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Yulan Mini
Overview :
YuLan-Mini is a lightweight language model developed by the AI Box team at Renmin University of China. With 240 million parameters, it achieves performance comparable to industry-leading models trained on larger datasets, despite using only 1.08 terabytes of pre-trained data. The model excels in mathematics and coding domains, and to facilitate reproducibility, the team will open-source relevant pre-training resources.
Target Users :
The target audience includes researchers and developers in the field of natural language processing, as well as companies in need of efficient language models. YuLan-Mini, known for its lightweight design and high efficiency, is particularly well-suited for resource-constrained environments that require high-performance models, such as small businesses and academic research.
Use Cases
Case Study 1: Researchers using YuLan-Mini for automatic problem solving and validation in mathematics.
Case Study 2: Developers leveraging YuLan-Mini to generate high-quality code snippets, enhancing development efficiency.
Case Study 3: Educational institutions adopting YuLan-Mini to assist teaching, providing personalized learning materials and answering questions.
Features
? A lightweight language model with 240 million parameters, delivering outstanding performance.
? Pre-trained using only 1.08 terabytes of data, demonstrating high data efficiency.
? Excellent at understanding and generating language in the realms of mathematics and programming.
? Open-source pre-training resources, including code and data, to enhance research transparency and reproducibility.
? Supports long contexts (up to 28K), suitable for complex tasks.
? Provides model weights and optimizer states for easier research and further training.
? Accommodates various usage scenarios, including pre-training, fine-tuning, and learning rate annealing.
How to Use
1. Visit the GitHub page of YuLan-Mini to learn about the project details and documentation.
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2. Follow the guidelines provided on the page to download and install the necessary pre-trained models and code.
3. Use the interface provided by Huggingface to load the model and tokenizer for inference testing.
4. Adjust model parameters as needed and fine-tune or further train for specific tasks.
5. Apply the model for practical applications such as text generation and question-answering systems.
6. Engage in community discussions to provide feedback on issues encountered and suggestions for improvements.
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