One half-day of training using a few hundred dollars yields similar results to mainstream large models, open-source and commercial-free domain-specific LLM solution
The most prominent distinction between LLaMA-1 and LLaMA-2 lies in the incorporation of higher-quality corpora, a pivotal factor contributing to significant performance enhancements in LLaMA-2. This, coupled with its commercial availability, extends the potential for creative applications of large models within the open-source community.
Nevertheless, it’s widely recognized that the cost of pre-training large models from scratch is exorbitant, often humorously referred to as a domain accessible only to those with “50 million dollars” to spare. This deters many companies and developers, so how can we build our own large models at a lower cost?
Being at the forefront of cost reduction and efficiency enhancement for large models, the Colossal-AI team maximizes the core capabilities of LLaMA-2. Through innovative training techniques, Colossal-AI has achieved remarkable results by utilizing only approximately 0.0085 trillion tokens of data, investing 15 hours, and incurring training costs in the range of a few hundred dollars. This strategy has yielded a high-performance Chinese LLaMA-2 model that consistently outperforms competitors across multiple evaluation benchmarks.
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