WangFei-2019

WangFei-2019's Stars

• vnpy/vnpy

  基于Python的开源量化交易平台开发框架

  Language:Python25.3k1.4k2.1k8.7k

• Hannibal046/Awesome-LLM

  Awesome-LLM: a curated list of Large Language Model

  18.3k370241.5k

• HqWu-HITCS/Awesome-Chinese-LLM

  整理开源的中文大语言模型，以规模较小、可私有化部署、训练成本较低的模型为主，包括底座模型，垂直领域微调及应用，数据集与教程等。

  15.5k202261.4k

• BradyFU/Awesome-Multimodal-Large-Language-Models

  :sparkles::sparkles:Latest Advances on Multimodal Large Language Models

  12.3k271115783

• kyrolabs/awesome-langchain

  😎 Awesome list of tools and projects with the awesome LangChain framework

  7.6k14211519

• EleutherAI/lm-evaluation-harness

  A framework for few-shot evaluation of language models.

  Language:Python6.7k391.1k1.8k

• ccfddl/ccf-deadlines

  ⏰ Collaboratively track deadlines of conferences recommended by CCF (Website, Python Cli, Wechat Applet) / If you find it useful, please star this project, thanks~

  Language:Vue6.1k2281430

• steven2358/awesome-generative-ai

  A curated list of modern Generative Artificial Intelligence projects and services

  5.8k14627631

• wgwang/awesome-LLMs-In-China

  **大模型

  5.4k10727445

• OpenBMB/ToolBench

  [ICLR'24 spotlight] An open platform for training, serving, and evaluating large language model for tool learning.

  Language:Python4.8k49291406

• baichuan-inc/Baichuan2

  A series of large language models developed by Baichuan Intelligent Technology

  Language:Python4.1k41394293

• tomgoldstein/loss-landscape

  Code for visualizing the loss landscape of neural nets

  Language:Python2.8k3341397

• Zjh-819/LLMDataHub

  A quick guide (especially) for trending instruction finetuning datasets

  2.5k463165

• Paitesanshi/LLM-Agent-Survey

  2.5k7219150

• TigerResearch/TigerBot

  TigerBot: A multi-language multi-task LLM

  Language:Python2.2k31126194

• MLGroupJLU/LLM-eval-survey

  The official GitHub page for the survey paper "A Survey on Evaluation of Large Language Models".

  1.4k131790

• SimonBlanke/Gradient-Free-Optimizers

  Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

  Language:Python1.2k214684

• horseee/Awesome-Efficient-LLM

  A curated list for Efficient Large Language Models

  Language:Python1.2k42387

• princeton-nlp/MeZO

  [NeurIPS 2023] MeZO: Fine-Tuning Language Models with Just Forward Passes. https://arxiv.org/abs/2305.17333

  Language:Python1k193862

• facebookresearch/ToMe

  A method to increase the speed and lower the memory footprint of existing vision transformers.

  Language:Python9551093968

• pengxiao-song/awesome-chinese-legal-resources

  📝 An Awesome Collection of Chinese Legal Dataset and Relevant Resources. 致力于收集全面的中文法律数据源

  7566870

• hkust-nlp/deita

  Deita: Data-Efficient Instruction Tuning for Alignment [ICLR2024]

  Language:Python48262727

• Shuai-Xie/genetic-algorithm

  遗传算法 - Matlab

  Language:MATLAB41491139

• marcellodebernardi/loss-landscapes

  Approximating neural network loss landscapes in low-dimensional parameter subspaces for PyTorch

  Language:Python2973652

• Atomic-man007/Awesome_Multimodel_LLM

  Awesome_Multimodel is a curated GitHub repository that provides a comprehensive collection of resources for Multimodal Large Language Models (MLLM). It covers datasets, tuning techniques, in-context learning, visual reasoning, foundational models, and more. Stay updated with the latest advancement.

  2608116

• ZO-Bench/ZO-LLM

  [ICML 2024] Official code for the paper "Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark ".

  Language:Python704116

• WangFei-2019/Image-text-Retrieval

  Language:Python436

• ylsung/ECoFLaP

  Code for "ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models" (ICLR 2024)

  Language:Jupyter Notebook16281

• WangFei-2019/SNARE

  Project for SNARE benchmark

  Language:Python10220

• alphadl/DeepLearning-500-questions

  深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06

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