TAIDE-LX-7B-Chat-GGUF

taide/TAIDE-LX-7B-Chat-GGUF

• This is quantized version of taide/TAIDE-LX-7B-Chat created using llama.cpp

Model Description

• The TAIDE project aims to develop a generative AI dialogue engine model that is tailored to the linguistic and cultural characteristics of Taiwan, while also establishing a trustworthy AI environment. By combining academic, industrial, and research resources, the project seeks to advance the development of trustworthy generative AI, enhancing Taiwan's international competitiveness, promoting industrial development, and reducing dependence on foreign technologies.
• The large language models developed in this project are based on Meta's LLaMA2-7b release, incorporating text and training materials from various fields in Taiwan to enhance the model's ability to respond in Traditional Chinese and perform well in specific tasks. The publicly released models are as follows:
  • TAIDE-LX-7B: This model is a continuous pretrained version with Traditional Chinese data based on LLaMA2-7b. It is suitable for scenarios where users intend to further fine-tune the model. Since the pre-trained model has not undergone fine-tuning and preference alignment, it may produce malicious or unsafe outputs. Please use with caution.
  • TAIDE-LX-7B-Chat: This model enhances office-related tasks and multi-turn question-and-answer dialogue capabilities through instruction tuning based on TAIDE-LX-7B. It is suitable for scenarios such as chat conversations or task assistance. TAIDE-LX-7B-Chat also provides a 4-bit quantization model. The quantization model is primarily offered for user convenience but may affect performance and introduce unforeseen issues. Users are advised to understand and take note of this.

Model Parameters

• Parameters: 7B
• Max context length: 4K
• Training token in Traditional Chinese: 41.44B
• Training time: 1531.82 H100 GPU Hours

Features

• Expanding the character and word count by 24,720 to enhance the model's ability to process Traditional Chinese.
• Strictly vetting training data for the model to improve its trustworthiness and applicability.
• Enhancing the model's performance for tasks such as summarization, writing articles, writing letters, and translating between Chinese and English, which are commonly used in office settings.
• Strengthening the model's understanding of Taiwan-specific culture, terminology, and context.
• Equip the model with multi-turn conversation capabilities.

Examples

Training methods

• Software / hardware spec
  • GPU: H100
  • Training Framework: PyTorch
• Data preprocessing
  • Character normalization
  • Deduplication
  • Denoise
    • Html tag、javascript in web content
    • Non-standard characters or garbage characters
    • Posts with an insufficient number of characters
    • Removing specific formats such as extra line breaks added for formatting purposes
  • Removing personal information such as emails and phone numbers.
  • Remove inappropriate content such as gambling, pornography, etc..
• Character and word expanding
  • Enhancing the performance of Traditional Chinese input and output, the expanded data include the following two parts:
    • Obtaining Chinese characters from the Ministry of Education's "Variant Chinese Characters Dictionary" and "Corrected Characters Table".
    • Collecting over 5,000,000 sentences with more than 100 characters each from the Traditional Chinese Wikipedia, news articles, and the Chinese Common Crawl data (2.1G), used to train the tokenizer for Chinese characters and words.
• Continuous pretraining (CP)
  • Supplementing the model with a large amount of reliable Traditional Chinese knowledge.
  • Hyper parameters
    • optimizer: AdamW
    • learning rate: 1e-4
    • batch size: 1M tokens
    • epoch: 1
• Fine tune (FT)
  • Enabling the model to answer questions in Traditional Chinese.
  • Hyper parameters
    • optimizer: AdamW
    • learning rate: 5e-5
    • batch size: 256K tokens
    • epoch: 3

