| |
| |
| """ |
| 👑 QUILLAN-RONIN v5.3.1 / v5.4 — STREAMING INTERACTIVE SOVEREIGN TERMINAL |
| ================================================================== |
| Run this script to chat directly with Quillan-Ronin v5.3.1 (Canonical) / v5.4 (Frontier). |
| Automatically selects the best available checkpoint. |
| """ |
|
|
| import sys |
| import time |
| from pathlib import Path |
| import torch |
|
|
| SCRIPTS_DIR = Path(r"C:\02_QUILLAN\scripts") |
| CHECKPOINT_DIR = Path(r"C:\02_QUILLAN\checkpoints") |
|
|
| sys.path.insert(0, str(SCRIPTS_DIR)) |
|
|
| from quillan_v10_unrolled_sovereign import QuillanConfig, QuillanSovereignUnifiedModel |
| from sovereign_inference_engine import SovereignTokenizer, SovereignInferenceEngine, SamplingParams |
|
|
| if hasattr(sys.stdout, "reconfigure"): |
| sys.stdout.reconfigure(encoding="utf-8", errors="replace") |
|
|
|
|
| def select_best_checkpoint() -> Path: |
| """Auto-select best available checkpoint in priority order.""" |
| candidates = [ |
| CHECKPOINT_DIR / "quillan_frontier_v2_best.pt", |
| CHECKPOINT_DIR / "quillan_ronin_v531_sovereign_production.pt", |
| CHECKPOINT_DIR / "checkpoints_sft" / "quillan_super_run_best.pt", |
| CHECKPOINT_DIR / "checkpoints_sft" / "quillan_sovereign_gold_best.pt", |
| ] |
| for p in candidates: |
| if p.exists(): |
| return p |
| raise FileNotFoundError("No Quillan checkpoint found. Run training first.") |
|
|
|
|
| def main(): |
| ckpt_path = select_best_checkpoint() |
|
|
| print("==================================================================", flush=True) |
| print(" 👑 QUILLAN-RONIN v5.3.1 / v5.4 — INTERACTIVE SOVEREIGN TERMINAL", flush=True) |
| print(f" Active Checkpoint: {ckpt_path.name}", flush=True) |
| print("==================================================================\n", flush=True) |
|
|
| device = torch.device("cpu") |
| tokenizer = SovereignTokenizer("gpt2") |
| cfg = QuillanConfig( |
| vocab_size=50257, |
| hidden_dim=1024, |
| num_layers=16, |
| num_heads=32, |
| num_experts=34, |
| num_experts_active=4, |
| max_seq_len=16384, |
| ) |
|
|
| print("[*] Loading Quillan-Ronin weights...", flush=True) |
| model = QuillanSovereignUnifiedModel(cfg).to(device) |
| engine = SovereignInferenceEngine.load_from_checkpoint( |
| model_factory=lambda: model, |
| checkpoint_path=ckpt_path, |
| device=device, |
| ) |
|
|
| print("[✓] Model ready! Type 'exit' or 'quit' to end session.\n", flush=True) |
|
|
| history = "" |
| params = SamplingParams( |
| max_new_tokens=512, |
| temperature=0.65, |
| top_p=0.85, |
| repetition_penalty=1.20, |
| frequency_penalty=0.30, |
| presence_penalty=0.30, |
| ) |
|
|
| while True: |
| try: |
| user_input = input("You > ").strip() |
| if not user_input: |
| continue |
| if user_input.lower() in ("exit", "quit"): |
| print("Exiting session.") |
| break |
|
|
| prompt = f"<|user|>\n{user_input}\n<|assistant|>\n" |
| print("Quillan > ", end="", flush=True) |
|
|
| def _stream_cb(token: str): |
| print(token, end="", flush=True) |
|
|
| engine.generate(prompt=prompt, params=params, stream_callback=_stream_cb) |
| print("\n") |
|
|
| except KeyboardInterrupt: |
| print("\nSession interrupted.") |
| break |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
