Started trying to make Ruby learn as we talk to her

dataset.py

@@ -0,0 +1,20 @@
+import torch
+from tokenizer import simple_tokenizer, load_vocab
+
+def create_dataset():
+    vocab = load_vocab()
+    data = [
+        ("a", "b"),
+        ("ab", "c"),
+        ("abc", "d"),
+    ]
+
+    inputs = [torch.tensor(simple_tokenizer(src, vocab), dtype=torch.long) for src, tgt in data]
+    targets = [torch.tensor(simple_tokenizer(tgt, vocab), dtype=torch.long) for src, tgt in data]
+
+    return inputs, targets
+
+if __name__ == "__main__":
+    inputs, targets = create_dataset()
+    print(inputs)
+    print(targets)

main.py

@@ -1,70 +1,35 @@
 import discord
-import requests
+from model_manager import ModelManager
-import json
 import os
 from dotenv import load_dotenv
 
-# Load environment variables from .env file
+# Load environment variables
 load_dotenv()
 
-# Replace with your bot token
+# Discord bot setup
-BOT_TOKEN = os.getenv('DISCORD_TOKEN')
-
-# Ollama configuration
-OLLAMA_API_URL = 'http://192.168.1.159:11434/api/generate'  # Adjust if your Ollama setup is different
-
-# Set up the Discord client
 intents = discord.Intents.default()
 intents.messages = True
 intents.message_content = True
-
 client = discord.Client(intents=intents)
 
+# Initialize the ModelManager
+USE_CUSTOM_MODEL = True
+OLLAMA_URL = None  # Set to your Ollama endpoint if needed
+model_manager = ModelManager(use_custom_model=USE_CUSTOM_MODEL, ollama_url=OLLAMA_URL)
 
-# Function to query Ollama
-def query_ollama(prompt):
-    payload = {
-        "prompt": prompt,
-        "model": "nollama/mythomax-l2-13b:Q4_K_M"  # Replace with your Ollama model
-    }
-    try:
-        response = requests.post(OLLAMA_API_URL, json=payload, stream=True)
-        if response.status_code == 200:
-            collected_response = ""
-            # Stream and parse each line of JSON from the response
-            for line in response.iter_lines(decode_unicode=True):
-                if line.strip():  # Skip empty lines
-                    try:
-                        data = json.loads(line)  # Parse each line as JSON
-                        collected_response += data.get("response", "")
-                        if data.get("done", False):
-                            break
-                    except json.JSONDecodeError as e:
-                        print(f"Error decoding JSON line: {line}, Error: {e}")
-            return collected_response.strip() or "No response from model."
-        else:
-            return f"Error: {response.status_code} - {response.text}"
-    except requests.RequestException as e:
-        return f"Error connecting to Ollama: {str(e)}"
-
-
-# Event for when the bot is ready
 @client.event
 async def on_ready():
-    print(f'We have logged in as {client.user}')
+    print(f"Logged in as {client.user}")
 
-
-# Event for when a message is sent
 @client.event
 async def on_message(message):
-    # Ignore the bot's own messages
     if message.author == client.user:
         return
 
-    # Respond to all messages except those in DMs
+    # Generate response
-    if not isinstance(message.channel, discord.DMChannel):
+    user_input = message.content
-        response = query_ollama(message.content.strip())
+    bot_response = model_manager.generate_response(user_input)
-        await message.channel.send(response)
 
-# Run the bot
+    await message.channel.send(bot_response)
-client.run(BOT_TOKEN)
+
+client.run(os.getenv("DISCORD_TOKEN"))

memory_buffer.py

@@ -0,0 +1,11 @@
+from collections import deque
+
+class MemoryBuffer:
+    def __init__(self, capacity=10):
+        self.buffer = deque(maxlen=capacity)
+
+    def add_interaction(self, user_input, bot_response):
+        self.buffer.append((user_input, bot_response))
+
+    def get_data(self):
+        return list(self.buffer)

