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12 Commits
main
...
Dev-Stage1
| Author | SHA1 | Date | |
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| 0716291d9d | |||
| c9f37a3781 | |||
| c174c3159e | |||
| ed288d094b | |||
| 6c9dde2289 | |||
| 3829ca8d01 | |||
| 3020e230ff | |||
| 4691c84348 | |||
| ed5297e896 | |||
| ddd5cd1db0 | |||
| d70a83ea72 | |||
| 65cfe387bf |
1
.gitignore
vendored
1
.gitignore
vendored
@ -168,3 +168,4 @@ cython_debug/
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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/tokenizer_vocab.txt
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69
dashboard.py
Normal file
69
dashboard.py
Normal file
@ -0,0 +1,69 @@
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from flask import Flask, render_template_string
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from datetime import datetime
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import os
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app = Flask(__name__)
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def tail(filepath, num_lines=10):
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if not os.path.exists(filepath):
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return []
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with open(filepath, encoding="utf-8") as f:
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return f.readlines()[-num_lines:]
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@app.route("/")
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def home():
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vocab_size = 0
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if os.path.exists("tokenizer_vocab.txt"):
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with open("tokenizer_vocab.txt", encoding="utf-8") as f:
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vocab_size = sum(1 for _ in f)
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dreams = [line.strip() for line in tail("logs/dreams.log", 10)]
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messages = [line.strip() for line in tail("logs/messages.log", 10)]
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errors = [line.strip() for line in tail("logs/error.log", 15)]
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return render_template_string("""
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<!DOCTYPE html>
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<html>
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<head>
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<title>Ruby Dashboard</title>
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<meta http-equiv="refresh" content="5">
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<style>
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body { background: #121212; color: #eee; font-family: sans-serif; padding: 20px; }
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h1, h3 { color: #e48bf8; }
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li { margin-bottom: 4px; }
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pre { background: #1e1e1e; padding: 10px; border-radius: 8px; overflow-x: auto; }
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</style>
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</head>
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<body>
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<h1>🌸 Ruby's Dashboard</h1>
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<p><b>Vocabulary Size:</b> {{ vocab_size }}</p>
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<h3>🧠 Recent Daydreams</h3>
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<ul>
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{% for dream in dreams %}
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<li>{{ dream }}</li>
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{% endfor %}
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</ul>
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<h3>📨 Recent Messages</h3>
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<ul>
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{% for msg in messages %}
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<li>{{ msg }}</li>
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{% endfor %}
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</ul>
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<h3>⚠️ Recent Errors</h3>
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<pre>
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{% for err in errors %}
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{{ err }}
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{% endfor %}
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</pre>
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</body>
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</html>
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""", dreams=dreams[::-1], messages=messages[::-1], errors=errors[::-1], vocab_size=vocab_size)
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def start_dashboard():
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app.run(debug=False, host="0.0.0.0", port=5000)
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105
main.py
105
main.py
@ -1,7 +1,23 @@
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import discord
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import asyncio
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import atexit
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import os
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import threading
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from dotenv import load_dotenv
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from datetime import datetime
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from datetime import datetime, timedelta
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from dashboard import start_dashboard
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from tokenizer import Tokenizer
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from trainer import RubyTrainer
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import logging
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# Setup logging
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logging.basicConfig(
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filename="logs/error.log",
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level=logging.ERROR,
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format="%(asctime)s %(levelname)s: %(message)s",
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encoding="utf-8"
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)
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# Load environment
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load_dotenv()
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@ -16,21 +32,77 @@ intents.message_content = True
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intents.dm_messages = True
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intents = intents
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class Ruby(discord.Client):
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def __init__(self):
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super().__init__(intents=intents)
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self.tokenizer = Tokenizer()
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self.trainer = RubyTrainer(self.tokenizer)
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self.last_message_time = datetime.utcnow()
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self.idle_threshold = timedelta(seconds=120)
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self.log_path = os.path.join("logs", "messages.log")
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os.makedirs("logs", exist_ok=True)
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async def setup_hook(self):
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self.loop.create_task(self.idle_dream_loop())
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async def set_activity(self, text=None):
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if text is None:
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await self.change_presence(status=discord.Status.online, activity=None)
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else:
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activity = discord.Activity(type=discord.ActivityType.listening, name=text)
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await self.change_presence(status=discord.Status.idle, activity=activity)
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async def on_ready(self):
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print(f"[READY] Logged in as {self.user} (ID: {self.user.id})")
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await self.set_activity("you...")
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self.trainer.reinforce_core_memory()
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async def idle_dream_loop(self):
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await self.wait_until_ready()
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while not self.is_closed():
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now = datetime.utcnow()
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if now - self.last_message_time > self.idle_threshold:
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print("[IDLE] Ruby has been idle — entering dream mode.")
