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:
LLMS
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27 Mistral-7B Adapters Fine-Tuned for Under $8 Each
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We fine-tuned 27 adapters using #Mistral-7B on Predibase for < $8.00 each and 25 of them rival or outperform #GPT4 Check out our blog to see benchmarks, learn how we did it & get the link to download the #LLMs on @HuggingFace #TheFutureIsFineTuned https://
pbase.ai/49Hrwtn -
AI21 Jurassic-2 and Task-Specific Models in LangChain
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@AI21Labs Foundation and Task-Specific Models Unlock the full potential of AI21's Jurassic-2 and Task-Specific models directly in LangChain! Effortlessly build and scale generative AI applications that meet today's business needs. No NLP expertise required; just seamless
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JSON Agents with Mixtral: Open Source Graph Database Navigation
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JSON Agents with @ollama & LangChain Explore the cutting-edge application of the Open Source Mixtral model from @MistralAI to an agent that navigates a graph database in our latest blog. Authored by @tb_tomaz from @neo4j
, this insightful post demonstrates how leveraging a -
Abacus AI Launches Affordable LLM Fine-Tunes Inference API
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@AbacusAI introduces "LLM Fine-Tunes" inference API = Unlocks the power of affordable, Open-Source #LLMs, matches GPT-4’s performance and is 20X cheaper! Start here: https://
abacus.ai/llmapi
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#LLMOps #AI #GenerativeAI #MachineLearning #MLOps #GPT4 #DeepLearning #DataScience -
MinBPE: Minimal Byte Pair Encoding Implementation for LLM Tokenization
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Also, releasing new repository on GitHub: minbpe
Minimal, clean, code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization. https://
github.com/karpathy/minbpe In the video we essentially build minbpe from scratch.
Don't miss the http://
exercise.md to build your -
New Lecture: Building GPT Tokenizer with Byte Pair Encoding
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New (2h13m ) lecture: "Let's build the GPT Tokenizer" Tokenizers are a completely separate stage of the LLM pipeline: they have their own training set, training algorithm (Byte Pair Encoding), and after training implement two functions: encode() from strings to tokens, and
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Tokenization: The Root of LLM Problems and Quirks
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We will see that a lot of weird behaviors and problems of LLMs actually trace back to tokenization. We'll go through a number of these issues, discuss why tokenization is at fault, and why someone out there ideally finds a way to delete this stage entirely.
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RAG Query Translation: Decomposition Approaches From Scratch
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RAG From Scratch: Query Translation (Decomposition) Our RAG From Scratch video series walks through impt RAG concepts in short / focused videos w/ code. This is the third in our videos on Query Translation, focused on approaches for decomposition w/ ideas from Least-to-Most
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Google and its AI: Genius or Failure?
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What is #Google playing at with its AI? Gemini Pro, Gemini Ultra, Advanced… And now Gemini 1.5! Everyone’s calling it genius, but what if it’s a sign of defeat? We break it down in this video → https://youtu.be/K8uk68X7F4I #ArtificialIntelligence #GoogleGemini