Image-Text-to-Text
MLX
Safetensors
dots3_note
jang
apple-silicon
dots3
multimodal
Mixture of Experts
quantized
conversational
2-bit
Instructions to use JANGQ-AI/dots3-note-prev-JANG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use JANGQ-AI/dots3-note-prev-JANG with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("JANGQ-AI/dots3-note-prev-JANG") config = load_config("JANGQ-AI/dots3-note-prev-JANG") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use JANGQ-AI/dots3-note-prev-JANG with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "JANGQ-AI/dots3-note-prev-JANG"
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": "JANGQ-AI/dots3-note-prev-JANG" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use JANGQ-AI/dots3-note-prev-JANG 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 "JANGQ-AI/dots3-note-prev-JANG"
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 JANGQ-AI/dots3-note-prev-JANG
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use JANGQ-AI/dots3-note-prev-JANG with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "JANGQ-AI/dots3-note-prev-JANG"
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 "JANGQ-AI/dots3-note-prev-JANG" \ --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"
| { | |
| "image": { | |
| "pass": true, | |
| "matched": [ | |
| "square" | |
| ], | |
| "text": "To identify the shapes and their colors in the image, we can analyze each element step-by-step:\n\n1. **Top-Left Object:**\n * **Shape:** It has four equal sides, straight edges, and sharp corners (vertices). This is a **square**.\n * **Color:** It is filled" | |
| }, | |
| "video": { | |
| "pass": true, | |
| "matched": [ | |
| "right", | |
| "left" | |
| ], | |
| "text": "To determine the direction in which the red square moves, we can analyze its position frame-by-frame from the provided timestamps:\n\n1. **00:00**: The red square is located on the left side of the frame.\n2. **00:01**: The red square has shifted slightly to the right" | |
| }, | |
| "audio": { | |
| "pass": true, | |
| "matched": [ | |
| "tone", | |
| "sine", | |
| "pitch", | |
| "sound" | |
| ], | |
| "text": "The audio consists of a single, continuous, high-pitched electronic tone. It is a pure and steady sound, similar to a sine wave, with no variation in pitch or volume. The tone persists for the entire duration of the clip before stopping abruptly at the end.<|endofassistant|>" | |
| } | |
| } |