#LoRA

Firefly is a versatile tool for training large models, offering pre-training, instruction fine-tuning, and DPO functionality for a broad range of popular models, including Llama3 and Vicuna. It employs methodologies such as full parameter tuning, LoRA, and QLoRA for efficient resource usage, catering to users with limited computing power. Its user-friendly approach allows for straightforward model training with optimized configurations to minimize memory and time consumption. Discover open-source model weights and benefit from proven methods, achieving notable improvements in the Open LLM Leaderboard.

This guide provides insights into advanced techniques for efficiently fine-tuning large language models with tools like LoRA and Hugging Face. Featuring comprehensive tutorials on various methods such as PEFT, RLHF training, and transformer-based approaches, it offers clear, step-by-step guides for model enhancement—suitable for data scientists and AI researchers seeking to optimize machine learning processes and accuracy.

ChatGenTitle utilizes fine-tuned LLaMA models with extensive arXiv data to efficiently generate paper titles. This project offers open-source models, online trials, and flexibility for diverse AI research fields, facilitating straightforward deployment. Integrating with HuggingFace, it ensures seamless access and applications in scientific contexts, enriched by thorough data collection from arXiv.

LLMTuner provides an efficient solution for adjusting large language models, such as Whisper and Llama, using sophisticated techniques like LoRA and QLoRA. Featuring a user-friendly, scikit-learn-inspired interface, it facilitates streamlined parameter tuning and model deployment. The tool offers effortless demo creation and model deployment with minimal code, making it suitable for researchers and developers seeking fast and reliable ML outcomes. Its future features, including deployment readiness on platforms like AWS and GCP, are designed to significantly enhance model training and deployment capabilities.

The sd-webui-text2video extension implements state-of-the-art text-to-video models like ModelScope and VideoCrafter within AUTOMATIC1111's StableDiffusion WebUI without requiring logins. It supports LoRA training and offers features like in-painting and video looping, enabling efficient animation creation with minimal VRAM usage. Key updates feature Torch2 optimizations for extended video output on constrained VRAM and the introduction of WebAPI. Discover usage examples and fine-tuning options with leading models, enhancing creativity for video synthesis aficionados.

The project introduces a text-to-image diffusion process using a large language model that enhances semantic comprehension and a diffusion-based model for drawing. Comprising an LLM-based planning component and diffusion model, the system accurately aligns with text prompts in two phases. Listed as a CVPR 2024 oral paper, the package includes model weights such as a LoRA-finetuned LLaMa-2-7B and fully-finetuned SDv2.1. Users can explore image creation interactively through Gradio demos and apply continuous edits for targeted image changes.

The sd-webui-regional-prompter is an extension of AUTOMATIC1111's stable-diffusion-webui, designed to allow assigning different prompts to various image regions, enhancing output customizability. It supports several division modes such as vertical and horizontal, along with experimental features like inpaint and prompt-based region assignments. The tool improves LoRA application with step-stopping options to refine image quality and processing speed. It includes updates for SDXL, web-ui 1.5, and new prompt mode options for more precise control, making it suitable for users aiming for detailed and creative image generation.

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.

LoRA employs low-rank matrix adaptations, reducing trainable parameters and optimizing task adaptation in large language models. This approach minimizes storage needs and avoids inference delays. The Python package integrates with PyTorch and the Hugging Face PEFT library, ensuring competitive performance alongside full fine-tuning in benchmarks like GLUE. LoRA adapts specific Transformer elements, like query and value projections, offering flexibility across models such as RoBERTa, DeBERTa, and GPT-2. The 'loralib' can be installed to apply these techniques efficiently.

Parameter-Efficient Fine-Tuning (PEFT) offers a cost-effective way to adapt large pretrained models with reduced computational and storage needs, maintaining high performance similar to fully fine-tuned models. PEFT works with tools like Transformers, Diffusers, and Accelerate, making it versatile for model training and inference across different domains. This method helps in managing large models on consumer hardware by minimizing memory consumption without compromising accuracy.

Explore how AI models such as LLaMA and ChatGLM LoRA enhance software development efficiency. This project offers tutorials, models, training data, and records designed to improve productivity. It features techniques for user task creation, code generation, and test execution, providing valuable insights for developers aiming to leverage AI for automating and optimizing coding processes.

