Got Jade to exactly copy without extra characters - Version Solstice-Horizon
This commit is contained in:
Dan
parent
d7f116620a
commit
e0ea105872
135
main.py
135
main.py
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# main.py: Discord Bot Code
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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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from model import JadeModel
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import os
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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intents = discord.Intents.default()
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intents.messages = True
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intents.message_content = True
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client = discord.Client(intents=intents)
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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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# Initialize the model
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = JadeModel().to(device)
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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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@client.event
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async def on_ready():
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print(f'We have logged in as {client.user}')
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@client.event
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async def on_message(message):
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if message.author == client.user:
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return
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print(f"Received message from {message.author}: {message.content}")
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# Train Jade with the new message
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model.train_on_message(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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# Generate a response using Jade
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response = model.generate_response(message.content)
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await message.channel.send(response)
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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.message_content = True
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bot = DiscordBot(intents=intents)
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bot.run(DISCORD_TOKEN)
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# Start the bot with your token
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client.run(os.getenv('DISCORD_TOKEN'))
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275
model.py
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model.py
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# Suggested Refinements for Jade (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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import torch.optim as optim
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import random
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import string
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import numpy as np
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# Simple Tokenizer
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class SimpleTokenizer:
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class JadeModel(nn.Module):
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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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super(JadeModel, self).__init__()
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# GPT-like Transformer architecture
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self.vocab_size = 256 # Character-level tokenization (ASCII range)
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self.embedding_dim = 768 # GPT-like embedding dimension
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self.num_heads = 12 # Number of attention heads
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self.num_layers = 12 # Number of transformer layers
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self.max_position_embeddings = 512 # Maximum sequence length
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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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# Embedding layers
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self.embedding = nn.Embedding(self.vocab_size, self.embedding_dim)
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self.position_embedding = nn.Embedding(self.max_position_embeddings, self.embedding_dim)
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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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# Transformer layers
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self.transformer_layers = nn.ModuleList([
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nn.TransformerEncoderLayer(d_model=self.embedding_dim, nhead=self.num_heads)
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for _ in range(self.num_layers)
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])
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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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# Output layer
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self.fc = nn.Linear(self.embedding_dim, self.vocab_size)
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self.softmax = nn.Softmax(dim=-1)
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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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# Optimizer and loss function
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self.optimizer = optim.Adam(self.parameters(), lr=0.001)
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self.criterion = nn.CrossEntropyLoss()
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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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# Device setup
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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self.to(self.device)
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# Debug message to verify changes (updated unique message for each change)
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self.debug_message = "[DEBUG] Model initialized with version: Jade-Solstice-Horizon"
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print(self.debug_message)
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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, input_ids):
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# Create position ids for input sequence
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seq_length = input_ids.size(1)
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position_ids = torch.arange(0, seq_length, dtype=torch.long, device=self.device)
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position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
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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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# Embedding lookup
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x = self.embedding(input_ids) + self.position_embedding(position_ids)
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# Pass through transformer layers
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for layer in self.transformer_layers:
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x = layer(x)
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# Output layer
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x = self.fc(x)
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return x
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def generate_response(self, input_text, initial_temperature=0.85, top_p=0.8, repetition_penalty=1.4, max_token_frequency=2):
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# Convert input_text to token ids
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input_ids = self.tokenize(input_text)
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input_tensor = torch.tensor(input_ids).unsqueeze(0).to(self.device)
