File size: 2,415 Bytes
36a53a4 |
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 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
"""
NeuralQuantum Ollama Configuration for Hugging Face Transformers
"""
from transformers import PretrainedConfig
class NeuralQuantumOllamaConfig(PretrainedConfig):
"""Configuration class for NeuralQuantum Ollama model"""
model_type = "neuralquantum_ollama"
def __init__(
self,
vocab_size=50257,
hidden_size=768,
num_attention_heads=12,
num_hidden_layers=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=2048,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
use_cache=True,
quantum_enhancement=True,
quantum_layers=6,
quantum_circuit_depth=12,
quantum_optimization="vqe",
hybrid_mode=True,
ollama_optimized=True,
temperature=0.7,
top_p=0.9,
top_k=40,
repeat_penalty=1.1,
num_ctx=2048,
num_predict=512,
torch_dtype="float16",
**kwargs
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.use_cache = use_cache
# Quantum-specific parameters
self.quantum_enhancement = quantum_enhancement
self.quantum_layers = quantum_layers
self.quantum_circuit_depth = quantum_circuit_depth
self.quantum_optimization = quantum_optimization
self.hybrid_mode = hybrid_mode
self.ollama_optimized = ollama_optimized
# Ollama-specific parameters
self.temperature = temperature
self.top_p = top_p
self.top_k = top_k
self.repeat_penalty = repeat_penalty
self.num_ctx = num_ctx
self.num_predict = num_predict
self.torch_dtype = torch_dtype |