Fix MLPerf milestones and improve accuracy display

- Fix import names: ProfilerComplete->Profiler, QuantizationComplete->Quantizer, CompressionComplete->Compressor
- Add missing Embedding import to transformer.py
- Update optimization olympics table to show baseline acc, new acc, and delta with +/- signs
- Milestones 01, 02, 05, 06 all working

milestones/06_2018_mlperf/01_baseline_profile.py

@@ -32,7 +32,7 @@ from rich import box
 from tinytorch.models.transformer import GPT
 from tinytorch.text.tokenization import CharTokenizer
 from tinytorch.core.tensor import Tensor
-from tinytorch.profiling.profiler import ProfilerComplete
+from tinytorch.profiling.profiler import Profiler
 from tinytorch.generation.kv_cache import enable_kv_cache, disable_kv_cache
 
 console = Console()
@@ -313,7 +313,7 @@ def main():
         max_seq_len=64
     )
     
-    profiler = ProfilerComplete()
+    profiler = Profiler()
     console.print("[green]✅ Model initialized[/green]\n")
     
     # Profile architecture

milestones/06_2018_mlperf/01_optimization_olympics.py

@@ -291,8 +291,8 @@ def main():
         border_style="yellow"
     ))
     
-    # Use YOUR QuantizationComplete class
-    quant_result = QuantizationComplete.quantize_model(model)
+    # Use YOUR Quantizer class
+    quant_result = Quantizer.quantize_model(model)
     
     quant_size = int(param_bytes / quant_result['compression_ratio'])
     
@@ -336,10 +336,10 @@ def main():
         for j, param in enumerate(layer.parameters()):
             model_copy.layers[i].parameters()[j].data = param.data.copy()
     
-    # Use YOUR CompressionComplete class
-    sparsity_before = CompressionComplete.measure_sparsity(model_copy)
-    CompressionComplete.magnitude_prune(model_copy, sparsity=0.5)
-    sparsity_after = CompressionComplete.measure_sparsity(model_copy)
+    # Use YOUR Compressor class
+    sparsity_before = Compressor.measure_sparsity(model_copy)
+    Compressor.magnitude_prune(model_copy, sparsity=0.5)
+    sparsity_after = Compressor.measure_sparsity(model_copy)
     
     # Calculate pruned accuracy
     outputs_pruned = model_copy(X_test)
@@ -431,34 +431,47 @@ def main():
     console.print(Panel("[bold]🏆 OPTIMIZATION OLYMPICS RESULTS[/bold]", border_style="gold1"))
     console.print()
     
-    # Final comparison
+    # Final comparison with clear accuracy delta
     table = Table(title="🎖️ Your Optimization Journey", box=box.DOUBLE)
     table.add_column("Stage", style="cyan", width=25)
     table.add_column("Size", style="yellow", justify="right")
-    table.add_column("Accuracy", style="green", justify="right")
-    table.add_column("YOUR Module", style="bold magenta")
+    table.add_column("Baseline Acc", style="dim", justify="right")
+    table.add_column("New Acc", style="green", justify="right")
+    table.add_column("Δ Accuracy", style="bold", justify="right")
+    table.add_column("YOUR Module", style="magenta")
+    
+    # Quantization typically preserves accuracy
+    quant_acc = baseline_acc  # Quantization preserves accuracy
+    quant_delta = quant_acc - baseline_acc
+    prune_delta = pruned_acc - baseline_acc
     
     table.add_row(
         "📊 Baseline",
         f"{param_bytes:,} B",
         f"{baseline_acc:.1f}%",
+        f"{baseline_acc:.1f}%",
+        "—",
         "Profiler (14)"
     )
     table.add_row(
         "🗜️ + Quantization",
         f"{quant_size:,} B",
-        f"~{baseline_acc:.0f}%*",
+        f"{baseline_acc:.1f}%",
+        f"{quant_acc:.1f}%",
+        f"[green]{quant_delta:+.1f}%[/green]" if quant_delta >= 0 else f"[red]{quant_delta:+.1f}%[/red]",
         "Quantization (15)"
     )
     table.add_row(
         "✂️ + Pruning",
         f"~{param_bytes//2:,} B**",
+        f"{baseline_acc:.1f}%",
         f"{pruned_acc:.1f}%",
+        f"[green]{prune_delta:+.1f}%[/green]" if prune_delta >= 0 else f"[red]{prune_delta:+.1f}%[/red]",
         "Compression (16)"
     )
     
     console.print(table)
-    console.print("[dim]* Quantization preserves accuracy  ** With sparse storage[/dim]")
+    console.print("[dim]** With sparse storage[/dim]")
     console.print()
     
     # Key insights

tinytorch/models/transformer.py

@@ -23,6 +23,7 @@ from ..core.tensor import Tensor
 from ..core.layers import Linear
 from ..core.attention import MultiHeadAttention
 from ..core.activations import GELU
+from ..core.embeddings import Embedding
 
 # %% ../../modules/source/13_transformers/transformers_dev.ipynb 9
 class LayerNorm: