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yilinw92's Projects

examples icon examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

get-things-done-with-prompt-engineering-and-langchain icon get-things-done-with-prompt-engineering-and-langchain

LangChain & Prompt Engineering tutorials on Large Language Models (LLMs) such as ChatGPT with custom data. Jupyter notebooks on loading and indexing data, creating prompt templates, CSV agents, and using retrieval QA chains to query the custom data. Projects for using a private LLM (Llama 2) for chat with PDF files, tweets sentiment analysis.

gradio_rag icon gradio_rag

Code and resources showcasing the Retrieval-Augmented Generation (RAG) technique, a solution for enhancing data freshness in Large Language Models (LLMs). Incorporate up-to-date external knowledge into LLM-generated responses. Additionally, this repository includes a Gradio-based user interface for seamless model deployment.

gru-d icon gru-d

inspired by 'Recurrent Neural Networks for Multivariate Time Series with Missing Values' pytorch ver

hello-algo icon hello-algo

《Hello 算法》一本动画图解、能运行、可提问的数据结构与算法入门书

highway-env icon highway-env

A minimalist environment for decision-making in autonomous driving

hts-audio-transformer icon hts-audio-transformer

The official code repo of "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection"

ml-audio-start icon ml-audio-start

Suggestions for those interested in developing audio applications of machine learning

models icon models

Models and examples built with TensorFlow

passt icon passt

Efficient Training of Audio Transformers with Patchout

peft icon peft

🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.

pytorch-image-models icon pytorch-image-models

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

tuning_playbook icon tuning_playbook

A playbook for systematically maximizing the performance of deep learning models.

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