Build a Large Language Model (From Scratch) This repository contains the code for developing, pretraining, and finetuning a GPT-like LLM and is the official code repository for the book Build a Large Language Model (From Scratch). In Build a Large Language Model (From Scratch), you'll le...
【AI】Deep Dive into LLMs like ChatGPT_2 03:31:24 【AI】The Path To AGI, Deceptive AIs, Building a Virtual Cell 54:58 【AI】OpenAI Deep Research,让普通人一下变强好几倍的的主题研究 Agent 01:23:18 【AI】DeepSeek 的颠覆、冲击、争议和误解 01:20:33 【AI】Reinforcement Learning ...
[2401.05268] AUTOACT: Automatic Agent Learning from Scratch via Self-Planning (arxiv.org) Introduction 当前LLM Agent 在许多复杂任务上取得了相当可观的表现。尽管在这个领域进行了不断的探索,现有的 LLM Agent 系统仍然面临着昂贵的、不可重现的数据依赖性,并且面临将单一模型用于多个功能的挑战。为此,本文引入...
This is code implementation and learning from book:"LLM_from_scratch" - deepkshikha/LLM_from_scratch
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I'm an LLM Research Engineer with over a decade of experience in artificial intelligence. My work bridges academia and industry, with roles including senior staff at an AI company and a statistics professor. My expertise lies in LLM research and the deve
PySACX This repo contains a Pytorch implementation of the SAC-X RL Algorithm [1]. It uses the Lunar Lander v2 environment from OpenAI gym. The SAC-X algorithm enables learning of complex behaviors from scratch in the presence of multiple sparse reward signals. ...
Despite the impressive performance across numerous tasks, Large Language Models (LLMs) often fail in solving simple decision-making tasks due to the misalignment of the knowledge in LLMs with environments. On the contrary, Reinforcement Learning (RL) agents learn policies from scratch, which makes ...
Thus, they can better understand diverse user inputs than training from scratch, improving the user experience. Further, we automate the fine-tuning of an LLM to parse user utterances into the grammar by generating a training dataset of (utterance, parse) pairs. Compared with dataset-generation ...
Once you understand which settings work well, try a more accurate model, such asInception-v3orResNet-50, and see if that improves your results. Size When you deploy to edge devices such as Raspberry Pi®or FPGAs, choose a model with a low memory footprint, such asSqueezeNetorMobileNet-...