Based Code figured out. Now to just figure how to train her.
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Dan
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15
.vscode/launch.json
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.vscode/launch.json
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{
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// Use IntelliSense to learn about possible attributes.
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// Hover to view descriptions of existing attributes.
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// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
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"version": "0.2.0",
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"configurations": [
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{
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"name": "Jade",
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"type": "debugpy",
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"request": "launch",
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"program": "E:\\Development\\AI Development\\Jade\\main.py",
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"console": "integratedTerminal"
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}
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]
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}
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123
main.py
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main.py
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import discord
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import torch
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from model import SimpleTokenizer, initialize_model, train_on_conversation, save_model, update_model_vocab
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import torch.nn.functional as F
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import os
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from dotenv import load_dotenv
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load_dotenv()
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class DiscordBot(discord.Client):
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def __init__(self, **options):
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super().__init__(**options)
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self.tokenizer = SimpleTokenizer()
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self.tokenizer_vocab_path = 'tokenizer_vocab.json'
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self.tokenizer.load_vocab(self.tokenizer_vocab_path)
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self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
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self.model, self.optimizer, self.criterion = initialize_model(self.tokenizer, self.device)
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self.conversation_history = [] # Keep track of conversations for learning
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self.previous_reply = None # Store last reply for pattern recognition
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async def on_ready(self):
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print(f'Logged in as {self.user.name}')
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async def on_message(self, message):
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if message.author == self.user:
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return
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print(f"Received message from {message.author}: {message.content}")
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# Update tokenizer vocabulary with the new message
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previous_vocab_size = len(self.tokenizer.token2idx)
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self.tokenizer.build_vocab([message.content])
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new_vocab_size = len(self.tokenizer.token2idx)
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# Update model if vocabulary has changed
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if new_vocab_size != previous_vocab_size:
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self.model = update_model_vocab(self.model, self.tokenizer, self.device)
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self.optimizer = torch.optim.Adam(self.model.parameters(), lr=0.001)
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print("Model vocabulary updated.")
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# Generate a reply
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self.model.eval()
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with torch.no_grad():
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reply = self.generate_reply(message.content)
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print(f"Sending reply: {reply}")
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await message.channel.send(reply)
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# Append conversation to history for future learning
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self.conversation_history.append({
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"user_message": message.content,
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"bot_reply": reply,
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"channel": message.channel
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})
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# Continuous learning: Train on this conversation pair
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loss = train_on_conversation(
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self.model,
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self.optimizer,
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self.criterion,
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self.tokenizer,
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message.content,
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reply,
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self.device
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)
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# Save the model and tokenizer for future sessions
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save_model(self.model)
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self.tokenizer.save_vocab(self.tokenizer_vocab_path)
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# Store this reply to help Jade learn from repetition in future responses
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self.previous_reply = reply
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def generate_reply(self, input_text, max_length=20, temperature=1.0, top_k=10):
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# Prepare the input sequence with special tokens
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input_sequence = ['<SOS>'] + input_text.split() + ['<EOS>']
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input_indices = self.tokenizer.encode(' '.join(input_sequence))
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input_tensor = torch.tensor([input_indices], dtype=torch.long, device=self.device)
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generated_indices = []
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for _ in range(max_length):
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output = self.model(input_tensor)
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if output.size(0) == 0:
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print("Model output is empty. Breaking out of generation loop.")
