Update README.md

README.md

@@ -1,7 +1,112 @@
 ---
-language: en
+license: gemma
+library_name: mlx
 pipeline_tag: text-generation
 tags:
+- transformers
 - mlx
-library_name: 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](https://huggingface.co/mlx-community/YanoljaNEXT-Rosetta-27B-2511-mlx-bf16) 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
+
+## 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-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)
+```