ggml-org/Qwen3-Reranker-0.6B-Q8_0 - This model was converted to GGUF format from Qwen/Qwen3-Reranker-0.6B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.
2,912 Pulls 1 Tag Updated 3 months ago
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534 Pulls 1 Tag Updated 3 months ago
Model Overview Qwen3-Reranker-8B-f16 has the following features: Model Type: Text Reranking Supported Languages: 100+ Languages Number of Paramaters: 8B Context Length: 32k
20.7K Pulls 1 Tag Updated 11 months ago
The 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.
3,611 Pulls 3 Tags Updated 7 months ago
bge-reranker-base
2,070 Pulls 1 Tag Updated 6 months ago
29 Pulls 1 Tag Updated 4 months ago
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671 Pulls 1 Tag Updated 8 months ago
Model Overview Qwen3-Reranker-8B-q8_0 has the following features: Model Type: Text Reranking Supported Languages: 100+ Languages Number of Paramaters: 8B Context Length: 32k
287 Pulls 1 Tag Updated 11 months ago
quantize https://huggingface.co/BAAI/bge-reranker-v2-m3 to f16 / q8_0 (latest) / q4_k_m
281.3K Pulls 4 Tags Updated 1 year ago
Alibaba's text reranking model.Qwen3-Reranker-8B has the following features: Model Type: Text Reranking. Supported Languages: 100+ Languages. Number of Paramaters: 8B. Context Length: 32k.
216.6K Pulls 5 Tags Updated 1 year ago
66 Pulls 1 Tag Updated 7 months ago
follow https://huggingface.co/BAAI/bge-reranker-v2-m3
31.1K Pulls 1 Tag Updated 1 year ago
Alibaba's text reranking model.Qwen3-Reranker-0.6B has the following features: Model Type: Text Reranking Supported Languages: 100+ Languages Number of Paramaters: 0.6B Context Length: 32k
22.8K Pulls 2 Tags Updated 1 year ago
Alibaba's text reranking model.Qwen3-Reranker-4B has the following features: Model Type: Text Reranking Supported Languages: 100+ Languages Number of Paramaters: 4B Context Length: 32k...
14.9K Pulls 3 Tags Updated 1 year ago
BGE-Reranker-v2-M3 是轻量级重排序模型，基于 BGE-M3-0.5B 架构优化，专为多语言检索任务设计，尤其强化了中英文混合场景下的性能。其核心定位是为RAG流程提供高效的上下文重排序能力。
13.3K Pulls 1 Tag Updated 1 year ago
quantize https://huggingface.co/maidalun1020/bce-reranker-base_v1 to f16 / q8_0 (latest) / q4_k_m
14.2K Pulls 4 Tags Updated 1 year ago
quantize https://huggingface.co/BAAI/bge-reranker-large to f16 / q8_0 (latest) / q4_k_m
10.8K Pulls 4 Tags Updated 1 year ago