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AI モデル 84760

• TimeLens-7B↗

  TencentARC

• WizardLM-7B-uncensored-GPTQ↗

  TheBloke

• flexivit_base.300ep_in1k↗

  timm

• rexnet_200.nav_in1k↗

  timm

• Skywork-OR1-7B-GGUF↗

  tinyopsec

• GPT-OSS-20B-P-EAGLE-GGUF↗

  TULLUS

• bge-m3↗

  unsloth

  In this project, we introduce BGE-M3, which is distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity. - Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval. - Multi-Linguality: It can support more than 100 working languages. - Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens. We recommend to use the following pipeline: hybrid retrieval + re-ranking. - Hybrid retrieval leverages the strengths of various methods, offering higher accuracy and stronger generalization capabilities. A classic example: using both embedding retrieval and the BM25 algorithm. Now, you can try to use BGE-M3, which supports both embedding and sparse retrieval. This allows you to obtain token weights (similar to the BM25) without any additional cost when generate dense embeddings. To use hybrid retrieval, you can refer to Vespa and Milvus. The previous test results were lower because we mistakenly removed the passages that have the same id as the query from the search results. After correcting this mistake, the overall performance of BGE-M3 on MIRACL is higher than the previous results, but the experimental conclusion remains unchanged. The other results are not affected by this mistake. To reproduce the previous lower results, you need to add the `--remove-query` parameter when using `pyserini.search.faiss` or `pyserini.search.lucene` to search the passages.

• Qwen3.8-Flash-Next-Splash-Quality-GGUF↗

  Vardaan97

• magneto-schema-retriever-gdc↗

  vida-nyu

  This is a fine-tuned version of sentence-transformers/all-mpnet-base-v2 for schema matching tasks in the biomedical domain, specifically targeting the GDC (Genomic Data Commons) data standard. The model was fine-tuned using a self-supervised approach that leverages LLMs to generate diverse training data from the GDC target schema. The key innovation is combining LLM-based augmentation with structure-based augmentation: **1. LLM-Based Augmentation (`llm-aug`):** - For each anchor column in the GDC target schema, an LLM generates semantically equivalent but syntactically diverse variations - Captures syntactic heterogeneity common in real biomedical datasets - Example transformations: - `patient_id` → `subject_identifier`, `participant_number`, `patient_identifier` - `diagnosis_date` → `date_of_diagnosis`, `dx_date`, `diagnostic_date` - Creates diverse positive examples while preserving semantic meaning

• viduq3-mix↗

  Vidu

  Vidu ビデオモデル、公式 API 形式に対応