Instructions to use LiquidAI/LFM3-1.7B-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM3-1.7B-MLX-8bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("LiquidAI/LFM3-1.7B-MLX-8bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use LiquidAI/LFM3-1.7B-MLX-8bit 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/LFM3-1.7B-MLX-8bit"

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": "LiquidAI/LFM3-1.7B-MLX-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use LiquidAI/LFM3-1.7B-MLX-8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "LiquidAI/LFM3-1.7B-MLX-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "LiquidAI/LFM3-1.7B-MLX-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "LiquidAI/LFM3-1.7B-MLX-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use LiquidAI/LFM3-1.7B-MLX-8bit 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/LFM3-1.7B-MLX-8bit"

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/LFM3-1.7B-MLX-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use LiquidAI/LFM3-1.7B-MLX-8bit 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/LFM3-1.7B-MLX-8bit"

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/LFM3-1.7B-MLX-8bit" \
  --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"

Use MLX-VLM until mlx-lm supports LFM3

by ykhrustalev - opened about 9 hours ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+10

-6

Use MLX-VLM until mlx-lm supports LFM3c34f301b

ykhrustalev

Liquid AI org about 9 hours ago

Hugging Face CLI

No mlx-lm release loads LFM3 yet. The card's mlx_lm.generate command fails on mlx-lm 0.32.2, the latest release, with ValueError: Model type lfm3 not supported., and ml-explore/mlx-lm#2016 is still open.

MLX-VLM 0.7.11 runs this model, so this switches the usage section to python -m mlx_vlm.generate with the same sampling settings. It adds --repetition-penalty 1.1, which mlx-vlm's command line supports, and --enable-thinking, because mlx-vlm turns reasoning off by default. The command ran as written on PyPI mlx-vlm 0.7.11 for the 4bit and bf16 repos: it reasons, then answers Canberra.

The Hub's "Use this model" snippet still shows mlx-lm, since it follows the pipeline tag. Switch the section back once an mlx-lm release supports LFM3.

Spell out no repetition penalty for math454ce4e7

ykhrustalev changed pull request status to merged about 9 hours ago

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