Update model.safetensors

model.safetensors

@@ -1,3 +1,11 @@
+#!/usr/bin/env python3
+# smartbloom_transformer.py - Smartbloom 1.1 Advanced Transformer Model
+# A hypothetical, ultra-advanced transformer with ~674T parameters to surpass BaGuaLu's 174T
+# Sharded into 974 files for practicality
+# Incorporates hierarchical MoE, dynamic multi-query attention with RoPE, and optimization
+# Created for maximal power and intelligence, inspired by xAI principles
+# Current date: March 10, 2025
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -30,7 +38,7 @@ class RotaryPositionEmbedding(nn.Module):
         return (x * cos + x_rot * sin).view_as(x)
 
 # ========================
-# ✅ Dynamic Multi-Query Attention with RoPE and Speculative Decoding
+# ✅ Dynamic Multi-Query Attention with RoPE
 # ========================
 class DynamicMultiQueryAttention(nn.Module):
     def __init__(self, hidden_size: int, num_heads: int, dropout: float = 0.05, max_position_embeddings: int = 65536):
@@ -159,14 +167,14 @@ class SmartbloomLayer(nn.Module):
 class SmartbloomTransformer(nn.Module):
     def __init__(
         self,
-        vocab_size: int = 250000,       # Larger than BaGuaLu
-        hidden_size: int = 81920,       # Ultra-wide
-        num_layers: int = 98304,        # Ultra-deep to beat BaGuaLu
-        num_heads: int = 640,           # More heads
-        num_experts: int = 32768,       # Double BaGuaLu's 90,000 experts
-        top_k: int = 4,                 # Top-k routing
-        intermediate_size: int = 327680,# Massive FFN
-        max_position_embeddings: int = 65536  # Double BaGuaLu's context
+        vocab_size: int = 250000,
+        hidden_size: int = 81920,
+        num_layers: int = 98304,
+        num_heads: int = 640,
+        num_experts: int = 32768,
+        top_k: int = 4,
+        intermediate_size: int = 327680,
+        max_position_embeddings: int = 65536
     ):
         super(SmartbloomTransformer, self).__init__()
         
@@ -223,41 +231,66 @@ model = SmartbloomTransformer(
 )
 
 # ========================
-# ✅ Sharded Save Model Weights to Safetensors
+# ✅ Sharded Save Model Weights to 974 Files
 # ========================
 def save_smartbloom():
     os.makedirs("smartbloom_shards", exist_ok=True)
-    # Save embeddings and output layer
+    total_shards = 974
+    layers_per_shard = 98304 // (total_shards - 2)  # 972 shards for layers, 2 for embeddings/output
+    
+    # Shard 0: Embeddings
     embed_state_dict = {
         "embedding.weight": model.embedding.weight,
-        "pos_embedding.weight": model.pos_embedding.weight,
+        "pos_embedding.weight": model.pos_embedding.weight
+    }
+    save_model(embed_state_dict, "smartbloom_shards/shard_000.safetensors")
+    
+    # Shards 1 to 972: Layers
+    for shard_idx in range(total_shards - 2):  # 972 shards
+        start_layer = shard_idx * layers_per_shard
+        end_layer = min((shard_idx + 1) * layers_per_shard, 98304)
+        shard_state_dict = {}
+        for i in range(start_layer, end_layer):
+            layer = model.layers[i]
+            for k, v in layer.state_dict().items():
+                shard_state_dict[f"layer_{i}.{k}"] = v
+        save_model(shard_state_dict, f"smartbloom_shards/shard_{shard_idx + 1:03d}.safetensors")
+    
+    # Shard 973: Output layer and final norm
+    output_state_dict = {
         "norm.weight": model.norm.weight,
         "norm.bias": model.norm.bias,
         "output_layer.weight": model.output_layer.weight,
         "output_layer.bias": model.output_layer.bias
     }
-    save_model(embed_state_dict, "smartbloom_shards/embeddings.safetensors")
-    
-    # Save each layer separately
-    for i, layer in enumerate(model.layers):
-        layer_state_dict = {f"layer_{i}.{k}": v for k, v in layer.state_dict().items()}
-        save_model(layer_state_dict, f"smartbloom_shards/layer_{i}.safetensors")
+    save_model(output_state_dict, f"smartbloom_shards/shard_{total_shards - 1:03d}.safetensors")
 
 # ========================
-# ✅ Sharded Load Model Weights from Safetensors
+# ✅ Sharded Load Model Weights from 974 Files
 # ========================
 def load_smartbloom():
-    # Load embeddings and output layer
-    embed_state_dict = load_model("smartbloom_shards/embeddings.safetensors")
+    total_shards = 974
+    layers_per_shard = 98304 // (total_shards - 2)
+    
+    # Load Shard 0: Embeddings
+    embed_state_dict = load_model("smartbloom_shards/shard_000.safetensors")
     model.embedding.load_state_dict({"weight": embed_state_dict["embedding.weight"]})
     model.pos_embedding.load_state_dict({"weight": embed_state_dict["pos_embedding.weight"]})
-    model.norm.load_state_dict({"weight": embed_state_dict["norm.weight"], "bias": embed_state_dict["norm.bias"]})
-    model.output_layer.load_state_dict({"weight": embed_state_dict["output_layer.weight"], "bias": embed_state_dict["output_layer.bias"]})
     
-    # Load each layer
-    for i, layer in enumerate(model.layers):
-        layer_state_dict = load_model(f"smartbloom_shards/layer_{i}.safetensors")
-        layer.load_state_dict({k.split('.', 1)[1]: v for k, v in layer_state_dict.items()})
+    # Load Shards 1 to 972: Layers
+    for shard_idx in range(total_shards - 2):
+        start_layer = shard_idx * layers_per_shard
+        end_layer = min((shard_idx + 1) * layers_per_shard, 98304)
+        shard_state_dict = load_model(f"smartbloom_shards/shard_{shard_idx + 1:03d}.safetensors")
+        for i in range(start_layer, end_layer):
+            layer = model.layers[i]
+            layer_state_dict = {k.split('.', 1)[1]: v for k, v in shard_state_dict.items() if k.startswith(f"layer_{i}.")}
+            layer.load_state_dict(layer_state_dict)
+    
+    # Load Shard 973: Output layer and norm
+    output_state_dict = load_model(f"smartbloom_shards/shard_{total_shards - 1:03d}.safetensors")
+    model.norm.load_state_dict({"weight": output_state_dict["norm.weight"], "bias": output_state_dict["norm.bias"]})
+    model.output_layer.load_state_dict({"weight": output_state_dict["output_layer.weight"], "bias": output_state_dict["output_layer.bias"]})
 
 # ========================
 # 🚀 Example Usage