v.dundigalla (vyomakesh09)

vyomakesh09

Geek Repo

Location:latent space

Twitter:@VyomDundigalla

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v.dundigalla's repositories

longagent

LONGAGENT: Scaling Language Models to 128k Context through Multi-Agent Collaboration

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notes

my notes

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AGPT

An experimental open-source attempt to make GPT-4 fully autonomous.

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ai-trade

ethtokyo 2023 project

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Ares

AI that looks out for hackathons

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autogen

Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ

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exoplanet-ml

Machine learning models and utilities for exoplanet science.

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hedgehogT

Implementation of hedgehog transformer which is a Linear Attention Mechanism inspired by the concept described in "The Hedgehog & the Porcupine: Expressive Linear Attentions with Softmax Mimicry."

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it-cert-automation-practice

Google IT Automation with Python Professional Certificate - Practice files

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llama2.mojo

Inference Llama 2 in one file of pure 🔥

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moonbase

A brief introduction to Git hub.

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deploys-

pipelines

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git_template

A simple reproducible template to implement AI r

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gpt-2

Code for the paper "Language Models are Unsupervised Multitask Learners"

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gym-pybullet-drones

PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control

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HRTX

Multi-Modal Multi-Embodied Hivemind-like Iteration of RTX-2

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open-interpreter

OpenAI's Code Interpreter in your terminal, running locally

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Python

All Algorithms implemented in Python

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RT-X

Pytorch implementation of the models RT-1-X and RT-2-X from the paper: "Open X-Embodiment: Robotic Learning Datasets and RT-X Models"

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swarms

Build, Deploy, and Scale Reliable Swarms of Autonomous Agents for Workflow Automation. Join our Community: https://discord.gg/DbjBMJTSWD

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template

a template for my projects

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vswarms-cloud

Deploy your autonomous agents to production grade environments with 99% Uptime Guarantee, Infinite Scalability, and self-healing.

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vzeta

Build high-performance AI models with modular building blocks

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webapps

A simple web application where users could choose their own Ml algorithm(logistic regression, support vector classifier and random forest classifier) on mushrooms data set.

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