Gemini 1.5 Pro was overshadowed by OpenAI's Sora but is probably the biggest boost in LLM capabilities this year (so far). It's not available to the public yet, but Google DeepMind was kind enough to grant me early access. Let's get into the tests below.
LLMS
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Google DeepMind Releases Gemini 1.5 Pro With 1M Token Context
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First, what is Gemini 1.5 Pro?
— Rowan Cheung (@rowancheung) 18 février 2024
Gemini 1.5 Pro is Google DeepMind's newest update to the free version of its LLM.
It most notably has a 1M+ token context length, meaning it can understand much larger amounts of data compared to other LLMs/chatbots. pic.twitter.com/lIjeHE8W9uFirst, what is Gemini 1.5 Pro? Gemini 1.5 Pro is Google DeepMind's newest update to the free version of its LLM. It most notably has a 1M+ token context length, meaning it can understand much larger amounts of data compared to other LLMs/chatbots.
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Gemini 1.5 Pro’s Million Token Context Length Capabilities
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Gemini 1.5 Pro's 1,000,000+ token context length is incredible. I got early access and spent my Saturday night running tests. Here are 6 impressive capabilities I found:
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OpenAI’s Deliberate Strategy: GPT-5 Ready, Gradual Sora Rollout
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Yes, but that was foreseeable from the start. In any case, I don't share the view that OpenAI is acting hastily. On the contrary. I am firmly convinced that GPT5 has been ready for a long time and that they are slowly making progress through sora. It has to happen like this.
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Chat Application with LangChain, LLMs, Streamlit SQL Database
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Want a fun *16* minute weekend read? Building a Chat Application with LangChain, LLMs, and Streamlit for Complex SQL Database Interaction Using LLMs to interact with SQL databases can be complicated – this is a great deepdive https://
towardsdatascience.com/building-a-cha
t-app-with-langchain-llms-and-streamlit-for-complex-sql-database-interaction-7433245079f3
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Building Chatbots with LangChain and OpenAI Guide
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How to Build Your Own Chatbot with LangChain and OpenAI Chat is the dominant UX for LLM applications. Great blog by Tahreem Rasul walks through how to build one Covers how to keep it on track and only responding to specific topics! https://
medium.com/@tahreemrasul/
how-to-build-your-own-chatbot-with-langchain-and-openai-f092822b6ba6
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Dissecting Transformers in Natural Language Processing
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Dissecting Transformers with #NLProc. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Dissecting-NLP
roc
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Building RAG Chatbot with Upstash Vector Stack
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Building your own RAG chatbot with Upstash Great blog by the @upstash team on how to build a RAG chatbot with a top tier tech stack: Upstash Vector, Upstash Redis, Hugging Face Inference API, Replicate LLAMA-2-70B Chat model, and Vercel https://
upstash.com/blog/rag-chatb
ot-upstash
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Gemini’s Impressive Information Filtering from Million Token Context
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Strange, imo most tester were impressed by the capability of Gemini to filter information from the ~1mio token
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ReWOO Agents: Reasoning Without Observations Framework
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Agents that Reason WithOut Observations (ReWOO)⍱ Take the “plan + execute” agent design up a notch via variable assignment. Can generate better plans (and avoid re-planning) while letting task executors focus. JS: https://
github.com/langchain-ai/l
anggraphjs/blob/main/examples/rewoo/rewoo.ipynb
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Python: https://
github.com/langchain-ai/l
anggraph/blob/main/examples/rewoo/rewoo.ipynb
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