nomic-embed-text-v1.5-GGUF

Required usage: task prefixes

nomic-embed-text requires task-instruction prefixes. Without them, retrieval quality collapses.

• Prepend search_query: to every query.
• Prepend search_document: to every document you index.

Other supported prefixes: classification: , clustering: . The MTEB score below (SciFact 0.7056 nDCG@10) was measured with these prefixes.

Long context (8192 tokens): the GGUF defaults to 2048. For the full window, serve with --rope-scaling yarn --rope-freq-scale 0.75 -c 8192.

nomic-embed-text-v1.5 — 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

• nomic-embed-text-v1.5-Q4_K_M.gguf (90.1 MB)
• nomic-embed-text-v1.5-Q5_K_M.gguf (101.2 MB)
• nomic-embed-text-v1.5-Q8_0.gguf (146.0 MB)

Quantization drift

Run embedding_quant_drift.py to populate.

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.