#finetuning

Offers an inclusive toolbox for efficient finetuning of large-scale machine learning models, accessible to the community while supporting diverse optimizers, conversation templates such as Llama-3 and Phi-3, and advanced techniques like speculative decoding and LISA for memory-efficient training. Recognized with the Best Demo Paper Award at NAACL 2024, it provides essential tools for chatbot deployment and model evaluation, suited for professionals aiming to enhance and deploy large models effectively in an objective and unbiased manner.

Torchtune supports the creation, finetuning, and experimentation with large language models (LLMs) like Llama, Gemma, and Mistral through PyTorch. It offers training recipes that improve memory efficiency and performance scaling. YAML configurations and diverse dataset support simplify model management. Integration with libraries such as Hugging Face ensures efficient collaboration and logging. The latest update includes support for Llama 3.2 Vision models, enhancing both vision and text processing.

Investigate the tuning of language models with leading-edge approaches on publicly accessible datasets. This project provides a unified codebase for training and assessing, featuring modern enhancements like LoRA, QLoRA, and efficient parameter updates. Find further insights and advancements through related research publications. The repository contains datasets, evaluation scripts for key benchmarks, and offers models such as Tülu tailored to diverse datasets, facilitating improved language model outcomes. Engage in fine-tuning for instruction adherence, employing advanced practices and reliable evaluation techniques.

This comprehensive guide covers the entire process of building a GPT-like large language model, starting from coding the basics. It provides step-by-step instructions with clear explanations and examples, making it a valuable resource for understanding model development, pretraining, and finetuning techniques. The guide parallels techniques used in technologies like ChatGPT and includes information on loading and refining larger pre-trained models. Access the official code repository for updates and additional resources.

nanoGPT is a simple and fast repository for training and fine-tuning medium-sized GPT models. As a rewrite of minGPT, it emphasizes simple code for easy adaptation, allowing for both new model training and fine-tuning of pre-trained checkpoints. By leveraging popular frameworks such as PyTorch and Hugging Face Transformers, nanoGPT supports training on a range of hardware from advanced GPUs to basic computers, showcasing versatility in reproducing GPT-2 results with OpenWebText.

Explore how slowllama facilitates fine-tuning of Llama2 and CodeLLama models on Apple M1/M2 and nVidia GPUs without quantization. Learn about SSD and RAM offloading for efficient model management, focused exclusively on fine-tuning using LoRA, ensuring effective parameter updates on consumer-grade hardware. Review experimental results to understand GPU and memory optimization for large model fine-tuning.

Unsloth facilitates faster and memory-efficient finetuning for models such as Llama 3.2 and Mistral Small 22B, operating up to five times quicker. The tool offers support for various model versions with accessible Colab and Kaggle notebooks. Models can be finetuned, exported, or uploaded to platforms like Hugging Face without needing specialized hardware. Unsloth's open-source nature enhances finetuning efficiency and serves as a valuable resource, with comprehensive documentation and installation guides available for optimizing usage.

This project provides a streamlined implementation of Llama 3.1 with minimal dependencies, simplifying training, finetuning, and inference. Unlike Meta's official release, this version focuses on the 8B base model, minimizing complexity and dependencies. It offers early finetuning on the Tiny Stories dataset and avenues for future enhancements, making it suitable for developers seeking a simplified Llama 3.1 application.

Llama2-Code-Interpreter enables efficient code generation and execution across diverse languages and frameworks. It features a sophisticated code interpreter with an impressive 70.12% success rate on HumanEval benchmarks. Interact with models such as CodeLlama-7B through the Gradio UI and benefit from seamless Python variable tracking and execution. This tool is an asset for developers looking to expedite debugging and enhance coding efficiency through improved data interpretation.

Discover a novel method in robotic control using transformer-based diffusion models trained on 800k diverse robot trajectories. Integrating language commands and RGB inputs, Octo efficiently handles various action spaces with limited resources. Adaptations through zero-shot evaluations and custom finetuning allow seamless transitions to new robotic settings. Pretrained checkpoints and detailed guides facilitate deployment while advanced attention mechanisms enhance adaptability and resource efficiency.