π° Frugal GPT is a three-step process introduced in a paper that helps reduce the cost of LLM APIs by up to 98% while maintaining performance.
π The three steps of Frugal GPT are prompt adoption, LLM approximation, and LLM cascade, which collectively reduce the inference cost associated with LLM APIs while improving accuracy.
π Implementing Frugal GPT not only saves money but also promotes sustainability and improves response latency in applications that use LLM APIs.
FrugalGPT is introducing a new approach to reduce GPT-4 API costs.
The approach includes three steps: prompt adaption, llm approximation, and llm Cascade.
FrugalGPT allows for using a combination of multiple models instead of relying solely on gpt4.
π Frugal GPT aims to balance performance and cost by combining open source models with GPT-4.
π The architecture of Frugal GPT consists of prompt selection, query concatenation, completion cache, model fine-tuning, and LLM cascade.
βοΈ Prompt adaption and LLM approximation are key steps in Frugal GPT's architecture.
β The FrugalGPT prompt selector is important for reducing API costs while maintaining accuracy.
π Using a query concatenator can help decrease the number of API calls and improve response time.
π Prompt adaption techniques such as prompt selector and query concatenator can help optimize the GPT API usage.
π Creating a cache of previously asked questions can reduce GPT-4 API costs
π― Model fine-tuning allows for using a smaller and cheaper language model like gptj
π Using an LLM Cascade approach reduces the need for querying the GPT-4 API
π‘ Reducing GPT-4 API costs can be achieved by implementing three effective steps: prompt adoption, LLM approximation, and LLM Cascade.
β Prompt adoption involves using simple if-else rules to stop the search once an accepted answer is found.
π LLM approximation includes strategies like query concatenation, completion cache, and model fine-tuning to minimize the use of expensive APIs.
βοΈ In LLM Cascade, the query is sent through multiple models, stopping at the most accurate one if the result meets expectations.
π Implementing these steps not only reduces costs but also improves accuracy, as Frugal GPT has outperformed GPT-4 in certain instances.
π° FrugalGPT offers cost savings of up to 98% for using large language models.
π‘ This paper provides practical and common-sense strategies for reducing API costs.
π The paper shares research on cost reduction and performance improvement with large language models.
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