lqh 0.2

Liquid Harness – customize Liquid AI foundation models into task-specific models.

Closed beta — visit lqh.ai to request access.

lqh — Liquid Harness

From zero to a fine-tuned LFM in under an hour.

Liquid Harness is a terminal agent that turns a plain-English task description into a deployable model. It writes the data pipeline, scores and filters samples, runs baselines, fine-tunes, and iterates. No ML experience required. Built and maintained by Liquid AI as the official way to customize Liquid Foundation Models, powered by LQH Cloud.

pip install lqh
lqh

✨ Features

💬 Fully agentic

You chat, the agent works. It interviews you about your task, writes and manages the specifications, then drives every downstream stage end-to-end.

📝 Specify in plain English

No DSL, no boilerplate, no ML jargon. Just describe what you want the model to do — the agent captures requirements through dialogue and turns them into structured specs you can refine over time.

🧪 Synthetic data, scored & filtered

The agent authors a per-task data generation pipeline, generates samples concurrently on LQH Cloud, and scores each one with an LLM judge against your rubric. The dataset that hits training is already curated.

🏋️ Fine-tune locally or in the cloud

Eval and data generation run on LQH Cloud. Training can run locally on your own GPUs, or hand off to a beefier machine — just sync the dataset and continue.

🤗 HuggingFace integration

Push and pull datasets from the Hub. Set HF_TOKEN to enable private dataset access and dataset publishing.

🤖 Hands-off --auto mode

Point lqh at a directory and walk away. It either delivers a checkpoint that beats baseline or returns an explicit failure with the reason — never a hang, never a prompt.

🖥️ Interactive TUI

Provide input, guide the agent, visualize progress, and inspect dataset samples — all from a single terminal session with a slash-command palette and a live status bar.

📦 Project-as-directory

Any directory is a project — fully git-compatible, so you can version, branch, and collaborate on specs, datasets, and runs like any other code. cd to switch projects.

🚀 The pipeline

One command runs all nine stages. Each is a real component you can inspect, stop at, or hand off.

spec → rubric → data gen → filter → baseline → SFT → DPO → eval → checkpoint

$ lqh --auto ./my-task
[stage: rubric]            writing scorer from spec
[stage: data_gen_draft]    5 samples generated, all valid
[stage: filter_validation] 1,427 / 2,000 kept
[stage: sft_initial]       score 6.8/10  (baseline 4.1)
[stage: dpo]               iter 3/5, score 7.4/10
[final: success]           DPO checkpoint beats baseline by +3.3

🔧 Requirements

• Python 3.10+
• A Liquid Harness account (request access)
• Optional: torch + transformers for local fine-tuning
• Optional: HF_TOKEN for HuggingFace dataset sync

🔐 Authentication

lqh
> /login

The CLI stores your token in ~/.lqh/config.json and authenticates all requests to LQH Cloud.

🧬 Base model

Default: LFM2-1.2B-Instruct — small, capable, runs anywhere.

🗺️ Roadmap / work in progress

Things we're actively building or planning. Open an issue if you want to weigh in.

• Quantized evals — run the local evaluation using a quantized model (llama.cpp) so we measure the exact artifact that will be deployed (e.g. Q4_0). Includes checkpoint → GGUF conversion as part of the pipeline.
• Quantization-aware training (QAT) — once quantized evals are in place, train against the quantization noise so the deployed quantized model matches the full-precision score.
• Sub-agent spawning — today there's a single main agent loop. We're adding the ability for the agent to fork or spawn sub-agent processes in parallel for independent subtasks (e.g. drafting multiple data pipelines, running evals concurrently).
• Training via API or SLURM — local and direct-SSH training work today. The training backend is already abstracted so SSH+SLURM cluster submission and a hosted training API can drop in, but neither is implemented or end-to-end tested yet.
• Multi-modal support — scaffolding for image and audio inputs is in place, but the concrete pipelines, data generators, and end-to-end tests have not been written yet.

🤝 Contributing

Please read CONTRIBUTING.md before opening a pull request. Random PRs will be rejected — discuss first via an issue, and the discussion will usually conclude with a prompt (not a patch) that a coding agent will execute. Security hot fixes are the one exception. See the contribution policy for details.

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Made with care by Liquid AI.