The neural network that encodes the LLM has no canonical form, its structure depends on the architecture, the training data, and the order in which it is processed. It is still very difficult to extract a semantically interpretable representation from an LLM.
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6x Performance Speedup Achievement Technical Optimization
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got around a 6x speedup today by doing this just once. my results speak for themselves
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GitLab Uses Claude for Trustworthy Code Generation
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DevOps leader @GitLab uses Claude for consistent and accurate code that its developer community can trust. “Claude is compatible with GitLab’s principles of transparency and privacy by design and provides a high-integrity foundation for code generation.” Learn more below.
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Fine-tuning Chat Models with Cohere Python SDK
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Learn about fine-tuning a model for Chat to improve its performance at a specific task. This article includes practical code examples using Cohere's Python SDK. Learn more about it now.
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Poor Man’s Profiler: Manual Code Performance Debugging Technique
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when my code is getting too slow, i just run it a couple times and ctrl-C on the slow part, see where in my code it stops me. i call it the poor man's profiler
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AlphaGeometry Solves IMO 2015 Problem with Neural Assistance
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In case you wonder how an #AlphaGeometry solution looks like, check out the full solution (109 step!) here. In this IMO 2015, problem #3, the symbolic component asked for help from the neural language models 3 times (the auxiliary constructions in blue) before succeeding 🙂
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Fine-tune Zephyr-7B for Customer Service Intent Detection
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ICYMI: We recently released a new #LLM tutorial for #finetuning open-source #Zephyr-7B to determine the intent of customer service tickets. Sample data, code and notebooks included. Check it out to get started! https://
pbase.ai/3vCIdr3 -
Flow Engineering Boosts Code Generation Performance Significantly
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Prompt engineering (or rather "Flow engineering") intensifies for code generation. Great reading and a reminder of how much alpha there is (pass@5 19% to 44%) in moving from a naive prompt:answer paradigm to a "flow" paradigm, where the answer is constructed iteratively.
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Foundations of Vector Retrieval: Essential Deep Learning Techniques
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Foundations of Vector Retrieval Sebastian Bruch : https://
arxiv.org/abs/2401.09350 #ArtificialIntelligence #DeepLearning #MachineLearning -
AI Code Generation Integrity: Ensuring Logical Correctness
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I found this amazing. Code Integrity+Code generation is what we need at this moment. Fed up of having AI tools generating code that is not logically correct or not passes test cases. Future is amazing.