Usage:
# macOS / Linux (latest release)
curl -fsSL https://kaja.io/setup.sh | bash
# Pin version or repo (put env after the pipe)
curl -fsSL https://kaja.io/setup.sh | REPO=subztep/kaja VERSION=v0.0.1 bash

# Windows (PowerShell)
irm https://kaja.io/setup.ps1 | iex
# e.g. fork:  $env:REPO = 'me/kaja'; irm https://kaja.io/setup.ps1 | iex

Model info

parameter_size: 1.7B

Parameters = the “knobs” the model learned during training. More knobs = more knowledge/capability, but also more RAM and slower.

Size RAM Needed (rough) Vibe
1B–3B 1–3 GB Tiny, fast, dumb-ish. Fine for autocomplete, simple tasks.
7B–8B 5–8 GB The “sweet spot” for laptops.
13B–30B 10–25 GB Smarter, needs a good GPU or lots of RAM.
70B+ 40+ GB Serious hardware territory.
400B+ Data center GPT-4 / Claude class.

So 1.7B = small and fast, runs on pretty much anything, but won’t be winning any IQ contests.

quantization_level: Q8_0

This is a compression trick. Originally each parameter is a 32-bit or 16-bit float (fancy decimal number). Quantization rounds them to smaller integers to save space.

Think JPEG for model weights — lose a little quality, save a lot of size.

Common levels (smaller number = more compressed = worse quality but faster/smaller):

Quant Bits/weight Quality File size (for 1.7B)
F16 16 Original-ish ~3.4 GB
Q8_0 8 Nearly identical to F16 ~1.8 GB
Q6_K ~6 Very good ~1.4 GB
Q5_K_M ~5 Good ~1.2 GB
Q4_K_M ~4 Decent, most popular ~1.0 GB
Q2_K ~2 Noticeably dumber ~0.7 GB

Q8_0 is basically “lossless-ish” — about half the size of the original with almost no quality drop. Good default for small models where you’ve got the RAM to spare.


Useful CLI Commands

Check for dependency updates:

bun outdated -r

Update depdendencies for all packages:

bunx npm-check-updates -w -u

Print full output with errors into a file:

bun dev > output.txt 2>&1

Generate Better-Auth secret:

openssl rand -base64 32

Find unused dependencies:

bunx knip

Docker

Start database only:

docker compose up -d db

Connect with any PostgreSQL client to port 5433.


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