Introduction to Mathematical Optimisation in Python
by @zluvsand @Medium Read more: https://
buff.ly/46ZquaB #DataScience #BigData #ArtificialIntelligence #MI cc: @pascal_bornet @yvesmulkers @kuriharan
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Introduction to Mathematical Optimisation in Python
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Claude 3 Automates Code Testing and Documentation Tasks
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This is probably the most interesting Claude 3 transcript – I pipe in my entire codebase, prompt Claude to "fill out the test_files_to_prompt.py with tests", feed it a test failure for it to fix, then tell it to "Fill out the Usage section of the README"
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Files-to-Prompt Tool: Building with Claude 3 Opus
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I released a new tool called files-to-prompt, which concatenates together files and directories to help pipe them into an LLM I built it almost entirely through prompting Claude 3 Opus. Here's a detailed write-up of how I did that and what I learned:
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Forward/Backward Implementation Complete, Now Optimizing with CUDA
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Once you have the forward/backward, the rest of it (data loader, Adam update, etc) are mostly trivial. The real fun starts now though: I am now porting this to CUDA layer by layer so that it can be made efficient, perhaps even coming within reasonable fraction of PyTorch, but
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Memory Allocation Strategy in LLM Training Implementation
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You can look at the raw training implementation here: https://
github.com/karpathy/llm.c
/blob/master/train_gpt2.c
… You'll see that we allocate all the required memory a single time in the beginning in one large block of 1D memory. From there on during training, no memory gets created or destroyed, so we stay at -
Implementing Neural Network Layers with Memory Pointer Management
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Once you have all the layers, you just string all it all together. Not gonna lie, this was quite tedious and masochistic to write because you have to make sure all the pointers and tensor offsets are correctly arranged. Left: we allocate a single 1D array of memory and then
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llm.c: Train GPT-2 in Pure C Without Heavy Dependencies
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Have you ever wanted to train LLMs in pure C without 245MB of PyTorch and 107MB of cPython? No? Well now you can! With llm.c: https://
github.com/karpathy/llm.c To start, implements GPT-2 training on CPU/fp32 in only ~1,000 lines of clean code. It compiles and runs instantly, and exactly -
OpenRouter Implementation of AI-Oracle Tool Released
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Thanks to @abdiisan for implementing an OpenRouter version of `ai-oracle`! https://
x.com/abdiisan/statu
/abdiisan/status/1777423304086753725
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Using Cursor AI to Write JSON Mode Unit Tests
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also today i learned that you can just use @cursor_ai to write JSON mode unit tests
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Serverless ChatGPT RAG with LangChain.js and Azure
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🚀Serverless ChatGPT with RAG using LangChain.js
— LangChain (@LangChain) 8 avril 2024
Great OSS repo that shows how to build a serverless ChatGPT-like experience with Retrieval-Augmented Generation using LangChain.js and Azurehttps://t.co/Bh95rgu7Bb pic.twitter.com/UlLf38DKrbServerless ChatGPT with RAG using LangChain.js Great OSS repo that shows how to build a serverless ChatGPT-like experience with Retrieval-Augmented Generation using LangChain.js and Azure https://
github.com/Azure-Samples/
serverless-chat-langchainjs?tab=readme-ov-file
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