Instructions to use mlx-community/Fara1.5-9B-OptiQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Fara1.5-9B-OptiQ-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("mlx-community/Fara1.5-9B-OptiQ-4bit")
config = load_config("mlx-community/Fara1.5-9B-OptiQ-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

How to use mlx-community/Fara1.5-9B-OptiQ-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 "mlx-community/Fara1.5-9B-OptiQ-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": "mlx-community/Fara1.5-9B-OptiQ-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/Fara1.5-9B-OptiQ-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 "mlx-community/Fara1.5-9B-OptiQ-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 mlx-community/Fara1.5-9B-OptiQ-4bit

Run Hermes

hermes

OpenClaw new

How to use mlx-community/Fara1.5-9B-OptiQ-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 "mlx-community/Fara1.5-9B-OptiQ-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 "mlx-community/Fara1.5-9B-OptiQ-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"

Fara1.5-9B-OptiQ-4bit / README.md

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	---
	license: mit
	language:
	- en
	tags:
	- mlx
	- optiq
	- computer-use
	- web-agent
	- agent
	library_name: mlx
	pipeline_tag: image-text-to-text
	base_model: microsoft/Fara1.5-9B
	---

	# Fara1.5-9B-OptiQ-4bit

	> Built with [mlx-optiq](https://mlx-optiq.com), the MLX-native toolkit to quantize, fine-tune, and serve LLMs locally on Apple Silicon (no PyTorch, no cloud). [Try the Lab](https://mlx-optiq.com/docs/lab/) · [All OptiQ quants](https://mlx-optiq.com/models) · [Docs](https://mlx-optiq.com/docs/)
	>
	> Supported loaders: [mlx-optiq](https://mlx-optiq.com) (text, vision, and MTP) and stock [mlx-lm](https://github.com/ml-explore/mlx-lm) (text). Other front-ends load MLX weights through their own stack, so support there depends on that stack rather than on these files.

	An [OptiQ](https://mlx-optiq.com) mixed-precision MLX quant of [microsoft/Fara1.5-9B](https://huggingface.co/microsoft/Fara1.5-9B), a Qwen3.5-based computer-use / web-agent vision-language model.

	- Mixed 4/8-bit, 5.21 bits per weight (7.5G on disk).
	- The per-layer bit allocation is transferred from the published `mlx-community/Qwen3.5-9B-OptiQ-4bit` quant. Fara1.5 is a finetune of Qwen3.5-9B with identical architecture, so the OptiQ allocation matches the Qwen3.5 family exactly, with no separate sensitivity pass.
	- Vision tower kept at bf16 in `optiq/optiq_vision.safetensors`. The one repo loads text-only under stock `mlx-lm` and full image+text under OptiQ.

	## Running it

	```bash
	pip install -U optiq
	optiq serve --model mlx-community/Fara1.5-9B-OptiQ-4bit
	```

	Use the OpenAI-compatible endpoint at `http://localhost:8000/v1`. Send an `image_url` part for the computer-use / vision path.