gpt2 tiny chinese LLM Model

Introduction

The CKIP GPT2-Tiny-Chinese project offers traditional Chinese transformers models, including ALBERT, BERT, and GPT2, along with NLP tools such as word segmentation, part-of-speech tagging, and named entity recognition.

Architecture

The CKIP GPT2-Tiny-Chinese model is a transformer-based architecture built for text generation tasks in traditional Chinese using the PyTorch framework. It is a smaller version of the GPT2 model, optimized for inference in Chinese text.

Training

The model is pre-trained on a vast corpus of traditional Chinese text to enhance its language generation capabilities. Users can leverage the pre-trained model for various NLP tasks without the need for additional training.

Guide: Running Locally

Install Dependencies: Ensure you have Python and PyTorch installed. Use the transformers library from Hugging Face for model and tokenizer utilities.
```
pip install torch transformers
```

Load Model and Tokenizer:

from transformers import BertTokenizerFast, AutoModel

tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/gpt2-tiny-chinese')

Running Inference: Use the loaded model and tokenizer to process input text and generate predictions.
Cloud GPUs: To enhance performance, consider using cloud-based GPU services like AWS, Google Cloud, or Azure for faster inference times.

License

The CKIP GPT2-Tiny-Chinese model is licensed under the GPL-3.0 license, which allows for free use, modification, and distribution under the same license terms.

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