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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+library_name: transformers
+tags:
+  - translation
+license: gemma
+language:
+  - en
+  - es
+  - fr
+  - de
+  - pt
+  - ja
+  - ko
+  - zh
+  - ar
+  - ru
+  - hi
+---
+
+# yanolja/YanoljaNEXT-Rosetta-4B
+
+This model is a fine-tuned version of [`google/gemma-3-4b-pt`](https://huggingface.co/google/gemma-3-4b-pt). As it is intended solely for text generation, we have extracted and utilized only the `Gemma3ForCausalLM` component from the original architecture.
+
+While the model name includes "YanoljaNEXT," our well-known model brand, this specific model does not feature an expanded tokenizer. The `YanoljaNEXT` branding reflects our commitment to developing high-quality, multilingual models.
+
+- **Model Name:** `yanolja/YanoljaNEXT-Rosetta-4B`
+- **Base Model:** `google/gemma-3-4b-pt`
+
+## Model Description
+
+This model is a 4-billion parameter, decoder-only language model built on the Gemma3 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:
+- English
+- Spanish
+- French
+- German
+- Portuguese
+- Japanese
+- Korean
+- Chinese
+- Arabic
+- Russian
+- Hindi
+
+While optimized for these languages, it may also perform effectively on other languages supported by the base Gemma3 model.
+
+## How to use
+
+You can use this model with the `transformers` library as follows:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "yanolja/YanoljaNEXT-Rosetta-4B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16
+)
+
+# Example prompt
+target_language = "Korean"
+messages = [
+    {"role": "system", "content": f"Translate the user's text to {target_language}.\nContext: Technical support conversation about USB modem configuration\nTone: Informative and helpful\nGlossary:\n- USB modem -> USB 모뎀\n- configuration -> 설정\n- technical support -> 기술 지원\nProvide the final translation immediately without any other text."},
+    {"role": "user", "content": "Yanolja NEXT is a company that provides global cutting-edge technology for the hospitality industry."}
+]
+
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=4096)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+The model outputs the final translation in JSON format when appropriate, or plain text for simple translations.
+
+## Training Procedure
+
+### Training Data
+The translation datasets were compiled from several sources, including:
+- [AI Hub](https://aihub.or.kr/)
+- [Europarl](https://www.statmt.org/europarl/)
+
+The model was fine-tuned on multilingual translation data to optimize performance across the supported language pairs.
+
+## Performance
+
+### Translation Quality Benchmarks
+
+The following CHrF++ scores demonstrate the model's competitive performance against other state-of-the-art translation models on English to Korean translation:
+
+| Model | CHrF++ Score |
+|-------|--------------|
+| yanolja/YanoljaNEXT-Rosetta-20B | 33.87 |
+| google/gemini-2.0-flash-001 | 33.81 |
+| openai/gpt-oss-120b | 31.51 |
+| **yanolja/YanoljaNEXT-Rosetta-4B** | **31.31** |
+| openai/gpt-4.1-nano | 31.15 |
+| Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 | 31.02 |
+| openai/gpt-oss-20b | 30.56 |
+| google/gemma-3-27b-it | 30.05 |
+| google/gemma-3-4b-pt | 27.53 |
+
+YanoljaNEXT-Rosetta-4B achieves competitive translation quality while maintaining efficiency as a 4B parameter model.
+
+## Intended Uses & Limitations
+
+This model is intended for translating structured data (JSON format) while preserving the original structure. It is particularly well-suited for tasks such as localizing product catalogs, translating hotel reviews, or handling any other structured content that requires accurate translation.
+
+### Limitations
+The model's primary focus is on JSON data. Performance on unstructured text or other data formats may vary.
+
+### License
+This model is released under the Gemma license, inherited from its base model, [`google/gemma-3-4b-pt`](https://huggingface.co/google/gemma-3-4b-pt). Please consult the official [Gemma license terms](https://ai.google.dev/gemma/terms) for detailed usage guidelines.
+
+## Citation
+
+If you use this model, please consider citing:
+
+```
+@misc{yanolja2025yanoljanextrosetta,
+  author = {Yanolja NEXT},
+  title = {YanoljaNEXT-Rosetta-4B},
+  year = {2025},
+  publisher = {Hugging Face},
+  journal = {Hugging Face repository},
+  howpublished = {\\url{https://huggingface.co/yanolja/YanoljaNEXT-Rosetta-4B}}
+}
+```
+
+## References
+
+This work utilizes several models and datasets. We would like to acknowledge the original authors for their valuable contributions to the field.
+
+```
+@misc{gemma3,
+  author = {Google},
+  title = {Gemma 3},
+  year = {2024},
+  publisher = {Google DeepMind},
+  howpublished = {\\url{https://deepmind.google/models/gemma/gemma-3/}}
+}
+
+
+@misc{aihub,
+  author = {National Information Society Agency (NIA)},
+  title = {AI-Hub: AI Integrated Platform},
+  year = {2025},
+  publisher = {National Information Society Agency},
+  howpublished = {\\url{https://aihub.or.kr}}
+}
+
+@article{europarl,
+  author    = {Koehn, Philipp},
+  title     = {Europarl: A Parallel Corpus for Statistical Machine Translation},
+  journal   = {MT Summit},
+  year      = {2005},
+  pages     = {79--86}
+}
+```

chat_template.jinja

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+{{ bos_token }}{% for message in messages %}<start_of_turn>{% if message['role']=='system' %}instruction
+{% elif message['role']=='user' %}source
+{% elif message['role']=='assistant' %}translation
+{% endif %}{{ message['content'] | trim }}<end_of_turn>
+{% endfor %}{% if add_generation_prompt %}<start_of_turn>translation
+{% endif %}

config.json

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+{
+  "_sliding_window_pattern": 6,
+  "architectures": [
+    "Gemma3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": null,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": 106,
+  "final_logit_softcapping": null,
+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 10240,
+  "layer_types": [
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+    "sliding_attention",
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+    "sliding_attention",
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+  "max_position_embeddings": 131072,
+  "model_type": "gemma3_text",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 34,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_local_base_freq": 10000.0,
+  "rope_scaling": {
+    "factor": 8.0,
+    "rope_type": "linear"
+  },
+  "rope_theta": 1000000.0,
+  "sliding_window": 1024,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.2",
+  "use_cache": false,
+  "vocab_size": 262208
+}

generation_config.json

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+{
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "do_sample": true,
+  "eos_token_id": [
+    1,
+    106
+  ],
+  "pad_token_id": 0,
+  "top_k": 64,
+  "top_p": 0.95,
+  "transformers_version": "4.55.2"
+}

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