metadata
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-bf16
This model mlx-community/YanoljaNEXT-Rosetta-27B-2511-mlx-bf16 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
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-bf16")
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)