pipeline() will soon be Turing Complete, programmable by kwargs
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DeepSeek-Coder-V2-Instruct Outperforms GPT-4o on Coding Tasks
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DeepSeek-Coder-V2-Instruct is the open-weights model that beat GPT-4o at coding tasks. Try the 236B model on Replicate today
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Building with Claude Sonnet 3.5 Initial Prompt Iteration
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The idea morphed a bit as I was building, but here’s the initial prompt on Claude Sonnet 3.5 with no special system message
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Keras Models Direct Save to Hugging Face Hub Integration
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Further, in the next Keras version we're making it possible to save *any* Keras model to Hugging Face Hub directly, via the built-in model `.save()` method (and likewise for model loading!)
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KerasNLP now integrates with Hugging Face Hub models
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You can now use any Hugging Face Hub model with KerasNLP (as long as the corresponding architecture is in KerasNLP)! What's more, you can also upload your own fine-tuned KerasNLP models to Hugging Face in one line.
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Mastering AI-Powered Quick Prototyping Process
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Thanks! I feel I’ve mastered the AI powered “quick prototype” process.
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Claude Sonnet 3.5 Builds Full App in One Hour
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Built entirely using Claude Sonnet 3.5, hosted on Replit. Probably less than hour from start to finish. 52 lines of Python, 102 lines of html, 182 lines of css. Uses local storage, no user accounts, won’t scale and will break with heavy traffic.
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Claude 3 Haiku Fine-tuning Achieves 99.6% Classification Accuracy
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In testing, we tuned Haiku to act as a moderator on an example dataset. Fine-tuning improved classification accuracy from 81.5% to 99.6% and reduced prompt tokens per query by 89%. Fine-tuning for Claude 3 Haiku is available today in preview in Amazon Bedrock.
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RAG Optimization with MongoDB Vector Search Course
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Learn to optimize RAG for cost and performance in our new short course, Prompt Compression and Query Optimization, created with @MongoDB and taught by @richmondalake.
— Andrew Ng (@AndrewYNg) 10 juillet 2024
This course teaches you to combine traditional database capabilities with vector search using MongoDB for RAG.… pic.twitter.com/jHOXsEbG66Learn to optimize RAG for cost and performance in our new short course, Prompt Compression and Query Optimization, created with @MongoDB and taught by @richmondalake
. This course teaches you to combine traditional database capabilities with vector search using MongoDB for RAG.