Llama.cpp Quantizations of nomic embed text v2 moe: Multilingual Mixture of Experts Text Embeddings Blog Technical Report AWS SageMaker Atlas Embedding and Unstructured Data Analytics Platform This model was presented in the paper Training Sparse Mixture Of Experts Text Embedding Models. Using llama.cpp commit e3a9421b7 for quantization. Original model: nomic embed text v2 moe Usage This model can be used with the llama.cpp server and other software that supports llama.cpp embedding models. Embedding text with nomic embed text requires task instruction prefixes at the beginning of each string. For example, the code below shows how to use the search query prefix to embed user questions, e.g. in a RAG application. Start a llama.cpp server: And run this code: You should see output similar to this: Download a file (not the whole branch) from below: Filename Quant Type File Size Description : nomic embed text v2 moe.f32.gguf f32 1820MiB Full FP32 weights. nomic embed text v2 moe.f16.gguf f16 913MiB Full FP16 weights. nomic embed text v2 moe.bf16.gguf bf16 913MiB Full BF16 weights. nomic embed text v2 moe.Q8\ 0.gguf Q8\ 0 488MiB Extremely high quality, generally unneeded but max availabl…
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