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Timothy Pogue's Projects

aiodocker icon aiodocker

Python Docker API client based on asyncio and aiohttp

alpaca.cpp icon alpaca.cpp

Locally run an Instruction-Tuned Chat-Style LLM

autogptq icon autogptq

An easy-to-use model quantization package with user-friendly apis, based on GPTQ algorithm.

chatbot-ui icon chatbot-ui

A ChatGPT clone for running locally in your browser.

coco-dr icon coco-dr

[EMNLP 2022] This is the code repo for our EMNLP‘22 paper "COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning".

dalle-flow icon dalle-flow

A Human-in-the-Loop workflow for creating HD images from text

docarray icon docarray

🧬 Represent, send, and store multimodal data · Neural Search · Vector Search · Document Store

epicbox icon epicbox

Run untrusted code in secure Docker based sandboxes

fauxpilot icon fauxpilot

FauxPilot - an open-source alternative to GitHub Copilot server

freqai icon freqai

An interface that lets users build, test, and deploy custom adaptive machine learning models in freqtrade. Mirrored development on github.com/freqtrade/freqtrade

google-clone icon google-clone

A Google clone using Next JS, Tailwind CSS & Heroicons for the front-end and the Google Programmable Search Engine

haystack icon haystack

:mag: Haystack is an open source NLP framework that leverages Transformer models. It enables developers to implement production-ready neural search, question answering, semantic document search and summarization for a wide range of applications.

langchain icon langchain

⚡ Building applications with LLMs through composability ⚡

llama2.c icon llama2.c

Inference Llama 2 in one file of pure C

localai icon localai

:robot: Self-hosted, community-driven simple local OpenAI-compatible API written in go. Can be used as a drop-in replacement for OpenAI, running on CPU with consumer-grade hardware. Supports ggml compatible models, for instance: LLaMA, alpaca, gpt4all, vicuna, koala, gpt4all-j, cerebras

ray icon ray

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.

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