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--- |
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license: apache-2.0 |
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language: |
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- ar |
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- bg |
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- bn |
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- ca |
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- cs |
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- da |
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- de |
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- el |
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- es |
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- et |
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- fa |
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- fi |
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- fil |
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- fr |
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- gu |
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- he |
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- hi |
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- hr |
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- hu |
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- id |
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- is |
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- it |
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- ja |
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- kn |
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- ko |
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- lt |
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- lv |
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- ml |
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- mr |
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- nl |
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- 'no' |
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- pa |
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- pl |
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- pt |
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- ro |
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- ru |
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- sk |
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- sl |
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- sr |
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- sv |
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- sw |
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- ta |
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- te |
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- th |
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- tr |
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- uk |
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- ur |
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- vi |
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- zh |
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- zu |
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base_model: |
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- winninghealth/WiNGPT-Babel-2 |
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tags: |
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- GGUF |
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- multilingual |
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datasets: |
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- google/wmt24pp |
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pipeline_tag: translation |
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library_name: transformers |
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--- |
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# WiNGPT-Babel-2: A Multilingual Translation Language Model |
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[](https://huggingface.co/collections/winninghealth/wingpt-babel-68463d4b2a28d0d675ff3be9) |
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[](https://opensource.org/licenses/Apache-2.0) |
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> This is the quantization version (llama.cpp) of [WiNGPT-Babel-2](https://huggingface.co/winninghealth/WiNGPT-Babel-2). |
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> |
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> Example |
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> |
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> ```shell |
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> ./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 |
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> ``` |
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> |
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> - **--jinja**: This flag activates the Jinja2 chat template processor. |
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> - **--chat-template-file**: This flag points the server to the required template file that defines the WiNGPT-Babel-2's custom prompt format. |
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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. |
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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. |
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## Core Improvements in Version 2.0 |
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WiNGPT-Babel-2 introduces the following key technical upgrades over its predecessor: |
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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). |
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2. **Enhanced Chinese Translation:** The translation pipeline from other source languages to Chinese (xx โ zh) has been specifically optimized, improving the accuracy and fluency of the results. |
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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. |
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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. |
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## Training Methodology |
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The performance improvements in WiNGPT-Babel-2 are attributed to a continuous, data-driven, iterative training process: |
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1. **Data Collection:** Collecting anonymous, real-world translation task logs from integrated applications (e.g., Immersive Translate, Videolingo). |
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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. |
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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. |
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## Technical Specifications |
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* **Base Model:** [GemmaX2-28-2B-Pretrain](https://huggingface.co/ModelSpace/GemmaX2-28-2B-Pretrain) |
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* **Primary Training Data:** "Human-in-the-loop" in-house dataset, [WMT24++](https://huggingface.co/datasets/google/wmt24pp) dataset |
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* **Maximum Context Length:** 4096 tokens |
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* **Chat Capability:** Supports multi-turn dialogue, allowing for contextual follow-up and translation refinement. |
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## Language Support |
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| Direction | Description | Supported Languages (Partial List) | |
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| :---------------------- | :--------------------------------------------------- | :----------------------------------------------------------- | |
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| **Core Support** | Highest quality, extensively optimized. | `en โ zh` | |
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| **Expanded Support** | Supported via `wmt24pp` dataset training. | `en โ 55+ languages`, including: `fr`, `de`, `es`, `ru`, `ar`, `pt`, `ko`, `it`, `nl`, `tr`, `pl`, `sv`... | |
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| **Enhanced to Chinese** | Specifically optimized for translation into Chinese. | `xx โ zh` | |
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## Performance |
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<table> |
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<thead> |
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<tr> |
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<th rowspan="2" align="center">Model</th> |
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<th colspan="2" align="center">FLORES-200</th> |
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</tr> |
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<tr> |
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<th align="center">xx โ en</th> |
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<th align="center">xx โ zh</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td align="center">WiNGPT-Babel-AWQ</td> |
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<td align="center">33.91</td> |
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<td align="center">17.29</td> |
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</tr> |
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<tr> |
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<td align="center">WiNGPT-Babel-2-AWQ</td> |
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<td align="center">36.43</td> |
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<td align="center">30.74</td> |
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</tr> |
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</tbody> |
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</table> |
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**Note**: |
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1. The evaluation metric is spBLEU, using the FLORES-200 tokenizer. |
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3. 'xx' represents the 52 source languages from the wmt24pp dataset. |
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## Usage Guide |
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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. |
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**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. |
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### Example |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "winninghealth/WiNGPT-Babel-2-AWQ" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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# Example: Translation of text within a JSON object to Chinese |
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prompt_json = """{ |
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"product_name": "High-Performance Laptop", |
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"features": ["Fast Processor", "Long Battery Life", "Lightweight Design"] |
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}""" |
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messages = [ |
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{"role": "system", "content": "Translate this to Simplified Chinese Language"}, |
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{"role": "user", "content": prompt_json} # Replace with the desired prompt |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=4096, |
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temperature=0 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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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). |
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## LICENSE |
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1. This project's license agreement is the Apache License 2.0 |
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2. Please cite this project when using its model weights: https://huggingface.co/winninghealth/WiNGPT-Babel-2 |
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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. |
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## Contact Us |
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- Apply for a token through the WiNGPT platform |
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- Or contact us at [email protected] to request a free trial API_KEY |
