  Large language models have exhibited robust performance across diverse
natural language processing tasks. This report introduces TechGPT-2.0, a
project designed to enhance the capabilities of large language models
specifically in knowledge graph construction tasks, including named entity
recognition (NER) and relationship triple extraction (RTE) tasks in NLP
applications. Additionally, it serves as a LLM accessible for research within
the Chinese open-source model community. We offer two 7B large language model
weights and a QLoRA weight specialized for processing lengthy texts.Notably,
TechGPT-2.0 is trained on Huawei's Ascend server. Inheriting all
functionalities from TechGPT-1.0, it exhibits robust text processing
capabilities, particularly in the domains of medicine and law. Furthermore, we
introduce new capabilities to the model, enabling it to process texts in
various domains such as geographical areas, transportation, organizations,
literary works, biology, natural sciences, astronomical objects, and
architecture. These enhancements also fortified the model's adeptness in
handling hallucinations, unanswerable queries, and lengthy texts. This report
provides a comprehensive and detailed introduction to the full fine-tuning
process on Huawei's Ascend servers, encompassing experiences in Ascend server
debugging, instruction fine-tuning data processing, and model training. Our
code is available at https://github.com/neukg/TechGPT-2.0