VisualCLA is a Chinese multimodal language model that builds upon Chinese-LLaMA/Alpaca by incorporating image encoding features. Pre-trained with Chinese image-text data, it synchronizes visual and text elements to enhance multimodal understanding. Additionally, it is fine-tuned on a range of multimodal command datasets to improve comprehension, execution, and dialogue with complex instructions. Still in its testing phase, the project aims to refine model performance in understanding and conversational tasks, offering inference code and deployment scripts via Gradio/Text-Generation-WebUI. Available in a test version as VisualCLA-7B-v0.1, it exhibits promising advancements in multimodal interaction, encouraging further exploration in diverse applications.

Delve into step-by-step guides on LoRA and DoRA implementation, designed to supplement learning and practical application. Ideal for those interested in gaining comprehensive insights and practical skills in advanced technology solutions.

ControlNeXt introduces a highly efficient approach for generating controllable images and videos, optimizing parameter usage for enhanced speed and efficiency. The integration with LoRA allows for versatile style modifications with consistent results. Various models such as ControlNeXt-SDXL for images and ControlNeXt-SVDv2 for video showcase noticeable improvements in quality and execution. The ongoing development phase offers accessible demos and regular updates, ensuring a progressive user experience.

Designed for scalable neural retrieval, this toolkit facilitates efficient model training and inference. It integrates parameter-efficient methods such as LoRA and advanced technologies like DeepSpeed and flash attention. Users can access and finetune top pre-trained models, including BGE-Embedding and Instruct-E5, via HuggingFace. Self-contained datasets support various tasks, ensuring efficient training on billion-scale LLMs with GPUs and TPUs. This makes it an excellent choice for researchers seeking to enhance retrieval systems using sophisticated techniques.

Stable Diffusion WebUI Forge facilitates development through efficient resource management, rapid inference, and innovative features. Taking inspiration from Minecraft Forge, this platform enhances Stable Diffusion WebUI by integrating popular extensions and supporting sophisticated image editing. It features an easy setup compatible with multiple CUDA and Pytorch versions, allowing for seamless updates and effective GPU usage. Users can access comprehensive guides, various extensions, and report on performance issues or enhancements, ensuring a reliable platform for image creation and enhancement.

Discover Punica, a novel solution for serving multiple LoRA finetuned models with only 1% additional memory overhead, utilizing a special CUDA kernel for efficient computation. Achieve up to 12x throughput boosts compared to leading systems using segmentation gathering techniques. Punica is available via binaries or source code to match your configuration needs, with comprehensive examples and benchmarks provided.

The library supports a range of techniques like QLoRA, DeepSpeed, and Gradient Checkpointing to enhance the efficiency of Large Language Model training. It offers features such as checkpoint integration with the HuggingFace Hub and training progress tracking with W&B, allowing for customizable configurations that meet various training requirements. The library accommodates a wide range of models and integrates seamlessly with existing projects, facilitating both rapid prototyping and production-level deployments.

This project leverages the Mixtral-8x7B model, enhanced with an expanded Chinese vocabulary to improve NLP capabilities. It offers open-source access to both the expanded model and incremental pre-training code, which notably boosts encoding and decoding efficiency in Chinese, ensuring strong comprehension and generation potentials. Users should remain attentive to potential biases or inaccuracies in outputs. The model is compatible with various acceleration techniques within the Mixtral-8x7B ecosystem and can be downloaded including options for integrating LoRA weights.

The mistral-finetune project provides an efficient platform for fine-tuning Mistral models by leveraging the LoRA training framework. This method focuses on memory conservation by locking most weights and adjusting only a small fraction. Tailored for multi-GPU environments, it also accommodates single GPU use for smaller models, like the 7B. Recently, it includes support for models such as Mistral Large v2 and Mistral Nemo, demanding more memory for larger tasks but enhancing finetuning capabilities. It serves as a straightforward entry point for finetuning Mistral models, emphasizing specific data formatting and installation instructions, essential for advanced training across various systems.

