---
license: gemma
library_name: mlx
pipeline_tag: text-generation
tags:
- transformers
- mlx
- translation
language:
  - ar
  - bg
  - zh
  - cs
  - da
  - nl
  - en
  - fi
  - fr
  - de
  - el
  - gu
  - he
  - hi
  - hu
  - id
  - it
  - ja
  - ko
  - fa
  - pl
  - pt
  - ro
  - ru
  - sk
  - es
  - sv
  - tl
  - th
  - tr
  - uk
  - vi
base_model:
- yanolja/YanoljaNEXT-Rosetta-27B-2511
---

# mlx-community/YanoljaNEXT-Rosetta-27B-2511-mlx-8bit-gs32

This model [mlx-community/YanoljaNEXT-Rosetta-27B-2511-mlx-8bit-gs32](https://huggingface.co/mlx-community/YanoljaNEXT-Rosetta-27B-2511-mlx-8bit-gs32) was
converted to MLX format from [yanolja/YanoljaNEXT-Rosetta-27B-2511](https://huggingface.co/yanolja/YanoljaNEXT-Rosetta-27B-2511)
using mlx-lm version **0.28.4**.

You can find more similar translation-related MLX model quants for an Apple Mac Studio at https://huggingface.co/bibproj

## Recipe:
* 8-bit
* group-size 32
* 9 bits per weight (bpw)

---

## Model Description

This model is a 27-billion parameter, decoder-only language model built on the Gemma3 27B architecture and fine-tuned by Yanolja NEXT. It is specifically designed to translate structured data (JSON format) while preserving the original data structure.

The model was trained on a multilingual dataset covering the following languages equally:
- Arabic
- Bulgarian
- Chinese
- Czech
- Danish
- Dutch
- English
- Finnish
- French
- German
- Greek
- Gujarati
- Hebrew
- Hindi
- Hungarian
- Indonesian
- Italian
- Japanese
- Korean
- Persian
- Polish
- Portuguese
- Romanian
- Russian
- Slovak
- Spanish
- Swedish
- Tagalog
- Thai
- Turkish
- Ukrainian
- Vietnamese

While optimized for these languages, it may also perform effectively on other languages supported by the base Gemma3 model.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/YanoljaNEXT-Rosetta-27B-2511-mlx-8bit-gs32")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

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