winninghealth
/

WiNGPT-Babel-2-GGUF

@@ -1,163 +1,166 @@
----
-license: apache-2.0
-language:
-- en
-- zh
-base_model:
-- ModelSpace/GemmaX2-28-2B-Pretrain
-tags:
-- GGUF
----
-# 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
-<table>
-<thead>
-<tr>
-<th rowspan="2" align="center">Model</th>
-<th colspan="2" align="center">FLORES-200</th>
-</tr>
-<tr>
-<th align="center">xx → en</th>
-<th align="center">xx → zh</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center">WiNGPT-Babel-AWQ</td>
-<td align="center">33.91</td>
-<td align="center">17.29</td>
-</tr>
-<tr>
-<td align="center">WiNGPT-Babel-2-AWQ</td>
-<td align="center">36.43</td>
-<td align="center">30.74</td>
-</tr>
-</tbody>
-</table>
-**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 [email protected] to request a free trial API_KEY

+---
+license: apache-2.0
+language:
+- en
+- zh
+base_model:
+- winninghealth/WiNGPT-Babel-2
+tags:
+- GGUF
+- translation
+- llm
+- multilingual
+---
+# 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
+<table>
+<thead>
+<tr>
+<th rowspan="2" align="center">Model</th>
+<th colspan="2" align="center">FLORES-200</th>
+</tr>
+<tr>
+<th align="center">xx → en</th>
+<th align="center">xx → zh</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">WiNGPT-Babel-AWQ</td>
+<td align="center">33.91</td>
+<td align="center">17.29</td>
+</tr>
+<tr>
+<td align="center">WiNGPT-Babel-2-AWQ</td>
+<td align="center">36.43</td>
+<td align="center">30.74</td>
+</tr>
+</tbody>
+</table>
+**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 [email protected] to request a free trial API_KEY