---
language:
- en
license: eupl-1.2
tags:
- mlx
- safetensors
- 4-bit
- transformers
- 8-bit
- gguf
- lek
- lethean
base_model:
- google/gemma-4-E2B-it
base_model_relation: quantized
pipeline_tag: any-to-any
library_name: mlx
datasets:
- TIGER-Lab/MMLU-Pro
---

# Lemer

A [Gemma 4 E2B](https://huggingface.co/google/gemma-4-E2B-it) finetune by [lthn.ai](https://lthn.ai) — EUPL-1.2

## Benchmarks

### Lemer vs Stock Gemma 4 E2B (bf16)

Columns: **(Think, Temperature)** — `G4` = Stock Gemma 4 E2B, `Lemer` = LEK-activated

|  | G4(1,0) | G4(1,1) | G4(0,0) | G4(0,1) | Lemer(1,0) | Lemer(1,1) | Lemer(0,0) | Lemer(0,1) |
| :---- | :----: | :----: | :----: | :----: | :----: | :----: | :----: | :----: |
| Biology | 40.0% | TBC | TBC | TBC | **60.0%** | TBC | TBC | TBC |
| Math | 10.0% | 30.0% | 15.0% | 10.0% | **55.0%** | **60.0%** | **25.0%** | **25.0%** |
| Business | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Chemistry | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Computer Science | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Economics | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Engineering | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Health | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| History | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Law | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Other | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Philosophy | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Physics | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Psychology | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| **Average** | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |

MMLU-Pro ([TIGER-Lab/MMLU-Pro](https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro), test split, 20 samples per category).
Evaluated using [rapid-mlx](https://github.com/LetheanNetwork/Rapid-MLX) + [OpenAI SDK](https://github.com/openai/openai-python) + Google [parse_response()](https://huggingface.co/google/gemma-4-E2B-it).

### Lemer Quantisation Benchmarks (MMLU-Pro, all categories)

|  | bf16 | 8bit | 6bit | 5bit | 4bit | mxfp8 | mxfp4 | nvfp4 |
| :---- | :----: | :----: | :----: | :----: | :----: | :----: | :----: | :----: |
| Biology | 60.0% | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Math | 55.0% | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Business | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Chemistry | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Computer Science | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Economics | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Engineering | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Health | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| History | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Law | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Other | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Philosophy | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Physics | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| Psychology | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |
| **Average** | TBC | TBC | TBC | TBC | TBC | TBC | TBC | TBC |

## Use

### MLX (recommended for Apple Silicon)

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("lthn/lemer", revision="4bit")
response = generate(model, tokenizer, prompt="Hello", max_tokens=200)
```

### Rapid-MLX (OpenAI-compatible server)

```bash
pip install rapid-mlx
rapid-mlx serve lthn/lemer --port 8100
```

```python
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8100/v1", api_key="not-needed")
response = client.chat.completions.create(
    model="default",
    messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)
```

### HF Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("lthn/lemer", revision="bf16-hf")
tokenizer = AutoTokenizer.from_pretrained("lthn/lemer", revision="bf16-hf")
```

## Branches

### MLX

| Branch | Size |
|--------|------|
| `bf16` | 8.7G |
| `8bit` | 4.6G |
| `6bit` | 3.6G |
| `5bit` | 3.0G |
| `4bit` | 2.5G |
| `mxfp8` | 4.5G |
| `mxfp4` | 2.3G |
| `nvfp4` | 2.5G |

### GGUF

| Branch | Size |
|--------|------|
| `bf16-gguf` | 8.7G |
| `8bit-gguf` | 4.6G |
| `6bit-gguf` | 3.6G |
| `5bit-gguf` | 3.0G |
| `4bit-gguf` | 2.5G |
| `3bit-gguf` | 2.0G |

### HF Transformers

| Branch | Size |
|--------|------|
| `bf16-hf` | 8.7G |

## Base

[google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it)

## More

- [lthn.ai](https://lthn.ai)
- [Lethean Network](https://github.com/LetheanNetwork)

## Licence

Training data and adapter: [EUPL-1.2](https://joinup.ec.europa.eu/collection/eupl/eupl-text-eupl-12)
Base model: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)