#model fine-tuning

LazyLLM is a low-code development tool streamlining the creation of multi-agent AI applications. It facilitates a gradual enhancement process through prototype development, data feedback, and iterative optimization. Key features include easy AI application assembly, single-click deployment, cross-platform adaptability, efficient model fine-tuning, and advanced parameter searching. It supports seamless model integration, offering uniform user experiences whether using local or cloud resources. This tool eases the complexity of AI application development, aiding both novices and seasoned developers in focusing on algorithm innovation without tedious engineering burdens.

The flan-alpaca project offers improved problem-solving through the fine-tuning of Vicuna-13B on the Flan dataset, as well as demonstrating FLAN-T5's capability in text-to-audio generation. This approach extends Stanford Alpaca's instruction tuning to various models, with all pre-trained models accessible via HuggingFace. It includes practical tools for interactive demo, benchmarking, data preprocessing, training, and efficient integration. This objective overview highlights the project's focus on accessible, high-performance language model tuning.

Learn to efficiently use Llama 3.1 and 3.2 models, covering everything from installation to customization with datasets. This repository provides insights into local inference, model fine-tuning, and performance optimization, including advanced methods like synthetic data generation and chatbot creation. Benefit from techniques like assisted decoding for enhanced text generation speed. Discover API integration for large models, protection strategies using Llama Guard and Prompt Guard, and RAG pipelines for effective deployment. Designed for both newcomers and experienced machine learning enthusiasts.

The project aims to enhance the Whisper speech recognition model through Lora fine-tuning, accommodating diverse training contexts such as timestamped and non-timestamped data. It also accelerates inference using CTranslate2 and GGML. Capable of recognizing and translating 98 languages into English, the Whisper model is deployable on Windows, Android, and servers, adaptable to both original and fine-tuned model versions. Discover how models from whisper-tiny to whisper-large-v3 help minimize word error rates across different datasets and utilize available tools for effortless integration into diverse applications.