Source URL: https://simonwillison.net/2024/Nov/18/jack-clark/
Source: Simon Willison’s Weblog
Title: Quoting Jack Clark
Feedly Summary: The main innovation here is just using more data. Specifically, Qwen2.5 Coder is a continuation of an earlier Qwen 2.5 model. The original Qwen 2.5 model was trained on 18 trillion tokens spread across a variety of languages and tasks (e.g, writing, programming, question answering). Qwen 2.5-Coder sees them train this model on an additional 5.5 trillion tokens of data. This means Qwen has been trained on a total of ~23T tokens of data – for perspective, Facebook’s LLaMa3 models were trained on about 15T tokens. I think this means Qwen is the largest publicly disclosed number of tokens dumped into a single language model (so far).
— Jack Clark
Tags: jack-clark, generative-ai, training-data, ai, qwen, llms
AI Summary and Description: Yes
Summary: The text discusses the advancements in the Qwen2.5 Coder model, highlighting its training on an unprecedented amount of data, which positions it as a significant player in the generative AI landscape. This has implications for AI security and the future of language models.
Detailed Description: The content centers around the Qwen2.5 Coder language model, emphasizing its notable training volumes which have implications both for its performance and potential security risks. Key points include:
– **Model Evolution**: Qwen2.5 Coder is an enhancement of the previous Qwen 2.5 model, signifying a trend in generative AI toward continuous improvement through larger data sets.
– **Data Utilization**: The combined training data of approximately 23 trillion tokens sets a record in public disclosures for language models, surpassing notable benchmarks set by other models like Facebook’s LLaMa3 (which was trained with about 15 trillion tokens).
– **Implications for AI Development**: The vast amount of training data not only enhances the capabilities of generative AI but also raises questions about the management of this data, particularly regarding security, privacy, and compliance issues.
Considering the growth trajectory of models like Qwen, security professionals should be mindful of:
– **Data Privacy**: Understanding how training data is sourced and whether any sensitive information is inadvertently included.
– **Model Security**: As models grow in complexity and capability, they also become more attractive targets for adversarial attacks. Robust security measures must be in place.
– **Compliance**: Ensuring that the expansive datasets used comply with relevant data protection laws and regulations.
In summary, advancements like Qwen2.5 Coder not only represent significant technical achievements but also come with increased responsibility towards security and compliance in AI development.