Instructions to use mlx-community/LFM2.5-2.6B-OptiQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/LFM2.5-2.6B-OptiQ-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("mlx-community/LFM2.5-2.6B-OptiQ-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 mlx-community/LFM2.5-2.6B-OptiQ-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 "mlx-community/LFM2.5-2.6B-OptiQ-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": "mlx-community/LFM2.5-2.6B-OptiQ-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use mlx-community/LFM2.5-2.6B-OptiQ-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 "mlx-community/LFM2.5-2.6B-OptiQ-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 "mlx-community/LFM2.5-2.6B-OptiQ-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 mlx-community/LFM2.5-2.6B-OptiQ-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 "mlx-community/LFM2.5-2.6B-OptiQ-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/LFM2.5-2.6B-OptiQ-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/LFM2.5-2.6B-OptiQ-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use mlx-community/LFM2.5-2.6B-OptiQ-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 "mlx-community/LFM2.5-2.6B-OptiQ-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 mlx-community/LFM2.5-2.6B-OptiQ-4bit

Run Hermes

hermes

mlx-community/LFM2.5-2.6B-OptiQ-4bit

Built with mlx-optiq, the MLX-native toolkit to quantize, fine-tune, and serve LLMs locally on Apple Silicon, no PyTorch and no cloud. All OptiQ quants · Docs · LFM2.5 family

An OptiQ mixed-precision quant of LiquidAI/LFM2.5-2.6B. 1.93 GB on disk, down from 5.2 GB at bf16, with a 128k context.

LFM2.5 is a hybrid architecture: convolutional blocks interleaved with full attention, so only a few blocks carry a KV cache at all. OptiQ measures each layer's sensitivity and assigns per-layer bit-widths, keeping 8-bit where it matters and 4-bit elsewhere.

What it is

Property	Value
Base	LiquidAI/LFM2.5-2.6B
Architecture	`lfm2` — hybrid conv + full attention, 30 layers
Method	OptiQ mixed-precision, sensitivity-driven (bf16 reference)
On disk	1.93 GB (bf16: 5.2 GB)
Context	128k

Capability Score

Six-metric mean, the standard OptiQ eval.

Metric	Score
MMLU (5-shot, 969 samples)	59.5%
GSM8K (1000 samples)	38.1%
IFEval (full set, strict)	33.1%
BFCL-V3 simple (200 calls)	48.5%
HumanEval (164 problems, pass@1)	18.9%
HashHop (long-context retrieval)	13.0%
Capability Score (mean of 6)	35.19

Read that profile against what the model is for. Liquid position LFM2.5-2.6B for agentic workloads, tool use, data extraction and RAG, and state plainly that it is not recommended for agentic coding or knowledge-heavy tasks. Tool calling is the strongest result here and coding the weakest, which is the shape you would expect. World knowledge at 59.5% is the highest number in the table.

Run it

pip install mlx-optiq
optiq serve --model mlx-community/LFM2.5-2.6B-OptiQ-4bit

That gives you an OpenAI and Anthropic compatible endpoint with mixed-precision KV cache, tool-call healing and prompt caching.

Liquid's recommended sampling for this model is temperature 0.1, top_k 50, repetition_penalty 1.1. Those ship in generation_config.json in this repo and optiq serve applies them automatically, so you get the publisher's settings without passing any flags.

Or straight from Python:

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/LFM2.5-2.6B-OptiQ-4bit")
prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Extract the dates from this invoice."}],
    add_generation_prompt=True, tokenize=False,
)
print(generate(model, tokenizer, prompt=prompt, max_tokens=512))

Model tree for mlx-community/LFM2.5-2.6B-OptiQ-4bit

Base model

LiquidAI/LFM2.5-2.6B-Base

Finetuned

LiquidAI/LFM2.5-2.6B

Quantized

(40)

this model

mlx-community
/

LFM2.5-2.6B-OptiQ-4bit

mlx-community/LFM2.5-2.6B-OptiQ-4bit

What it is

Capability Score

Run it

Links

Model tree for mlx-community/LFM2.5-2.6B-OptiQ-4bit