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Embed, compare and rerank

Retrieval systems stand on three operations: turn inputs into dense vectors, compare vectors, and rerank candidates against a query with a model that reads both together. Modelverse's embedding and reranking families provide all three as commands, and the same models serve them over HTTP.

Compare texts​

Any text-embedding family provides similar: the positional texts embed in one batch, and the payload names the inputs by index and prints their cosine matrix:

clika-modelverse Qwen/Qwen3-Embedding-0.6B-GGUF:Q8_0 similar \
    "How do I reset my password?" \
    "Password reset instructions" \
    "Quarterly revenue rose 4 percent"
[0] How do I reset my password?
[1] Password reset instructions
[2] Quarterly revenue rose 4 percent
scores (cosine; 1 = identical direction):
         [0]    [1]    [2]
[0]   1.0000  0.7265  0.2591
[1]   0.7265  1.0000  0.3705
[2]   0.2591  0.3705  1.0000

--top-k N switches the rendering to a per-input ranking (the nearest N others per row), the form you want when the list is long; --json emits the same result as a document for scripts.

Compare images, or texts against images​

A dual-tower family (CLIP, SigLIP) embeds texts and images into one space, so similar grows two more forms: with --image attachments the matrix is texts against images, and with only images it is images against images:

clika-modelverse google/siglip-base-patch16-224 similar \
    "a photo of a cat" "a photo of a dog" --image pet1.jpg --image pet2.jpg

For the raw vectors, embed takes images and prints a summary row per input (vector width, L2 norm, a quantized-vector signature, and each row's cosine against the first), with --output PREFIX writing PREFIX.csv (one row per image, the full vector as columns) and PREFIX.json (run configuration, summary and vectors) for whatever indexes them next:

clika-modelverse google/siglip-base-patch16-224 embed catalog/*.jpg --output catalog_vectors

Rerank documents against a query​

Embedding similarity is a coarse first pass; a reranker is a cross-encoder that reads the query and each document together and scores actual relevance. The reranking families provide rerank, with every document scored through one packed forward:

clika-modelverse Qwen/Qwen3-Reranker-0.6B rerank \
    "how to reset a password" \
    "Password reset instructions" \
    "Changing your username" \
    "Quarterly revenue rose 4 percent"
0.9231  [0] Password reset instructions
0.4106  [1] Changing your username
0.0312  [2] Quarterly revenue rose 4 percent

--instruction prepends the task instruction the checkpoint was trained with, when your use differs from the default retrieval phrasing. The everyday pipeline is both stages in order: similar --top-k over the corpus to shortlist, rerank over the shortlist to decide.

Over HTTP​

The same models serve the retrieval routes (the full route table):

clika-modelverse Qwen/Qwen3-Embedding-0.6B-GGUF:Q8_0 serve --port 8000
curl -s http://127.0.0.1:8000/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{"input": ["How do I reset my password?", "Password reset instructions"]}'

POST /v1/embeddings answers in the OpenAI shape, so client.embeddings.create(...) works against the base URL unchanged; POST /v1/similarity returns the cosine matrix directly, saving the round trip through raw vectors when the comparison is all you need.

Embedding models are small (Model requirements carries the figures), and the GGUF quantization selector on the source works here exactly as in Run a specific GGUF quantization.