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| File | Quantization | Size |
|---|---|---|
| mxbai-embed-large-v1-q4_k.gguf | Q4_K | 196 MB |
| mxbai-embed-large-v1-q8_0.gguf | Q8_0 | 341 MB |
| mxbai-embed-large-v1.gguf | F32 | 1279 MB |
1# Download
2huggingface-cli download cstr/mxbai-embed-large-v1-GGUF mxbai-embed-large-v1-q4_k.gguf --local-dir .
3
4# Run with CrispEmbed
5./crispembed -m mxbai-embed-large-v1-q4_k.gguf "Hello world"
6
7# Or with auto-download
8./crispembed -m mxbai-embed-large-v1 "Hello world"| Property | Value |
|---|---|
| Architecture | BERT |
| Parameters | 335M |
| Embedding Dimension | 1024 |
| Layers | 24 |
| Pooling | CLS |
| Tokenizer | WordPiece |
| Base Model | mixedbread-ai/mxbai-embed-large-v1 |
1# Build CrispEmbed
2git clone https://github.com/CrispStrobe/CrispEmbed
3cd CrispEmbed
4cmake -S . -B build && cmake --build build -j
5
6# Encode
7./build/crispembed -m mxbai-embed-large-v1-q4_k.gguf "query text"
8
9# Server mode
10./build/crispembed-server -m mxbai-embed-large-v1-q4_k.gguf --port 8080
11curl -X POST http://localhost:8080/v1/embeddings \
12 -d '{"input": ["Hello world"], "model": "mxbai-embed-large-v1"}'convert-bert-embed-to-gguf.pymixedbread-ai.apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.