Instructions to use LiquidAI/LFM2.5-VL-3B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2.5-VL-3B-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("LiquidAI/LFM2.5-VL-3B-MLX-4bit")
config = load_config("LiquidAI/LFM2.5-VL-3B-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

How to use LiquidAI/LFM2.5-VL-3B-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 "LiquidAI/LFM2.5-VL-3B-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": "LiquidAI/LFM2.5-VL-3B-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use LiquidAI/LFM2.5-VL-3B-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 "LiquidAI/LFM2.5-VL-3B-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 "LiquidAI/LFM2.5-VL-3B-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"

Hermes Agent

How to use LiquidAI/LFM2.5-VL-3B-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 "LiquidAI/LFM2.5-VL-3B-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 LiquidAI/LFM2.5-VL-3B-MLX-4bit

Run Hermes

hermes

Atomic Chat

LFM2.5-VL-3B-MLX-4bit / tokenizer_config.json

samuelstevens

Initial release

6720d37 about 13 hours ago

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	{
	"backend": "tokenizers",
	"bos_token": "<\|startoftext\|>",
	"clean_up_tokenization_spaces": true,
	"eos_token": "<\|im_end\|>",
	"image_end_token": "<\|image_end\|>",
	"image_start_token": "<\|image_start\|>",
	"image_thumbnail": "<\|img_thumbnail\|>",
	"image_token": "<image>",
	"is_local": true,
	"legacy": false,
	"local_files_only": true,
	"max_length": null,
	"model_max_length": 1000000000000000019884624838656,
	"model_specific_special_tokens": {
	"image_end_token": "<\|image_end\|>",
	"image_start_token": "<\|image_start\|>",
	"image_thumbnail": "<\|img_thumbnail\|>",
	"image_token": "<image>"
	},
	"pad_to_multiple_of": null,
	"pad_token": "<\|pad\|>",
	"pad_token_type_id": 0,
	"padding_side": "left",
	"processor_class": "Lfm2VlProcessor",
	"return_token_type_ids": false,
	"spaces_between_special_tokens": false,
	"tokenizer_class": "TokenizersBackend",
	"use_default_system_prompt": false,
	"use_fast": true
	}