Instructions to use Isaac74/qwen3-0.6b-lightweight-semantic-mathlib-search-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Isaac74/qwen3-0.6b-lightweight-semantic-mathlib-search-adapter with PEFT:
Task type is invalid.
- sentence-transformers
How to use Isaac74/qwen3-0.6b-lightweight-semantic-mathlib-search-adapter with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Isaac74/qwen3-0.6b-lightweight-semantic-mathlib-search-adapter") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Lightweight Semantic Mathlib Search โ LoRA Adapter (Demo)
What this is:
A small, reproducible demo adapter for Qwen/Qwen3-Embedding-0.6B that enables lightweight, LLM-free semantic search over mathlib (Lean 4).
Supports both natural language and formula-based search (can be used in combination) for finding Lean theorems or mathematical statements.
Does not support LaTeX syntax search yet.
Data is built from ~180k Lean theorems extracted via LeanDojo for Lean 4.20.1 (there may be minor gaps vs. the full mathlib).
Part of the AITP 2025 submission: โTowards Lightweight and LLM-Free Semantic Search for mathlib4.โ
Contents
adapter/adapter_config.json&adapter/adapter_model.safetensorsโ LoRA weights for the encoderfaiss/<*.idx>โ HNSW FAISS index (built for cosine/inner-product search)faiss/*public_index.jsonlโ public mapping{ idx, full_name, statement }
DEMO
https://github.com/IsaacLi74/Lightweight-and-LLM-Free-Semantic-Search-for-mathlib4
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