Instructions to use OsaurusAI/LFM2.5-8B-A1B-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OsaurusAI/LFM2.5-8B-A1B-MXFP4 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("OsaurusAI/LFM2.5-8B-A1B-MXFP4")

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 OsaurusAI/LFM2.5-8B-A1B-MXFP4 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OsaurusAI/LFM2.5-8B-A1B-MXFP4"

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": "OsaurusAI/LFM2.5-8B-A1B-MXFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use OsaurusAI/LFM2.5-8B-A1B-MXFP4 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "OsaurusAI/LFM2.5-8B-A1B-MXFP4"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "OsaurusAI/LFM2.5-8B-A1B-MXFP4"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "OsaurusAI/LFM2.5-8B-A1B-MXFP4",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use OsaurusAI/LFM2.5-8B-A1B-MXFP4 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 "OsaurusAI/LFM2.5-8B-A1B-MXFP4"

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 OsaurusAI/LFM2.5-8B-A1B-MXFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use OsaurusAI/LFM2.5-8B-A1B-MXFP4 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OsaurusAI/LFM2.5-8B-A1B-MXFP4"

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 "OsaurusAI/LFM2.5-8B-A1B-MXFP4" \
  --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-MXFP4 / README.md

Osaurus-AI

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metadata

language:
  - en
  - ar
  - zh
  - fr
  - de
  - ja
  - ko
  - es
  - pt
license: other
license_name: lfm1.0
license_link: LICENSE
base_model: LiquidAI/LFM2.5-8B-A1B
pipeline_tag: text-generation
library_name: mlx
tags:
  - mlx
  - mxfp4
  - lfm2.5
  - liquid
  - text-generation

LFM2.5-8B-A1B-MXFP4

MLX MXFP4 conversion of LiquidAI/LFM2.5-8B-A1B, built for Apple Silicon inference.

This is a text-only LFM2.5 hybrid model with LIV convolution layers, GQA attention layers, and MoE feed-forward layers. It keeps the original Liquid chat template in chat_template.jinja.

Format

Quantization: MLX MXFP4
Converter output: 4.251 bits per weight
Quantization config: mode=mxfp4, bits=4, group_size=32
Router/gate tensors: preserved at 8-bit groups where emitted by MLX
Local size before upload: 4.2G
Source model: LiquidAI/LFM2.5-8B-A1B

Runtime

Use an MLX runtime with LFM2/LFM2.5 support.

from mlx_lm import load, generate

model, tokenizer = load("OsaurusAI/LFM2.5-8B-A1B-MXFP4")
prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "What is 2+2? Answer briefly."}],
    add_generation_prompt=True,
    tokenize=False,
)
print(generate(model, tokenizer, prompt=prompt, max_tokens=64, verbose=True))

Chat Template And Reasoning

The bundled chat_template.jinja uses Liquid's ChatML-like format:

User and assistant turns use <|im_start|> / <|im_end|>.
The generation prompt ends at <|im_start|>assistant\n; it does not pre-open <think>.
Assistant reasoning may appear inside <think>...</think>.
Tool calls use Liquid's Python-call list format inside <|tool_call_start|> and <|tool_call_end|>.

Do not force an extra synthetic <think> prefix at runtime. Let the template and model handle reasoning normally.

Verification

Local smoke run on the converted bundle:

Prompt: What is 2+2? Answer briefly.
Result: generated reasoning identified 4
Reported generation speed: about 286 tok/s on a 96-token run
Peak memory reported by the smoke run: about 4.544 GB

This is a smoke test, not a benchmark suite or accuracy evaluation.

Korean

이 모델은 LiquidAI/LFM2.5-8B-A1B를 Apple Silicon용 MLX MXFP4 형식으로 변환한 버전입니다. chat_template.jinja의 기본 템플릿을 사용하고, 런타임에서 별도의 <think> 접두어를 강제로 추가하지 않는 것을 권장합니다.