Descriptions:
- Design and optimize large language models, devise fine-tuning strategies, and streamline the training process.
- Explore deep learning architectures like Seq2Seq, Transformer, and advanced techniques including Fine-tuning, Prompt Engineering, and Soft Prompting (SFT).
- Develop systems for efficient model training and deployment, involving data preprocessing, parallel training, and resource management.
- Establish performance evaluation systems and monitor training metrics to ensure model quality and iteration efficiency.
Requirements:
- Bachelor degree or higher in Computer Science, Artificial Intelligence, Mathematics, or related fields.
- LLM. AIGC required.
