Sentence Similarity
sentence-transformers
Safetensors
qwen3
lean4
mathlib
retrieval
premise-selection
text-embeddings-inference
Instructions to use hmzprover/hpsearch-embed-fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hmzprover/hpsearch-embed-fast with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hmzprover/hpsearch-embed-fast") 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
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Download README.md from hmzprover/hpsearch-embed-fast: direct link, hf CLI and curl.
- Browser
- Download file 2.28 kB
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https://huggingface.co/hmzprover/hpsearch-embed-fast/resolve/main/README.md
- Command line
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hf download hf://hmzprover/hpsearch-embed-fast/README.md
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curl -L -o README.md https://huggingface.co/hmzprover/hpsearch-embed-fast/resolve/main/README.md
2.28 kB
| license: apache-2.0 | |
| base_model: Qwen/Qwen3-Embedding-0.6B | |
| library_name: sentence-transformers | |
| pipeline_tag: sentence-similarity | |
| tags: [lean4, mathlib, retrieval, premise-selection] | |
| # hpsearch-embed-fast | |
| The query side of [hpsearch](https://github.com/humanfia/hpsearch)'s mode `fast` for proof states and Lean | |
| expressions: the first 8 layers of Qwen3-Embedding-0.6B and a linear map to 2,560 dimensions, trained so that a | |
| query lands next to the [`hmzprover/hpsearch-embed-4b-v2`](https://huggingface.co/hmzprover/hpsearch-embed-4b-v2) | |
| vectors of what it asks for (documents keep their hpsearch-embed-4b-v2 vectors; `pairs_with` names it). About | |
| 3 ms per query on an A100 with CUDA-graph replay. | |
| Queries: a proof state (prompt `Instruct: Given a Lean 4 proof state, retrieve Mathlib lemmas that help prove its | |
| goal\nQuery: `, goal first), its goal alone, or a Lean expression (a statement's conclusion or Pi type; prompt | |
| `Instruct: Given Lean 4 code, retrieve the Mathlib declaration it states or names\nQuery: `). Full softmax over the | |
| frozen document vectors, plus distillation (KL over the documents) from hpsearch-embed-4b-v2's own query side on | |
| expressions. Lean expressions come from the training corpora's own statements (no evaluation gold among them). | |
| ## Training data | |
| Public training splits only: | |
| - LeanDojo Benchmark 4 (v10) `random/train`: each traced tactic's state paired with the premises it uses; | |
| - LeanStateSearch (`ruc-ai4math/mathlib_handler_benchmark_410`) `random/train`, the same way. | |
| For part of the states, a one-to-three-sentence English description of the next step (written by an LLM from | |
| the state and the tactic) follows the state, as in Lean Finder's "augmented" proof states. | |
| Decontamination (strict): a state is dropped when its theorem is in a held-out split or is the theorem of any | |
| evaluation query, when its text equals any evaluation query, or when its goal equals the goal of any | |
| proof-state evaluation query; a premise is dropped when its name or statement is a gold of any evaluation | |
| (MathlibQR/MPR, Lean Finder, LeanStateSearch, TBPS, TheoremSearch, MIRB, LeanDojo, hpsearch's own); such | |
| premises are also left out of the contrastive softmax. Kept: 147,412 states | |
| (89,719 LeanDojo, 57,693 LeanStateSearch), 89,298 premises. | |