---
library_name: mlx
license: apache-2.0
license_link: https://ai.google.dev/gemma/docs/gemma_4_license
pipeline_tag: text-generation
base_model: google/gemma-4-E4B-it
tags:
- mlx
- gemma
- gemma4
- bfloat16
- bf16
- unquantized
- apple-silicon
---

# Gemma-4-E4B-it MLX BF16

Unquantized **bfloat16 MLX** conversion of [`google/gemma-4-E4B-it`](https://huggingface.co/google/gemma-4-E4B-it) for Apple Silicon inference with [`mlx-lm`](https://github.com/ml-explore/mlx-lm).

This repo is the plain 16-bit reference variant: no 8-bit, 4-bit, RotorQuant, TurboQuant, AWQ, GPTQ, or GGUF quantization is applied.

## Provenance

| Field | Value |
|---|---|
| Source model | [`google/gemma-4-E4B-it`](https://huggingface.co/google/gemma-4-E4B-it) |
| Format | MLX safetensors |
| Weight dtype | `bfloat16` |
| Tensor check | 665 tensors, all `mlx.core.bfloat16` |
| Local conversion tool | `mlx-lm` |
| License | Apache 2.0 / Gemma license terms from upstream |

Conversion command:

```bash
mlx_lm.convert \
  --hf-path google/gemma-4-E4B-it \
  --mlx-path gemma-4-e4b-it-MLX-bf16 \
  --dtype bfloat16
```

## Why BF16?

Gemma-4 is distributed natively in bfloat16. Keeping BF16 preserves the upstream numerical format while avoiding the quality/runtime tradeoffs of weight quantization.

## Use with MLX

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

```python
from mlx_lm import load, generate

model, tokenizer = load("majentik/gemma-4-e4b-it-MLX-bf16")

messages = [{"role": "user", "content": "Explain Singapore's MRT system in one paragraph."}]
prompt = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_dict=False,
)

response = generate(model, tokenizer, prompt=prompt, max_tokens=256, verbose=True)
print(response)
```

## Relationship to quantized variants

Use this repo when you want the unquantized BF16 reference decoder. For smaller/faster variants, use the existing quantized MLX repos under `majentik`, such as:

- [`majentik/gemma-4-E4B-RotorQuant-MLX-8bit`](https://huggingface.co/majentik/gemma-4-E4B-RotorQuant-MLX-8bit)
- [`majentik/gemma-4-e4b-it-mlx-4bit`](https://huggingface.co/majentik/gemma-4-e4b-it-mlx-4bit)

## Notes

- This is a format conversion of the upstream Gemma-4 E4B instruct model, not a fine-tune.
- The weights remain unquantized BF16.
- For licensing and acceptable use, follow the upstream Gemma terms linked above.