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Model: winninghealth/WiNGPT-Babel Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- en
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- zh
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tags:
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- translation
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- multilingual
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base_model:
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||||
- Qwen/Qwen2.5-1.5B
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pipeline_tag: text-generation
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new_version: winninghealth/WiNGPT-Babel-2
|
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---
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||||
# 🌐 WiNGPT-Babel
|
||||
|
||||
WiNGPT-Babel(巴别塔)是一个基于大语言模型(LLM)为翻译应用定制的模型,致力于提供便捷的多语言信息母语级体验。
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||||
|
||||
和其他机器翻译模型最大的不同是,WiNGPT-Babel 是采用 human-in-the-loop 数据生产采集闭环策略训练而成。因此 WiNGPT-Babel 更适应真实使用场景,例如新闻、研究成果以及观看带有实时翻译字幕的视频。通过一系列的工具插件 WiNGPT-Babel 会将这些内容翻译成用户的母语,以更好的体验呈现在用户面前。
|
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|
||||
我们的目标是利用先进的 LLM 技术,降低语言障碍,帮助用户更轻松地获取全球范围内的互联网信息,包括学术论文、社交媒体、网页内容和视频字幕等各种数据格式。虽然实现这一目标还需要时间,但 LLM 技术的发展为其提供了可能性。
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## ✨ 核心特点
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||||
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||||
- **human-in-the-loop 🌱:** 首先,使用少量数据进行初步训练;然后,通过API收集我们使用各种工具的日志数据,并利用这些日志构建新的训练数据。使用WiNGPT-2.6 模型和奖励模型对这些数据进行rejection sampling,并辅以人工审核以确保数据质量。经过几轮迭代训练,模型性能将逐步提升,直至达到预期水平停止。
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- **多格式翻译 📄 🌐 🎬:** 支持多种文本格式的翻译,包括网页、社交媒体内容、学术论文、视频字幕、以及数据集等。
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- **高精度翻译 🧠:** 基于先进的 LLM 架构,我们致力于提供准确、自然、流畅的翻译结果。
|
||||
- **高性能翻译 ⏱️:** 采用1.5B模型,支持实时字幕翻译等应用场景,满足用户对实时翻译的需求。
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- **多语言支持 🗣️:** 目前支持超过 20 种语言,并不断扩展语言支持范围。
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- **应用适配 🪒:** 目前已适配的工具有:沉浸式翻译、videolingo。
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## 🧪 适用场景
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- 🌐 **网页内容翻译:** 适用于日常网页浏览,快速理解网页信息
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- 📄 **学术论文翻译:** 适用于辅助理解多语言研究论文,提高阅读效率
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- 📰 **新闻资讯翻译:** 适用于快速了解全球新闻动态,获取一手信息
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- 🎬 **视频字幕翻译:** 适用于观看外语视频,辅助理解视频内容
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- 📊 **数据集多语言处理:** 适用于多语言数据集的初步翻译,辅助数据分析
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## 🔤 语言支持(更多语言待验证)
|
||||
🇺🇸 English ↔️ 🇨🇳 Chinese | 🇯🇵 Japanese ➡️ 🇨🇳 Chinese
|
||||
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||||
## 🚀 快速开始
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||||
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WiNGPT-Babel 采用 Qwen2.5-1.5B 作为基础模型 ,是在测试比较了各种参数规模模型平衡推理速度和翻译质量的选择。在各种应用场景下的翻译速度可以达到甚至超过谷歌翻译,这样的体验对于使用翻译模型来说是至关重要的。 为了帮助大家快速上手,我们提供了以下示例,并使用 Hugging Face Transformers 库进行加载和推理,当然推荐大家使用vllm、llama.cpp、ollama等推理工具或框架:
|
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||||
```python
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
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||||
model_name = "WiNGPT/WiNGPT-Babel"
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|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_name,
|
||||
torch_dtype="auto",
|
||||
device_map="auto"
|
||||
)
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||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
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prompt = "Give me a short introduction to large language model."
