Added Metrics Explanation

README-huggingface.md

@@ -113,6 +113,20 @@ The model shows strong performance across key metrics:
 - **Response Coherence:** 82.1%
 - **Peer Network Efficiency:** 91.2%
 
+### Understanding the Metrics
+
+- **Training Progress**: Two complete passes over training data, totaling 20,000 batched steps (10,000 steps per epoch with 8 samples per batch).
+
+- **Model Scale**: Core neural network and distributed coordination components combine to 1.82 GB, including parameter tensors and peer synchronization data structures.
+
+- **Convergence Metric**: Final validation phase showed 7.11 cross-entropy divergence between model predictions and reference sequences, computed at the token level.
+
+- **Token Precision**: In out-of-sample testing, 78.5% of the model's next-token selections matched the reference completions across all validation sequences.
+
+- **Output Quality**: Automated analysis of 82.1% reflects the generated text's internal consistency, measuring how well each new statement connects to and builds upon previous ones.
+
+- **Network Performance**: Distributed training achieved 91.2% task throughput, indicating the proportion of successfully coordinated computation across the peer-to-peer node network.
+
 ## Limitations & Biases
 
 1. **Current Limitations:**

README.md

@@ -105,6 +105,20 @@ Initial testing shows promising results:
 - **Response Coherence:** 82.1%
 - **Peer Network Efficiency:** 91.2%
 
+### Metrics Explanation
+
+- **Training Progress**: Two complete passes over training data, totaling 20,000 batched steps (10,000 steps per epoch with 8 samples per batch).
+
+- **Model Scale**: Core neural network and distributed coordination components combine to 1.82 GB, including parameter tensors and peer synchronization data structures.
+
+- **Convergence Metric**: Final validation phase showed 7.11 cross-entropy divergence between model predictions and reference sequences, computed at the token level.
+
+- **Token Precision**: In out-of-sample testing, 78.5% of the model's next-token selections matched the reference completions across all validation sequences.
+
+- **Output Quality**: Automated analysis of 82.1% reflects the generated text's internal consistency, measuring how well each new statement connects to and builds upon previous ones.
+
+- **Network Performance**: Distributed training achieved 91.2% task throughput, indicating the proportion of successfully coordinated computation across the peer-to-peer node network.
+
 ## Limitations & Biases
 
 1. **Current Limitations:**