Instructions to use hmzprover/hpsearch-embed-4b-state with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hmzprover/hpsearch-embed-4b-state with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hmzprover/hpsearch-embed-4b-state") 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
hpsearch-embed-4b-state
The proof-state query embedder of hpsearch's GPU modes. It is the
query side only of hmzprover/hpsearch-embed-4b:
LoRA (r=16, merged) trained so that a proof state (prompt Instruct: Given a Lean 4 proof state, retrieve Mathlib lemmas that help prove its goal\nQuery: ) lands next to the hpsearch-embed-4b vectors of the premises
its next tactic uses; documents keep their hpsearch-embed-4b vectors. Full softmax over all premise vectors.
Training data: the public train split of LeanDojo Benchmark 4 v10 (Zenodo 12740403, mathlib4
29dcec07): each traced tactic's state_before paired with every premise its annotation locates in the
premise corpus (367,730 (state, premise) pairs from 118,517 theorems).
Decontamination (strict): dropped every pair whose theorem is in the val or test split of either LeanDojo split (random, novel_premises; 20,761 pairs), whose state equals a LeanDojo val/test state (100), whose state equals a query of any other evaluation hpsearch is measured on (4,775), or whose premise's name (161,550) or statement (307) is a gold of any other evaluation (MathlibQR/MPR, Lean Finder, LeanStateSearch, TheoremSearch, TBPS, hmzprover, MIRB premise and Mathlib). Kept: 180,237 pairs, 107,936 states, 51,145 distinct premises.
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Model tree for hmzprover/hpsearch-embed-4b-state
Base model
Qwen/Qwen3-4B-Base