| |
| """ |
| setup_model.py - One-time model download for offline SLM reasoning rewriter |
| ============================================================================ |
| Run this ONCE to download the model weights locally. |
| After this, rewrite_reasoning.py works fully offline forever. |
| |
| Usage: |
| py setup_model.py |
| |
| What this downloads: |
| Model : Qwen2.5-1.5B-Instruct-Q4_K_M.gguf |
| Size : ~986 MB (~1 GB) |
| Source: Hugging Face (bartowski/Qwen2.5-1.5B-Instruct-GGUF) |
| Dest : ./models/Qwen2.5-1.5B-Instruct-Q4_K_M.gguf |
| |
| Why Qwen2.5-1.5B: |
| - Best 1 GB model for instruction-following + paraphrasing |
| - Handles Indian company/tech jargon well |
| - Apache 2.0 license (commercial use OK) |
| - Runs on CPU, no GPU required |
| |
| After download, install the runtime if not done: |
| pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu |
| |
| Then run the rewriter: |
| py rewrite_reasoning.py --input top100_reasoned.jsonl --out submission.csv |
| """ |
|
|
| import os |
| import sys |
| import urllib.request |
| import urllib.error |
| import shutil |
| from pathlib import Path |
|
|
| |
|
|
| MODEL_REPO = "bartowski/Qwen2.5-1.5B-Instruct-GGUF" |
| MODEL_FILE = "Qwen2.5-1.5B-Instruct-Q4_K_M.gguf" |
| MODEL_URL = ( |
| "https://huggingface.co/bartowski/Qwen2.5-1.5B-Instruct-GGUF" |
| "/resolve/main/Qwen2.5-1.5B-Instruct-Q4_K_M.gguf" |
| ) |
| EXPECTED_MB = 986 |
| MODELS_DIR = Path(__file__).parent / "models" |
| MODEL_PATH = MODELS_DIR / MODEL_FILE |
|
|
|
|
| |
|
|
| class ProgressBar: |
| def __init__(self, total_mb): |
| self.total = total_mb * 1024 * 1024 |
| self.done = 0 |
| self.last_pct = -1 |
|
|
| def __call__(self, block_num, block_size, total_size): |
| if total_size > 0: |
| self.total = total_size |
| self.done = min(self.done + block_size, self.total) |
| pct = int(self.done * 100 / self.total) |
| if pct != self.last_pct and pct % 2 == 0: |
| filled = pct // 5 |
| bar = "#" * filled + "." * (20 - filled) |
| mb_done = self.done / 1024 / 1024 |
| mb_total = self.total / 1024 / 1024 |
| print(f"\r [{bar}] {pct:3d}% {mb_done:.0f}/{mb_total:.0f} MB", |
| end="", flush=True) |
| self.last_pct = pct |
|
|
|
|
| |
|
|
| def try_huggingface_hub(): |
| """Try huggingface_hub if installed (resumable downloads).""" |
| try: |
| from huggingface_hub import hf_hub_download |
| print(" Using huggingface_hub (resumable)...") |
| hf_hub_download( |
| repo_id=MODEL_REPO, |
| filename=MODEL_FILE, |
| local_dir=str(MODELS_DIR), |
| local_dir_use_symlinks=False, |
| ) |
| return True |
| except ImportError: |
| return False |
| except Exception as e: |
| print(f" huggingface_hub failed: {e}. Falling back to urllib...") |
| return False |
|
|
|
|
| def download_urllib(): |
| print(f" From: {MODEL_URL}") |
| print(f" To: {MODEL_PATH}") |
| print(f" Size: ~{EXPECTED_MB} MB\n") |
| bar = ProgressBar(EXPECTED_MB) |
| try: |
| urllib.request.urlretrieve(MODEL_URL, MODEL_PATH, reporthook=bar) |
| print() |
| except urllib.error.URLError as e: |
| print(f"\n ERROR: Download failed: {e}") |
| if MODEL_PATH.exists(): |
| MODEL_PATH.unlink() |
| sys.exit(1) |
|
|
|
|
| |
|
|
| def main(): |
| print(""" |
| +===================================================+ |
| | REDROB SLM SETUP - One-time model download | |
| | Qwen2.5-1.5B-Instruct-Q4_K_M.gguf (~1 GB) | |
| +===================================================+ |
| """) |
|
|
| MODELS_DIR.mkdir(exist_ok=True) |
|
|
| |
| if MODEL_PATH.exists(): |
| size_mb = MODEL_PATH.stat().st_size / 1024 / 1024 |
| if size_mb > 900: |
| print(f" [OK] Model already at: {MODEL_PATH}") |
| print(f" Size: {size_mb:.0f} MB") |
| _check_llama_cpp() |
| return |
| else: |
| print(f" Partial download ({size_mb:.0f} MB). Re-downloading...") |
| MODEL_PATH.unlink() |
|
|
| |
| free_gb = shutil.disk_usage(MODELS_DIR).free / 1024 ** 3 |
| if free_gb < 1.2: |
| print(f" ERROR: Need 1.2 GB free, only {free_gb:.1f} GB available.") |
| sys.exit(1) |
| print(f" Free disk: {free_gb:.1f} GB [OK]") |
| print(f" Downloading {MODEL_FILE} ...") |
| print() |
|
|
| downloaded = try_huggingface_hub() |
| if not downloaded: |
| download_urllib() |
|
|
| |
| size_mb = MODEL_PATH.stat().st_size / 1024 / 1024 |
| if size_mb < 900: |
| print(f" ERROR: File too small ({size_mb:.0f} MB) — may be corrupted.") |
| sys.exit(1) |
|
|
| print(f"\n [OK] Model ready: {MODEL_PATH} ({size_mb:.0f} MB)") |
| _check_llama_cpp() |
|
|
| print("\n NEXT STEP:") |
| print(" py rewrite_reasoning.py --input top100_reasoned.jsonl --out submission.csv\n") |
|
|
|
|
| def _check_llama_cpp(): |
| print() |
| try: |
| import llama_cpp |
| print(f" [OK] llama-cpp-python: {llama_cpp.__version__}") |
| except ImportError: |
| print(" [!] llama-cpp-python not installed. Run ONE of:") |
| print() |
| print(" # CPU only (recommended if no NVIDIA GPU):") |
| print(" pip install llama-cpp-python \\") |
| print(" --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu") |
| print() |
| print(" # CUDA 12.1 (if you have an NVIDIA GPU):") |
| print(" pip install llama-cpp-python \\") |
| print(" --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
