nlpxucan/WizardLM: WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions
WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions - nlpxucan/WizardLM: WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions
Realistically, we should all be preparing for a world where AI is not trustworthy. Because AI tools can be so incredibly useful, they will increasingly pervade our lives, whether we trust them or not. Being a digital citizen of the next quarter of the twenty-first century will require learning the basic ins and outs of LLMs so that you can assess their risks and limitations for a given use case. This will better prepare you to take advantage of AI tools, rather than be taken advantage by them.
Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs
Introducing MPT-7B, the latest entry in our MosaicML Foundation Series. MPT-7B is a transformer trained from scratch on 1T tokens of text and code. It is open source, available for commercial use, and matches the quality of LLaMA-7B. MPT-7B was trained on the MosaicML platform in 9.5 days with zero human intervention at a cost of ~$200k. Starting today, you can train, finetune, and deploy your own private MPT models, either starting from one of our checkpoints or training from scratch. For inspiration, we are also releasing three finetuned models in addition to the base MPT-7B: MPT-7B-Instruct, MPT-7B-Chat, and MPT-7B-StoryWriter-65k+, the last of which uses a context length of 65k tokens!
LangChain Retrieval QA Over Multiple Files with ChromaDB
Colab: https://colab.research.google.com/drive/1gyGZn_LZNrYXYXa-pltFExbptIe7DAPe?usp=sharing
In this video I look at how to load multiple docs into a single Vectors Store retriever and then do QA over all the docs and return their source info along with answers.
Imagine a bunch of product managers sitting in a sprint planning meeting where, after signing off on the tasks to be done this sprint and starting the sprint, ChatGPT was deployed on those tasks.
vasilecampeanu/obsidian-weaver: Weaver is a useful Obsidian plugin that integrates ChatGPT/GPT-3 into your note-taking workflow. This plugin makes it easy to access AI-generated suggestions and insights within Obsidian, helping you improve your writing and brainstorming process.
Weaver is a useful Obsidian plugin that integrates ChatGPT/GPT-3 into your note-taking workflow. This plugin makes it easy to access AI-generated suggestions and insights within Obsidian, helping y...
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Poe lets you ask questions, get instant answers, and have back-and-forth conversations with AI. Gives access to GPT-4, gpt-3.5-turbo, Claude from Anthropic, and a variety of other bots. Poe includes both free and subscription bots. Payments for subscriptions will be charged to your Apple ID at conf…
Colab LaMini-LM Neo 1.3B: https://colab.research.google.com/drive/1JkbeqGDp_UIi12lv0CltJhFyzIqvY_1N?usp=sharing
Colab LaMini-LM GPT1.5B: https://colab.research.google.com/drive/1wAfYXYECNIeub0hS05c7BRTY2a26qd53?usp=sharing
Colab LaMini-Flan-T5-783M: https://colab.research.google.com/drive/1fJrwbqYFQa1wJ3xJelZ9gjTxcDnCqumb?usp=sharing
Github: https://github.com/mbzuai-nlp/LaMini-LM
Dataset: https://huggingface.co/datasets/MBZUAI/LaMini-instruction/
Paper: https://arxiv.org/abs/2304.14402
In this video I go through the paper LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions and examine how they created the dataset and models for this paper and project.
00:00 Intro
00:45 Key Idea
01:23 Diagram
01:45 Dataset
02:10 Hugging Face Dataset
02:27 Trained on a lot of Models
03:05 Paper
04:36 Prompts on ChatGPT
09:37 Code Time
Omni is a contextual real-time information retrieval system to augment intellectual work with the most relevant information across all websites, all books, all scientific literature, and all personal writing.
LangChain has become a tremendously popular toolkit for building a wide range of LLM-powered applications, including chat, Q&A and document search. In this blogpost I re-implement some of the novel LangChain functionality as a learning exercise, looking at the low-level prompts it uses to create these higher level capabilities.
Fixing LLM Hallucinations with Retrieval Augmentation in LangChain #6
Large Language Models (LLMs) have a data freshness problem. Even some of the most powerful models, like ChatGPT's gpt-3.5-turbo and GPT-4, have no idea about...
GitHub - 1rgs/jsonformer: A Bulletproof Way to Generate Structured JSON from Language Models
A Bulletproof Way to Generate Structured JSON from Language Models - GitHub - 1rgs/jsonformer: A Bulletproof Way to Generate Structured JSON from Language Models
Improving Document Retrieval with Contextual Compression
Note: This post assumes some familiarity with LangChain and is moderately technical.
💡 TL;DR: We’ve introduced a new abstraction and a new document Retriever to facilitate the post-processing of retrieved documents. Specifically, the new abstraction makes it easy to take a set of retrieved documents and extract from them