Open Source Minimal Multimodal GPT Projects | Generated by AI
Question: What are good open source projects for multimodal GPT-like models, similar to nanoGPT, that can generate or edit images?
Answer:
nanoGPT is a popular minimal, educational implementation for training GPT-like language models from scratch in pure PyTorch. For multimodal models (handling both text and images, similar to GPT-4V), the closest equivalent in terms of simplicity and educational value is nanoVLM.
Top Recommendation: nanoVLM
- Description: nanoVLM is a lightweight, minimal Vision-Language Model (VLM) built entirely in pure PyTorch, explicitly inspired by nanoGPT. It features a LLaMA-style decoder for language, a SigLiP vision encoder, and a modality projector to connect them.
- Why it’s similar to nanoGPT: It’s designed to be compact, easy to understand, and trainable from scratch with minimal resources (e.g., achieves strong performance with just 6 hours on a single H100 GPU or even in a free Google Colab notebook).
- Capabilities: Primarily for multimodal understanding (e.g., image captioning, visual question answering), not native image generation or editing.
- GitHub: https://github.com/huggingface/nanoVLM
Other Notable Minimal/Educational Multimodal Projects
- Seemore: A from-scratch PyTorch implementation of a vision-language model (image encoder + multimodal projector + decoder LLM). It’s homage to Andrej Karpathy’s makemore/nanoGPT series, using a simple character-level autoregressive model for simplicity.
- These are great for learning how multimodal models work under the hood.
For Multimodal Understanding (Image Input + Text Output, like most “multimodal GPT”)
Popular open-source projects include:
- LLaVA (and variants like LLaVA-NeXT): One of the pioneering visual instruction-tuned models aiming for GPT-4V-level capabilities. Easy to fine-tune and run locally.
- GitHub: https://github.com/haotian-liu/LLaVA
- Many others like Qwen-VL, InternVL, but they are larger and less “nano”-like.
For Models That Can Generate or Edit Images
True any-to-any multimodal models (text/image input → text/image output, like GPT-4o) are rarer in open source, especially minimal ones:
- DeepSeek Janus-Pro: Open-source series that supports both understanding and generating images.
- MMaDA: Multimodal diffusion-based models for text and image generation/editing.
- OmniGen 2: Unified model for text-to-image generation, image editing, and multimodal tasks (blends like GPT-4o).
- These are more advanced and resource-intensive, not as minimal as nanoGPT/nanoVLM.
Most open-source multimodal projects focus on vision-language understanding (input: images + text, output: text), not generation/editing of images. For image generation/editing, projects often combine VLMs with separate diffusion models (e.g., Stable Diffusion for editing).
If you’re looking for educational code to train your own multimodal model, start with nanoVLM—it’s the direct “nanoGPT for vision-language” analog.
References:
- nanoVLM GitHub
- Awesome Multimodal Large Language Models List
- LLaVA GitHub
- DeepSeek Janus Series
- MMaDA GitHub