Transformers
ColPali
visual-rag
colqwen
ethnopharmacology
retrieval
llm-reranking
Eval Results (legacy)
Instructions to use SAINTHALF/layra-v1-hybrid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SAINTHALF/layra-v1-hybrid with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SAINTHALF/layra-v1-hybrid", dtype="auto") - ColPali
How to use SAINTHALF/layra-v1-hybrid with ColPali:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
LAYRA: Large Academic Visual RAG (v1.1)
This model entry represents the optimized version of the LAYRA retrieval system, featuring LLM-based reranking.
Performance Summary (v1.1)
- Strategy: Hybrid Retrieval (ColQwen + BM25) $ ightarrow$ LLM Reranking (Qwen3-VL-Cloud)
- Recall@20: 1.00 (Perfect retrieval on Kanna Gold Set)
- MRR@20: 0.740 (Significant improvement over v1.0 baseline of 0.365)
- Recall@1: 0.600 (Correct answer is #1 result 60% of the time)
System Configuration
- Visual Encoder:
vidore/colqwen2.5-v0.2 - Vector DB: Milvus 2.6
- Fusion: RRF (Candidate Pool: 500)
- Reranker:
qwen3-vl-235b-instruct-cloud(Listwise Ranking)
Evaluation
Metrics verified on 2025-12-31 using the SAINTHALF/layra-kanna-goldset repository.
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Evaluation results
- Recall@20 on LAYRA Kanna Gold Settest set self-reported1.000
- MRR@20 (SOTA) on LAYRA Kanna Gold Settest set self-reported0.740
- Recall@1 on LAYRA Kanna Gold Settest set self-reported0.600
- Average Latency (s) on LAYRA Kanna Gold Settest set self-reported1.500