Image-Text-to-Text
MLX
Safetensors
qwen3_5_moe
4-bit precision
4bit
apple-silicon
chat
conversational
edge-ai
function-calling
image-to-text
instruct
local-llm
m1
m2
m3
m4
mac
mac-mini
mac-studio
macbook-air
macbook-pro
macos
metal
mixture-of-experts
mlx-lm
mlx_vlm
mmlu-verified
Mixture of Experts
multimodal
no-cloud
offline
on-device
outlier
outlier-app
private
private-ai
quantized
qwen
qwen3.6
reasoning
text-generation
thinking
tool-use
vision
visual-question-answering
vqa
Eval Results (legacy)
Instructions to use Outlier-Ai/Outlier-Vision-35B-A3B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Outlier-Ai/Outlier-Vision-35B-A3B-MLX-4bit 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("Outlier-Ai/Outlier-Vision-35B-A3B-MLX-4bit") config = load_config("Outlier-Ai/Outlier-Vision-35B-A3B-MLX-4bit") # 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 Outlier-Ai/Outlier-Vision-35B-A3B-MLX-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Outlier-Ai/Outlier-Vision-35B-A3B-MLX-4bit"
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": "Outlier-Ai/Outlier-Vision-35B-A3B-MLX-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use Outlier-Ai/Outlier-Vision-35B-A3B-MLX-4bit 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 "Outlier-Ai/Outlier-Vision-35B-A3B-MLX-4bit"
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 Outlier-Ai/Outlier-Vision-35B-A3B-MLX-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Outlier-Ai/Outlier-Vision-35B-A3B-MLX-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Outlier-Ai/Outlier-Vision-35B-A3B-MLX-4bit"
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 "Outlier-Ai/Outlier-Vision-35B-A3B-MLX-4bit" \ --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"
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