model.py

@@ -0,0 +1,25 @@
+import torch
+import torch.nn as nn
+
+class TinyGPT(nn.Module):
+    def __init__(self, vocab_size, embed_size, num_heads, num_layers):
+        super().__init__()
+        self.embedding = nn.Embedding(vocab_size, embed_size)
+        self.transformer = nn.Transformer(
+            d_model=embed_size,
+            nhead=num_heads,
+            num_encoder_layers=num_layers,
+            num_decoder_layers=num_layers,
+            batch_first=True  # Ensures batch is the first dimension
+        )
+        self.fc = nn.Linear(embed_size, vocab_size)
+
+    def forward(self, src, tgt):
+        # Embed inputs
+        src_embed = self.embedding(src)  # Shape: (batch_size, seq_len, embed_size)
+        tgt_embed = self.embedding(tgt)  # Shape: (batch_size, seq_len, embed_size)
+        # Pass through transformer
+        transformer_out = self.transformer(src_embed, tgt_embed)
+        # Linear projection to vocabulary size
+        output = self.fc(transformer_out)
+        return output

model_manager.py

@@ -0,0 +1,62 @@
+import torch
+from torch.optim import Adam
+from torch.nn import CrossEntropyLoss
+from memory_buffer import MemoryBuffer
+from model import TinyGPT
+from tokenizer import simple_tokenizer, detokenizer, load_vocab
+
+class ModelManager:
+    def __init__(self, use_custom_model=True, ollama_url=None):
+        self.use_custom_model = use_custom_model
+        self.ollama_url = ollama_url
+        self.memory = MemoryBuffer(capacity=10)  # Memory for 10 recent interactions
+        if self.use_custom_model:
+            self._load_custom_model()
+
+    def _load_custom_model(self):
+        """Load the custom GPT model."""
+        self.vocab = load_vocab()
+        self.model = TinyGPT(vocab_size=len(self.vocab), embed_size=32, num_heads=2, num_layers=2).cuda()
+        self.model.load_state_dict(torch.load("ruby_model.pth", weights_only=True))
+        self.model.eval()
+        self.optimizer = Adam(self.model.parameters(), lr=0.0001)
+        self.criterion = CrossEntropyLoss()
+
+    def query_custom_model(self, input_text):
+        """Generate a response using the custom GPT model."""
+        tokens = torch.tensor(simple_tokenizer(input_text, self.vocab), dtype=torch.long).cuda()
+        with torch.no_grad():
+            output = self.model(tokens.unsqueeze(0), tokens.unsqueeze(0))
+            predicted_idx = output.argmax(-1).squeeze()[-1].item()
+        return detokenizer([predicted_idx], self.vocab)
+
+    def train_on_interaction(self, user_input, bot_response):
+        """Train the model on a single interaction."""
+        self.model.train()
+        input_tokens = torch.tensor(simple_tokenizer(user_input, self.vocab), dtype=torch.long).cuda()
+        target_tokens = torch.tensor(simple_tokenizer(bot_response, self.vocab), dtype=torch.long).cuda()
+        
+        # Padding to ensure equal lengths
+        max_len = max(len(input_tokens), len(target_tokens))
+        input_tokens = torch.cat([input_tokens, torch.zeros(max_len - len(input_tokens), dtype=torch.long).cuda()])
+        target_tokens = torch.cat([target_tokens, torch.zeros(max_len - len(target_tokens), dtype=torch.long).cuda()])
+        
+        # Perform a single training step
+        self.optimizer.zero_grad()
+        output = self.model(input_tokens.unsqueeze(0), target_tokens.unsqueeze(0))
+        loss = self.criterion(output.view(-1, len(self.vocab)), target_tokens.view(-1))
+        loss.backward()
+        self.optimizer.step()
+        self.model.eval()
+
+    def generate_response(self, input_text):
+        """Generate a response using the selected model."""
+        if self.use_custom_model:
+            bot_response = self.query_custom_model(input_text)
+            self.memory.add_interaction(input_text, bot_response)
+            self.train_on_interaction(input_text, bot_response)
+            return bot_response
+        elif self.ollama_url:
+            return self.query_ollama(input_text)
+        else:
+            raise ValueError("No valid model selected or configured.")

