aq changelog
- aq v0.0.1
Introducing aq
Concept
aqis the developer environment for foundation models: frameworks, libraries, tools, and autonomous agents that help you design, test, and refine models. Built for precision and intent. Think Next.js for JS, but for ML: architecture, experiments, and building AI the way it should be built, deliberately, not by guessing.Trains live on disk. A train is a folder with your data, recipe, and results.
aqis the CLI that runs them: train, eval, queue jobs, and chat with an agent that works inside that folder, with the same precision as writing code.No account required for local training. Optional login for Aquin cloud features.
Install
curl -fsSL https://aq.aquin.app/framework/install.sh | bash
Needs Node 18+, Python 3, and npm. After install:
aq helpandaq doctor.From a git checkout:
./install.shorcd aq && npm install.New in 0.0.1
Start a project
aq init: new experiment folder (aq-experiment, or-new1,-new2, … if that name exists)aq init my-project: same, with your name-
aq fork: copy an experiment (skips old job logs and checkpoints)
Train & evaluate
aq train: fit fromrecipe.yamlaq eval: score against files inevals/aq serve: try generation from a checkpoint- Tabular ML: linear/logistic regression, ridge, lasso, elastic net, trees, forests, boosting, Gaussian processes
- LLM work: pretrain, SFT, full fine-tune, LoRA, QLoRA (NVIDIA + 4-bit), masked LM, span corruption, fill-in-the-middle, continued training from a checkpoint
- Transformers: encoder, decoder, encoder-decoder (set
model:in the recipe)
Work in the background
-
aq job run: run a command as a queued job aq job list | log | cancel | resume | tree
Agent & chat
-
aq: interactive chat in the terminal aq ask: one-shot question, no UIaq chat list: pick up old conversations (stored under~/.aq/chats/)-
aq spawn: start a background agent on a task - Plug in your own scripts (
tools/) and skill packs (skills/)
Other
aq provider: OpenAI, Anthropic, Grok, or Ollamaaq schedule: chains like train → eval, sweeps, cron-style rerunsaq stage: sub-experiments insidestages/aq status,aq diff,aq checkout: see what ran and go back to an older stateaq version,aq doctor
Fixes in 0.0.1
- Install:
aqcommand works after curl install (no broken path / missing binary) - Install: Python deps on Ubuntu/Debian (venv + pip setup)
- Training: faster LM runs on typical GPUs; less likely to OOM on a T4-class card
- Chat: conversations live in one global place, not scattered per folder
This is only the first cut. We want aq to feel like a calm desk for teaching models: yours to keep, easy to share, honest about what it can do. If you are curious about how machines learn, you already belong here. Build something small. See what it becomes.
