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README.md

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+---
+library_name: pytorch
+tags:
+  - vae
+  - genomics
+  - genome-minimization
+  - e-coli
+---
+
+# Genome Minimizer 2
+
+VAE-powered pipeline for generating minimal *E. coli* genomes. Models are trained on a binary gene presence/absence matrix of ~10,000 *E. coli* strains across ~55,000 genes.
+
+## Model Variants
+
+Each preset is stored on its own branch:
+
+| Branch | Architecture | Loss Functions | Description |
+|--------|---|---|---|
+| [`v0`](https://huggingface.co/McClain/genome-minimizer-2/tree/v0) | 55,039 → 1024 → 64 | Recon + KL (linear) | Baseline VAE |
+| [`v1`](https://huggingface.co/McClain/genome-minimizer-2/tree/v1) | 55,039 → 512 → 32 | Recon + KL (linear) + Abundance + L1 | + gene frequency control |
+| [`v2`](https://huggingface.co/McClain/genome-minimizer-2/tree/v2) | 55,039 → 512 → 32 | Recon + KL (cosine) + Abundance + L1 | Improved convergence |
+| [`v3`](https://huggingface.co/McClain/genome-minimizer-2/tree/v3) | 55,039 → 512 → 32 | Recon + KL (cosine) + Weighted Abundance + L1 | Best minimal genomes |
+
+## Quick Start
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+from src.genome_minimizer_2.training.model import VAE
+
+# Download v3 (best for minimal genomes)
+path = hf_hub_download("McClain/genome-minimizer-2", "final.pt", revision="v3")
+checkpoint = torch.load(path, map_location="cpu")
+
+model = VAE(input_dim=55039, hidden_dim=512, latent_dim=32)
+model.load_state_dict(checkpoint["model_state_dict"])
+```
+
+## Links
+
+- [W&B Experiment Tracking](https://wandb.ai/mcclain/genome-minimizer-2)
+- [GitHub Repository](https://github.com/ucl-cssb/genome-minimizer-2)