2023年3月13日 · This is the repo for the Stanford Alpaca project, which aims to build and share an instruction-following LLaMA model. The repo contains: The 52K data used for fine-tuning the model.

This is the repo for the Stanford Alpaca project, which aims to build and share an instruction-following LLaMA model. The repo contains: The 52K data used for fine-tuning the model

This is the repo for the Stanford Alpaca project, which aims to build and share an instruction-following LLaMA model. The repo contains: The 52K data used for fine-tuning the model.

This is the repo for the Stanford Alpaca project, which aims to build and share an instruction-following LLaMA model. The repo contains: A web demo to interact with our Alpaca model; The 52K data used for fine-tuning the model; The code for generating the data

Code and documentation to train Stanford's Alpaca models, and generate the data. - tatsu-lab/stanford_alpaca

This is the repo for the Stanford Alpaca project, which aims to build and share an instruction-following LLaMA model. The repo contains: A web demo to interact with our Alpaca model; The 52K data used for fine-tuning the model; The code for generating the data

2023年3月22日 · This is the repo for the Stanford Alpaca project, which aims to build and share an instruction-following LLaMA model. The repo contains: A web demo to interact with our Alpaca model; The 52K data used for fine-tuning the model; The code for generating the data; The code for fine-tuning the model

Code for "Meta-Learning Priors for Efficient Online Bayesian Regression" by James Harrison, Apoorva Sharma, and Marco Pavone - StanfordASL/ALPaCA

2023年3月13日 · This is the repo for the Stanford Alpaca project, which aims to build and share an instruction-following LLaMA model. The repo contains: A web demo to interact with our Alpaca model; The 52K data used for fine-tuning the model; The code for generating the data

The aim of Efficient Alpaca is to utilize LLaMA to build and enhance the LLM-based chatbots, including but not limited to reducing resource consumption (GPU memory or training time), improving inference speed, and more facilitating researchers' use (especially for fairseq users). This project will be constantly updated and maintained.