Instructions to use AlphaOxO/Maincoder-1B-8bits-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AlphaOxO/Maincoder-1B-8bits-mlx 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("AlphaOxO/Maincoder-1B-8bits-mlx") 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 AlphaOxO/Maincoder-1B-8bits-mlx with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AlphaOxO/Maincoder-1B-8bits-mlx"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "AlphaOxO/Maincoder-1B-8bits-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use AlphaOxO/Maincoder-1B-8bits-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "AlphaOxO/Maincoder-1B-8bits-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "AlphaOxO/Maincoder-1B-8bits-mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AlphaOxO/Maincoder-1B-8bits-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use AlphaOxO/Maincoder-1B-8bits-mlx 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 "AlphaOxO/Maincoder-1B-8bits-mlx"
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 AlphaOxO/Maincoder-1B-8bits-mlx
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use AlphaOxO/Maincoder-1B-8bits-mlx with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AlphaOxO/Maincoder-1B-8bits-mlx"
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 "AlphaOxO/Maincoder-1B-8bits-mlx" \ --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"
Update README.md
Browse files
README.md
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pipeline_tag: text-generation
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tags:
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- mlx
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---
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pipeline_tag: text-generation
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tags:
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- mlx
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base_model:
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- Maincode/Maincoder-1B
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---
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# Maincoder-1B — MLX 8bits
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Quantized to 8bits format.
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Converted from [Maincode/Maincoder-1B](Maincode/Maincoder-1B) using mlx-lm.
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## Requirements
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The `Maincoder` model type is support in my mlx-lm fork([AlphaKure/mlx-lm](https://github.com/AlphaKure/mlx-lm/issues/4)).
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Update at: 2026/09/09
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```bash
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pip install git+https://github.com/AlphaKure/mlx-lm.git@19ed84b1e3333bc9c320109b15dec38fe0784d29
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```
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## Use
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* use --extra-eos-token '<|im_end|>' to avoid loop response
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```bash
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mlx_lm.generate --model AlphaOxO/Maincoder-1B-8bits-mlx --prompt "def insert_sort(nums: list[int]):" --max-tokens 2048 --extra-eos-token '<|im_end|>'
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```
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