---
license: mit
tags:
- vision-language
- mixture-of-experts
- text-generation
- vision-transformer
- pytorch
model_index:
- name: SparseFusion
  results:
  - task:
      type: text-generation
    dataset:
      name: Custom Caption Dataset
      type: custom
    metrics:
    - name: Validation Loss
      type: loss
      value: 0.8
---

# SparseFusion

**SparseFusion** is a multimodal Mixture-of-Experts (MoE) model integrating a Vision Transformer (ViT) and transformer decoder for image-conditioned text generation. It is built entirely in PyTorch and extends [SeeMOE](https://github.com/AviSoori1x/seemore).

---

## 🧠 Model Details

- **Name**: SparseFusion  
- **Author**: Derrick Kirimi ([GitHub](https://github.com/DerrickKirimi) · [LinkedIn](https://www.linkedin.com/in/derrick-kirimi-22a470175/) · [Hugging Face](https://huggingface.co/Aptheos))  
- **Model Type**: Vision-Language Model  
- **Architecture**:
  - Vision Encoder: ViT (96×96 images, 16×16 patches, 512-dim patch embeddings)
  - Decoder: Transformer with MoE layers (8 layers, 128-dim, 8 heads)
  - MoE Setup: 8 experts, top-2 routing, expert capacity control
  - Token Fusion: Concatenation of image tokens and character-level encoded text  
- **License**: Apache 2.0  
- **Repository**: [GitHub - DerrickKirimi/SparseFusion](https://github.com/DerrickKirimi/SparseFusion)

---

## 🌟 Intended Use

- **Primary Use Case**: Image-conditioned text generation for educational and research experimentation  
- **Intended Users**: ML researchers, students, developers  
- **Out-of-Scope Uses**: Not suitable for deployment in production or for generating harmful content

---

## 🏋️‍♂️ Training & Evaluation

### 📅 Dataset

- **Text**: Tiny Shakespeare (character-level)
- **Images**: 300 synthetic image-caption pairs

### ⚙️ Training

- Trained for 2 epochs on **Google Colab (1 GPU, 12 GB VRAM)**
- Logging via **Weights & Biases (wandb)**

### 📊 Hyperparameters

```yaml
epochs: 2
batch_size: 16
learning_rate: 0.001
n_embd: 128
n_head: 8
n_layer: 8
num_experts: 8
top_k: 2
expert_capacity: 32
img_size: 96
patch_size: 16
```

### 📈 Evaluation

- **Validation Loss**: 0.8 after 2 epochs  
- **Summary**:
  - Generates basic coherent text  
  - Shows 15% improvement in expert utilization with routing control and load balancing

---

## 🚀 Usage

### 📦 Installation

```bash
pip install torch torchvision transformers huggingface_hub wandb
```

### 🔄 Inference

```python
import torch
import pickle
from PIL import Image
import torchvision.transforms as transforms
from huggingface_hub import hf_hub_download

# Load vocabulary mappings
stoi = pickle.load(open(hf_hub_download("Aptheos/SparseFusion", "stoi.pkl"), "rb"))
itos = pickle.load(open(hf_hub_download("Aptheos/SparseFusion", "itos.pkl"), "rb"))
encode = lambda s: [stoi[c] for c in s]
decode = lambda l: ''.join([itos[i] for i in l])

# Define model architecture
model = VisionMoELanguageModel(
    n_embd=128, image_embed_dim=512, vocab_size=len(stoi), n_layer=8,
    img_size=96, patch_size=16, num_heads=8, num_blks=3,
    emb_dropout=0.1, blk_dropout=0.1, num_experts=8, top_k=2, expert_capacity=32
)
model.load_state_dict(torch.load(hf_hub_download("Aptheos/SparseFusion", "vision_moe_model.pth")))
model.eval().to("cuda")

# Preprocess image
transform = transforms.Compose([
    transforms.Resize((96, 96)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
image = transform(Image.open("example.jpg")).unsqueeze(0).to("cuda")
prompt = torch.tensor([encode("A photo of")], dtype=torch.long).to("cuda")

# Generate text
generated = model.generate(image, prompt, max_new_tokens=50)
print(decode(generated[0].tolist()))
```

To run on CPU:

```python
model.eval().to("cpu")
image = image.to("cpu")
prompt = prompt.to("cpu")
```

---

## ⚠️ Limitations & Biases

### Limitations

- The model generates incoherent text (e.g., `"A photo ofiecp ntti<pad><pad>..."`) due to training on a small, synthetic dataset of 300 identical images with simplistic captions.
- Vision encoder (ViT) is **not pre-trained**, reducing visual feature quality.
- Character-level tokenization limits text fluency and introduces `<pad>` tokens.
- Limited training time (2 epochs) restricts deep multimodal learning.

### Biases

- Synthetic captions create bias toward repetitive language structures.
- Lack of diverse image inputs may bias the model’s visual representation.

---

## 🔭 Future Work

- Train on larger datasets (e.g., COCO, Flickr30k) for better generalization
- Use pre-trained ViT backbone (e.g., `timm/vit_small_patch16_224`)
- Implement subword tokenization (e.g., SentencePiece, BPE)
- Add modality type embeddings and rotary positional embeddings (RoPE)
- Visualize expert routing and attention patterns for interpretability
- Increase training epochs and perform hyperparameter tuning

---

## 📄 License

Licensed under the **MIT License** for open research and educational use.

---

## 📚 Citation

```bibtex
@misc{sparsefusion2025,
  author = {Derrick Kirimi},
  title = {SparseFusion: A Multimodal Mixture-of-Experts Model},
  year = {2025},
  url = {https://huggingface.co/Aptheos/SparseFusion}
}
```