Instructions to use npario/LFM2.5-8B-A1B-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use npario/LFM2.5-8B-A1B-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("npario/LFM2.5-8B-A1B-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 npario/LFM2.5-8B-A1B-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 "npario/LFM2.5-8B-A1B-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": "npario/LFM2.5-8B-A1B-MLX-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use npario/LFM2.5-8B-A1B-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 "npario/LFM2.5-8B-A1B-MLX-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "npario/LFM2.5-8B-A1B-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": "npario/LFM2.5-8B-A1B-MLX-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use npario/LFM2.5-8B-A1B-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 "npario/LFM2.5-8B-A1B-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 npario/LFM2.5-8B-A1B-MLX-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use npario/LFM2.5-8B-A1B-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 "npario/LFM2.5-8B-A1B-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 "npario/LFM2.5-8B-A1B-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"

LFM2.5-8B-A1B-MLX-8bit / README.md

npario

ykhrustalev

Duplicate from LiquidAI/LFM2.5-8B-A1B-MLX-8bit

39f520a about 1 month ago

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metadata

library_name: mlx
license: other
license_name: lfm1.0
license_link: LICENSE
language:
  - en
  - ar
  - zh
  - fr
  - de
  - ja
  - ko
  - es
  - pt
pipeline_tag: text-generation
tags:
  - liquid
  - lfm2.5
  - edge
  - moe
  - mlx
base_model: LiquidAI/LFM2.5-8B-A1B

Try LFM • Docs • LEAP • Discord

LiquidAI/LFM2.5-8B-A1B-MLX-8bit

This model LiquidAI/LFM2.5-8B-A1B-MLX-8bit was converted to MLX format from LiquidAI/LFM2.5-8B-A1B using mlx-lm version 0.31.3.

About this quantization

Quantized to 8 bits per weight (group size 64, affine mode). MoE routing gates retained at 8 bits.

For base model details, capabilities, and recommended generation settings, see the original model card.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler, make_logits_processors

model, tokenizer = load("LiquidAI/LFM2.5-8B-A1B-MLX-8bit")

prompt = "What is C. elegans?"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

sampler = make_sampler(temp=0.2, top_k=80)
logits_processors = make_logits_processors(repetition_penalty=1.05)

response = generate(
    model, tokenizer, prompt=prompt,
    sampler=sampler, logits_processors=logits_processors,
    verbose=True,
)

Chat Template

LFM2.5 uses a ChatML-like format. See the Chat Template documentation for details. Example:

<|startoftext|><|im_start|>system
You are a helpful assistant trained by Liquid AI.<|im_end|>
<|im_start|>user
What is C. elegans?<|im_end|>
<|im_start|>assistant

Because LFM2.5-8B-A1B is a reasoning model, assistant turns contain an explicit chain of thought before the final answer. tokenizer.apply_chat_template() formats messages automatically.

Tool Use

LFM2.5 supports function calling. Pass tools to tokenizer.apply_chat_template(..., tools=...). See the Tool Use documentation for the full guide.

License

LFM Open License v1.0 — see the LICENSE file or the original model card for terms.

Citation

@article{liquidAI20268BA1B,
  author  = {Liquid AI},
  title   = {LFM2.5-8B-A1B: Personal Assistant On Your Laptop},
  journal = {Liquid AI Blog},
  year    = {2026},
  note    = {www.liquid.ai/blog/lfm2-5-8b-a1b},
}

@article{liquidai2025lfm2,
  title   = {LFM2 Technical Report},
  author  = {Liquid AI},
  journal = {arXiv preprint arXiv:2511.23404},
  year    = {2025}
}