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

Libraries

How to use LetheanNetwork/lemer-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("LetheanNetwork/lemer-mlx-8bit")
config = load_config("LetheanNetwork/lemer-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 LetheanNetwork/lemer-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 "LetheanNetwork/lemer-mlx-8bit"

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": "LetheanNetwork/lemer-mlx-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use LetheanNetwork/lemer-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 "LetheanNetwork/lemer-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 LetheanNetwork/lemer-mlx-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use LetheanNetwork/lemer-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 "LetheanNetwork/lemer-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 "LetheanNetwork/lemer-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"

lemer-mlx-8bit / README.md

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	---
	library_name: mlx
	license: apache-2.0
	pipeline_tag: image-text-to-text
	base_model:
	- google/gemma-4-E2B-it
	base_model_relation: quantized
	tags:
	- gemma4
	- mlx
	- apple-silicon
	- 8bit
	- on-device
	- conversational
	---

	# LetheanNetwork/lemer-mlx-8bit

	Gemma 4 E2B in MLX format, 8-bit quantized, converted from
	[LetheanNetwork/lemer](https://huggingface.co/LetheanNetwork/lemer)'s
	bf16 safetensors via `mlx_lm.convert`. Higher-precision sibling of
	[`LetheanNetwork/lemer-mlx`](https://huggingface.co/LetheanNetwork/lemer-mlx)
	(which is 4-bit). For the LEK-merged variant see
	[`lthn/lemer`](https://huggingface.co/lthn/lemer).

	## Variants in this family

	\| Repo \| Format \| Bits \| Use case \|
	\|---\|---\|---\|---\|
	\| [`LetheanNetwork/lemer`](https://huggingface.co/LetheanNetwork/lemer) \| safetensors + gguf Q4_K_M \| bf16 / 4 \| Source weights + llama.cpp/Ollama \|
	\| [`LetheanNetwork/lemer-mlx`](https://huggingface.co/LetheanNetwork/lemer-mlx) \| mlx \| 4 \| Apple Silicon default \|
	\| `LetheanNetwork/lemer-mlx-8bit` \| mlx \| 8 \| This repo — higher precision \|
	\| [`LetheanNetwork/lemer-mlx-bf16`](https://huggingface.co/LetheanNetwork/lemer-mlx-bf16) \| mlx \| bf16 \| Full-precision reference \|

	## Usage

	```python
	from mlx_lm import load, generate

	model, tokenizer = load("LetheanNetwork/lemer-mlx-8bit")
	response = generate(
	model, tokenizer,
	prompt=tokenizer.apply_chat_template(
	[{"role": "user", "content": "Hello"}],
	add_generation_prompt=True,
	enable_thinking=True,
	),
	max_tokens=512,
	)
	```

	## Provenance

	- Source: `LetheanNetwork/lemer` bf16 safetensors (= `google/gemma-4-E2B-it`)
	- Converter: `mlx_lm.convert` (mlx-lm — LM Studio / Apple ML Research)
	- Quant: 8-bit group quantization, ~8.5 bits/weight effective
	- License: Apache 2.0 (Gemma Terms of Use)

	## License

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