfig": "configuration_negative.NegativeConfig",
"AutoModel": "modeling_negative.NegativeModel",
"AutoModelForCausalLM": "modeling_negative.NegativeModelForCausalLM",
}
super().__init__(
tie_word_embeddings=tie_word_embeddings,
use_cache=use_cache,
**kwargs,
)
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