Yan's repositories
GNNs-for-Node-Classification
Some GNNs are implemented using PyG for node classification tasks, including: GCN, GraphSAGE, SGC, GAT, R-GCN and HAN (Heterogeneous Graph Attention Network), which will continue to be updated in the future.
CS224W-Colab
Solutions for CS224W Winter 2021 Colab
FF-G2M
Code for AAAI 2023 (Oral) paper "Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting it into MLPs: An Effective GNN-to-MLP Distillation Framework"
Grace
The implementation of "Grace: Graph Self-Distillation and Completion to Mitigate Degree-Related Biases"
tttlm
Test-time-training on nearest neighbors for large language models
Scikit-LLM-Text-Classification-Streamlit-App
Scikit LLM Text Classification Streamlit App repository from AI Anytime YouTube channel.
gdsr
⚖️ A Graph-augmented Dense Statute Retriever. (EACL 2023)
gpt4free
decentralising the Ai Industry, just some language model api's...
embeddings
Embeddings: State-of-the-art Text Representations for Natural Language Processing tasks, an initial version of library focus on the Polish Language
LLM-Text-Classification
A set of Jupyter Notebooks for training and evaluating large-language model-based linguistic classification
GRAD
[ECML-PKDD 2023] Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
flair
A very simple framework for state-of-the-art Natural Language Processing (NLP)
InPars
Inquisitive Parrots for Search
NLP_Disaster_Tweets_Classification
Using a distilled version of a LLM to obtain the hidden states of tweet texts, then training a classifier head on top to distinguish tweets talking about natural disasters fron non-disasters.
pyllama
LLaMA: Open and Efficient Foundation Language Models
ColossalAI
Making large AI models cheaper, faster and more accessible
DPR
Dense Passage Retriever - is a set of tools and models for open domain Q&A task.
LLM-Adapters
LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models
LLaMA-Adapter
Fine-tuning LLaMA to follow instructions within 1 Hour and 1.2M Parameters
peft
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
TeSLA
[CVPR 2023] TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation
Transformers-for-NLP-2nd-Edition
Transformer models from BERT to GPT-4, environments from Hugging Face to OpenAI. Fine-tuning, training, and prompt engineering examples. A bonus section with ChatGPT, GPT-3.5-turbo, GPT-4, and DALL-E including jump starting GPT-4, speech-to-text, text-to-speech, text to image generation with DALL-E and more
Promptify
Prompt Engineering | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research
self-instruct
Aligning pretrained language models with instruction data generated by themselves.
EasyInstruct
An easy-to-use framework to instruct Large Language Models.