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--- |
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license: apache-2.0 |
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language: |
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- th |
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- zh |
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- en |
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metrics: |
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- accuracy |
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base_model: |
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- Qwen/Qwen2.5-7B |
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pipeline_tag: text-generation |
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tags: |
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- chemistry |
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- biology |
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- finance |
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- legal |
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- code |
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- medical |
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- text-generation-inference |
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--- |
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# OpenThaiLLM-Prebuilt-7B: Thai & China & English Large Language Model |
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**OpenThaiLLM-Prebuilt-7B** is an 7 billion parameter instruct model designed for Thai 🇹🇭 & China 🇨🇳 language. |
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It demonstrates competitive performance with llama-3-typhoon-v1.5-8b-instruct, and is optimized for application use cases, Retrieval-Augmented Generation (RAG), |
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constrained generation, and reasoning tasks.is a Thai 🇹🇭 & China 🇨🇳 large language model with 7 billion parameters, and it is based on Qwen2-7B. |
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For release notes, please see our [blog](https://medium.com/@superkingbasskb/openthaillm-prebuilt-release-f1b0e22be6a5). |
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## Requirements |
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The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. |
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With `transformers<4.37.0`, you will encounter the following error: |
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``` |
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KeyError: 'qwen2' |
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## Training details |
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We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization. |
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## Implementation |
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Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. |
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## Evaluation Performance |
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| Model | ONET | IC | TGAT | TPAT-1 | A-Level | Average (ThaiExam) | M3Exam (1 shot) | MMLU | |
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| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | |
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| OpenthaiLLM-Prebuilt-7B | **0.5185** | **0.6421** | **0.6461** | **0.4224** | **0.3937** | **0.5245** | **0.5355** | 0.6644 | |
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| SeaLLM-v3-7B | 0.4753 | 0.6421 | 0.6153 | 0.3275 | 0.3464 | 0.4813 | 0.7037 | 0.4907 | 0.4625 | |
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| llama-3-typhoon-v1.5-8B | 0.3765 | 0.3473 | 0.5538 | 0.4137 | 0.2913 | 0.3965 | 0.6451 | 0.4312 | 0.4125 | |
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| Qwen-2-7B | 0.4814 | 0.621 | 0.6153 | 0.3448 | 0.3385 | 0.4802 | 0.7073 | 0.4949 | 0.4807 | |
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| Meta-Llama-3.1-8B | 0.3641 | 0.2631 | 0.2769 | 0.3793 | 0.1811 | 0.2929 | 0.6591 | 0.4239 | 0.3583 | |
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## Citation |
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If you find our work helpful, feel free to give us a cite. |
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``` |
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@misc{qwen2.5, |
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title = {Qwen2.5: A Party of Foundation Models}, |
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url = {https://qwenlm.github.io/blog/qwen2.5/}, |
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author = {Qwen Team}, |
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month = {September}, |
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year = {2024} |
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} |
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@article{qwen2, |
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title={Qwen2 Technical Report}, |
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author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, |
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journal={arXiv preprint arXiv:2407.10671}, |
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year={2024} |
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} |
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