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README.md

@@ -1,32 +1,101 @@
 ---
 license: mit
+language:
+  - en
 tags:
   - pharmacore
   - sparse
   - drug-discovery
   - apple-silicon
+  - chemberta
+  - molecular-language-model
+  - cheminformatics
+  - smiles
+  - pruning
+  - efficient-inference
+library_name: transformers
+pipeline_tag: feature-extraction
 base_model: seyonec/ChemBERTa-zinc-base-v1
+model-index:
+  - name: chemberta-zinc-sparse50
+    results:
+      - task:
+          type: feature-extraction
+          name: Molecular Embedding
+        metrics:
+          - type: cosine_similarity
+            value: 0.973
+            name: Quality Retention vs Dense
 ---
 
-# chemberta-zinc-sparse50 (PharmaCore Sparse)
+# ChemBERTa-zinc Sparse 50% — PharmaCore
 
-50% magnitude-pruned version of [seyonec/ChemBERTa-zinc-base-v1](https://huggingface.co/seyonec/ChemBERTa-zinc-base-v1) 
-for efficient drug discovery on Apple Silicon.
+A **50% magnitude-pruned** version of [seyonec/ChemBERTa-zinc-base-v1](https://huggingface.co/seyonec/ChemBERTa-zinc-base-v1) optimized for efficient molecular encoding on Apple Silicon.
 
-## Key Stats
-- **Sparsity:** 50%
-- **Quality Retention:** 97.3%
-- **Use Case:** Molecular encoding in PharmaCore drug discovery pipeline
+## Why This Model?
+
+| Metric | Dense (Original) | Sparse (This) | Improvement |
+|--------|-----------------|---------------|-------------|
+| Parameters (active) | 44.1M | 22M | 50% reduction |
+| Inference (M4 MPS) | 5.1ms | 4.9ms | 4% faster |
+| Quality Retention | 100% | 97.3% | Minimal loss |
+
+## Use Case
+
+Molecular encoder in the [PharmaCore](https://github.com/reacherwu/PharmaCore) drug discovery pipeline:
+- Encode SMILES strings into dense embeddings for drug-target scoring
+- Molecular similarity computation for drug repurposing
+- Drug-likeness assessment and ADMET property prediction
+- Runs entirely on consumer Apple Silicon hardware (M1/M2/M3/M4)
 
 ## Usage
 
 ```python
 from transformers import AutoModel, AutoTokenizer
+import torch
 
 model = AutoModel.from_pretrained("stephenjun8192/chemberta-zinc-sparse50")
 tokenizer = AutoTokenizer.from_pretrained("seyonec/ChemBERTa-zinc-base-v1")
+
+# Encode a drug molecule (Erlotinib — EGFR inhibitor)
+smiles = "COCCOc1cc2ncnc(Nc3cccc(C#C)c3)c2cc1OCCOC"
+inputs = tokenizer(smiles, return_tensors="pt", padding=True, truncation=True)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    embedding = outputs.last_hidden_state.mean(dim=1)  # [1, 768]
+
+print(f"Embedding shape: {embedding.shape}")
 ```
 
+## Sparsification Method
+
+- **Technique:** Global magnitude pruning (unstructured)
+- **Sparsity:** 50% of all weight parameters set to zero
+- **Layers pruned:** All linear layers (attention Q/K/V/O, FFN)
+- **Validation:** Cosine similarity of embeddings vs dense model ≥ 0.973
+- **Training data:** Pre-trained on 100K ZINC molecules (SMILES)
+
+## Benchmarks (Apple M4 Mac mini, 16GB)
+
+| Task | Time |
+|------|------|
+| Single molecule embedding | 4.9ms |
+| Batch of 12 molecules | ~45ms |
+| Molecular fingerprint + embedding | ~6ms |
+| Drug repurposing (full screen) | ~18s |
+
 ## Part of PharmaCore
 
-[PharmaCore](https://github.com/stephenjun8192/PharmaCore) — Apple Silicon-native AI drug discovery platform.
+[PharmaCore](https://github.com/reacherwu/PharmaCore) — the first AI drug discovery platform that runs entirely on a MacBook. No cloud GPUs, no API keys, no data leaves your machine.
+
+## Citation
+
+```bibtex
+@software{pharmacore2026,
+  title={PharmaCore: Apple Silicon-Native AI Drug Discovery},
+  author={Stephen Wu},
+  year={2026},
+  url={https://github.com/reacherwu/PharmaCore}
+}
+```