Niklas Muennighoff (Muennighoff)

Muennighoff

Geek Repo

Company:Peking University

Location:Beijing

Home Page:muennighoff.github.io

Twitter:@Muennighoff

Github PK Tool:Github PK Tool

Niklas Muennighoff's repositories

sgpt

SGPT: GPT Sentence Embeddings for Semantic Search

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vilio

🥶Vilio: State-of-the-art VL models in PyTorch & PaddlePaddle

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FLAN

Provides a minimal implementation to extract FLAN datasets for further processing

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ytclipcc

Create Captions for any YouTube Clip using Wav2Vec2

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promptsource

Toolkit for creating, sharing and using natural language prompts.

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matrixshapes

Language modelling task to infer shapes of matrices - One of the most difficult tasks for models like GPT-3, GPT-J

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vllm

A high-throughput and memory-efficient inference and serving engine for LLMs

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alpaca_eval

An automatic evaluator for instruction-following language models. Human-validated, high-quality, cheap, and fast.

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bigcode-evaluation-harness

A framework for the evaluation of autoregressive code generation language models.

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CS231n

CS231n at Stanford University

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CS50

Computer Science Course taught at Harvard

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CS50xAI

AI-specialization of CS50

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FastChat

An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.

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FlagEmbedding

Open-source Embeddings

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gritlm

Generative Representational Instruction Tuning

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licensed-pile

Repo to hold code and track issues for the collection of permissively licensed data

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lm-evaluation-harness

A framework for few-shot evaluation of autoregressive language models.

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Megatron-DeepSpeed

Ongoing research training transformer language models at scale, including: BERT & GPT-2

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mteb

Massive Text Embedding Benchmark - Internal Development Git

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open_lm

A repository for research on medium sized language models.

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OpenDevin

🐚 OpenDevin: Code Less, Make More

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prompt_semantics

This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?”

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t-zero

Reproduce results and replicate training fo T0 (Multitask Prompted Training Enables Zero-Shot Task Generalization)

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transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

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udacity-dl

Udacity Deep-Learning Nanodegree 2020

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