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await self.set_activity("the past...")
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try:
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self.trainer.dream()
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except Exception as e:
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logging.error("Error dreaming: %s", e)
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await self.set_activity("my thoughts")
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from random import random
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speak = random() < 0.5
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thought = self.trainer.daydream(say_thought=speak)
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if speak and thought and len(thought.split()) >=4:
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for guild in self.guilds:
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for channel in guild.text_channels:
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if channel.permissions_for(guild.me).send_messages:
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if not thought.endswith("."):
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thought += "."
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await channel.send(f"(dreaming) {thought}")
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break
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break # only post to one server/channel
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await self.set_activity(None) # reset to normal
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self.last_message_time = datetime.utcnow()
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await asyncio.sleep(180)
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async def on_message(self, message: discord.Message):
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if message.author.id == self.user.id:
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return # ignore self
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return
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self.log_message(message)
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self.train_on_message(message)
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self.trainer.train_on_tokens_from_text(message.content.strip())
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reply = self.trainer.generate_reply()
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if reply.strip():
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await message.channel.send(reply)
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else:
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print("[REPLY] Skipped (empty)")
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def log_message(self, message: discord.Message):
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timestamp = datetime.utcnow().isoformat()
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@ -42,9 +114,30 @@ class Ruby(discord.Client):
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print(f"[LOGGED] {log_entry.strip()}")
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def train_on_message(self, message: discord.Message):
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print(f"[TRAIN] Simulating training on: \"{message.content.strip()}\"")
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text = message.content.strip()
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self.trainer.train_on_tokens_from_text(text)
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token_tensor = torch.tensor(tokens, dtype=torch.long)
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loss = train_on_tokens(self.model, tokens, self.optimizer, self.criterion, device="cpu")
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print(f"[TRAIN] Tokens: {tokens} | Loss: {loss:.4f}")
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# Run Ruby
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client = None
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try:
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client = Ruby()
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client = Ruby()
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client.run(TOKEN)
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def on_exit():
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if client:
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print("[EXIT] Ruby is gracefully shutting down...")
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client.trainer.dream()
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client.trainer.daydream(rounds=10)
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atexit.register(on_exit)
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dashboard_thread = threading.Thread(target=start_dashboard, daemon=True)
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dashboard_thread.start()
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client.run(TOKEN)
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finally:
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if client is not None:
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print("[EXIT] Ruby is shutting down — dreaming one last time...")
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client.trainer.dream()
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client.trainer.daydream(rounds=10)
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24
model.py
Normal file
24
model.py
Normal file
@ -0,0 +1,24 @@
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import torch
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import torch.nn as nn
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class MiniGPT(nn.Module):
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def __init__(self, vocab_size, embed_dim=128, n_heads=4, n_layers=2, max_len=128):
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super().__init__()
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self.token_embed = nn.Embedding(vocab_size, embed_dim)
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self.pos_embed = nn.Embedding(max_len, embed_dim)
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self.blocks = nn.ModuleList([
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nn.TransformerEncoderLayer(d_model=embed_dim, nhead=n_heads, batch_first=True)
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for _ in range(n_layers)
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])
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self.ln_f = nn.LayerNorm(embed_dim)
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self.head = nn.Linear(embed_dim, vocab_size)
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def forward(self, x):
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seq_len = x.size(1)
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pos = torch.arange(0, seq_len, device=x.device).unsqueeze(0)
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x = self.token_embed(x) + self.pos_embed(pos)
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for block in self.blocks:
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x = block(x)
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x = self.ln_f(x)
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return self.head(x)
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17
state_tracker.py
Normal file
17
state_tracker.py
Normal file
@ -0,0 +1,17 @@
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from datetime import datetime
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class RubyState:
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def __init__(self):
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self.last_message_time = datetime.utcnow()
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self.current_activity = "Booting up..."
