Getting a model

Any sentence-transformers-style ONNX export works: a directory containing model.onnx and tokenizer.json. embeddings.hub/fetch-model downloads one from the Hugging Face Hub into ~/.cache/embeddings-clj (tries onnx/model.onnx, falls back to model.onnx; already-cached files are not re-downloaded):

(require '[embeddings.hub :as hub])

(def model-dir (hub/fetch-model "sentence-transformers/all-MiniLM-L6-v2"))
;; opts: {:cache-dir "..." :revision "main" :variant ...}

Quantized exports: :variant :quantized tries the common quantized paths (onnx/model_quantized.onnx, onnx/model_qint8_avx512_vnni.onnx, onnx/model_int8.onnx, model_quantized.onnx) in order; a string :variant names an explicit repo-relative .onnx path (e.g. "onnx/model_q4.onnx"). Variants are cached in their own subdirectory, so full-precision and quantized copies of the same model coexist:

(hub/fetch-model "sentence-transformers/all-MiniLM-L6-v2" {:variant :quantized})

Or fetch manually:

mkdir -p models/all-MiniLM-L6-v2
curl -fL -o models/all-MiniLM-L6-v2/model.onnx \
  https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/resolve/main/onnx/model.onnx
curl -fL -o models/all-MiniLM-L6-v2/tokenizer.json \
  https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/resolve/main/tokenizer.json

Compatible model families include all-MiniLM, all-mpnet, BGE, GTE, and E5 (anything whose ONNX graph takes input_ids/attention_mask/token_type_ids and outputs token embeddings or a pre-pooled sentence embedding).

E5-style models expect instruction prefixes; pass :prefix per call:

(emb/embed model "how do I bake bread" {:prefix "query: "})
(emb/embed-batch model documents {:prefix "passage: "})