Realistic Multimodal LLM Project Ideas | Generated by AI
Question: What are good projects to train small multimodal LLM models (especially with video/data-heavy ideas)?
Answer:
You’re thinking in the right direction (moving from ideas → building systems), but I should ground one part: training a video foundation model from scratch is usually far beyond “small project” scale. Even “small” video models need huge datasets + multi-GPU clusters.
What is realistic and very valuable is building small multimodal systems that combine pretrained components + targeted fine-tuning or lightweight training (LoRA / adapters / small encoders). That’s exactly how most practical research prototypes are built today.
Here are strong, realistic project directions:
1. Video → Text Understanding (Best starter project)
Goal: Given a short video, describe what happens.
What you build:
- Video encoder (pretrained, e.g. CLIP / VideoMAE / InternVideo features)
- Small LLM (LLaMA-family small model)
- Connector (projection layer or LoRA adapter)
Training idea:
- Freeze video encoder
- Train a small projector + LLM adapter
Dataset:
- MSR-VTT
- WebVid
- ActivityNet Captions
Why this is good:
- Feels like “video intelligence”
- Cheap to train (1–4 GPUs possible)
- Very publishable / demo-friendly
2. Image + Text Multimodal Assistant (Mini GPT-4 style)
Goal: Chat with images.
Stack:
- CLIP / SigLIP image encoder
- Small LLM (1B–7B)
- Projection layer (like LLaVA style)
Training:
-
Only train:
- projection layer
- LoRA on LLM
Dataset:
- LAION-400M subset
- COCO captions
- ShareGPT-4V style datasets (open variants)
Outcome:
A “mini GPT-4V-like chatbot”
3. Video Event Q&A Model
Goal: Ask questions about short clips:
“What did the person do before sitting down?”
Key idea:
Instead of full video generation, focus on:
- temporal reasoning
- event sequencing
Architecture:
- sample frames (8–32 frames per clip)
- frame encoder (shared weights)
- temporal pooling (attention / transformer)
- LLM for reasoning
Dataset:
- TGIF-QA
- ActivityNet-QA
- TVQA
4. “FineWeb-style multimodal dataset builder” (VERY valuable)
Since you mentioned FineWeb:
Goal: build a pipeline that collects and filters multimodal data.
What you build:
- crawler for image/video-text pairs
- quality scoring model (CLIP similarity + heuristics)
- deduplication pipeline
- dataset versioning system
Why this is powerful:
This is actually what big labs spend massive effort on.
5. Lightweight Video Captioning Fine-tune System
Goal: improve caption quality of videos.
Approach:
- Start from pretrained video encoder
-
Train only:
- LoRA on decoder LLM
Add-ons:
- style control (“funny / technical / short captions”)
- structured captions (objects + actions + time)
6. Multimodal “Agent Memory” System
Goal: model remembers images/videos you show it.
Idea:
- embed media → vector DB
- LLM retrieves past memories
- answer based on multimodal history
Stack:
- CLIP embeddings
- FAISS / Milvus
- small LLM
Practical advice (important reality check)
If your goal is “train a video foundation model”, reality is:
- From scratch video models = 💰 millions + distributed compute
- Small projects = adapter tuning + dataset engineering + system design
So the highest-value path is:
Pretrained model + smart data + fine-tuning + system integration
That’s also what most “real world AI products” do.
If you want a strong direction
Given your background (engineering + ML curiosity), best 3 options:
- Video QA model (most research-like, impressive demo)
- Multimodal chat assistant (fastest to ship)
- Dataset + filtering pipeline (most underrated, closest to FineWeb idea)
If you want, I can help you go one level deeper and design:
- exact model architecture (layer-by-layer)
- training code structure (PyTorch / HF)
- compute estimate (how many GPUs, cost)
- or a “weekend MVP plan” for one of these projects