BAAI-DCAI/Bunny

Health v2 · maintenance only

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1053 stars · 75 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Bunny is a family of lightweight multimodal vision-language models that combine plug-and-play vision encoders (EVA-CLIP, SigLIP) with language backbones like Llama-3, Phi-3-mini, and Qwen1.5. The repository provides model weights, training code, curated data, and evaluation for building efficient image-understanding chat models that support Chinese and English.

Use cases

• run a lightweight vision-language model for image question answering
• fine-tune a small multimodal LLM on my own image-text data
• compare small VLMs against larger 7B and 13B models
• deploy a multimodal chat model locally with GGUF weights
• build an image captioning or visual chat assistant
• train a LLaVA-style model with a custom vision encoder and language backbone

When to choose

• you need strong multimodal performance from a small (4B-8B) model
• you want to swap vision encoders and language backbones plug-and-play
• you need high-resolution image input up to 1152x1152
• you want open training data and a reproducible training tutorial
• you need bilingual (Chinese/English) visual chat

When to avoid

• you need text-only LLM inference without image input
• you need production-grade serving infrastructure rather than research code
• you need audio, video, or speech modality support
• you need a managed API instead of self-hosted weights

Facets

library · maturity active

llm-inference machine-learning deep-learning image-processing chatbot large-language-models computer-vision artificial-intelligence python cross-platform vision-language-model multimodal lightweight-models llava-style image-understanding gguf huggingface chinese-english visual-question-answering model-training natural-language-processing gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="BAAI-DCAI/Bunny")