ahpuchend's repositories
nlp_chinese_corpus
大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP
accelerate
🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
awesome-virtual-try-on
A curated list of awesome research papers, projects, code, dataset, workshops etc. related to virtual try-on.
Chinese-LLaMA-Alpaca-2
中文LLaMA-2 & Alpaca-2大模型二期项目 + 16K超长上下文模型 (Chinese LLaMA-2 & Alpaca-2 LLMs, including 16K long context models)
DB-GPT
Revolutionizing Database Interactions with Private LLM Technology
dify
Dify is an open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production.
Efficient-Computing
Efficient computing methods developed by Huawei Noah's Ark Lab
embedchain
The Open Source RAG framework
FasterTransformer
Transformer related optimization, including BERT, GPT
FlagEmbedding
Retrieval and Retrieval-augmented LLMs
halo
强大易用的开源建站工具。
LLaMA-Factory
Unify Efficient Fine-Tuning of 100+ LLMs
llama.onnx
LLaMa/RWKV onnx models, quantization and testcase
Llama2-Chinese
Llama中文社区，最好的中文Llama大模型，完全开源可商用
llama3-from-scratch
llama3 implementation one matrix multiplication at a time
MaxKB
💬 基于 LLM 大语言模型的知识库问答系统。开箱即用，支持快速嵌入到第三方业务系统，1Panel 官方出品。
modelscope
ModelScope: bring the notion of Model-as-a-Service to life.
peft
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
pytorch-image-models
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Video-LLaMA
[EMNLP 2023 Demo] Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
VQGAN-CLIP
Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.