README.md

metadata

library_name: mlx
license: eupl-1.2
pipeline_tag: image-text-to-text
base_model_relation: quantized
tags:
  - gemma4
  - mlx
  - apple-silicon
  - 4bit
  - on-device
  - conversational
base_model:
  - google/gemma-4-E2B-it

LetheanNetwork/lemer-mlx

Gemma 4 E2B in MLX format, 4-bit quantized, converted from LetheanNetwork/lemer's bf16 safetensors via mlx_lm.convert. This is the unmodified Google Gemma 4 E2B-IT weights — no LEK shift, no fine-tuning — hosted in our namespace so downstream tools (benchmarks, apps) don't have to depend on external mlx-community mirrors.

For the LEK-merged (consent-based ethical kernel) variant of the same model, see lthn/lemer.

Variants in this family

Repo	Format	Bits	Use case
LetheanNetwork/lemer	safetensors + gguf Q4_K_M	bf16 / 4	Source weights + llama.cpp/Ollama
LetheanNetwork/lemer-mlx	mlx	4	This repo — Apple Silicon default
LetheanNetwork/lemer-mlx-8bit	mlx	8	Apple Silicon higher-precision
LetheanNetwork/lemer-mlx-bf16	mlx	bf16	Apple Silicon full-precision reference

Usage

from mlx_lm import load, generate

model, tokenizer = load("LetheanNetwork/lemer-mlx")
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: 4-bit group quantization, ~4.5 bits/weight effective
• License: Apache 2.0 (Gemma Terms of Use)

License

Apache 2.0, subject to the Gemma Terms of Use.