微调 Phi-3 和 Gemma 2：以较低成本实现 GPT-4 性能的预算路径
📄 中文摘要
Phi-3 和 Gemma 2 的微调为企业提供了在预算有限的情况下进行高质量 AI 模型训练的解决方案。研究显示，拥有 38 亿参数的 Phi-3-mini 在金融分类任务中超越了 GPT-4o，准确率达到 96%，而 GPT-4o 为 80%。一项多机构的研究进行了超过 200 次训练实验，结果表明 Phi-3-mini 在 7 项金融 NLP 基准测试中胜出 6 项。此外，推理成本方面，Phi-3-mini 的每百万个标记成本为 0.13 美元，而 GPT-4o 的平均成本约为 3.75 美元，前者的成本大约便宜 29 倍。Google 的 Gemma 2 模型同样展现了类似的优势。
📄 English Summary
Fine-Tuning Phi-3 & Gemma 2: The Budget Path to GPT-4 Performance at a Fraction of the Cost
Fine-tuning of Phi-3 and Gemma 2 provides enterprises with a solution for high-quality AI model training on a budget. Research indicates that the 3.8 billion parameter Phi-3-mini outperforms GPT-4o in financial classification tasks, achieving an accuracy of 96% compared to GPT-4o's 80%. A multi-institutional study conducted over 200 training experiments found that Phi-3-mini surpassed GPT-4o in 6 out of 7 financial NLP benchmarks. Furthermore, the inference cost for Phi-3-mini is $0.13 per million tokens, while GPT-4o's blended average is approximately $3.75, making it roughly 29 times cheaper. Google’s Gemma 2 model tells a similar story.
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数据源: OpenAI, Google AI, DeepMind, AWS ML Blog, HuggingFace 等