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| """Fetch + convert the LinguaBridge many-to-many translation model (one-time setup). | |
| Downloads facebook/nllb-200-distilled-600M from Hugging Face and converts it to | |
| an int8 CTranslate2 model at models/nllb200-600M-ct2 (~600MB on disk, ~1GB RAM | |
| at inference, ~100-300ms/sentence on CPU). The site's translator uses it for | |
| all 110 language directions natively (no English pivot, no API key), with the | |
| persistent translation_cache still freezing every output for determinism. | |
| Setup step, like src/miniweb/scripts/fetch_brand_fonts.py — run once per machine/deploy: | |
| ~/.conda/envs/miniweb/bin/python src/miniweb/scripts/fetch_translation_model.py | |
| The model directory is gitignored; on Railway either bake this into the image | |
| build or put models/ on the persistent volume. Without the model the site | |
| falls back to the LLM (if keyed) and then the built-in dictionaries. | |
| """ | |
| import pathlib | |
| import shutil | |
| import subprocess | |
| import sys | |
| ROOT = pathlib.Path(__file__).resolve().parents[3] # the repository | |
| MODEL_DIR = ROOT / "models" / "nllb200-600M-ct2" | |
| HF_REPO = "facebook/nllb-200-distilled-600M" | |
| # the weights every cached translation came from; with transformers 4.57.6 and ctranslate2 4.8.1 the conversion | |
| # gives the same model.bin byte for byte (deploy/space/Dockerfile pins those) | |
| HF_REVISION = "f8d333a098d19b4fd9a8b18f94170487ad3f821d" | |
| def main(): | |
| if (MODEL_DIR / "model.bin").exists() and (MODEL_DIR / "sentencepiece.model").exists(): | |
| print(f"model already present at {MODEL_DIR}") | |
| return | |
| from huggingface_hub import snapshot_download | |
| print(f"downloading {HF_REPO} ...") | |
| src = snapshot_download(HF_REPO, revision=HF_REVISION, allow_patterns=[ | |
| "*.json", "*.model", "pytorch_model.bin", "*.safetensors", "*.txt"]) | |
| print("converting to CTranslate2 int8 ...") | |
| MODEL_DIR.parent.mkdir(exist_ok=True) | |
| tmp = str(MODEL_DIR) + ".tmp" | |
| subprocess.run([ | |
| sys.executable, "-m", "ctranslate2.converters.transformers", | |
| "--model", src, "--output_dir", tmp, | |
| "--quantization", "int8", "--force", | |
| ], check=True) | |
| # keep the sentencepiece tokenizer next to the model | |
| sp = pathlib.Path(src) / "sentencepiece.bpe.model" | |
| shutil.copy2(sp, pathlib.Path(tmp) / "sentencepiece.model") | |
| shutil.move(tmp, MODEL_DIR) | |
| size = sum(f.stat().st_size for f in MODEL_DIR.rglob("*")) / 1e6 | |
| print(f"done: {MODEL_DIR} ({size:.0f} MB)") | |
| if __name__ == "__main__": | |
| main() | |