Instructions to use LiquidAI/LFM2.5-230M-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2.5-230M-MLX-4bit 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/LFM2.5-230M-MLX-4bit")

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/LFM2.5-230M-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/LFM2.5-230M-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/LFM2.5-230M-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use LiquidAI/LFM2.5-230M-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/LFM2.5-230M-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/LFM2.5-230M-MLX-4bit

Run Hermes

hermes

OpenClaw new

How to use LiquidAI/LFM2.5-230M-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/LFM2.5-230M-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/LFM2.5-230M-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"

MLX LM

How to use LiquidAI/LFM2.5-230M-MLX-4bit 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/LFM2.5-230M-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "LiquidAI/LFM2.5-230M-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "LiquidAI/LFM2.5-230M-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

LFM2.5-230M-MLX-4bit / README.md

ykhrustalev

Add LFM2.5-230M-MLX-4bit MLX export

5e2984e verified 30 days ago

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	---
	library_name: mlx
	license: other
	license_name: lfm1.0
	license_link: LICENSE
	language:
	- en
	- ar
	- zh
	- fr
	- de
	- ja
	- ko
	- es
	- pt
	- it
	pipeline_tag: text-generation
	tags:
	- liquid
	- lfm2.5
	- edge
	- mlx
	base_model: LiquidAI/LFM2.5-230M
	---

	<div align="center">
	<img
	src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"
	alt="Liquid AI"
	style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
	/>
	<div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
	<a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> •
	<a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> •
	<a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> •
	<a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
	</div>
	</div>
	<br>

	# LFM2.5-230M-MLX-4bit

	MLX export of [LFM2.5-230M](https://huggingface.co/LiquidAI/LFM2.5-230M) for Apple Silicon inference.

	LFM2.5-230M is a compact multilingual model built on LiquidAI's hybrid architecture, combining convolutional and attention layers for efficient long-context processing.

	## Model Details

	\| Property \| Value \|
	\|----------\|-------\|
	\| Parameters \| 230M \|
	\| Precision \| 4-bit \|
	\| Group Size \| 64 \|
	\| Size \| 139 MB \|
	\| Context Length \| 128K \|

	## Use with mlx

	```bash
	pip install mlx-lm
	```

	```python
	from mlx_lm import load, generate
	from mlx_lm.sample_utils import make_sampler

	model, tokenizer = load("LiquidAI/LFM2.5-230M-MLX-4bit")

	response = generate(
	model,
	tokenizer,
	prompt="The capital of France is",
	max_tokens=100,
	sampler=make_sampler(temp=0.7),
	verbose=True,
	)
	```

	## Other Precisions

	- [LFM2.5-230M-MLX-bf16](https://huggingface.co/LiquidAI/LFM2.5-230M-MLX-bf16) (438 MB)
	- [LFM2.5-230M-MLX-8bit](https://huggingface.co/LiquidAI/LFM2.5-230M-MLX-8bit) (233 MB)
	- [LFM2.5-230M-MLX-6bit](https://huggingface.co/LiquidAI/LFM2.5-230M-MLX-6bit) (178 MB)
	- [LFM2.5-230M-MLX-5bit](https://huggingface.co/LiquidAI/LFM2.5-230M-MLX-5bit) (159 MB)
	- [LFM2.5-230M-MLX-4bit](https://huggingface.co/LiquidAI/LFM2.5-230M-MLX-4bit) (139 MB)

	## License

	This model is released under the [LFM 1.0 License](LICENSE).