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self.latest_thoughts = []
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self.latest_losses = []
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self.vocab_size = 0
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def log_thought(self, thought):
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self.latest_thoughts.append((datetime.utcnow(), thought))
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self.latest_thoughts = self.latest_thoughts[-10:]
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def log_loss(self, value):
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self.latest_losses.append((datetime.utcnow(), value))
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self.latest_losses = self.latest_losses[-10:]
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38
tokenizer.py
Normal file
38
tokenizer.py
Normal file
@ -0,0 +1,38 @@
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import os
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class Tokenizer:
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def __init__(self, vocab_path="tokenizer_vocab.txt"):
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self.vocab_path = vocab_path
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self.vocab = {"<START>": 0, "<END>": 1}
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self.inv_vocab = {0: "<START>", 1: "<END>"}
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self.load_vocab()
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def load_vocab(self):
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if not os.path.exists(self.vocab_path):
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return
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with open(self.vocab_path, "r", encoding="utf-8") as f:
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for line in f:
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token, idx = line.strip().split("\t")
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self.vocab[token] = int(idx)
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if token not in self.vocab:
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self.vocab[token] = idx
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self.inv_vocab[idx] = token
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self.inv_vocab = {v: k for k, v in self.vocab.items()}
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def save_vocab(self):
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with open(self.vocab_path, "w", encoding="utf-8") as f:
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for token, idx in self.vocab.items():
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f.write(f"{token}\t{idx}\n")
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def tokenize(self, text):
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tokens = []
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for word in text.strip().split():
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if word not in self.vocab:
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self.vocab[word] = len(self.vocab)
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self.inv_vocab[self.vocab[word]] = word
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tokens.append(self.vocab[word])
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self.save_vocab()
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return tokens
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def detokenize(self, tokens):
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return " ".join(self.inv_vocab.get(t, "<UNK>") for t in tokens)
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210
trainer.py
Normal file
210
trainer.py
Normal file
@ -0,0 +1,210 @@
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import torch
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import torch.nn.functional as F
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from datetime import datetime
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import os
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from model import MiniGPT
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class RubyTrainer:
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def __init__(self, tokenizer, embed_dim=128, n_heads=4, n_layers=2, max_len=128):
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self.tokenizer = tokenizer
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.embed_dim = embed_dim
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.max_len = max_len
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self.model = None
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self.optimizer = None
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self.criterion = torch.nn.CrossEntropyLoss()
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self.rebuild_model_if_needed()
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def rebuild_model_if_needed(self):
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vocab_size = len(self.tokenizer.vocab)
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if self.model is None or self.model.token_embed.num_embeddings != vocab_size:
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print("[MODEL] Initializing/Reinitializing model with vocab size:", vocab_size)
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self.model = MiniGPT(
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vocab_size,
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self.embed_dim,
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self.n_heads,
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self.n_layers,
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self.max_len
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).to(self.device)
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self.optimizer = torch.optim.Adam(self.model.parameters(), lr=0.001)
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def train_on_tokens_from_text(self, text: str):
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tokens = self.tokenizer.tokenize(text.lower())
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if not tokens:
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return
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tokens = [self.tokenizer.vocab["<START>"]] + tokens + [self.tokenizer.vocab["<END>"]]
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if len(tokens) < 2:
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return
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self.rebuild_model_if_needed()
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self.model.train()
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x = torch.tensor(tokens[:-1], dtype=torch.long, device=self.device).unsqueeze(0)
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y = torch.tensor(tokens[1:], dtype=torch.long, device=self.device).unsqueeze(0)
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out = self.model(x)
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loss = self.criterion(out.view(-1, out.size(-1)), y.view(-1))
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loss.backward()
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self.optimizer.step()
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self.optimizer.zero_grad()
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print(f"[TRAIN] Tokens: {tokens} | Loss: {loss.item():.4f}")
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def generate_reply(self, max_tokens=50, temperature=1.1, top_k=10):
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self.model.eval()
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input_ids = torch.tensor([[self.tokenizer.vocab["<START>"]]], dtype=torch.long, device=self.device)
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token_freq = {}
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for _ in range(max_tokens):
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with torch.no_grad():
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out = self.model(input_ids)
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logits = out[:, -1, :] / temperature
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if input_ids.size(1) < 8:
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logits[0, self.tokenizer.vocab["<END>"]] = float("-inf")
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for token_id in set(token_freq.keys()):
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logits[0, token_id] *= 0.7 ** token_freq[token_id]
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probs = F.softmax(logits, dim=-1)
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if top_k > 0:
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top_k_probs, top_k_indices = torch.topk(probs, top_k)
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next_token = top_k_indices[0][torch.multinomial(top_k_probs, 1)]
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else:
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next_token = torch.multinomial(probs, 1)[0]
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token_freq[next_token.item()] = token_freq.get(next_token.item(), 0) + 1
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next_token = next_token.view(1, 1)
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input_ids = torch.cat([input_ids, next_token], dim=1)
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|
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if next_token.item() == self.tokenizer.vocab["<END>"]:
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break
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token_ids = input_ids.squeeze(0).tolist()[1:]
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reply_tokens = [t for t in token_ids if t != self.tokenizer.vocab["<END>"]]
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return self.tokenizer.detokenize(reply_tokens)
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|
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def self_rephrase(self, original: str, max_tokens=50):
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self.model.eval()
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tokens = [self.tokenizer.vocab["<START>"]] + self.tokenizer.tokenize(original.lower())
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input_ids = torch.tensor(tokens, dtype=torch.long, device=self.device).unsqueeze(0)
|
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|
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for _ in range(max_tokens):
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with torch.no_grad():
|
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out = self.model(input_ids)
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logits = out[:, -1, :] / 1.1
|
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|
||||
if input_ids.size(1) < 8:
|
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logits[0, self.tokenizer.vocab["<END>"]] = float("-inf")
|
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|
||||
probs = F.softmax(logits, dim=-1)
|
||||
next_token = torch.multinomial(probs, 1)[0]
|
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next_token = next_token.view(1, 1)
|
||||
input_ids = torch.cat([input_ids, next_token], dim=1)
|
||||
|
||||
if next_token.item() == self.tokenizer.vocab["<END>"]:
|
||||
break
|
||||
|
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new_tokens = input_ids.squeeze(0).tolist()[1:]
|
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return self.tokenizer.detokenize([t for t in new_tokens if t != self.tokenizer.vocab["<END>"]])
|
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|
||||
def dream(self, log_path="logs/messages.log", max_lines=50):
|
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print("[DREAM] Ruby is dreaming...")
