qdrant/qdrantbdd4bb5180f4a4fb378dd3dedf5c307e8a8b74e5int8 and binary quantization, with search-performance tuning guidance for float32 / float16 / uint8 vector storage.Qdrant implements two different approximate-vector compression families relevant to this request:
| #!/usr/bin/env python3 | |
| # /// script | |
| # requires-python = ">=3.12,<3.13" | |
| # dependencies = [ | |
| # "plamo-translate-cli", | |
| # "pdf2zh-next", | |
| # ] | |
| # /// | |
| # NOTE: PDFMathTranslate-next is published on PyPI as pdf2zh-next. | |
| # License: MIT |
| #!/usr/bin/env python3 | |
| """Run NanoBEIR-ja evaluation for a SentenceTransformer model (CLI). | |
| Usage example (ndcg@10 only by default): | |
| uv run scripts/nano_beir_ja_eval_cli.py \ | |
| --model-path cl-nagoya/ruri-v3-30m \ | |
| --batch-size 512 --autocast-dtype bf16 \ | |
| --output output/nano_beir_ja_eval_ruri-v3-30m.json | |
| Use --all-metrics to emit the full metric set. |
| #!/usr/bin/env python3 | |
| """Translate recent PDFs in Downloads using pdf2zh_next via uvx. | |
| Defaults: | |
| - Looks for PDFs downloaded within the last day. | |
| - Saves outputs to ~/Downloads/pdf2translated/{filename}.translated.pdf | |
| - Skips files that already have a translated output. | |
| - Uses pdf2zh_next with Google Translate, Japanese output, no watermark, and | |
| alternating bilingual pages. |
| from sentence_transformers import CrossEncoder, export_dynamic_quantized_onnx_model, export_optimized_onnx_model | |
| # モデル名の定義 | |
| MODEL_NAME = "hotchpotch/japanese-reranker-xsmall-v2" | |
| # 基本モデルの読み込み（CPUを使用、ONNXバックエンド） | |
| model = CrossEncoder(MODEL_NAME, device="cpu", backend="onnx") | |
| # 1. 基本モデル (model.onnx) | |
| # Hubにプッシュして、必要に応じてPRを作成 |
| """ | |
| query-crafter-japanese のサンプルコード。 | |
| 実際に大量に処理するときは、vllm などを利用することで、高速処理が可能 | |
| """ | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_name = "hotchpotch/query-crafter-japanese-Qwen3-1.7B" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| from transformers import AutoModel, AutoTokenizer | |
| import torch | |
| from tqdm import tqdm | |
| import numpy as np | |
| def adapt_model_to_new_tokenizer(model_name, new_tokenizer_name): | |
| # 元のモデルとトークナイザーをロード | |
| original_model = AutoModel.from_pretrained(model_name) | |
| original_tokenizer = AutoTokenizer.from_pretrained(model_name) |
| # %% | |
| from datasets import load_dataset | |
| dataset = load_dataset("hpprc/jawiki-paragraphs", split="train") | |
| # %% | |
| len(dataset) | |
| # %% | |
| # head N |
| """ | |
| This script converts an ONNX model to float16 precision using the onnxruntime transformers package. | |
| It takes an input ONNX model file as a mandatory argument. The output file name is optional; if not provided, | |
| the script generates the output file name by appending "_fp16" to the base name of the input file. | |
| """ | |
| import argparse | |
| import onnx | |
| from onnxruntime.transformers.float16 import convert_float_to_float16 | |
| import os |
| # base: https://github.com/facebookresearch/faiss/blob/main/benchs/bench_gpu_sift1m.py | |
| # base code License: MIT License | |
| # | |
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import os | |
| import time |