Post
2757
What is the best LLM for RAG systems? 🤔
In a business setting, it will be the one that gives the best performance at a great price! 💼💰
And maybe it should be easy to fine-tune, cheap to fine-tune... FREE to fine-tune? 😲✨
That's @Google Gemini 1.5 Flash! 🚀🌟
It now supports fine-tuning, and the inference cost is the same as the base model! <coughs LORA adopters> 🤭🤖
So the base model must be expensive? 💸
For the base model, the input price is reduced by 78% to $0.075/1 million tokens and the output price by 71% to $0.3/1 million tokens. 📉💵
But is it any good? 🤷♂️
On the LLM Hallucination Index, Gemini 1.5 Flash achieved great context adherence scores of 0.94, 1, and 0.92 across short, medium, and long contexts. 📊🎯
Google has finally given a model that is free to tune and offers an excellent balance between performance and cost. ⚖️👌
Happy tuning... 🎶🔧
Gemini 1.5 Flash: https://developers.googleblog.com/en/gemini-15-flash-updates-google-ai-studio-gemini-api/ 🔗
LLM Hallucination Index: https://www.rungalileo.io/hallucinationindex 🔗
In a business setting, it will be the one that gives the best performance at a great price! 💼💰
And maybe it should be easy to fine-tune, cheap to fine-tune... FREE to fine-tune? 😲✨
That's @Google Gemini 1.5 Flash! 🚀🌟
It now supports fine-tuning, and the inference cost is the same as the base model! <coughs LORA adopters> 🤭🤖
So the base model must be expensive? 💸
For the base model, the input price is reduced by 78% to $0.075/1 million tokens and the output price by 71% to $0.3/1 million tokens. 📉💵
But is it any good? 🤷♂️
On the LLM Hallucination Index, Gemini 1.5 Flash achieved great context adherence scores of 0.94, 1, and 0.92 across short, medium, and long contexts. 📊🎯
Google has finally given a model that is free to tune and offers an excellent balance between performance and cost. ⚖️👌
Happy tuning... 🎶🔧
Gemini 1.5 Flash: https://developers.googleblog.com/en/gemini-15-flash-updates-google-ai-studio-gemini-api/ 🔗
LLM Hallucination Index: https://www.rungalileo.io/hallucinationindex 🔗
