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typhoon-7B-2.5bpw-exl2

Text Generation
Transformers
PyTorch
Thai
mistral
pretrained
text-generation-inference
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metadata
license: apache-2.0
language:
  - th
library_name: transformers
pipeline_tag: text-generation
tags:
  - pretrained

Typhoon-7B: Thai Large Language Model

Typhoon-7B is a pretrained Thai 🇹🇭 large language model with 7 billion parameters, and it is based on Mistral-7B.

Typhoon-7B outperforms all open-source Thai language models at the time of writing as evaluated on Thai examination benchmarks, and its instruction-tuned variant achieves the best results in instruction-following tasks. Also, its performance in Thai is on par with GPT-3.5 while being 2.62 times more efficient in tokenizing Thai text.

Typhoon benchmark

For full details of this model, please read our paper.

Model Description

  • Model type: A 7B pretrained decoder-only model
  • Requirement: transformers 4.34.0 or newer.
  • Primary Language(s): Thai 🇹🇭 and English 🇬🇧
  • License: Apache-2.0 (Commercial)

Performance on Thai Benchmark

Model ONET IC TGAT TPAT-1 A-Level
Typhoon-7B 0.379 0.393 0.700 0.414 0.324
SeaLLM-7B 0.342 0.256 0.589 0.336 0.305
OpenThaiGPT-beta-7B 0.180 0.278 0.411 0.319 0.243
WangChanGLM 0.192 0.271 0.167 0.172 0.175
SEA-LION-7B 0.179 0.290 0.244 0.198 0.175
Avg. Human 0.318 - 0.472 0.406 -

Intended Uses & Limitations

This model is a pretrained base model. Thus, it may not be able to follow human instructions without using one/few-shot learning or instruction fine-tuning. The model does not have any moderation mechanisms, and may generate harmful or inappropriate responses.

SCB10X AI Team

  • Kunat Pipatanakul, Phatrasek Jirabovonvisut, Potsawee Manakul, Sittipong Sripaisarnmongkol, Ruangsak Patomwong, Pathomporn Chokchainant, Kasima Tharnpipitchai
  • If you find Typhoon-7B useful for your work, please cite it using:
@article{pipatanakul2023typhoon,
    title={Typhoon: Thai Large Language Models}, 
    author={Kunat Pipatanakul and Phatrasek Jirabovonvisut and Potsawee Manakul and Sittipong Sripaisarnmongkol and Ruangsak Patomwong and Pathomporn Chokchainant and Kasima Tharnpipitchai},
    year={2023},
    journal={arXiv preprint arXiv:2312.13951},
    url={https://arxiv.org/abs/2312.13951}
}

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