content.raw: What is the best LLM for RAG systems? 🤔
content.type: text
content.value: What is the best LLM for RAG systems? 🤔
content.type: new_line
content.type: new_line
content.raw: In a business setting, it will be the one that gives the best performance at a great price! 💼💰
content.type: text
content.value: In a business setting, it will be the one that gives the best performance at a great price! 💼💰
content.type: new_line
content.type: new_line
content.raw: And maybe it should be easy to fine-tune, cheap to fine-tune... FREE to fine-tune? 😲✨
content.type: text
content.value: And maybe it should be easy to fine-tune, cheap to fine-tune... FREE to fine-tune? 😲✨
content.type: new_line
content.type: new_line
content.raw: That's 
content.type: text
content.value: That's 
content.raw: @Google
content.type: mention
content.user: Google
content.raw:  Gemini 1.5 Flash! 🚀🌟
content.type: text
content.value:  Gemini 1.5 Flash! 🚀🌟
content.type: new_line
content.type: new_line
content.raw: It now supports fine-tuning, and the inference cost is the same as the base model! <coughs LORA adopters> 🤭🤖
content.type: text
content.value: It now supports fine-tuning, and the inference cost is the same as the base model! <coughs LORA adopters> 🤭🤖
content.type: new_line
content.type: new_line
content.raw: So the base model must be expensive? 💸
content.type: text
content.value: So the base model must be expensive? 💸
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content.raw: 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. 📉💵
content.type: text
content.value: 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. 📉💵
content.type: new_line
content.type: new_line
content.raw: But is it any good? 🤷‍♂️
content.type: text
content.value: But is it any good? 🤷‍♂️
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content.raw: 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. 📊🎯
content.type: text
content.value: 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. 📊🎯
content.type: new_line
content.type: new_line
content.raw: Google has finally given a model that is free to tune and offers an excellent balance between performance and cost. ⚖️👌
content.type: text
content.value: Google has finally given a model that is free to tune and offers an excellent balance between performance and cost. ⚖️👌
content.type: new_line
content.type: new_line
content.raw: Happy tuning... 🎶🔧
content.type: text
content.value: Happy tuning... 🎶🔧
content.type: new_line
content.type: new_line
content.raw: Gemini 1.5 Flash: 
content.type: text
content.value: Gemini 1.5 Flash: 
content.href: https://developers.googleblog.com/en/gemini-15-flash-updates-google-ai-studio-gemini-api/
content.raw: https://developers.googleblog.com/en/gemini-15-flash-updates-google-ai-studio-gemini-api/
content.type: link
content.raw:  🔗
content.type: text
content.value:  🔗
content.type: new_line
content.type: new_line
content.raw: LLM Hallucination Index: 
content.type: text
content.value: LLM Hallucination Index: 
content.href: https://www.rungalileo.io/hallucinationindex
content.raw: https://www.rungalileo.io/hallucinationindex
content.type: link
content.raw:  🔗
content.type: text
content.value:  🔗
rawContent: 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 🔗
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commentators.fullname: Tech Meld
commentators.name: Tech-Meld
commentators.type: user