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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](https://huggingface.co/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`](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`** | mlx | 4 | **This repo** — Apple Silicon default |
| [`LetheanNetwork/lemer-mlx-8bit`](https://huggingface.co/LetheanNetwork/lemer-mlx-8bit) | mlx | 8 | Apple Silicon higher-precision |
| [`LetheanNetwork/lemer-mlx-bf16`](https://huggingface.co/LetheanNetwork/lemer-mlx-bf16) | mlx | bf16 | Apple Silicon full-precision reference |
## Usage
```python
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](https://ai.google.dev/gemma/docs/gemma_4_license). |