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break
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next_token_logits = output[-1, 0, :] / temperature
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# Penalize <UNK>
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unk_token_idx = self.tokenizer.token2idx.get('<UNK>', None)
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if unk_token_idx is not None:
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next_token_logits[unk_token_idx] = -float('inf')
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# Apply Top-K sampling
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top_k = min(top_k, next_token_logits.size(-1))
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values, indices = torch.topk(next_token_logits, top_k)
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probabilities = F.softmax(values, dim=-1)
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predicted_index = indices[torch.multinomial(probabilities, 1)].item()
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# Stop if <EOS> token is generated
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if predicted_index == self.tokenizer.token2idx.get('<EOS>'):
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break
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generated_indices.append(predicted_index)
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input_indices.append(predicted_index)
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input_tensor = torch.tensor([input_indices], dtype=torch.long, device=self.device)
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# Filter out special tokens from generated indices
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special_token_indices = set(self.tokenizer.token2idx[token] for token in ['<PAD>', '<UNK>', '<SOS>', '<EOS>'])
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filtered_indices = [idx for idx in generated_indices if idx not in special_token_indices]
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# Decode the filtered indices
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reply = self.tokenizer.decode(filtered_indices)
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return reply
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DISCORD_TOKEN = os.getenv('DISCORD_TOKEN')
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# Initialize and run the Discord bot
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intents = discord.Intents.default()
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intents.messages = True
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bot = DiscordBot(intents=intents)
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bot.run(DISCORD_TOKEN)
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183
model.py
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model.py
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import torch
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import torch.nn as nn
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import threading
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import os
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import json
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# Simple Tokenizer
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class SimpleTokenizer:
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def __init__(self):
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self.token2idx = {'<PAD>': 0, '<UNK>': 1, '<SOS>': 2, '<EOS>': 3}
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self.idx2token = {idx: token for token, idx in self.token2idx.items()}
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self.lock = threading.Lock()
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def build_vocab(self, texts):
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with self.lock:
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for text in texts:
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tokens = text.split()
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for token in tokens:
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if token not in self.token2idx:
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idx = len(self.token2idx)
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self.token2idx[token] = idx
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self.idx2token[idx] = token
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def encode(self, text):
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with self.lock:
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return [self.token2idx.get(token, self.token2idx['<UNK>']) for token in text.split()]
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def decode(self, indices):
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with self.lock:
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return ' '.join([self.idx2token.get(idx, '<UNK>') for idx in indices])
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def save_vocab(self, path):
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with open(path, 'w') as f:
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json.dump({'token2idx': self.token2idx, 'idx2token': self.idx2token}, f)
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def load_vocab(self, path):
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if os.path.exists(path):
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with open(path, 'r') as f:
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vocab = json.load(f)
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self.token2idx = vocab['token2idx']
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self.idx2token = {int(k): v for k, v in vocab['idx2token'].items()}
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print('Tokenizer vocabulary loaded from', path)
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# Ensure special tokens are present
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special_tokens = {'<PAD>': 0, '<UNK>': 1, '<SOS>': 2, '<EOS>': 3}
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for token, idx in special_tokens.items():
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if token not in self.token2idx:
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self.token2idx[token] = idx
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self.idx2token[idx] = token
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else:
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print('No existing tokenizer vocabulary found. Starting fresh.')
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self.token2idx = {'<PAD>': 0, '<UNK>': 1, '<SOS>': 2, '<EOS>': 3}
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self.idx2token = {idx: token for token, idx in self.token2idx.items()}
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# Positional Encoding
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class PositionalEncoding(nn.Module):
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def __init__(self, d_model, max_len=5000):
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super(PositionalEncoding, self).__init__()
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pe = torch.zeros(max_len, d_model)
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position = torch.arange(0, max_len).unsqueeze(1).float()
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div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-torch.log(torch.tensor(10000.0)) / d_model))
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pe[:, 0::2] = torch.sin(position * div_term)
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if d_model % 2 == 1:
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pe[:, -1] = torch.cos(position.squeeze() * div_term[-1])
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else:
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pe[:, 1::2] = torch.cos(position * div_term)
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pe = pe.unsqueeze(1) # Shape: [max_len, 1, d_model]
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self.register_buffer('pe', pe)
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def forward(self, x):
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# x: [seq_len, batch_size, d_model]
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x = x + self.pe[:x.size(0)]
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return x
