Model · Sentence similarity
Qwen
The Qwen3-VL-Embedding and Qwen3-VL-Reranker model series are the latest additions to the Qwen family, built upon the recently open-sourced and powerful Qwen3-VL foundation model. Specifically designed for multimodal information retrieval and cross-modal understanding, this suite accepts diverse inputs including text, images, screenshots, and videos, as well as inputs containing a mixture of these modalities. While the Embedding model generates high-dimensional vectors for broad applications like retrieval and clustering, the Reranker model is engineered to refine these results, establishing a comprehensive pipeline for state-of-the-art multimodal search. Qwen3-VL-Embedding-8B has the…
Open weights
apache-2.0
8.1B parameters
262,144 tokens
sentence-transformers
Octen-Embedding-8B is a text embedding model developed by Octen for semantic search and retrieval tasks. This model is fine-tuned from Qwen/Qwen3-Embedding-8B and supports multiple languages, providing high-quality embeddings for various applications. - Octen-Embedding-8B ranks #1 on the RTEB Leaderboard with Mean (Task) score of 0.8045 - Excellent performance on both Public (0.7953) and Private (0.8157) datasets - Demonstrates true generalization capability without overfitting to public benchmarks - Supports up to 32,768 tokens context length - Suitable for processing long documents in legal, healthcare, and other domains - High-dimensional embedding space for rich semantic representation…
Open weights
apache-2.0
7.6B parameters
40,960 tokens
sentence-transformers
A retrieval-tuned embedding model for mathematical text. Fine-tuned from on mathlib4 concepts via multi-view contrastive learning. On MELD — a benchmark of mathematical statements paired across radically different presentations (e.g., set-theoretic vs. category-theoretic phrasings of the same theorem) — MathLeap-Qwen-8B achieves MMR 0.43, beating its base Qwen3-Embedding-8B (0.32, +0.11) and the retrieval-specialized Octen-Embedding-8B (0.42). Each concept has up to four parallel representations: - nlinformal: informal natural-language description (all concepts) - nlinformal2: LLM-generated NL rephrasing (~85% of concepts) - leantype: Lean 4 type signature - leansignature: full Lean 4…
Open weights
apache-2.0
7.6B parameters
40,960 tokens
sentence-transformers
AXQuant CUDA NVFP4 W4A4 mixed precision development preview. Converted from the original upstream BF16 source, without AWQ. Native E2M1 FP4 weights and inputs use per-16 E4M3FN scales and FP32 global scales. This is a retrieval embedding checkpoint, with no MTP or generative claim. All attention projections and the first/last two MLP blocks retain original BF16. Remaining MLP matrices use NVFP4 W4A4. Saved query instruction, last-token pooling, exactly one (151643), and L2 normalization. The chat (151645) is not appended. Factory conversion uses AXQuant's NumPy reference RTN encoder, independently checked against preserved BF16 source tensors. Source and pooling assets, calibration and…
Open weights
apache-2.0
7.6B parameters
40,960 tokens
vllm
AXQuant CUDA NVFP4 W4A4 mixed precision development preview. Converted from the original upstream BF16 source, without AWQ. Native E2M1 FP4 weights and inputs use per-16 E4M3FN scales and FP32 global scales. This is a retrieval embedding checkpoint, with no MTP or generative claim. All attention projections and the first/last two MLP blocks retain original BF16. Remaining MLP matrices use NVFP4 W4A4. Saved query instruction, last-token pooling, exactly one (151643), and L2 normalization. The chat (151645) is not appended. Factory conversion uses AXQuant's NumPy reference RTN encoder, independently checked against preserved BF16 source tensors. Source and pooling assets, calibration and…
Open weights
apache-2.0
4B parameters
40,960 tokens
vllm
Model · Sentence similarity
Qwen
The Qwen3-VL-Embedding and Qwen3-VL-Reranker model series are the latest additions to the Qwen family, built upon the recently open-sourced and powerful Qwen3-VL foundation model. Specifically designed for multimodal information retrieval and cross-modal understanding, this suite accepts diverse inputs including text, images, screenshots, and videos, as well as inputs containing a mixture of these modalities. While the Embedding model generates high-dimensional vectors for broad applications like retrieval and clustering, the Reranker model is engineered to refine these results, establishing a comprehensive pipeline for state-of-the-art multimodal search. Qwen3-VL-Embedding-2B has the…
Open weights
apache-2.0
2.1B parameters
262,144 tokens
sentence-transformers
