Update README.md

README.md

@@ -7,7 +7,73 @@ tags:
 - pre_training
 paper: "Tokens-per-Parameter Coverage Is Critical for Robust LLM Scaling Law Extrapolation"
 venue: "NeurIPS 2026"
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "**/*.json"
+dataset_info:
+  features:
+  - name: checkpoint_prefix
+    dtype: string
+  - name: timestamp
+    dtype: string
+  - name: status
+    dtype: string
+  - name: model_size
+    dtype: int64
+  - name: d_model
+    dtype: int64
+  - name: n_layers
+    dtype: int64
+  - name: n_heads
+    dtype: int64
+  - name: d_ff
+    dtype: int64
+  - name: vocab_size
+    dtype: int64
+  - name: max_seq_len
+    dtype: int64
+  - name: learning_rate
+    dtype: float64
+  - name: epochs
+    dtype: int64
+  - name: batch_size
+    dtype: int64
+  - name: weight_decay
+    dtype: float64
+  - name: seed
+    dtype: int64
+  - name: deterministic
+    dtype: bool
+  - name: cudnn_deterministic
+    dtype: bool
+  - name: cudnn_benchmark
+    dtype: bool
+  - name: total_tokens
+    dtype: int64
+  - name: total_steps
+    dtype: int64
+  - name: cumulative_time_seconds
+    dtype: float64
+  - name: val_epochs_count
+    dtype: int64
+  - name: mean_val_loss
+    dtype: float64
+  - name: best_val_loss
+    dtype: float64
+  - name: final_train_loss
+    dtype: float64
+  - name: std_train_loss
+    dtype: float64
+  - name: mean_train_loss
+    dtype: float64
+  - name: global_batch_tokens
+    dtype: int64
+  - name: final_val_loss
+    dtype: float64
 ---
+
 # Collinear/Non-Collinear Scaling Models
 
 Checkpoint repository for scaling law experiments comparing **collinear (CO)** and **non-collinear (NC)** experimental designs for the paper *Tokens-per-Parameter Coverage Is Critical for Robust LLM Scaling Law Extrapolation* under review for NeurIPS 2026.