--- title: Pick a model sidebar_label: 1. Pick a model description: "Read the catalog, resolve a model's identity, and see what a download would cost, all before any weights move." --- This tutorial takes one model from the catalog to a served endpoint in four parts, each a complete session, and ends with the same model running inside a C++ program. Core concepts are explained where they first appear. This part picks the model and learns everything about it without downloading a single weight. It assumes the install directory exists and `clikart-cli` is on your `PATH` (see [Quick install](../installation.md)). The model is Qwen2.5 0.5B Instruct, small enough to run on any machine in the [system requirements](/clikart/system-requirements); every command works the same with any other model in the catalog. ## The catalog knows the families `list` prints every registered model family: its modalities, the commands it provides, and whether it is runnable on this build. No network is involved; the catalog is compiled into the `clikart-cli` executable. ```bash clikart-cli list ``` ```text registered model families (47) bert runnable * Input Modalities: text * Output Modalities: embedding * Valid Combos: text -> embedding * Model Types: bert * Commands: embed, similar, bench, serve * Web UI: generic page * Vendor: Google * License page: https://github.com/google-research/bert/blob/master/LICENSE ------------------------------------------ ... ``` A family is the model architecture Modelverse knows how to run; a model you fetch is a checkpoint of that family. The `Commands` row is the contract for parts 2 and 3: whatever it lists is what that model can do. ## A source names a model Everything model-specific starts from a source: a Hugging Face Hub repo id (`/`), a pasted Hugging Face URL, or a local directory. Modelverse runs checkpoints in the formats model publishers ship on the Hub: safetensors, torch containers (`pytorch_model.bin`), and GGUF. (ONNX exports are a different lane: the ClikaRT runtime runs ONNX models directly; its how-to guide covers that path.) `info` resolves a source's identity: ```bash clikart-cli info Qwen/Qwen2.5-0.5B-Instruct ``` ```text Qwen/Qwen2.5-0.5B-Instruct family=qwen model_type=qwen2 architecture=Qwen2ForCausalLM variant: dense components: tokenizer=yes image=no audio=no video=no license page: https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT model: Qwen/Qwen2.5-0.5B-Instruct provider: Qwen source: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct license: apache-2.0 https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE ``` Identity resolution reads configuration files only. `clikart-cli` downloads a few KB of JSON, matches it against the registered families, and reports what it found; weights do not move. This is deliberate: you can interrogate a 70B model from a laptop. The last three lines are the licensing report: the family's license page, the checkpoint's provider and source, and the checkpoint's own license with whether the repository is gated. Read them before you ship what the model produces. ## `--dry` shows what a fetch would cost Add `--dry` and `info` also lists the files a fetch downloads, with sizes, split into companions (configs, tokenizer) and weights, and says how much of the repository it leaves behind (other weight formats, files the fetch never takes): ```bash clikart-cli info Qwen/Qwen2.5-0.5B-Instruct --dry ``` ```text Qwen/Qwen2.5-0.5B-Instruct family=qwen model_type=qwen2 architecture=Qwen2ForCausalLM variant: dense components: tokenizer=yes image=no audio=no video=no license page: https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT model: Qwen/Qwen2.5-0.5B-Instruct provider: Qwen source: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct license: apache-2.0 https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE companions: config.json 659 B generation_config.json 242 B merges.txt 1.6 MiB tokenizer.json 6.7 MiB tokenizer_config.json 7.1 KiB vocab.json 2.6 MiB weights: model.safetensors 942 MiB total: 953 MiB (weights 942 MiB, 7 files) repository: 953 MiB in 10 files; the rest is not fetched (other weight formats, files the fetch never takes) (dry; nothing downloaded) ``` Nothing here is Qwen-specific. `info google/gemma-4-E2B-it` reports `variant: dense + multimodal`; `info openai/whisper-large-v3-turbo` reports an audio component. Some repos ship several weight options to choose between; [Run a specific GGUF quantization](../../how-to/run-a-gguf-quantization.md) covers picking one. ## The model's own commands `clikart-cli` has four commands that work without naming a model (`list`, `devices`, `info` and `fetch`; `generate-template`, their file-form helper, rides beside them). Every other command runs on a model you name: pass the source and `--help`, and `clikart-cli` resolves its family and prints that family's own command surface: ```bash clikart-cli Qwen/Qwen2.5-0.5B-Instruct --help ``` ```text Qwen/Qwen2.5-0.5B-Instruct family=qwen text->text usage: clikart-cli Qwen/Qwen2.5-0.5B-Instruct [options] commands the 'qwen' family provides for this model options: -h, --help show this help and exit --verbose, -v debug diagnostics (the effective-options report) --quiet, -q warnings and errors only --no-color plain output (also honored: $NO_COLOR, TERM=dumb, non-tty) commands: prompt one templated generation of the positional user message serve run the http server (OpenAI and Anthropic APIs) bench the family-owned benchmark flow mm_bench the multi-modality benchmark flow (media axes over the serving path) ``` Asking a model for a command its family does not provide refuses precisely and names what it does provide, with exit code 3 ([Script clikart-cli](../../how-to/script-the-cli.md) has the full exit contract). Next: [part 2](02-fetch-and-prompt.md), the weights arrive and the model speaks.