Full RAG Application Code to Chat with GitHub Repo using Llama-3
LLMS
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Configure Embedchain App with Llama-3 and Chroma Database
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3. Configure the Embedchain App For this application we will use Llama-3, you can choose from OpenAI, cohere, anthropic or any other LLM of your choice. Select the vector database as the opensource chroma db (you are free to choose any other vector database of your choice)
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Build Local LLM RAG App Chat GitHub Llama-3
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Build a LLM app with RAG to chat with GitHub using Llama-3 running locally on your computer (100% free and without internet):
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Data Decontamination and Generalization in Advanced Math Benchmarks
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That's a valid point. The team tried hard to decontaminate the data. Also note that there's generalization into more challenging benchmarks such as HiddenMath and IMO-Bench.
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Slack LLM training on user data raises privacy concerns
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Imagine paying $120,000/year for years so then Slack can train an LLM on your community chat's DMs Not today P.S. I already switched Nomad List to Telegram http://
t.me/nomadlist a year ago -
Gemini 1.5 Pro Achieves 90% MATH Benchmark Milestone
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Also astonishing to see how fast the field has advanced: Gemini 1.5 Pro was first to surpass the 90% mark on MATH (6.9% 3 years ago). For comparison, on ImageNet, it took us close to 10 years to achieve the same from AlexNet (40%) to Meta Pseudo Labels (90.2%, our work
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Comparing Claude 1.5 Pro Flash and Pro Performance Benchmarks
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It's good but not as good as 1.5 Pro, which is expected since it's in a different size category. You can get a good sense by looking at the 1.5 Pro vs. 1.0 Pro column here and comparing that with the 1.5 Flash versus 1.0 Pro column.
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11 ChatGPT prompts to increase sales and customer acquisition
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If you are not using ChatGPT, you're living under a rock. Here're 11 ChatGPT prompts that can get you 10x more sales and customers. [Bookmark this for later]
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Blended AI Systems: Reduced Computational Requirements for Efficient Inference
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3. Efficient Inference: Blended systems require significantly less computational power. Each response is generated by a single model, maintaining the speed and efficiency of smaller models.
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Blending Smaller AI Models for Cost-Effective Performance
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Blending smaller AI model enables: 1. Cost-Effective Performance: Combining three mid-sized models (6B/13B parameters) can rival or surpass the capabilities of a single large model without the hefty computational demands.