#RLHF

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 a broad range of resources and methodologies for AI prompt creation and language model optimization. This project includes open-source models, fine-tuning datasets, and instructional alignment methods, providing valuable insights for enhancing AI functions. Access detailed series on advanced tuning techniques and explore diverse AIGC applications across fields. Regularly updated content includes thorough surveys and pioneering papers on language models and dialogue systems, offering deep insights into AI's human-like reasoning.

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 Although the project is no longer actively maintained, it has significantly contributed to the efficient tuning of language models.

Explore an open-source framework for language model training emphasizing safety and alignment using Safe RLHF methods. It supports leading pre-trained models, extensive datasets, and customizable training. Features include multi-scale safety metrics and thorough evaluation, assisting researchers in optimizing models with reduced risks. Developed by the PKU-Alignment team at Peking University.

Explore a diverse array of open-source datasets designed to improve chat-focused Large Language Models (LLMs) including ChatGPT, LLaMA, and Alpaca. This collection offers comprehensive datasets that support Instruction Tuning and Reinforcement Learning from Human Feedback (RLHF), crucial for developing instruction-following LLMs. Ideal for researchers and developers, it provides access to datasets spanning various languages and tasks, utilizing techniques such as human data generation, self-instruct, and mixed methodologies. This resource expedites advancements in natural language processing, fostering innovation.

OpenRLHF is a high-performance RLHF framework built on Ray, DeepSpeed, and HuggingFace Transformers. It focuses on simplicity and compatibility, enhancing training with vLLM and PPO optimizations for models over 70 billion parameters, supporting advanced distributed systems and multi-GPU setups to boost training stability.

This project develops Chinese language models based on Llama-2 with an expanded Chinese vocabulary, bringing the Chinese LLaMA-2 and Alpaca-2 models. These models improve performance through incremental training on large Chinese datasets and employ FlashAttention-2 for training efficiency. Supporting up to 64K context length, they enhance semantics and instruction comprehension. RLHF integration allows for alignment with human preferences, reflecting values better. Open-source training and fine-tuning scripts are provided to deploy the models on local devices within the LLaMA ecosystem, enhancing user access and interaction.

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.

Pykoi is an open-source Python library that facilitates the optimization of large language models utilizing Reinforcement Learning with Human Feedback (RLHF). It features a unified interface for collecting user feedback in real-time, finetuning, and comparing different models. Key functionalities include a UI for chat history storage, tools for efficient model performance comparison, and RAG chatbot integration. Compatible with CPU and GPU environments, Pykoi supports models from OpenAI, Amazon Bedrock, and Huggingface, aiding in fine-tuning models with custom datasets for improved precision and relevance.

Explore a comprehensive selection of publicly available human preference datasets suitable for fine-tuning language models, reinforcement learning from human feedback, and assessment. Highlighted collections encompass OpenAI WebGPT Comparisons, OpenAI Summarization, and Anthropic Helpfulness and Harmlessness Dataset, among others. Offering resources aimed at NLP development, these datasets are derived from sources including Reddit, StackExchange, and ShareGPT, enriching understanding of human preferences in AI. They support the development of reward models and offer insights into evaluating human-generated content across varied fields, ideal for researchers and developers working on the advancement of language model alignment.