A Github Gist containing a Python script for text classification using the TxTail API
AI agent helping write and fix code, running tests and iterating till code passes tests or matches designs. Uses OpenAI API and aims to make coding easier.
Micro Agent is an AI agent that assists with coding, helping with code generation and iteration processes. It's a focused agent that aims to write code based on provided test cases or design screenshots. It can work in tandem with OpenAI and Anthropic APIs for better visual matching. The agent is designed with a specific focus - creating a clear test case and providing feedback on code that helps improve the generated code. Installation requires Node.js v14 or later, and it can be installed globally using npm. To get started, running the agent in interactive mode is recommended. Micro Agent can work in both unit test matching mode and visual matching mode for coding assistance. It uses a multi-agent approach and connects with Figma for high fidelity design-to-code conversions. Configuration options are available via CLI or UI.
Micro Agent is an AI agent that assists with coding, helping with code generation and iteration processes. It's a focused agent that aims to write code based on provided test cases or design screenshots. It can work in tandem with OpenAI and Anthropic APIs for better visual matching. The agent is designed with a specific focus - creating a clear test case and providing feedback on code that helps improve the generated code. Installation requires Node.js v14 or later, and it can be installed globally using npm. To get started, running the agent in interactive mode is recommended. Micro Agent can work in both unit test matching mode and visual matching mode for coding assistance. It uses a multi-agent approach and connects with Figma for high fidelity design-to-code conversions. Configuration options are available via CLI or UI.
A light-weight codebase that enables memory-efficient and performant finetuning of Mistral's models. It is based on LoRA, a training paradigm where most weights are frozen and only 1-2% additional weights in the form of low-rank matrix perturbations are trained.
This GitHub repository contains a course on Large Language Models (LLMs) with roadmaps and Colab notebooks. The course is divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. Each part covers various topics, including mathematics, Python, neural networks, instruction datasets, pre-training, supervised fine-tuning, reinforcement learning from human feedback, evaluation, quantization, new trends, running LLMs, building a vector storage, retrieval augmented generation, advanced RAG, inference optimization, and deployment.
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