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generated_tokens = input_ids.copy()
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recent_tokens = list(input_ids[-10:]) # Expanded recent tokens window to 10
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temperature = initial_temperature
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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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with torch.no_grad():
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for i in range(50): # Generate up to 50 more tokens
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output = self.forward(input_tensor)
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logits = output[:, -1, :] # Consider only the last token's logits
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logits = logits / (temperature + 1e-2) # Apply temperature for sampling diversity
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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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# Apply repetition penalty
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for token in set(generated_tokens):
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if generated_tokens.count(token) > 1:
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logits[0, token] /= (repetition_penalty + generated_tokens.count(token) * 0.02) # Frequency-based scaling for penalty
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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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# Apply slight logits smoothing to avoid overly confident peaks
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logits = logits - torch.mean(logits) * 0.01
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# Dynamic Nucleus (top-p) sampling with adjusted threshold
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sorted_logits, sorted_indices = torch.sort(logits, descending=True)
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cumulative_probs = torch.cumsum(self.softmax(sorted_logits), dim=-1)
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top_p_mask = cumulative_probs < top_p
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top_p_logits = sorted_logits[top_p_mask]
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top_p_indices = sorted_indices[top_p_mask]
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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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if len(top_p_logits) > 1:
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top_p_probs = self.softmax(top_p_logits)
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sampled_token = top_p_indices[torch.multinomial(top_p_probs, num_samples=1).item()].item()
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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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sampled_token = sorted_indices[0, 0].item() # Fallback to the most probable token if none pass the top-p threshold
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# Enforce diversity constraint by limiting token frequency
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if generated_tokens.count(sampled_token) >= max_token_frequency:
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logits[0, sampled_token] -= 1.5 # Adjusted penalty to limit token frequency
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continue # Skip adding this token if it has reached the max frequency
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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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# Stop repetition if the sampled token was recently repeated
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if len(generated_tokens) > 1 and generated_tokens[-1] == sampled_token:
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continue
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# Add token and update state
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generated_tokens.append(sampled_token)
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recent_tokens.append(sampled_token)
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if len(recent_tokens) > 10:
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recent_tokens.pop(0) # Maintain a window of recent tokens to suppress
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def update_model_vocab(model, tokenizer, device):
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vocab_size = len(tokenizer.token2idx)
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# Update input tensor to include the generated token
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input_tensor = torch.tensor(generated_tokens).unsqueeze(0).to(self.device)
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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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# Gradually decrease temperature to reduce randomness more smoothly
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temperature = max(0.75, temperature * 0.98)
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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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response = self.detokenize(generated_tokens)
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print("[DEBUG] Generated response:", response) # Debug statement to verify changes
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print(f"[DEBUG] Generation loss rate (approximated): {temperature}") # Approximate loss rate
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return response
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return model
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def tokenize(self, text):
|
||||
# Character-level tokenizer: converts text to ASCII values
|
||||
token_ids = [ord(char) for char in text if ord(char) < self.vocab_size]
|
||||
return token_ids
|
||||
|
||||
def detokenize(self, token_ids):
|
||||
# Detokenizer to convert ASCII values back to characters
|
||||
return "".join([chr(id) for id in token_ids])
|
||||
|
||||
def train_on_conversation(model, optimizer, criterion, tokenizer, input_text, target_text, device):
|
||||
tokenizer.build_vocab([input_text, target_text])
|
||||
input_indices = tokenizer.encode(input_text)
|
||||
target_indices = tokenizer.encode(target_text)
|
||||
def train_on_message(self, message):
|
||||
# Tokenize the message
|
||||
input_ids = self.tokenize(message)
|
||||
input_tensor = torch.tensor(input_ids).unsqueeze(0).to(self.device)
|
||||
|
||||
# Concatenate input and target indices to create a single sequence
|
||||
full_indices = input_indices + target_indices
|
||||
# Create target labels (next character prediction task)
|
||||
labels = input_ids[1:] + [input_ids[-1]] # Shift tokens for training
|
||||
labels_tensor = torch.tensor(labels).unsqueeze(0).to(self.device)
|
||||
|
||||
# Create input and target sequences for training
|
||||
input_sequence = full_indices[:-1] # All tokens except the last
|
||||
target_sequence = full_indices[1:] # All tokens except the first
|
||||
# Training step
|
||||
self.optimizer.zero_grad()
|
||||
outputs = self.forward(input_tensor)
|
||||
loss = self.criterion(outputs.view(-1, outputs.size(-1)), labels_tensor.view(-1))
|
||||
loss.backward()
|
||||
self.optimizer.step()
|
||||
print(f"Training loss: {loss.item()}")
|
||||
|
||||
# Update model if vocabulary has changed
|
||||
if len(tokenizer.token2idx) != model.embedding.num_embeddings:
|
||||
model = update_model_vocab(model, tokenizer, device)
|
||||
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
|
||||
# Changes made:
|
||||
# Version: Jade-Solstice-Horizon
|
||||
# - Reverted temperature, top_p, and repetition penalty settings to be closer to Solstice.
|
||||
# - Introduced explicit stop criteria to prevent repeating tokens consecutively.
|
||||
# - Applied slight smoothing to logits to prevent high peaks and excessive repetition.
|
||||
# - Updated debug message to reflect the new version.
|
||||
|
||||
input_tensor = torch.tensor([input_sequence], dtype=torch.long, device=device)
|
||||
target_tensor = torch.tensor([target_sequence], dtype=torch.long, device=device)
|
||||
|
||||
loss = train_step(model, optimizer, criterion, input_tensor, target_tensor)
|
||||
return loss
|
||||
# Observations:
|
||||
# - Aimed to retain the strengths of Solstice while reducing remaining issues with repetitive tokens by adding specific repetition stop criteria.
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user