Instructions to use lthn/lemma-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lthn/lemma-mlx-8bit 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("lthn/lemma-mlx-8bit")
config = load_config("lthn/lemma-mlx-8bit")

# 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 lthn/lemma-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 "lthn/lemma-mlx-8bit"

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": "lthn/lemma-mlx-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use lthn/lemma-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 "lthn/lemma-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 lthn/lemma-mlx-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use lthn/lemma-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 "lthn/lemma-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 "lthn/lemma-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"

lemma-mlx-8bit / README.md

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	---
	library_name: mlx
	pipeline_tag: image-text-to-text
	tags:
	- gemma4
	- lemma
	- mlx
	- 8bit
	- apple-silicon
	- multimodal
	- on-device
	- conversational
	license: eupl-1.2
	license_link: https://ai.google.dev/gemma/docs/gemma_4_license
	base_model:
	- lthn/lemma
	base_model_relation: quantized
	---

	# Lemma — Gemma 4 E4B — MLX 8-bit

	The mid-sized member of the Lemma model family by Lethean. An EUPL-1.2 fork of Gemma 4 E4B with the Lethean Ethical Kernel (LEK) merged into the weights.

	This repo hosts the MLX 8-bit build for native Apple Silicon inference via [`mlx-lm`](https://github.com/ml-explore/mlx-lm) and [`mlx-vlm`](https://github.com/Blaizzy/mlx-vlm). For the GGUF playground (Ollama, llama.cpp) see [`lthn/lemma`](https://huggingface.co/lthn/lemma). For the unmodified Google base see [`LetheanNetwork/lemma`](https://huggingface.co/LetheanNetwork/lemma).

	## Family

	\| Repo \| Format \| Bits \|
	\|---\|---\|---\|
	\| [`lthn/lemma`](https://huggingface.co/lthn/lemma) \| GGUF multi-quant \| Q4_K_M → BF16 \|
	\| [`lthn/lemma-mlx`](https://huggingface.co/lthn/lemma-mlx) \| MLX \| 4-bit \|
	\| [`lthn/lemma-mlx-8bit`](https://huggingface.co/lthn/lemma-mlx-8bit) \| MLX \| 8-bit \|
	\| [`lthn/lemma-mlx-bf16`](https://huggingface.co/lthn/lemma-mlx-bf16) \| MLX \| bf16 \|

	## License

	EUPL-1.2. See [Gemma Terms of Use](https://ai.google.dev/gemma/docs/gemma_4_license) for upstream base model terms.