Summary of A Practice Of Post-training on Llama-3 70b with Optimal Selection Of Additional Language Mixture Ratio, by Ningyuan Xi et al.
A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio
by Ningyuan Xi, Yetao Wu, Kun Fan, Teng Chen, Qingqing Gu, Peng Yu, Jinxian Qu, Chenxi Liu, Zhonglin Jiang, Yong Chen, Luo Ji
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A study on Continual Pre-Training (CPT) of Large Language Models (LLMs) optimizes hyperparameters for effective adaptation to new domains. Researchers fine-tuned Llama-3 models, with 8B and 70B sizes, to enhance their Chinese language skills. The optimal mixture ratio of additional languages was correlated with the learning rate, enabling a precise experimental setup. This led to improved model performance on Chinese-related benchmarks and specific domains like math, coding, and emotional intelligence. The deployed 70B LLM achieved satisfying results in a real-life chat system. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models need to learn new skills or adapt to different areas, but this requires a lot of training. To make the most of this process, scientists need to choose the right settings for their models. This paper looks at how to do this by fine-tuning a Large Language Model called Llama-3. The researchers made the model better at understanding Chinese and even improved its skills in math, coding, and emotional intelligence. They then tested the final version of the model on a real-life chat system and it performed well. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Llama
