--- license: apache-2.0 language: - ar - bg - bn - ca - cs - da - de - el - es - et - fa - fi - fil - fr - gu - he - hi - hr - hu - id - is - it - ja - kn - ko - lt - lv - ml - mr - nl - 'no' - pa - pl - pt - ro - ru - sk - sl - sr - sv - sw - ta - te - th - tr - uk - ur - vi - zh - zu base_model: - winninghealth/WiNGPT-Babel-2 tags: - GGUF - multilingual datasets: - google/wmt24pp pipeline_tag: translation library_name: transformers --- # WiNGPT-Babel-2: A Multilingual Translation Language Model [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-WiNGPT--Babel-blue)](https://huggingface.co/collections/winninghealth/wingpt-babel-68463d4b2a28d0d675ff3be9) [![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-yellow.svg)](https://opensource.org/licenses/Apache-2.0) > This is the quantization version (llama.cpp) of [WiNGPT-Babel-2](https://huggingface.co/winninghealth/WiNGPT-Babel-2). > > Example > > ```shell > ./llama-server -m WiNGPT-Babel-2-GGUF/WiNGPT-Babel-2-IQ4_XS.gguf --jinja --chat-template-file WiNGPT-Babel-2-GGUF/WiNGPT-Babel-2.jinja > ``` > > - **--jinja**: This flag activates the Jinja2 chat template processor. > - **--chat-template-file**: This flag points the server to the required template file that defines the WiNGPT-Babel-2's custom prompt format. WiNGPT-Babel-2 is a language model optimized for multilingual translation tasks. As an iteration of WiNGPT-Babel, it features significant improvements in language coverage, data format handling, and translation accuracy for complex content. The model continues the "Human-in-the-loop" training strategy, iteratively optimizing through the analysis of log data from real-world application scenarios to ensure its effectiveness and reliability in practical use. ## Core Improvements in Version 2.0 WiNGPT-Babel-2 introduces the following key technical upgrades over its predecessor: 1. **Expanded Language Support:** Through training with the `wmt24pp` dataset, language support has been extended to **55 languages**, primarily enhancing translation capabilities from English (en) to other target languages (xx). 2. **Enhanced Chinese Translation:** The translation pipeline from other source languages to Chinese (xx → zh) has been specifically optimized, improving the accuracy and fluency of the results. 3. **Structured Data Translation:** The model can now identify and translate text fields embedded within **structured data (e.g., JSON)** while preserving the original data structure. This feature is suitable for scenarios such as API internationalization and multilingual dataset preprocessing. 4. **Mixed-Content Handling:** Its ability to handle mixed-content text has been improved, enabling more accurate translation of paragraphs containing **mathematical expressions (LaTeX), code snippets, and web markup (HTML/Markdown)**, while preserving the format and integrity of these non-translatable elements. ## Training Methodology The performance improvements in WiNGPT-Babel-2 are attributed to a continuous, data-driven, iterative training process: 1. **Data Collection:** Collecting anonymous, real-world translation task logs from integrated applications (e.g., Immersive Translate, Videolingo). 2. **Data Refinement:** Using a reward model for rejection sampling on the collected data, supplemented by manual review, to filter high-quality, high-value samples for constructing new training datasets. 3. **Iterative Retraining:** Using the refined data for the model's incremental training, continuously improving its performance in specific domains and scenarios through a cyclical iterative process. ## Technical Specifications * **Base Model:** [GemmaX2-28-2B-Pretrain](https://huggingface.co/ModelSpace/GemmaX2-28-2B-Pretrain) * **Primary Training Data:** "Human-in-the-loop" in-house dataset, [WMT24++](https://huggingface.co/datasets/google/wmt24pp) dataset * **Maximum Context Length:** 4096 tokens * **Chat Capability:** Supports multi-turn dialogue, allowing for contextual follow-up and translation refinement. ## Language Support | Direction | Description | Supported Languages (Partial List) | | :---------------------- | :--------------------------------------------------- | :----------------------------------------------------------- | | **Core Support** | Highest quality, extensively optimized. | `en ↔ zh` | | **Expanded Support** | Supported via `wmt24pp` dataset training. | `en → 55+ languages`, including: `fr`, `de`, `es`, `ru`, `ar`, `pt`, `ko`, `it`, `nl`, `tr`, `pl`, `sv`... | | **Enhanced to Chinese** | Specifically optimized for translation into Chinese. | `xx → zh` | ## Performance
Model FLORES-200
xx → en xx → zh
WiNGPT-Babel-AWQ 33.91 17.29
WiNGPT-Babel-2-AWQ 36.43 30.74
**Note**: 1. The evaluation metric is spBLEU, using the FLORES-200 tokenizer. 3. 'xx' represents the 52 source languages from the wmt24pp dataset. ## Usage Guide For optimal inference performance, it is recommended to use frameworks such as `vllm`. The following provides a basic usage example using the Hugging Face `transformers` library. **System Prompt:** For optimal automatic language inference, it is recommended to use the unified system prompt: `Translate this to {{to}} Language`. Replace `{{to}}` with the name of the target language. For instance, use `Translate this to Simplified Chinese Language` to translate into Chinese, or `Translate this to English Language` to translate into English. This method provides precise control over the translation direction and yields the most reliable results. ### Example ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "winninghealth/WiNGPT-Babel-2-AWQ" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) # Example: Translation of text within a JSON object to Chinese prompt_json = """{ "product_name": "High-Performance Laptop", "features": ["Fast Processor", "Long Battery Life", "Lightweight Design"] }""" messages = [ {"role": "system", "content": "Translate this to Simplified Chinese Language"}, {"role": "user", "content": prompt_json} # Replace with the desired prompt ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=4096, temperature=0 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` For additional usage demos, you can refer to the original [WiNGPT-Babel](https://huggingface.co/winninghealth/WiNGPT-Babel#%F0%9F%8E%AC-%E7%A4%BA%E4%BE%8B). ## LICENSE 1. This project's license agreement is the Apache License 2.0 2. Please cite this project when using its model weights: https://huggingface.co/winninghealth/WiNGPT-Babel-2 3. Comply with [gemma-2-2b](https://huggingface.co/google/gemma-2-2b), [GemmaX2-28-2B-v0.1](https://huggingface.co/ModelSpace/GemmaX2-28-2B-v0.1), [immersive-translate](https://github.com/immersive-translate/immersive-translate), [VideoLingo](https://github.com/immersive-translate/immersive-translate) protocols and licenses, details on their website. ## Contact Us - Apply for a token through the WiNGPT platform - Or contact us at wair@winning.com.cn to request a free trial API_KEY