Open-Source Multimodal LLM Projects | Generated by AI
Question: Can you recommend good open-source projects for training small multimodal LLMs (image/video + language)?
Answer:
Below is a curated, practical set of real open-source projects you can actually study, clone, and build on for small multimodal LLM systems (image/video + LLM). I grouped them by usefulness for learning → training → scaling up.
🧠 1. Best entry-level multimodal LLM (Image + Text)
🔹 LLaVA (must-start project)
- GitHub: https://github.com/haotian-liu/LLaVA
- Idea: Vision encoder + LLM + projection layer
-
What you learn:
- How GPT-4V-style models are built
- How to fine-tune with LoRA
- How image features connect to LLM tokens
👉 This is the “hello world” of multimodal LLMs
🔹 BLIP-2 / InstructBLIP
- Strong baseline for image → text reasoning
- Uses frozen vision + frozen LLM + Q-former bridge
Why it matters:
- Very stable training recipe
- Less compute than full fine-tuning
🎥 2. Best open-source video LLM projects (your main interest)
🔹 Video-LLaVA (VERY recommended)
- Repo: https://github.com/PKU-YuanGroup/Video-LLaVA (SourcePulse)
- Paper: EMNLP 2024 style system
What it does:
- Extends LLaVA to videos
- Aligns frames → LLM reasoning
Why it’s good:
- Clean architecture
- Works for video Q&A / captioning
- Good for small-scale training experiments
🔹 LLaMA-VID
Key idea:
- Compress long videos into “context tokens”
- Enables hour-long video reasoning
Why it’s useful:
- Teaches long-context multimodal design
- Good research direction for “video memory”
🔹 PLLaVA (lightweight video extension)
Key idea:
- “parameter-free” video extension of image LLaVA
- Efficient adaptation from image model → video model
Why it matters:
- Extremely practical for small compute
- Good for experiments on 1–2 GPUs
🧱 3. Strong video foundation models (for feature extraction)
These are NOT small training projects, but extremely useful as backbone:
🔹 InternVideo (very important ecosystem)
What it provides:
- Video encoders (ViT-based)
- Pretrained video representations
- Huge dataset + pretrained models
Why you should care:
- You can freeze this and build your own LLM on top
- This is like “CLIP for video”
🔹 InternVideo2 / InternVid dataset
- 7M+ videos, massive scale dataset (InternVideo)
Why it matters:
- This is “FineWeb equivalent for video”
- Useful for dataset engineering inspiration
🧪 4. Minimal / hackable multimodal frameworks
🔹 nanoVLM (VERY good for learning)
- Lightweight VLM (~750 lines concept)
- Runs on Colab-level GPUs
Why it’s important:
- Shows full pipeline in minimal code
- Great for “build your own multimodal LLM”
🧠 5. Best “realistic training stack” (what you should actually do)
If your goal is small multimodal LLM training project, the best combo is:
⭐ Recommended stack:
- Vision encoder: CLIP / SigLIP / InternVideo features
- LLM: LLaMA 3 / Qwen 2.5 (small version)
- Connector: MLP projector or Q-former
- Training method: LoRA / QLoRA
-
Dataset:
- WebVid
- MSR-VTT
- COCO captions
🚀 6. Best project ideas using these repos
If you want “real buildable projects”:
1. Video Q&A assistant
Use:
- Video-LLaVA + LoRA
2. “FineWeb for video”
Build:
- dataset crawler + filtering + dedup pipeline
3. Long video memory model
Use:
- LLaMA-VID style compression idea
4. Lightweight multimodal chatbot
Use:
- LLaVA + small LoRA tuning
⚡ Key insight (important)
You don’t actually need to “train video LLM from scratch”.
The modern winning formula is:
Frozen pretrained encoders + small trainable adapters + good dataset engineering
That’s exactly how Video-LLaVA, InternVideo2, and PLLaVA-style systems work.
If you want next step, I can design for you:
- a 1-week build plan
- exact GPU cost estimate
- or a minimal repo architecture (folders + code skeleton) for your first multimodal video LLM
Just tell me 👍