Training Data

• Continuous pre-training data (about 140GB) | Dataset | Description | | --- | -------- | | Litigation Data | Civil litigation data from various levels of courts in the judicial rulings, including data from 2013/01 to 2023/12. | | CNA news | The CNA news includes daily news articles from June 1993 to June 2023, spanning a period of 30 years. The content covers various domains such as domestic and international politics, society, economy, culture, education, and lifestyle. | | ETtoday news | ETtoday news data, including data from 2011/10 to 2023/12. | | Legislative Yuan Gazette | The Legislative Yuan Gazette contains data from the 1st session of the 8th term to the 7th session of the 10th term. | | Publisher Website Book Introduction | Includes book introduction data from the websites of SunColor, Gotop publishers. | | Abstracts of GRB research projects | GRB is an information system that compiles research projects funded by government grants and their outcome reports. This dataset primarily includes research project abstracts from 1993 to 2023, including both Chinese and their English counterparts. | | Academic conference proceedings abstracts | The database contains academic conference proceedings held in Taiwan from 1988 to 2009. | | Taiwan Panorama magazine | Taiwan Panorama magazine contains articles from July 1993 to June 2023, spanning 30 years. The content focuses on Taiwanese culture, tourism, and local customs. | | 樂詞網 | 《樂詞網》covers approximately 187,000 academic terms in the humanities and social sciences, along with their translations. | | Data from various ministries and commissions | Including partial data from government department websites such as the Executive Yuan's "National Overview", the Ministry of Culture's "National Cultural Memory Bank", the National Development Council's "Archives Support Teaching Network", the Ministry of Transportation's "Traffic Safety Portal", etc. | | Business Today | Business Today Magazine is a weekly magazine focused on finance. The dataset includes articles from 2008/01 to 2023/07. | | Mandarin and idiom dictionary from the Ministry of Education | Dataset including:<br>Idiom Dictionary: Contains 5,338 idioms, including definitions, original stories, usage explanations, and example sentences.<br>Revised Mandarin Dictionary: contains Chinese words and various vocabulary, including pronunciation, radicals, definitions, and other information, totaling approximately 165,539 entries.<br>Concise Mandarin Dictionary: is a condensed version of the "Revised Mandarin Dictionary", containing a total of 45,247 entries. | | SCITechVista | The dataset includes science news and popular science articles from the SCITechVista website. | | iKnow | The iKnow platform provides information on market trends, strategic analysis, patent knowledge, and technology transaction information for Taiwan and the global technology industry. The dataset includes data from 2005/01 to 2023/07. | | Science Development Monthly Magazine | Science Development Monthly Magazine is a popular science publication published by the National Science Council (NSC) to promote science education. It includes articles from 2004/10 to 2020/12. In 2021, the magazine was relaunched as "CharmingSCITech" quarterly, providing new knowledge on international technology issues. | | Legislation Database | The Legislation Database includes the latest central regulations, rules, draft bills, and local regulations issued by government agencies as of 2023/10. | | Local Government Tourism Websites | Covering partial data from tourism websites of local government counties and cities in Taiwan. | | Curriculum Guidelines from the National Institute of Education | The dataset includes curriculum guidelines for different subjects at various levels of education. | | CNA's English and Chinese Name Translation Database | The English and Chinese Name Translation Database of the Central News Agency (CNA) collects translations of foreign and Chinese surnames, personal names, organizations, and place names used in news. | | Fairy tales | A total of 20 fairy tale books, including "Tom Sawyer," "Peter Pan," "Alice's Adventures in Wonderland," "Uncle Long Legs," and more. | | RedPajama-Data-V2 | Extracting English data from the RedPajama-Data-v2 multilingual dataset | | MathPile-commercial | A mathematics-focused dataset obtained from MathPile-commercial | | Traditional Chinese Wikipedia Articles | The content of all articles in Traditional Chinese Wikipedia, up to January 2023. | | github-code-clean | An open-source code dataset on GitHub. After removing unlicensed code and documents. |
• Fine tune data
  • The TAIDE team trains the LLaMA2 series models to generate fine-tuning data, which generates single or multi-turn conversations on topics such as world knowledge, creative writing, general knowledge, translation, summarization, programming, and Taiwanese values. The fine tune data consists of 128K prompt-response pairs and will be released publicly later.

Evaluation

• taide-bench
  • Data
    • Tasks include writing articles, writing letters, summarizing articles, translating from English to Traditional Chinese, translating from Traditional Chinese to English. There are 500 questions in total.
    • data link: taide-bench
  • Evaluation method
    • LLM as a Judge by GPT4
    • code link: taide-bench-eval
  • Scores | Model | Translating from Traditional Chinese to English | Translating from English to Traditional Chinese | Summerization | Writing articles | Writing letters | Average | | --- | ----- | ----- | ---- | ---- | ---- | --- | | TAIDE-LX-7B-Chat | 7.165 | 7.685 | 7.720 | 9.635 | 9.110 | 8.263 | | GPT3.5 | 8.880 | 8.810 | 7.450 | 9.490 | 8.750 | 8.676 | | LLAMA2 7B | 6.075 | 4.475 | 5.905 | 2.625 | 3.040 | 4.424 | | LLAMA2 13B | 6.480 | 6.135 | 6.110 | 2.565 | 3.000 | 4.858 | | LLAMA2 70B | 6.975 | 6.375 | 6.795 | 2.625 | 2.990 | 5.152 |

License

• TAIDE L Models Community License Agreement

Disclaimer

• Due to limitations in its design architecture and the inevitable biases in data, any response from the LLM model does not represent the stance of TAIDE. Additional security measures should be implemented before use, and responses may also contain incorrect information. Users are advised not to fully trust the responses.

Development Team

• https://taide.tw/index/teamList

Useful links

• TAIDE official website
• TAIDE Huggingface
• TAIDE Github
• Kuwa AI