Michael Goin (mgoin)

mgoin

Geek Repo

Company:@neuralmagic

Location:Boston

Home Page:https://www.linkedin.com/in/michael-goin/

Twitter:@mgoin_

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neuralmagic

Michael Goin's repositories

learned_indexes

Experiments on ideas proposed in Tim Kraska's "The Case for Learned Index Structures"

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MPT-Medical-Chatbot

This is a medical bot built using MPT and Sentence Transformers. The bot is powered by DeepSparse, Langchain, and Chainlit. The bot runs on a decent CPU machine with a minimum of 16GB of RAM.

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TTS

🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production

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advos

RISC-V OS in Rust with hardware support for SiFive's HiFive1 board

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torch_bitmask

Implementations for fast bitmask compression for weight sparsity in PyTorch

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AutoGPTQ

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

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BitBLAS

BitBLAS is a library to support mixed-precision matrix multiplications, especially for quantized LLM deployment.

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clip-retrieval

Easily compute clip embeddings and build a clip retrieval system with them

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dev_env

Holds dotfiles, scripts, and notes to quickly construct my preferred development environment.

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flash-attention

Fast and memory-efficient exact attention

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huggingface.js

Utilities to use the Hugging Face Hub API

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inference

Reference implementations of MLPerf™ inference benchmarks

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langchain

⚡ Building applications with LLMs through composability ⚡

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llama-cpp-python

Python bindings for llama.cpp

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llmperf

LLMPerf is a library for validating and benchmarking LLMs

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lm-evaluation-harness

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

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mteb

MTEB: Massive Text Embedding Benchmark

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optimum

🏎️ Accelerate training and inference of 🤗 Transformers with easy to use hardware optimization tools

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rol

Game of Life implemented in Rust

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sparsegpt

Code for the ICML 2023 paper "SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot".

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transformers

🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.

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vllm

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

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