File size: 4,451 Bytes
0efd351 |
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 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 |
import torch
import torch.nn as nn
from huggingface_hub import PyTorchModelHubMixin
import hashlib
import random
# Simple DHT Implementation
class SimpleDHT:
def __init__(self, node_id):
self.node_id = node_id
self.storage = {}
def _hash(self, key):
"""Generate a hash for the key."""
return hashlib.sha256(key.encode()).hexdigest()
def put(self, key, value):
"""Store a key-value pair in the DHT."""
hashed_key = self._hash(key)
self.storage[hashed_key] = value
print(f"Stored {key} at {hashed_key}")
def get(self, key):
"""Retrieve a value from the DHT by key."""
hashed_key = self._hash(key)
return self.storage.get(hashed_key, None)
# Self Sovereign AI 1.0 with DeepSeek R1 Support
class SelfSovereignAI(nn.Module, PyTorchModelHubMixin):
def __init__(self, input_size=10, hidden_size=20, output_size=1, seq_length=8, num_heads=2):
super(SelfSovereignAI, self).__init__()
# Original feedforward layers
self.layer1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.layer2 = nn.Linear(hidden_size, output_size)
self.sigmoid = nn.Sigmoid()
# DeepSeek R1-inspired Transformer layer
self.embedding = nn.Linear(input_size, hidden_size)
self.transformer = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
d_model=hidden_size,
nhead=num_heads,
dim_feedforward=hidden_size * 4,
dropout=0.1
),
num_layers=1
)
self.seq_length = seq_length
# DHT for decentralized storage
self.dht = SimpleDHT(node_id=str(random.randint(1, 1000)))
self._store_metadata()
def forward(self, x, use_transformer=False):
if use_transformer:
# Transformer path (expects sequence data: batch_size, seq_length, input_size)
x = self.embedding(x)
x = self.transformer(x)
x = x.mean(dim=1) # Average over sequence length
x = self.layer2(x)
x = self.sigmoid(x)
else:
# Original feedforward path
x = self.layer1(x)
x = self.relu(x)
x = self.layer2(x)
x = self.sigmoid(x)
return x
def _store_metadata(self):
"""Store model metadata in DHT, including DeepSeek R1 details."""
metadata = {
"model_name": "Self Sovereign AI 1.0 with DeepSeek R1",
"input_size": 10,
"hidden_size": 20,
"output_size": 1,
"seq_length": self.seq_length,
"num_heads": 2,
"supports_transformer": True
}
self.dht.put("metadata", metadata)
# Store transformer weights in DHT (example)
transformer_weights = self.transformer.state_dict()
self.dht.put("transformer_weights", transformer_weights)
def load_from_dht(self):
"""Load transformer weights from DHT if available."""
weights = self.dht.get("transformer_weights")
if weights:
self.transformer.load_state_dict(weights)
print("Loaded transformer weights from DHT")
def get_metadata(self):
"""Retrieve model metadata from DHT."""
return self.dht.get("metadata")
# Example usage
if __name__ == "__main__":
# Create an instance of the model
model = SelfSovereignAI(input_size=10, hidden_size=20, output_size=1, seq_length=8, num_heads=2)
# Test feedforward path
input_data_ff = torch.randn(1, 10)
output_ff = model(input_data_ff, use_transformer=False)
print(f"Feedforward output: {output_ff}")
# Test transformer path (DeepSeek R1-style)
input_data_tr = torch.randn(1, 8, 10) # batch_size, seq_length, input_size
output_tr = model(input_data_tr, use_transformer=True)
print(f"Transformer output: {output_tr}")
# Retrieve metadata from DHT
metadata = model.get_metadata()
print(f"Stored metadata: {metadata}")
# Load transformer weights from DHT (simulated)
model.load_from_dht()
# Save the model locally
model.save_pretrained("self_sovereign_ai_1.0_deepseek")
# Push to Hugging Face Hub
model.push_to_hub(
repo_id="your-username/self-sovereign-ai-1.0-deepseek",
commit_message="Added DeepSeek R1 support to Self Sovereign AI 1.0"
) |