Instructions to use xocialize/LFM2.5-8B-A1B-MLX-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xocialize/LFM2.5-8B-A1B-MLX-bf16 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("xocialize/LFM2.5-8B-A1B-MLX-bf16")

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 xocialize/LFM2.5-8B-A1B-MLX-bf16 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "xocialize/LFM2.5-8B-A1B-MLX-bf16"

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": "xocialize/LFM2.5-8B-A1B-MLX-bf16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use xocialize/LFM2.5-8B-A1B-MLX-bf16 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 "xocialize/LFM2.5-8B-A1B-MLX-bf16"

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 xocialize/LFM2.5-8B-A1B-MLX-bf16

Run Hermes

hermes

OpenClaw new

How to use xocialize/LFM2.5-8B-A1B-MLX-bf16 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "xocialize/LFM2.5-8B-A1B-MLX-bf16"

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 "xocialize/LFM2.5-8B-A1B-MLX-bf16" \
  --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 xocialize/LFM2.5-8B-A1B-MLX-bf16 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "xocialize/LFM2.5-8B-A1B-MLX-bf16"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "xocialize/LFM2.5-8B-A1B-MLX-bf16"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "xocialize/LFM2.5-8B-A1B-MLX-bf16",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

LFM2.5-8B-A1B-MLX-bf16 — availability mirror

A verbatim mirror of LiquidAI/LFM2.5-8B-A1B-MLX-bf16. Full bf16 precision — the unquantized checkpoint.

Why this repo exists

We ship Swift/MLX packages that name a weight repo in code. When an upstream repo moves or disappears, every already-published version of those packages breaks for anyone installing fresh — there is no redirect and no way to patch software already in someone's hands.

That is not hypothetical: an unrelated model family we had ported was reorganized onto a new HuggingFace org without redirects, and six of our published packages broke the same day. This mirror exists so that the same failure cannot take LFM2.5 with it.

It exists for durability, not improvement. Nothing here is changed, tuned, or repackaged, and LiquidAI's original is the better place to get these weights if you want the newest revision. All model IP, training, and quantization are LiquidAI's work.

Integrity

Byte-for-byte identical to upstream. Every file was verified by SHA-256 against the corresponding LiquidAI/LFM2.5-8B-A1B-MLX-bf16 file immediately before upload, and the published copies were re-verified afterward. The hashes are listed below so the claim is checkable rather than merely asserted:

25e731c697c4fe4c52ee654cedf7106a827748d2  LICENSE
6b9b8a6656db7b3d3024cc96914dcc19f858bc9e  README.md
8bca4a545e9ec8dc0c5d4689a78886c0741c47e4  chat_template.jinja
b4510db0c169c5b0ac56e40a829a2ca79a12a4b6  config.json
d14c980b6f1908b204b4b448734a0a508624960a  generation_config.json
0da8064e6741c864171035b606b817527f04ebd00f42d3950879ec65b5c9a23f  model-00001-of-00004.safetensors
b0c5a46659f133579ebd1c15c697b18a9c47f3a742869cb9e3fef7b8627fdea7  model-00002-of-00004.safetensors
5e10c55559c48c71975491276dfbac310f5a5b99b20d6a51e5b333675661af90  model-00003-of-00004.safetensors
3bd4e88199145dda61dbd6e8b92650200bc581951e084d264aa0afb4d371c87a  model-00004-of-00004.safetensors
57256e34f5238ce1427b6e0bac9f6a67ff0af5d8  model.safetensors.index.json
695be7802a0e4b8a81048f0ff5ebb7fc811a0ba5a6be63dbb24deb5a81096f41  tokenizer.json
df190b8c97179c7b2fde48758a353b1bcfb18c83  tokenizer_config.json

Small non-LFS files (configs, tokenizer metadata) carry no upstream hash to compare against and were verified by exact byte size.

Licence — the commercial threshold matters

These weights are governed by the LFM Open License v1.0, included in full as LICENSE. Mirroring changes nothing about it.

It is Apache-2.0-shaped, with one condition that is easy to miss and important if you are building a product rather than experimenting:

Section 5 — Commercial Use limitation. Commercial use by a legal entity with annual revenue at or above $10,000,000 USD is not licensed under this agreement. Below that threshold, commercial use is permitted. Qualified non-profits are exempt for non-commercial and research purposes.

Redistribution is expressly permitted by §4, on four conditions: supply a copy of the licence, mark any files you modified, retain attribution notices, and carry forward any NOTICE file. We have modified nothing.

This summary is a pointer, not a substitute for reading the licence, and where the two differ the licence governs.

Credit

LFM2.5-8B-A1B is designed, trained, quantized, and released by LiquidAI. This mirror adds nothing but an address we can promise not to move.

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Safetensors

Model size

8B params

Tensor type

BF16

F32

MLX

Hardware compatibility

Quantized

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