We unified the interfaces of instruction-tuning data (e.g., CoT data), multiple LLMs and parameter-efficient methods (e.g., lora, p-tuning) together for easy use. We welcome open-source enthusiasts to initiate any meaningful PR on this repo and integrate as many LLM related technologies as possible. 我们打造了方便研究人员上手和使用大模型等微调平台，我们欢迎开源爱好者发起任何有意义的pr！
Die Hauptfunktionen von phoebussi/alpaca-cot sind: Data Expansion, Instruction Datasets.
Open-Source-Alternativen zu phoebussi/alpaca-cot sind unter anderem: lianjiatech/belle — BELLE is a specialized implementation of Chinese conversational large language models, encompassing a full instruction… facebookresearch/codellama — Code Llama is a large language model based on Llama 2 trained specifically for programming tasks and software… freedomintelligence/huatuo-26m — The Largest-scale Chinese Medical QA Dataset： with 26,000,000 question answer pairs. ibm/dromedary — Dromedary: towards helpful, ethical and reliable LLMs. instruction-tuning-with-gpt-4/gpt-4-llm — This project is an instruction tuning framework and synthetic data generator that uses high-capacity teacher models to… da-southampton/redgpt — [English Version].
BELLE is a specialized implementation of Chinese conversational large language models, encompassing a full instruction tuning framework. It provides a pipeline for training, evaluating, and deploying models optimized for natural language understanding and dialogue tasks in the Chinese language. The project is distinguished by its integrated approach to model refinement, combining the curation of multi-million entry instruction datasets with a distributed training pipeline. This pipeline supports both full fine-tuning and low-rank adaptation to optimize conversational performance. The system
Code Llama is a large language model based on Llama 2 trained specifically for programming tasks and software development. It provides specialized model types optimized for general code generation, instruction following, and context-aware infilling. The project includes an instruction-tuned programming model for executing technical tasks via natural language prompts and a code infilling model that predicts missing sections based on surrounding source context. A large context code model is also provided to analyze extensive blocks of source code for improved coherence. The system covers capab
The Largest-scale Chinese Medical QA Dataset： with 26,000,000 question answer pairs.
