mxbai-embed-large-v1-GGUF

Usage note: retrieval query prefix

For retrieval tasks, prepend this instruction to queries (documents get no prefix):

Represent this sentence for searching relevant passages:

The MTEB score below (SciFact 0.7388 nDCG@10) was measured with this query prefix. For similarity/clustering tasks, no prefix is needed. Context window is 512 tokens.

mxbai-embed-large-v1 — Embedding GGUF (quantization-verified)

Quantized embedding model in GGUF, served in --embedding mode via llama.cpp. This is an encoder — it outputs vectors, not text. It is validated for retrieval quality and quantization fidelity, not chat behavior.

Files

• mxbai-embed-large-v1-Q4_K_M.gguf (215.8 MB)
• mxbai-embed-large-v1-Q5_K_M.gguf (245.7 MB)
• mxbai-embed-large-v1-Q8_0.gguf (358.1 MB)

Quantization drift (vs f16)

Mean cosine similarity of embeddings vs the f16 baseline. 1.0 = identical.

Per-domain fidelity at Q4_K_M (which content types the quant preserves best):

Retrieval sanity (lightweight)

Built-in 12-query retrieval check (no external corpus): top-1 accuracy 1.0, MRR 1.0. healthy (top-1 >= 0.9)

Retrieval (MTEB)

Standardized MTEB retrieval scores (main metric, usually nDCG@10 — higher is better). These are comparable across models on the MTEB leaderboard.

Metric: main_score (retrieval tasks: nDCG@10). Measured on the Q8_0 quant served via llama.cpp.

Dense-retrieval mode. These scores are for standard single-vector dense retrieval (what llama.cpp serves). Models like BGE-M3 that also support sparse/multi-vector (ColBERT) modes score higher in hybrid setups — that capability isn't exercised here, so compare this number against other models' dense scores, not hybrid ones.

What this is NOT

This card carries no safety, red-team, or viewpoint scores: those do not apply to an embedding model. For chat-model governance cards, see the SmartTasks text-LLM line.