Sentence Similarity
sentence-transformers
Safetensors
qwen3
lean4
mathlib
retrieval
text-embeddings-inference
Instructions to use hmzprover/hpsearch-embed-4b-agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hmzprover/hpsearch-embed-4b-agent with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hmzprover/hpsearch-embed-4b-agent") 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-agent
The keyword-query embedder of hpsearch's best mode:
hmzprover/hpsearch-embed-4b (Qwen3-Embedding-4B) LoRA
fine-tuned on agents' search queries and merged. Queries use hpsearch's instructions
(Instruct: ...\nQuery: ); documents are a declaration's Lean statement then its NL statement.
Fine-tuning data:
- agent search queries from hmzprover/query-chains (keyword bags and guessed Lean names an agent typed into a theorem search tool), each paired with the declaration its chain finally found (in hmzprover/proofs); hard negatives are the lexical search hits that are not that declaration;
- replayed public pairs of the base model's own training set.
Decontamination: every chain of the hmzprover search benchmark (positive and negative chains) is excluded, and every pair whose normalised query text, or whose target's name or statement, occurs in any evaluation set used by hpsearch (MathlibQR / MathlibMPR, Lean Finder, LeanStateSearch, TheoremSearch, TBPS, hmzprover) is dropped.
- Downloads last month
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Model tree for hmzprover/hpsearch-embed-4b-agent
Base model
Qwen/Qwen3-4B-Base Finetuned
Qwen/Qwen3-Embedding-4B Finetuned
hmzprover/hpsearch-embed-4b