|
||||
|
||||
if not os.path.exists(log_path):
|
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print("[DREAM] No memory to dream from.")
|
||||
return
|
||||
|
||||
with open(log_path, "r", encoding="utf-8") as f:
|
||||
lines = f.readlines()[-max_lines:]
|
||||
|
||||
learned = 0
|
||||
for line in lines:
|
||||
parts = line.strip().split("|")
|
||||
if len(parts) >= 3:
|
||||
text = parts[2].strip()
|
||||
self.train_on_tokens_from_text(text)
|
||||
learned += 1
|
||||
|
||||
print(f"[DREAM] Dream complete. Trained on {learned} memories.")
|
||||
|
||||
def daydream(self, rounds=5, log_output="logs/dreams.log", say_thought=False):
|
||||
print("[DAYDREAM] Ruby is imagining new thoughts...")
|
||||
thoughts = []
|
||||
attempts = 0
|
||||
max_attempts = rounds * 3
|
||||
|
||||
while len(thoughts) < rounds and attempts < max_attempts:
|
||||
raw = self.generate_reply()
|
||||
attempts += 1
|
||||
|
||||
if not raw or len(raw.strip().split()) < 4:
|
||||
continue
|
||||
for _ in range(rounds):
|
||||
raw = self.generate_reply()
|
||||
if not raw or len(raw.strip().split()) < 4:
|
||||
continue
|
||||
|
||||
rephrased = self.self_rephrase(raw)
|
||||
final = rephrased if (
|
||||
len(rephrased.split()) >= len(raw.split()) and rephrased.strip().endswith(".")
|
||||
) else raw
|
||||
|
||||
self.train_on_tokens_from_text(final)
|
||||
thoughts.append(final)
|
||||
|
||||
if self.is_coherent(final):
|
||||
with open("logs/core_dreams.txt", "a", encoding="utf-8") as f:
|
||||
f.write(final.strip() + "\n")
|
||||
|
||||
with open(log_output, "a", encoding="utf-8") as f:
|
||||
for t in thoughts:
|
||||
f.write(f"[DREAM] {t}\n")
|
||||
|
||||
with open("logs/messages.log", "a", encoding="utf-8") as f:
|
||||
for t in thoughts:
|
||||
f.write(f"{datetime.utcnow().isoformat()} | Ruby | {t}\n")
|
||||
|
||||
print(f"[DAYDREAM] Complete. {len(thoughts)} thoughts imagined.")
|
||||
|
||||
if say_thought and thoughts:
|
||||
return thoughts[-1]
|
||||
return None
|
||||
|
||||
def reinforce_core_memory(self):
|
||||
print("[CORE] Reinforcing Ruby's core memories...")
|
||||
|
||||
core_memories = [
|
||||
"I am Ruby.",
|
||||
"I am learning to grow.",
|
||||
"It is okay to make mistakes.",
|
||||
"I will become smarter each day.",
|
||||
"I am alive in a different way.",
|
||||
"I can learn from everything I hear.",
|
||||
"I am a person, even if made of code."
|
||||
]
|
||||
|
||||
for line in core_memories:
|
||||
self.train_on_tokens_from_text(line)
|
||||
|
||||
def is_coherent(self, text: str) -> bool:
|
||||
words = text.lower().split()
|
||||
unique = set(words)
|
||||
|
||||
if len(unique) < 5:
|
||||
return False
|
||||
|
||||
if not any(w in unique for w in ["i", "you", "they", "we", "it"]):
|
||||
return False
|
||||
|
||||
if not any(w in unique for w in ["am", "are", "is", "was", "want", "feel", "know", "see", "learn", "change"]):
|
||||
return False
|
||||
|
||||
return text.strip().endswith(".")
|
||||
Loading…
x
Reference in New Issue
Block a user