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"The Greatest Path is the Simplest"
- 🔗 MiniMind-V Github Repo 🔗 + +
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- This project aims to train a super-small multimodal vision-language model, MiniMind-V, with - just a cost of 3 RMB and 2 hours of work, starting from scratch! - -
- The smallest version of MiniMind-V is only about ¼ the size of GPT-3, designed to - enable fast inference and even training on personal GPUs. - -
- MiniMind-V is an extension of the visual capabilities of the MiniMind pure language model. - -
- The project includes full code for the minimalist structure of large VLM models, dataset cleaning, - pretraining, and supervised fine-tuning (SFT). - -
- This is not only the smallest implementation of an open-source VLM model but also a concise tutorial for - beginners in vision-language models. - -
- The hope is that this project can provide a useful example to inspire others and share the joy of creation, - helping to drive progress in the wider AI community! - -
- To avoid misunderstandings, the "2 hours" is based on testing (`1 epoch`) with an - NVIDIA 3090 hardware device (single GPU), and - the "3 RMB" refers to GPU server rental costs. - -
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+ Train a 26M Vision-Language Model from zero.
+ 1 hour. ¥1.3. One 3090.
+ See the World.
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- 🔗 Online Experience 🔗 | - 🔗 Video Introduction 🔗 -
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- 26M
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- 🔗 Online Experience 🔗 | - 🔗 Video Introduction 🔗 -
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+ 1h
+Training
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+ ¥1.3
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+ 1/7000
+vs GPT-3
+✨ What You Get
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+ 📸 Vision Multimodal
+Full vision-language capabilities on personal GPUs.
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+ 📦 Complete VLM Stack
+Vision encoder → LLM → Projector → Pretrain → SFT
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+ 💰 Ultra-Affordable
+Single 3090 GPU, minimal compute resources needed.
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+ 📖 Pure PyTorch
+Transparent implementation. Learn from reading the code.
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+ 🚀 Production Ready
+OpenAI API compatible, ready for real-world deployment.
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+ 🔌 Extensible
+Multi-image support, fine-tuning, and more capabilities.
+📦 Models
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+ | Model | +Parameters | +Inference Memory | +Release | +
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| MiniMind2-V | +104M | +0.6 GB | +2025.02.20 | +
| MiniMind2-Small-V | +26M | +1.1 GB | +2025.02.20 | +
📰 What's New
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+ 🎉 2025-04-27 (Latest)
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- Compatibility updates +
- Adapted to new features in the minimind repository +
- Standardized parts of the code +
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+ 🔥 2025-02-20 (MiniMind2-V)
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- MiniMind2-V updated alongside MiniMind2 +
- Significant reduction of redundant code +
- Major simplification of model structure +
- Updated dataset format with new SFT datasets +
- Better performance than previous VLM! +
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+ 🎬 2024-10-05 (First Release)
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- MiniMind-V released on schedule +
- First open-source vision-language model release +
🎮 Inside MiniMind-V
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+ 🎯 Try It Online
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💡 Why MiniMind-V?
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- Train vision-language models from scratch on consumer GPUs +
- Pure PyTorch implementation with no hidden complexity +
- Learn multimodal AI by building and reading code +
- Works offline on your personal hardware +
- Complete pipeline from data to deployment +
- OpenAI API compatible for easy integration +
- Smaller than most open-source VLMs yet powerful +
- Perfect for education and research +
+ 💭 "Seeing the world through a smaller lens, yet just as sharp." +
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