Instructions to use mlx-community/LFM3-VL-2B-OptiQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/LFM3-VL-2B-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/LFM3-VL-2B-OptiQ-4bit")
config = load_config("mlx-community/LFM3-VL-2B-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/LFM3-VL-2B-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/LFM3-VL-2B-OptiQ-4bit"

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": "mlx-community/LFM3-VL-2B-OptiQ-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use mlx-community/LFM3-VL-2B-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/LFM3-VL-2B-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/LFM3-VL-2B-OptiQ-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mlx-community/LFM3-VL-2B-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/LFM3-VL-2B-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/LFM3-VL-2B-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"

mlx-community/LFM3-VL-2B-OptiQ-4bit

Built with mlx-optiq, the MLX-native toolkit to quantize, fine-tune, and serve LLMs locally on Apple Silicon, no PyTorch and no cloud. All OptiQ quants · Docs

An OptiQ mixed-precision quant of LiquidAI/LFM3-VL-2B, Liquid's LFM3 vision-language model. It reads images and text and answers in text. It is 1.7 GB on disk, down from 3.9 GB at bf16, with a 128k context.

The language model is the LFM3 backbone: short convolutions and attention, with sliding-window attention, so only 6 of its 24 layers keep a full-length KV cache. OptiQ measures each layer's sensitivity and assigns its bit-width from that. The vision tower stays at bf16 in a sidecar, optiq_vision.safetensors.

What it is

Property	Value
Base	LiquidAI/LFM3-VL-2B
Architecture	`lfm3_vl`: LFM3 language model (24 layers: 13 convolution, 5 sliding-window attention of 256 tokens, 6 full attention) plus a vision tower
Method	OptiQ mixed-precision, sensitivity-driven (bf16 reference); vision tower at bf16
On disk	1.7 GB (bf16: 3.9 GB)
Context	128k

About half of the language model's weight matrices stay at 8-bit (75 of 153), the ones whose quantization moved the output most. The rest are at 4-bit.

Capability Score

Six-metric mean, the standard OptiQ eval, run in reasoning mode because the model thinks before it answers by default. The baseline is a uniform 4-bit quant of the same model, scored the same way.

Metric	OptiQ	Uniform 4-bit
MMLU (generative)	68.9%	70.6%
GSM8K	83.2%	76.4%
IFEval (strict)	44.4%	35.7%
BFCL-V3 simple (200 calls)	91.0%	90.5%
HumanEval (164 problems, pass@1)	55.5%	43.9%
HashHop (long-context retrieval)	3.0%	0.0%
Capability Score (mean of 6)	57.67	52.85

The OptiQ quant is ahead by 4.8 points overall, with the largest gains on code (+11.6), instruction following (+8.7) and math (+6.8).

Run it

pip install -U mlx-optiq
optiq serve --model mlx-community/LFM3-VL-2B-OptiQ-4bit

That gives you an OpenAI and Anthropic compatible endpoint that takes images as well as text, with mixed-precision KV cache (kv_config.json in this repo), tool calling, and prompt caching. Send an image as an image_url content part, as with any OpenAI vision request.

The model reasons before it answers, so give it a generous max_tokens. Liquid's recommended sampling (temperature 0.1, top_k 50, repetition_penalty 1.1) ships in generation_config.json, and optiq serve applies it automatically.

Model tree for mlx-community/LFM3-VL-2B-OptiQ-4bit

Base model

LiquidAI/LFM3-1.7B-Base

Finetuned

LiquidAI/LFM3-VL-2B

Quantized

(33)

this model

mlx-community
/

LFM3-VL-2B-OptiQ-4bit

mlx-community/LFM3-VL-2B-OptiQ-4bit

What it is

Capability Score

Run it

Links

Model tree for mlx-community/LFM3-VL-2B-OptiQ-4bit