A growing local model snapshot, separated by chat, coding, retrieval, reranking and speech tasks. It spans generations, not the entire market or every latest release. Quality and speed are not uniformly benchmarked here.
Text embedding · Search reranking · Speech to text
Embedding models produce retrieval vectors, rerankers score candidate passages, and speech models transcribe audio. These tasks are not comparable by chat quality. Batch, activation and audio memory are not modeled; specialist capacity stays unverified instead of using the chat-cache formula.
Compare under identical settings; size uses public tensor-element metadata and can differ from the named parameter count. Read licenses with base-model and usage terms.
bge-reranker-v2-m3
Family
BGE Reranker
Weight size basis
0.57 B
Documented/configured context limit
Unknown
License identifier (see source)
apache-2.0
Use
Search reranking
Embedding models produce retrieval vectors, rerankers score candidate passages, and speech models transcribe audio. These tasks are not comparable by chat quality. Batch, activation and audio memory are not modeled; specialist capacity stays unverified instead of using the chat-cache formula.
Embedding models produce retrieval vectors, rerankers score candidate passages, and speech models transcribe audio. These tasks are not comparable by chat quality. Batch, activation and audio memory are not modeled; specialist capacity stays unverified instead of using the chat-cache formula.
Embedding models produce retrieval vectors, rerankers score candidate passages, and speech models transcribe audio. These tasks are not comparable by chat quality. Batch, activation and audio memory are not modeled; specialist capacity stays unverified instead of using the chat-cache formula.
Embedding models produce retrieval vectors, rerankers score candidate passages, and speech models transcribe audio. These tasks are not comparable by chat quality. Batch, activation and audio memory are not modeled; specialist capacity stays unverified instead of using the chat-cache formula.
Embedding models produce retrieval vectors, rerankers score candidate passages, and speech models transcribe audio. These tasks are not comparable by chat quality. Batch, activation and audio memory are not modeled; specialist capacity stays unverified instead of using the chat-cache formula.
Embedding models produce retrieval vectors, rerankers score candidate passages, and speech models transcribe audio. These tasks are not comparable by chat quality. Batch, activation and audio memory are not modeled; specialist capacity stays unverified instead of using the chat-cache formula.
Embedding models produce retrieval vectors, rerankers score candidate passages, and speech models transcribe audio. These tasks are not comparable by chat quality. Batch, activation and audio memory are not modeled; specialist capacity stays unverified instead of using the chat-cache formula.
Embedding models produce retrieval vectors, rerankers score candidate passages, and speech models transcribe audio. These tasks are not comparable by chat quality. Batch, activation and audio memory are not modeled; specialist capacity stays unverified instead of using the chat-cache formula.
File sizes and hashes come from author repository listings; weights were not downloaded. File size is not total runtime memory. A verified matching-precision file takes precedence over the generic weight estimate, before cache and runtime margins.
Capacity scenario, not a benchmark: public weight tensor elements × bits ÷ 8 plus a 20% quantization/mixed-precision margin; F16 KV from layers, KV heads and context, plus 1.5 GiB runtime margin. Reserve at least 4 GiB or 25% RAM, and 2 GiB dedicated VRAM. One model, one text sequence; not a measured GGUF size or run guarantee.
Hybrid architectures, special packing or missing configs require manual review. Image/audio overhead is excluded. MoE uses total weights, not active parameters. 4/8-bit scenarios do not establish a published quant or backend compatibility.