|
||||
messages = [
|
||||
{"role": "system", "content": "中英互译下面的内容"},
|
||||
{"role": "user", "content": prompt}
|
||||
]
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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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)
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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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快速使用 llama.cpp 推理示例
|
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```
|
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llama-cli -m WiNGPT-Babel-Q4_K_M.gguf -co -i -if -p "<|im_start|>system\n中英互译下面的内容<|im_end|>\n" --in-prefix "<|im_start|>user\n" --in-suffix "<|im_end|>\n<|im_start|>assistant\n" -fa -ngl 80 -n 512
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```
|
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|
||||
- **注意:** WiNGPT-Babel 默认系统提示词仅为:“中英互译下面的内容”。模型会自动根据用户的输入翻译成对应的语言,无需其他复杂的指令。支持的最大长度8192,且具备多轮对话的能力。
|
||||
|
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### 🎬 示例
|
||||
|
||||
以下是一些应用场景示例,展示如何使用模型进行翻译。
|
||||
|
||||
1. **网页翻译:**
|
||||
- **场景:** 用户通过工具及简单系统提示,将外文网页内容翻译成母语。
|
||||
- **工具:** 沉浸式翻译
|
||||
<figure><img src="assets/20241216084737.png" style="zoom:40%;" /></figure>
|
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<figure><img src="assets/20241216084744.png" style="zoom:40%;" /></figure>
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<figure><img src="assets/20241216084809.png" style="zoom:40%;" /></figure>
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<figure><img src="assets/20241216085303.png" style="zoom:40%;" /></figure>
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<figure><img src="assets/20241216085311.png" style="zoom:40%;" /></figure>
|
||||
|
||||
2. **学术论文翻译:**
|
||||
- **场景:** 用户使用工具翻译外文研究论文,辅助研究工作。
|
||||
- **工具:** 沉浸式翻译
|
||||
<figure><img src="assets/20241216084751.png" style="zoom:40%;" /></figure>
|
||||
<figure><img src="assets/20241216084757.png" style="zoom:40%;" /></figure>
|
||||
|
||||
3. **社交媒体翻译:**
|
||||
- **场景:** 用户可以使用模型,将不同语言的社交媒体内容翻译成母语
|
||||
- **工具:** 沉浸式翻译
|
||||
<figure><img src="assets/20241216084803.png" style="zoom:40%;" /></figure>
|
||||
|
||||
4. **视频字幕翻译:**
|
||||
- **场景:** 用户利用工具,结合模型,直接翻译字幕文件并保存为文件。
|
||||
- **工具:** 沉浸式翻译
|
||||
<figure><img src="assets/20241216085700.png" style="zoom:30%;" /></figure>
|
||||
|
||||
5. **PDF文件翻译:**
|
||||
- **场景:** 用户利用工具,结合模型,将PDF等文档翻译或作为双语对照。
|
||||
- **工具:** PDFMathTranslate
|
||||
<figure><img src="assets/20241218080815.png" style="zoom:50%;" /></figure>
|
||||
|
||||
6. **数据集翻译:**
|
||||
- **场景:** 用户利用模型,将外语数据集进行翻译。
|
||||
- **工具:** wingpt-web-client
|
||||
<figure><img src="assets/093402.png" style="zoom:35%;" /></figure>
|
||||
|
||||
7. **视频网站实时翻译:**
|
||||
- **场景:** 用户利用工具,结合模型,在观看互联网视频时实时生成字幕。
|
||||
- **工具:** 沉浸式翻译
|
||||
<figure><img src="assets/IMG_6875.GIF" style="zoom:60%;" /></figure>
|
||||
<figure><img src="assets/IMG_6877.GIF" style="zoom:65%;" /></figure>
|
||||
|
||||
8. **视频翻译与字幕压制:**
|
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- **场景:** 用户利用工具,结合模型,将外语视频生成带有翻译字幕的视频。
|
||||
- **工具:** VideoLingo
|
||||
<figure><img src="assets/IMG_6878.GIF" style="zoom:100%;" /></figure>
|
||||
|
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**注意:** 以上示例展示了如何利用工具并结合 WiNGPT-Babel 模型进行文本翻译。你可以根据自己的需求和习惯,通过工具并将其应用到更多场景。
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||||
|
||||
|
||||
### 🌱 局限性
|
||||
- **专业术语翻译:** 在法律、医学等高度专业领域、代码等,翻译结果可能存在偏差
|
||||
- **文学作品翻译:** 对于文学作品中的修辞、隐喻等,可能无法完美传达原文意境
|
||||
- **长文本翻译:** 在处理超长文本时,可能会出现翻译错误或者幻觉问题,需要进行分段处理
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- **多语言适配:** 目前主要在中英语言场景里进行使用,其他语言需要更多的测试和反馈
|
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|
||||
## 许可证
|
||||
|
||||
1. 本项目授权协议为 Apache License 2.0
|
||||
2. 使用本项目包括模型权重时请引用本项目:https://huggingface.co/winninghealth/WiNGPT-Babel
|
||||
3. 遵守 [Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B), [immersive-translate](https://github.com/immersive-translate/immersive-translate), [VideoLingo](https://github.com/Huanshere/VideoLingo) 相关协议及其许可证,详细内容参照其网站。
|
||||
|
||||
## 联系我们
|
||||
|
||||
- 通过 [WiNGPT 测试平台](https://wingpt.winning.com.cn) 申请密钥
|
||||
|
||||
- 或通过 [wair@winning.com.cn](mailto:wair@winning.com.cn) 与我们取得联系申请接口测试 API_KEY
|
||||
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config.json
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|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
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"bos_token_id": 151643,
|
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
|
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"rms_norm_eps": 1e-06,
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"rope_theta": 1000000.0,
|
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"sliding_window": 32768,
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"tie_word_embeddings": true,
|
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"torch_dtype": "bfloat16",
|
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"transformers_version": "4.43.1",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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14
generation_config.json
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{
|
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"bos_token_id": 151643,
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"pad_token_id": 151643,
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"do_sample": true,
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151645,
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151643
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_p": 0.8,
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"top_k": 20,
|
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"transformers_version": "4.37.0"
|
||||
}
|
||||
151387
merges.txt
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c9154afd388d25237ed675a9bd4ab9def70b99fdee7a5107e29e8da0a935172e
|
||||
size 3087467144
|
||||
303282
tokenizer.json
Normal file
207
tokenizer_config.json
Normal file
@@ -0,0 +1,207 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{% for message in messages %}{% if not loop.first %}{{- '\n' }}{% endif %}{{- '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' }}{% if loop.last and add_generation_prompt %}{{- '\n<|im_start|>assistant\n' }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"add_bos_token": false
|
||||
}
|
||||