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# GPT Model
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class GPTModel(nn.Module):
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def __init__(self, vocab_size, d_model=128, nhead=8, num_layers=2):
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super(GPTModel, self).__init__()
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self.model_type = 'Transformer'
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self.d_model = d_model
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self.embedding = nn.Embedding(vocab_size, d_model)
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self.pos_encoder = PositionalEncoding(d_model)
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encoder_layers = nn.TransformerEncoderLayer(d_model, nhead)
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self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers)
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self.fc_out = nn.Linear(d_model, vocab_size)
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self.src_mask = None
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def _generate_square_subsequent_mask(self, sz):
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mask = torch.triu(torch.ones(sz, sz) == 1).transpose(0, 1)
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mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
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return mask
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def forward(self, src):
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# src: [seq_len, batch_size]
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src = src.transpose(0, 1) # Shape: [seq_len, batch_size]
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src = self.embedding(src) * torch.sqrt(torch.tensor(self.d_model, dtype=torch.float32))
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src = self.pos_encoder(src)
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if self.src_mask is None or self.src_mask.size(0) != src.size(0):
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device = src.device
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self.src_mask = self._generate_square_subsequent_mask(src.size(0)).to(device)
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output = self.transformer_encoder(src, self.src_mask)
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logits = self.fc_out(output)
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return logits # Shape: [seq_len, batch_size, vocab_size]
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# Training function
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def train_step(model, optimizer, criterion, input_tensor, target_tensor):
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model.train()
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optimizer.zero_grad()
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output = model(input_tensor) # [seq_len, batch_size, vocab_size]
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output = output.view(-1, output.size(-1)) # [seq_len * batch_size, vocab_size]
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target = target_tensor.transpose(0,1).contiguous().view(-1) # [seq_len * batch_size]
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loss = criterion(output, target)
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loss.backward()
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optimizer.step()
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print(f'Training loss: {loss.item():.4f}')
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return loss.item()
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def initialize_model(tokenizer, device):
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vocab_size = len(tokenizer.token2idx)
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model = GPTModel(vocab_size=vocab_size).to(device)
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optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
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criterion = nn.CrossEntropyLoss(ignore_index=0)
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model_path = 'gpt_model.pth'
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# Load existing model if available
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if os.path.exists(model_path):
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model.load_state_dict(torch.load(model_path, map_location=device))
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print('Model loaded from', model_path)
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else:
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print('No existing model found. Starting fresh.')
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return model, optimizer, criterion
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def save_model(model):
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model_path = 'gpt_model.pth'
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torch.save(model.state_dict(), model_path)
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def update_model_vocab(model, tokenizer, device):
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vocab_size = len(tokenizer.token2idx)
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old_embedding_weight = model.embedding.weight.data
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old_vocab_size, embedding_dim = old_embedding_weight.shape
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new_embedding = nn.Embedding(vocab_size, model.d_model).to(device)
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new_embedding.weight.data[:old_vocab_size] = old_embedding_weight
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model.embedding = new_embedding
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old_fc_out_weight = model.fc_out.weight.data
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old_fc_out_bias = model.fc_out.bias.data
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new_fc_out = nn.Linear(model.d_model, vocab_size).to(device)
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new_fc_out.weight.data[:old_vocab_size] = old_fc_out_weight
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new_fc_out.bias.data[:old_vocab_size] = old_fc_out_bias
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model.fc_out = new_fc_out
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return model
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def train_on_conversation(model, optimizer, criterion, tokenizer, input_text, target_text, device):
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tokenizer.build_vocab([input_text, target_text])
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input_indices = tokenizer.encode(input_text)
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target_indices = tokenizer.encode(target_text)
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# Concatenate input and target indices to create a single sequence
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full_indices = input_indices + target_indices
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# Create input and target sequences for training
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input_sequence = full_indices[:-1] # All tokens except the last
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target_sequence = full_indices[1:] # All tokens except the first
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# Update model if vocabulary has changed
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if len(tokenizer.token2idx) != model.embedding.num_embeddings:
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model = update_model_vocab(model, tokenizer, device)
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optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
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input_tensor = torch.tensor([input_sequence], dtype=torch.long, device=device)
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target_tensor = torch.tensor([target_sequence], dtype=torch.long, device=device)
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loss = train_step(model, optimizer, criterion, input_tensor, target_tensor)
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return loss
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