This project delivers advanced solutions to enhance transformer architectures like LLaMA and LLaMA-2 using LoRA and PEFT. It focuses on efficiency and affordability, allowing users to access fine-tuned models on HuggingFace with seamless integration. Recent advancements include improved data processing and scripts for easy model setup and tuning. Discover various data refinement techniques to ensure model training accuracy and uniqueness, with detailed CLI guidelines for local deployment.

Learn how Low-rank Adaptation speeds up the fine-tuning of Stable Diffusion models, enhancing efficiency and reducing model size, ideal for easy sharing. This technique is compatible with diffusers, includes inpainting support, and can surpass traditional fine-tuning in performance. Discover integrated pipelines for enhancing CLIP, Unet, and token outputs, along with straightforward checkpoint merging. Delve into project updates, its web demo on Huggingface Spaces, and explore detailed features to understand its role in text-to-image diffusion fine-tuning.

Lora-scripts provides a streamlined GUI and script solution for LoRA and Dreambooth training, featuring a comprehensive one-click setup environment. It seamlessly integrates with kohya-ss/sd-scripts and incorporates advanced tools such as a web-based training interface, Tensorboard integration, and diverse configuration options for both Windows and Linux systems. Developers can utilize easy setup scripts and benefit from the WD 1.4 Tagger and Tag Editor, making it ideal for efficient Stable Diffusion model training.

Explore a straightforward interface for tuning language models with the LoRA method on NVIDIA GPUs. The Simple LLM Finetuner uses the PEFT library to provide easy-to-use tools for dataset handling, parameter tweaking, and evaluating model inference. Suitable for beginners, it supports small datasets and can run on standard Colab instances. Adjust settings with ease to boost model performance with minimal effort.

Discover detailed insights into LLM-RLHF-Tuning, implementing multi-stage training including instruction fine-tuning, reward model training, and PPO/DPO algorithms. The project leverages LLaMA and LLaMA2 model capabilities, endorsing efficient, distributed training with frameworks like accelerate and deepspeed. Its flexible configurations enable seamless integration of RM, SFT, Actor, and Critic models. This resource serves as a valuable guide for researchers interested in robust AI model training approaches.

Discover resources for optimizing language models using FireAct. The repository provides prompts, demo codes, and datasets tailored for language agent fine-tuning. It supports task exploration, OpenAI API integration, and SERP API utilization. FireAct guides through data generation, Alpaca and GPT format fine-tuning, and supervised learning for enhanced outcomes. Explore a model zoo with Llama family-based multitask models for effective language agent applications.

This project details a complete process for tuning the ChatGLM large language model through LoRA and Reinforcement Learning with Human Feedback (RLHF) on accessible hardware. It covers data processing, supervised fine-tuning, and reward modeling. The guide also addresses effective PEFT version utilization for model integration, overcoming Hugging Face transformer compatibility challenges. This enables efficient model development and tuning, specifically for those working with constrained resources.

Explore a system facilitating interactive image management using self-instructed language models founded on Vicuna (LLaMA). This tool utilizes 71K instruction datasets to autonomously manage visual foundation models, supporting user-led model teaching with LoRA enhancements while allowing interactive dialogue engagement with images. Stay updated on NIPS 2023 developments and explore features through demos and resources.

This repository provides fine-tuning scripts for the Flux model, utilizing LoRA and ControlNet technologies. With support for high-resolution output through DeepSpeed integration, it enables training of models like the IP-Adapter and various ControlNet versions at 1024x1024 resolution. Necessary tools include Python 3.10+, PyTorch 2.1+, and HuggingFace CLI for downloading models. Testing is supported through ComfyUI, Gradio, and CLI, with a low-memory mode available using Flux-dev-F8 on HuggingFace. Models are under the FLUX.1 Non-Commercial License.

The project implements advanced fine-tuning techniques for the ChatGLM-6B model, including LoRA, P-Tuning V2, and Reinforcement Learning with Human Feedback (RLHF). It features a comprehensive Web UI for single GPU-based training, evaluation, and inference, highlighting its role in optimizing large language models. The repository supports various datasets like Stanford Alpaca, BELLE, and GPT-4 generated data, enhancing ChatGLM's adaptability to diverse datasets and tuning methods. Although the project is no longer actively maintained, it has significantly contributed to the efficient tuning of language models.