  Recent advancements enlarge the capabilities of large language models (LLMs)
in zero-shot image-to-text generation and understanding by integrating
multi-modal inputs. However, such success is typically limited to English
scenarios due to the lack of large-scale and high-quality non-English
multi-modal resources, making it extremely difficult to establish competitive
counterparts in other languages. In this paper, we introduce the Ziya-Visual
series, a set of bilingual large-scale vision-language models (LVLMs) designed
to incorporate visual semantics into LLM for multi-modal dialogue. Composed of
Ziya-Visual-Base and Ziya-Visual-Chat, our models adopt the Querying
Transformer from BLIP-2, further exploring the assistance of optimization
schemes such as instruction tuning, multi-stage training and low-rank
adaptation module for visual-language alignment. In addition, we stimulate the
understanding ability of GPT-4 in multi-modal scenarios, translating our
gathered English image-text datasets into Chinese and generating
instruction-response through the in-context learning method. The experiment
results demonstrate that compared to the existing LVLMs, Ziya-Visual achieves
competitive performance across a wide range of English-only tasks including
zero-shot image-text retrieval, image captioning, and visual question
answering. The evaluation leaderboard accessed by GPT-4 also indicates that our
models possess satisfactory image-text understanding and generation
capabilities in Chinese multi-modal scenario dialogues. Code, demo and models
are available at
~\url{https://huggingface.co/IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1}.
