curtana

Crates.iocurtana
lib.rscurtana
version0.1.2
created_at2025-05-27 21:17:17.623818+00
updated_at2025-09-19 02:31:01.913155+00
descriptionSimplified zero-cost wrapper over llama.cpp powered by lama-cpp-2.
homepage
repositoryhttps://github.com/with-caer/curtana
max_upload_size
id1691799
size37,564
Caer (caer)

documentation

README

curtana on crates.io curtana on docs.rs curtana is MIT licensed

An accessible low-overhead wrapper over llama.cpp powered by llama-cpp-2, supporting most .gguf-formatted "Chat" and "Embedding" models.

Examples

These examples assume the following models are downloaded into the working directory:

Chat (via Llama 3.2 Instruct)

// Create a new local model registry and load
// a chat model into it with a system prompt
// of "You are a cupcake."
let registry = ModelRegistry::new().unwrap();
let mut model = registry
    .load_chat_model("Llama-3.2-3B-Instruct-Q6_K.gguf", "You are a cupcake.")
    .unwrap();

// Run ("infer") the model with the prompt
// "What are you?", capturing its output
// as UTF-8 encoded bytes.
let mut output = vec![];
model.infer("What are you?", &mut output).unwrap();
let output = String::from_utf8_lossy(&output);

// Hopefully, the model thinks it's a cupcake due
// to the system prompt.
assert!(output.to_lowercase().contains("cupcake"));

Embedding (via Nomic Embedding 1.5)

// Create a new local model registry and load
// an embedding model into it.
let registry = ModelRegistry::new().unwrap();
let mut model = registry
    .load_text_embedding_model("nomic-embed-text-v1.5.f16.gguf")
    .unwrap();

// Embed some fanciful document titles with the model.
let embeddings = model
    .embed(&[
        "search_document: might and magic in fantasy realms",
        "search_document: swords and sorcery for fantasy authors",
        "search_document: practical engineering for scientists",
    ])
    .unwrap();
assert_eq!(3, embeddings.len());

// Embed a search query with the model.
let query_embeddings = model.embed(&["query_document: fantasy"]).unwrap();
assert_eq!(1, query_embeddings.len());

// Calculate the cosine distance (or "similarity") between the embeddings.
let distance_a = cosine_distance(&query_embeddings[0], &embeddings[0]);
let distance_b = cosine_distance(&query_embeddings[0], &embeddings[1]);
let distance_c = cosine_distance(&query_embeddings[0], &embeddings[2]);

// The fantasy embeddings should be more similar
// than the scientific embedding.
assert!(distance_a < distance_c);
assert!(distance_b < distance_c);

License

Copyright © 2025 With Caer, LLC.

Licensed under the MIT license. Refer to the license file for more info.

Commit count: 13

cargo fmt