Liyang Fan

Education

• Specialized in Computer Technology
• Research conducted at Shenzhen Institute of Advanced Technology

• Ranked in the top 1% of the program
• Key courses: Software Engineering, Discrete Mathematics, Data Structures, Algorithm Design

Work Experience

• Worked on model architecture adaptation, data mining, and feature extension
• Contributed to the development of jina-reranker-v2-base-multilingual model

Projects

ChatAgent

• Developed an AI agent project based on FastAPI framework
• Implemented natural language database querying, improving data retrieval efficiency
• Achieved response times under 3 seconds for queries on tables with millions of records

MegaChat Text Generation Model

• Built a text generation system based on the MEGABYTE model
• Implemented efficient inference with extended length capabilities
• Developed character-level inference for multi-language support

ByteLLM

• Developed ByteLLM, focusing on end-to-end training of Large Language Models using ByteTokenizer
• Integrated byte-level tokenization into LLMs to simplify and enhance the training process
• To Demonstrate compatibility with existing LLM frameworks, facilitating seamless integration
• To Achieve significant improvements in tokenization and training efficiency

Skills

• Programming Languages: Python, C++, Java
• Machine Learning: Experienced in designing and implementing ML models
• Frameworks: PyTorch, FastAPI, Hugging Face Transformers
• Tools: Git, Docker

Publications

Liu, Z., Fang, F., Feng, X., Du, X., Zhang, C., Wang, Z., Bai, Y., Zhao, Q., Fan, L., Gan, C., et al. (2024). II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models. arXiv preprint arXiv:2406.05862.