Lizard Chat is the chat workspace built into the app, running on whatever model you activated. Since 1.2.52 it draws charts — and there is no chart button, no menu and no special syntax to learn. [image: lizard-chat.png] How it decides Ask a question that wants a picture and you get one: Show me a bar chart of my disk use in GB: models 22.2, benchmark history 0.17, logs 0.4 Plot the price over the last four months: 3.10, 3.40, 3.25, 3.90 Draw a diagram of how a release happens: build, sign, upload, announce Give me a pie chart of my week: coding 30 hours, meetings 8, reading 4 The wording only decides which example the model is shown. The model emits the chart type itself and can overrule that suggestion. Asked to "make a diagram of revenue by quarter", llama-3.2-3B returned a line chart — four quarters of revenue are a trend, not a flowchart. It can also decide a chart would not help and just answer. Six shapes bar · line · area · pie · scatter · flow (boxes and arrows, for processes and architectures) What it will not do It will not draw something misleading. A single data point, a value it cannot read, a diagram with nothing connected — you get the written answer and no chart, rather than a picture implying data that was never there. Where it runs Everything: your message, your documents, the answer, the chart. The charting library ships inside the installer, so charts render with the network switched off. Nothing in the chat crosses the network. The rest of the workspace Saved and searchable conversations grouped by day, pin/rename/export as Markdown. Questions about your own text, Markdown, CSV, log and source files, with citations. Separate workspaces (General, Code, Research) each with their own instructions and documents. MCP plugins for folders, web pages, SQLite files and Git history. Full details: https://lizard-llm.qendryx.com/chat.html Question for the room: what did you ask it to chart, and did it pick the shape you expected?

Welcome to the Lizard-LLM Community This is the official place to discuss local AI inference, share reproducible benchmark results, ask for help, and follow the development of lizard-native and Caterpillar. Start here Introduce yourself in General Discussion. Share hardware and measured results in Benchmarks. Ask implementation questions in Lizard Native or Caterpillar. Post support questions in Open Questions. Show integrations and local-AI projects in What are you building? Useful links: Lizard-LLM product overview Documentation Benchmarks Download Please include your hardware, model, quantization level, runtime provider, and the exact error or measurement when asking a technical question.

Worth stating plainly, because it's the most common question. The one-time $5 buys, permanently: Both engines running on your machine, forever Setup, hardware check, model recommendations and downloads Chatting locally The local API for your own code None of that is credit-based, and none of it expires. Five days of full benchmarks are included on top. After those five days, nothing you paid for is removed. Chat, the engines, setup — all still there. Only the full benchmark suite and the analytics dashboard pause, and a one-time credit pack reopens them. Credits never expire. Most people never need this. Credits are only ever spent on benchmark runs — 1 for a quick run, 5 for a standard comparison, 10 for a full Q1–Q9 suite. Terms: https://lizard-llm.qendryx.com/faq.html

Lizard serves the same API as OpenAI, on your machine. If you already have code written against a cloud AI service, you usually change one line. Start the server first: lizard-llm serve --model active Python from openai import OpenAI client = OpenAI(base_url="http://127.0.0.1:8013/v1", api_key="lizard-local") res = client.chat.completions.create( model="active", messages=[{"role": "user", "content": "Hello"}], ) print(res.choices[0].message.content) Node.js import OpenAI from "openai"; const client = new OpenAI({ baseURL: "http://127.0.0.1:8013/v1", apiKey: "lizard-local" }); curl curl http://127.0.0.1:8013/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{"model":"active","messages":[{"role":"user","content":"Hi"}]}' The key is ignored — nothing leaves your computer — but most clients insist on a value, so give it anything. Anything that reads OPENAI_BASE_URL (LangChain, LlamaIndex, lots of editor plugins) can point at Lizard the same way: OPENAI_BASE_URL=http://127.0.0.1:8013/v1 OPENAI_API_KEY=lizard-local Full docs: https://lizard-llm.qendryx.com/docs.html What have you wired it into?

Welcome. Short version of what this is: Lizard runs a real AI model on your own computer. No cloud account, no usage limits, and what you type never leaves your machine. It checks what your hardware can handle, suggests a model that fits, and gets you chatting in a few minutes. Getting started: Install and open it — everything's in the one installer, no Python or Docker needed first. Let it scan your machine. It only suggests models that will actually run well. Pick one, let it download, activate it. Talk to it — either the quick test chat in setup, or the full chat app from the tray icon. Two ways to chat: Quick chat in setup — a fast check that a model answers sensibly. Chat app (tray icon → Open chat app) — the full window, keeps your conversation, warms the model and lets you unload it when you're done. Docs: https://lizard-llm.qendryx.com/docs.html · FAQ: https://lizard-llm.qendryx.com/faq.html Introduce yourself and say what you're running it on — the hardware range here is genuinely wide.