Update model card

README.md

@@ -14,18 +14,18 @@ scCAFM is a causality-aware foundation model designed for large-scale single-cel
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-## Key Features
-**Structure Foundation Module (SFM)**
+## Key features
+**Structure foundation module (SFM)**
 * Efficient, context-aware causal GRN inference in a latent factor space.
 * Uses a Mixture-of-Experts (MoE) architecture so different latent experts capture distinct regulatory contexts; this enables per-cell GRN specialization without learning a full causal model per cell.
 * Outputs: per-cell directed edges with causal confidence, context assignment, and compact latent summaries.
 
-**Embedding Foundation Module (EFM)**
+**Embedding foundation module (EFM)**
 * Learns gene and cell embeddings guided by the SFM-inferred causal structure (e.g., contrastive/cause-aware objectives).
 * Embeddings are transferable: they improve downstream supervised and unsupervised tasks (drug sensitivity, perturbation response prediction, trajectory/lineage inference).
 
 
-## Model Assets
+## Model assets
 The release package separates biological resources, shared tokenizer assets, and model-specific weights:
 * `resources/human_tfs.csv`
 * `resources/mouse_tfs.csv`