test.py

@@ -0,0 +1,23 @@
+import torch
+from model import TinyGPT
+from tokenizer import simple_tokenizer, detokenizer, load_vocab
+
+def test_model():
+    vocab = load_vocab()
+
+    # Load model
+    model = TinyGPT(vocab_size=len(vocab), embed_size=32, num_heads=2, num_layers=2).cuda()
+    model.load_state_dict(torch.load("ruby_model.pth", weights_only=True))  # Set weights_only=True
+    model.eval()
+
+    # Test input
+    test_input = torch.tensor(simple_tokenizer("abc", vocab), dtype=torch.long).cuda()
+    with torch.no_grad():
+        output = model(test_input.unsqueeze(0), test_input.unsqueeze(0))
+        predicted_idx = output.argmax(-1).squeeze()[-1].item()
+
+    predicted_char = detokenizer([predicted_idx], vocab)
+    print(f"Ruby says: {predicted_char}")
+
+if __name__ == "__main__":
+    test_model()

tokenizer.py

@@ -0,0 +1,32 @@
+import json
+
+# Save vocabulary
+def save_vocab():
+    vocab = {char: idx for idx, char in enumerate("abcdefghijklmnopqrstuvwxyz ")}
+    vocab["<unk>"] = len(vocab)  # Add unknown token
+    with open('vocab.json', 'w') as f:
+        json.dump(vocab, f)
+
+
+# Load vocabulary
+def load_vocab():
+    with open('vocab.json', 'r') as f:
+        return json.load(f)
+
+# Tokenizer
+def simple_tokenizer(text, vocab):
+    # Convert text to lowercase and replace unknown characters with <unk>
+    text = text.lower()
+    unk_token = vocab.get("<unk>", None)
+    return [vocab[char] if char in vocab else unk_token for char in text]
+
+
+# Detokenizer
+def detokenizer(tokens, vocab):
+    reverse_vocab = {idx: char for char, idx in vocab.items()}
+    return ''.join(reverse_vocab[token] for token in tokens)
+
+if __name__ == "__main__":
+    save_vocab()
+    vocab = load_vocab()
+    print(simple_tokenizer("hello world", vocab))

train.py

@@ -0,0 +1,46 @@
+import torch
+from torch import nn
+from torch.optim import Adam
+from model import TinyGPT
+from dataset import create_dataset
+from tokenizer import load_vocab
+
+def pad_sequence(seq, max_len):
+    """Pads a sequence to the given maximum length."""
+    return torch.cat([seq, torch.zeros(max_len - len(seq), dtype=torch.long)], dim=0)
+
+def train_model():
+    vocab = load_vocab()
+    inputs, targets = create_dataset()
+
+    # Determine the maximum sequence length for padding
+    max_len = max(len(seq) for seq in inputs + targets)
+
+    # Pad inputs and targets
+    inputs = [pad_sequence(seq, max_len) for seq in inputs]
+    targets = [pad_sequence(seq, max_len) for seq in targets]
+
+    # Convert to batch tensors
+    inputs = torch.stack(inputs).cuda()
+    targets = torch.stack(targets).cuda()
+
+    # Model setup
+    model = TinyGPT(vocab_size=len(vocab), embed_size=32, num_heads=2, num_layers=2).cuda()
+    criterion = nn.CrossEntropyLoss()
+    optimizer = Adam(model.parameters(), lr=0.001)
+
+    # Training loop
+    for epoch in range(100):
+        optimizer.zero_grad()
+        output = model(inputs, targets)
+        loss = criterion(output.view(-1, len(vocab)), targets.view(-1))
+        loss.backward()
+        optimizer.step()
+        print(f"Epoch {epoch + 1}, Loss: {loss.item()}")
+
+    # Save the model
+    torch.save(model.state_dict(), "ruby_model.pth")
+    print("Model saved as ruby_model.pth")
+
+if __name__ == "__main__":
+    train_model()

vocab.json

@@ -0,0 +1 @@
+{"a": 0, "b": 1, "c": 2, "d": 3, "e": 4, "f": 5, "g": 6, "h": 7, "i": 8, "j": 9, "k": 10, "l": 11, "m": 12, "n": 13, "o": 14, "p": 15, "q": 16, "r": 17, "s": 18, "t": 19, "u": 20, "v": 21, "w": 22, "x": 23, "y": 24, "z": 25, " ": 26, "<unk>": 27}