# LLaMA

LLaSA_training is a speech synthesis training project based on LLaMA, aimed at enhancing the efficiency and performance of speech synthesis models by optimizing training and inference computational resources. This project leverages both open-source datasets and proprietary datasets for training, supports various configurations and training methods, and offers high flexibility and scalability. Its main advantages include efficient data processing capabilities, strong speech synthesis effects, and support for multiple languages. This project is suitable for researchers and developers in need of high-performance speech synthesis solutions, applicable to the development of intelligent voice assistants, speech broadcasting systems, and other scenarios.

Vicuna is an open-source chat robot trained by fine-tuning LLaMA on user-shared conversations. Initial evaluations using GPT-4 as a judge showed that Vicuna-13B achieved 90% of the quality of OpenAI ChatGPT and Google Bard in over 90% of cases, and outperformed other models like LLaMA and Stanford Alpaca in over 90% of cases. The training cost of Vicuna-13B is approximately $300. The code, model weights, and online demo are available for non-commercial use.

LlamaChat is a desktop client that allows you to chat with LLaMA, Alpaca, and GPT4All models. Alpaca is a 7B parameter LLaMA model fine-tuned by Stanford University based on OpenAI's text-davinci-003 data. You can import both raw PyTorch model checkpoint files or pre-converted .ggml model files with LlamaChat. LlamaChat is completely open-source and free, always maintaining transparency. You can find more information on GitHub.