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aca4nmt

Code of a novel model for NMT

auto-round

SOTA Weight-only Quantization Algorithm for LLMs

autoawq

AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference.

autogptq

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

axolotl

Go ahead and axolotl questions

bert-pytorch

Google AI 2018 BERT pytorch implementation

blip

PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

caffe

chinese-clip

Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.

ecosystem-graphs

elevater_toolkit_ic

Toolkit for Elevater Benchmark

justinlin610

justinlin610.github.io

kobe

Towards Knowledge-Based Personalized Product Description Generation in E-commerce

llama-factory

Easy-to-use LLM fine-tuning framework (LLaMA, BLOOM, Mistral, Baichuan, Qwen, ChatGLM)

llama.cpp

LLM inference in C/C++

mlx-examples

Examples in the MLX framework

ofa

Official repository of OFA. Paper: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

opendevin

🐚 OpenDevin: Code Less, Make More

python_for_linguists

Python for Linguists – a Gentle Introduction to Programming

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

qwenlm.github.io

sglang

SGLang is a structured generation language designed for large language models (LLMs). It makes your interaction with models faster and more controllable.

su4mlc

Code for the article "Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification" (EMNLP 2018)

swe-bench

Enhanced fork of SWE-bench, tailored for OpenDevin's ecosystem.

transformers

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

unilm

UniLM AI - Large-scale Self-supervised Pre-training across Tasks, Languages, and Modalities

vllm

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