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

This model mlx-community/YanoljaNEXT-Rosetta-27B-2511-mlx-8bit-gs32 was converted to MLX format from 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)

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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

pip install mlx-lm

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)