Instructions to use LetheanNetwork/lemrd-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LetheanNetwork/lemrd-mlx with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("LetheanNetwork/lemrd-mlx")
config = load_config("LetheanNetwork/lemrd-mlx")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use LetheanNetwork/lemrd-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "LetheanNetwork/lemrd-mlx"

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": "LetheanNetwork/lemrd-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use LetheanNetwork/lemrd-mlx with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "LetheanNetwork/lemrd-mlx"

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 "LetheanNetwork/lemrd-mlx" \
  --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"

Hermes Agent

How to use LetheanNetwork/lemrd-mlx 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 "LetheanNetwork/lemrd-mlx"

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 LetheanNetwork/lemrd-mlx

Run Hermes

hermes

Atomic Chat

lemrd-mlx / README.md

Snider

feat: initial mlx 4bit quant, converted from LetheanNetwork/lemrd bf16

8f83009 4 months ago

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	---
	library_name: mlx
	license: apache-2.0
	license_link: https://ai.google.dev/gemma/docs/gemma_4_license
	pipeline_tag: image-text-to-text
	base_model:
	- LetheanNetwork/lemrd
	base_model_relation: quantized
	tags:
	- gemma4
	- mlx
	- apple-silicon
	- 4bit
	- on-device
	- conversational
	---

	# LetheanNetwork/lemrd-mlx

	Gemma 4 in MLX format, 4-bit quantized, converted from
	[LetheanNetwork/lemrd](https://huggingface.co/LetheanNetwork/lemrd)'s bf16
	safetensors via `mlx_lm.convert`. Unmodified Google weights hosted
	in the Lethean namespace so downstream tools don't have to depend
	on external mlx-community mirrors.

	For the LEK-merged sibling see [`lthn/lemrd`](https://huggingface.co/lthn/lemrd).

	## License

	Apache 2.0, subject to the [Gemma Terms of Use](https://ai.google.dev/gemma/docs/gemma_4_license).