Marc Love (marclove)

marclove

Geek Repo

Company:@carbonfive

Location:New York, NY

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Marc Love's repositories

soothsayer

A Ruby client for Google's Prediction API

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dl-clip

CLI tool to download and clip videos for social media

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elixir-google-api

Elixir client libraries for accessing Google APIs.

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quiet-star

Code for Quiet-STaR

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AlpacaDataCleaned

Alpaca dataset from Stanford, cleaned and curated

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alpagasus

Unofficial implementation of AlpaGasus

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bert.cpp

ggml implementation of BERT

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calibrate-before-use

Few-shot Learning of GPT-3

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cog-demofusion-enhance

Cog wrapper for DemoFusion img2img enhancer

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dspy

DSPy: The framework for programming—not prompting—foundation models

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Fooocus

Focus on prompting and generating

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gitignore

A collection of useful .gitignore templates

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intercode

Code repository for InterCode benchmark https://arxiv.org/abs/2306.14898

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jsdom

A JavaScript implementation of various web standards, for use with Node.js

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language-elixir

Elixir language support for the Atom editor.

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nui

Style iOS apps with a stylesheet, similar to CSS

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openstates

Ruby wrapper for the Sunlight OpenStates API

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oss-text-generation-inference

Large Language Model Text Generation Inference

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Prompt-Engineering-Guide

:octopus: Guide and resources for prompt engineering

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qlora

QLoRA: Efficient Finetuning of Quantized LLMs

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quick-start-connectors

This open-source repository offers reference code for integrating workplace datastores with Cohere's LLMs, enabling developers and businesses to perform seamless retrieval-augmented generation (RAG) on their own data.

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raygun

Rails application generator that builds out an application with the common customization stuff already done.

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raygun-rails

Rails 7 application template for Raygun, the Carbon Five Rails application generator.

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razor

Phoenix application generator that builds applications with the common C5 customization stuff already done.

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reveal.js

The HTML Presentation Framework

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SnapKit

A Swift Autolayout DSL for iOS & OS X

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tuning_playbook

A playbook for systematically maximizing the performance of deep learning models.

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