chatbot_1b.py

# Copyright (c) 2025 CMS Manhattan
# All rights reserved.
# Author: Konstantin Vladimirovich Grabko
# Email: grabko@cmsmanhattan.com
# Phone: +1(516)777-0945
#
# MIT License
#
# Copyright (c) 2025 Konstantin Grabko
#
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import torch
import torch.nn.functional as F
from transformers import GPT2TokenizerFast
from gpt_modern_8b import JiRackPyTorch  # Same import used in fine-tuning
from pathlib import Path

# ============================= GENERATION SETTINGS =============================
# Temperature: Lower = more focused, conservative, and predictable responses
# Start with 0.7. Increase to 0.8–0.9 if the model starts repeating itself
TEMPERATURE = 0.7

# Top-K: Limits sampling to the K most likely next tokens
# Start with 50. Increase if output feels too safe/boring
TOP_K = 50

# Max Length: Maximum number of new tokens to generate per response
MAX_LENGTH = 120

# ============================= PATHS =============================
LAST_TRAINED_PATH = Path("build/fine_tuning_output/epoch2/gpt_finetuned.pt")
FINAL_OUTPUT_DIR = Path("build/fine_tuning_output/epoch2")  # Folder containing the .pt
MODEL_SAVE_NAME = "gpt_finetuned.pt"

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

# ============================= CHATBOT CLASS =============================
class Chatbot:
    def __init__(self, model_path: Path):
        # 1. Load tokenizer (offline-safe recommended — see note below)
        print("Loading standard GPT-2 tokenizer...")
        # For full offline use, replace "gpt2" with "./tokenizers/gpt2" after first download
        self.tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
        self.tokenizer.pad_token = self.tokenizer.eos_token

        # 2. Initialize model architecture
        print("Initializing JiRackPyTorch model...")
        self.model = JiRackPyTorch().to(device)
        self.model.eval()

        # 3. Load latest trained weights
        load_path = None
        candidate1 = FINAL_OUTPUT_DIR / MODEL_SAVE_NAME
        candidate2 = model_path if model_path.is_file() else None

        if candidate1.exists():
            load_path = candidate1
            print(f"Found weights in final folder: {load_path}")
        elif candidate2 and candidate2.exists():
            load_path = candidate2
            print(f"Loading weights from: {load_path}")
        else:
            print("Warning: No trained weights found. Running with randomly initialized model.")

        if load_path:
            print(f"Loading state dict from {load_path}...")
            self.model.load_state_dict(torch.load(load_path, map_location=device))
            print("Weights loaded successfully!")

        print(f"Model is now running on {device} — ready for chat!\n")

    def generate_response(self, prompt: str, max_length: int = MAX_LENGTH,
                          temperature: float = TEMPERATURE, top_k: int = TOP_K) -> str:
        input_ids = self.tokenizer.encode(prompt, return_tensors="pt").to(device)

        with torch.no_grad():
            for _ in range(max_length):
                # Forward pass
                logits, _ = self.model(input_ids)  # JiRackPyTorch returns (logits, past_kv)

                # Get logits for the last generated token
                next_token_logits = logits[:, -1, :]

                # Apply temperature
                if temperature != 1.0:
                    next_token_logits = next_token_logits / temperature

                # Apply Top-K sampling
                if top_k > 0:
                    values, indices = torch.topk(next_token_logits, top_k)
                    next_token_logits = torch.full_like(next_token_logits, float('-inf'))
                    next_token_logits.scatter_(1, indices, values)

                # Sample next token
                probabilities = F.softmax(next_token_logits, dim=-1)
                next_token = torch.multinomial(probabilities, num_samples=1)

                # Append to sequence
                input_ids = torch.cat([input_ids, next_token], dim=-1)

                # Early stop on EOS or custom end-of-utterance token
                token_str = self.tokenizer.decode(next_token.item())
                if "__eou__" in token_str or next_token.item() == self.tokenizer.eos_token_id:
                    break

        # Decode full output and strip prompt
        full_output = self.tokenizer.decode(input_ids[0], skip_special_tokens=False)
        response = full_output[len(prompt):].strip()

        # Clean up any leftover markers
        response = response.replace("__eou__", "").strip()

        return response


# ============================= MAIN CHAT LOOP =============================
def main():
    global TEMPERATURE, TOP_K

    print("Starting JiRack Chatbot...")
    chatbot = Chatbot(LAST_TRAINED_PATH)

    print("\n" + "=" * 70)
    print(f"JIRACK CHATBOT ONLINE")
    print(f"Temperature: {TEMPERATURE} | Top-K: {TOP_K} | Max Length: {MAX_LENGTH}")
    print("Type 'quit' or 'exit' to exit")
    print("Change settings: set temp=0.8   or   set k=80")
    print("=" * 70 + "\n")

    while True:
        try:
            user_input = input("You: ").strip()

            if user_input.lower() in {"quit", "exit", "bye"}:
                print("Goodbye!")
                break

            # Live parameter tuning
            if user_input.lower().startswith("set temp="):
                try:
                    TEMPERATURE = float(user_input.split("=")[1])
                    print(f"Temperature → {TEMPERATURE}")
                except:
                    print("Invalid format. Use: set temp=0.7")
                continue

            if user_input.lower().startswith("set k="):
                try:
                    TOP_K = int(user_input.split("=")[1])
                    print(f"Top-K → {TOP_K}")
                except:
                    print("Invalid format. Use: set k=50")
                continue

            if not user_input:
                continue

            print("Generating...", end="\r")
            response = chatbot.generate_response(user_input)
            print(f"JiRack: {response}\n")

        except KeyboardInterrupt:
            print("\n\nShutting down...")
            break
        except Exception as e:
            print(f"Error: {e}")

if __name__ == "__main__":
    main()