Open Academic Research on Improving LLaMA to SOTA LLM
Les fonctionnalités principales de aethercortex/llama-x sont : LLM Training and Optimization, Natural Language Processing.
Les alternatives open-source à aethercortex/llama-x incluent : chenking2020/findthechatgpter — ChatGPT爆火，开启了通往AGI的关键一步，本项目旨在汇总那些ChatGPT的开源平替们，包括文本大模型、多模态大模型等，为大家提供一些便利. jayzhang42/federatedgpt-shepherd — Shepherd: A foundational framework enabling federated instruction tuning for large language models. alexrozanski/llamachat — Chat with your favourite LLaMA models in a native macOS app. artidoro/qlora — This project is a quantized fine-tuning framework for large language models. It implements a low-rank adaptation… facico/chinese-vicuna — Chinese-Vicuna is a Chinese large language model and instruction-following AI based on the LLaMA architecture. It is… nomic-ai/gpt4all — GPT4All is a cross-platform runtime environment designed to execute large language models directly on local consumer…
This project is a quantized fine-tuning framework for large language models. It implements a low-rank adaptation library and a four-bit quantizer to reduce the GPU memory requirements needed to train large models. The framework utilizes four-bit quantization and low-rank adapters to enable model training on consumer-grade hardware. It further reduces the memory footprint through double quantization and a paged optimizer that offloads states to system RAM. The system supports distributed training across multiple GPUs to handle larger parameter scales and includes utilities for custom dataset
ChatGPT爆火，开启了通往AGI的关键一步，本项目旨在汇总那些ChatGPT的开源平替们，包括文本大模型、多模态大模型等，为大家提供一些便利
Chat with your favourite LLaMA models in a native macOS app
Chinese-Vicuna is a Chinese large language model and instruction-following AI based on the LLaMA architecture. It is specifically designed for natural language understanding and generation in the Chinese language, utilizing an instruction-tuned model to follow complex user prompts across conversations. The project provides a LoRA fine-tuning framework and quantization systems to enable model adaptation and inference on consumer hardware. It implements quantized inference to reduce memory usage on both CPUs and GPUs, supported by a low-level C++ implementation to minimize system resource requi