Instructions to use Ai-userman/Zacoda-Lite-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Ai-userman/Zacoda-Lite-1.0 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Ai-userman/Zacoda-Lite-1.0") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use Ai-userman/Zacoda-Lite-1.0 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Ai-userman/Zacoda-Lite-1.0"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Ai-userman/Zacoda-Lite-1.0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Ai-userman/Zacoda-Lite-1.0 with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Ai-userman/Zacoda-Lite-1.0"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Ai-userman/Zacoda-Lite-1.0
Run Hermes
hermes
- OpenClaw new
How to use Ai-userman/Zacoda-Lite-1.0 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Ai-userman/Zacoda-Lite-1.0"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Ai-userman/Zacoda-Lite-1.0" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- MLX LM
How to use Ai-userman/Zacoda-Lite-1.0 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Ai-userman/Zacoda-Lite-1.0"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Ai-userman/Zacoda-Lite-1.0" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ai-userman/Zacoda-Lite-1.0", "messages": [ {"role": "user", "content": "Hello"} ] }'
Zacoda Lite 1.0
The first release in the Lite line. Superseded by Zacoda Lite 1.1 β that's the one to use.
A note on this repo
This repo doesn't have a working download right now β the weights that were here didn't survive. If you're looking for a 1.0-generation Zacoda model to actually run, Zacoda Plus 1.0 still has its bf16 build up, and Lite 1.1 above is the real successor to this one.
(Separately: Plus 1.0's own 8-bit and 4-bit builds were lost the same way β only its bf16 version survived, which is what's live on that repo now.)
Made independently by Sean Zhang / Ai-userman
On benchmarks
Large labs sometimes train on the questions their models get benchmarked with β deliberately or through data that quietly overlaps. We can't audit anyone else's pipeline, so we won't make that claim about them. What we can do is show ours.
ZACBENCH-700 is our own evaluation set (700 questions across math, coding, general knowledge, logic, instruction-following, English, and agentic tool-use), and we checked β not assumed β that Zacoda was not trained on it: zero of its questions appear anywhere in our training corpus or distillation prompts. We also report GSM8K and HumanEval. GSM8K's train split is in our SFT data (standard practice β many labs do this); its test split is not, verified. HumanEval appears nowhere in training and is our cleanest independent signal.
Every model in any comparison here gets the identical token budget per section and the identical prompt β no model gets a bigger reasoning allowance than another. The full scoring code, every question, and the exact token limits are public: ZACBENCH-700. Run it yourself, against this model or any other.
- Downloads last month
- 241