Create README.md

README.md

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+---
+license: apache-2.0
+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 **Apache 2.0 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}
+}
+```