Modelverse at a glance

Modelverse is the CLIKA model library, built on the ClikaRT runtime. It is a catalog of model families, the clikart-cli executable, which inspects, fetches and runs any model in it, and the library behind both (C++ first, with Python and Kotlin bindings over the same surface). It is not a training framework, not a conversion pipeline, and not a hosted service. Models arrive as the checkpoint files their authors published; Modelverse knows which family they belong to and what that family can do.

The mental model

Five ideas carry the whole product, in the order you meet them.

1. The catalog is a registry of model families. A family (Llama, Qwen, Whisper, CLIP, ...) declares its input and output modalities, the checkpoints it matches, and the commands it provides. The catalog is built into clikart-cli; listing it needs no network.

   clikart-cli list

2. A model is a source you name. A Hugging Face Hub repo id, a pasted Hugging Face URL, or a local directory all name a model. Identity resolution reads config files only, so asking what something is never downloads weights.

   clikart-cli info Qwen/Qwen2.5-0.5B-Instruct
   clikart-cli info ./my-model-dir

   A GGUF repo that ships several quantizations takes a selector, <org>/<repo>:Q6_K; with several options and no selection, clikart-cli refuses and prints the option table instead of guessing.

3. The clikart-cli executable has four commands that work without naming a model: list, devices, info, and fetch. Every other command runs on a model you name: clikart-cli <model> <command>. Which commands a model supports depends on what kind of model it is: a text-generation model has prompt, serve, and bench; a speech model has transcribe. clikart-cli <model> --help prints the commands a model supports:

   clikart-cli <source> --help    # that model's commands
   clikart-cli <source> prompt "The capital of France is"

4. Serving is one of those commands. serve hosts the model behind an OpenAI-compatible HTTP server, with streaming chat completions, a built-in web chat page, and per-modality routes (transcription, embeddings, depth and more). Existing OpenAI clients connect by changing their base URL.

   clikart-cli <source> serve --port 8000

5. Everything clikart-cli does, the library does. The executable is a thin layer over the clika_modelverse library: a snapshot call fetches, the registry loads a runnable model, a serving pipeline generates. Your application makes the same two calls in its own language:

   C++

   mv::hub::SnapshotResult snapped = mv::hub::snapshot("Qwen/Qwen2.5-0.5B-Instruct", opts);
   mv::GenerativeModel model = mv::ModelRegistry::builtin().load_generative(snapped.local_dir, load);

   Python

   snapped = mv.hub.snapshot("Qwen/Qwen2.5-0.5B-Instruct", cache_dir="models")
   model   = mv.ModelRegistry.builtin().load_generative(snapped.local_dir)

   Kotlin

   val snapped = Hub.snapshot("Qwen/Qwen2.5-0.5B-Instruct", cacheDir = "models")
   val model   = ModelRegistry.builtin().loadGenerative(snapped.localDir)

The tutorial series turns these into working sessions, one idea per part.

Platforms

Modelverse ships inside the ClikaRT release archive, one archive per platform: Linux (x86_64, arm64), Windows (x86_64, arm64), macOS and Android, and from Python it ships inside the clika-runtime wheel as clika_runtime.modelverse. Extract the archive and run in place; the manifest inside pins the exact runtime this build linked against, so a mismatched pair refuses with a readable error. The ClikaRT system requirements apply unchanged; Modelverse adds no requirements of its own.