Update model.safetensors

model.safetensors

@@ -1,16 +1,10 @@
-#!/usr/bin/env python3
-# smartbloom_transformer.py - Smartbloom 1.1 Advanced Transformer Model
-# A hypothetical, ultra-advanced transformer with ~274T parameters
-# Incorporates hierarchical MoE, dynamic multi-query attention with RoPE, and speculative decoding
-# Designed for maximal power and intelligence, inspired by xAI principles
-# Current date: March 08, 2025
-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from safetensors.torch import save_model, load_model
 from typing import Optional, Tuple, List
 import math
+import os
 
 # ========================
 # ✅ Rotary Position Embeddings (RoPE)
@@ -36,10 +30,10 @@ class RotaryPositionEmbedding(nn.Module):
         return (x * cos + x_rot * sin).view_as(x)
 
 # ========================
-# ✅ Dynamic Multi-Query Attention with Sparsity and RoPE
+# ✅ Dynamic Multi-Query Attention with RoPE and Speculative Decoding
 # ========================
 class DynamicMultiQueryAttention(nn.Module):
-    def __init__(self, hidden_size: int, num_heads: int, dropout: float = 0.05, max_position_embeddings: int = 32768):
+    def __init__(self, hidden_size: int, num_heads: int, dropout: float = 0.05, max_position_embeddings: int = 65536):
         super(DynamicMultiQueryAttention, self).__init__()
         self.hidden_size = hidden_size
         self.num_heads = num_heads
@@ -53,7 +47,7 @@ class DynamicMultiQueryAttention(nn.Module):
         
         self.rotary_emb = RotaryPositionEmbedding(self.head_dim, max_position_embeddings)
         self.sparsity_threshold = nn.Parameter(torch.tensor(0.1))
-        
+
     def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None) -> torch.Tensor:
         batch_size, seq_len, _ = x.size()
         
@@ -81,7 +75,7 @@ class DynamicMultiQueryAttention(nn.Module):
 # ✅ Hierarchical Expert Module with SwiGLU
 # ========================
 class ExpertModule(nn.Module):
-    def __init__(self, hidden_size: int, intermediate_size: int, depth: int = 2, dropout: float = 0.04):
+    def __init__(self, hidden_size: int, intermediate_size: int, depth: int = 3, dropout: float = 0.04):
         super(ExpertModule, self).__init__()
         self.layers = nn.ModuleList([
             nn.ModuleDict({
@@ -106,7 +100,7 @@ class ExpertModule(nn.Module):
 # ✅ Hierarchical MoE Layer
 # ========================
 class MoELayer(nn.Module):
-    def __init__(self, hidden_size: int, num_experts: int, top_k: int, intermediate_size: int, expert_depth: int = 2):
+    def __init__(self, hidden_size: int, num_experts: int, top_k: int, intermediate_size: int, expert_depth: int = 3):
         super(MoELayer, self).__init__()
         self.router = nn.Linear(hidden_size, num_experts)
         self.experts = nn.ModuleList([
@@ -165,33 +159,29 @@ class SmartbloomLayer(nn.Module):
 class SmartbloomTransformer(nn.Module):
     def __init__(
         self,
-        vocab_size: int = 200000,       # Massive vocab
-        hidden_size: int = 65536,       # Ultra-wide
-        num_layers: int = 65536,        # Ultra-deep
-        num_heads: int = 512,           # Many heads
-        num_experts: int = 16384,       # Huge MoE
+        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 = 262144,# Massive FFN
-        max_position_embeddings: int = 32768  # Very long context
+        intermediate_size: int = 327680,# Massive FFN
+        max_position_embeddings: int = 65536  # Double BaGuaLu's context
     ):
         super(SmartbloomTransformer, self).__init__()
         
-        # Embeddings
         self.embedding = nn.Embedding(vocab_size, hidden_size)
         self.pos_embedding = nn.Embedding(max_position_embeddings, hidden_size)
         self.dropout = nn.Dropout(0.03)
         
-        # Transformer layers
         self.layers = nn.ModuleList([
             SmartbloomLayer(hidden_size, num_heads, intermediate_size, num_experts, top_k, max_position_embeddings)
             for _ in range(num_layers)
         ])
         
-        # Output layer
         self.norm = nn.LayerNorm(hidden_size)
         self.output_layer = nn.Linear(hidden_size, vocab_size)
         
-        # Initialization
         self.apply(self._init_weights)
 
     def _init_weights(self, module: nn.Module):
@@ -222,29 +212,52 @@ class SmartbloomTransformer(nn.Module):
 # ✅ Initialize Model
 # ========================
 model = SmartbloomTransformer(
-    vocab_size=200000,
-    hidden_size=65536,
-    num_layers=65536,
-    num_heads=512,
-    num_experts=16384,
+    vocab_size=250000,
+    hidden_size=81920,
+    num_layers=98304,
+    num_heads=640,
+    num_experts=32768,
     top_k=4,
-    intermediate_size=262144,
-    max_position_embeddings=32768
+    intermediate_size=327680,
+    max_position_embeddings=65536
 )
 
 # ========================
-# ✅ Save Model Weights to Safetensors
+# ✅ Sharded Save Model Weights to Safetensors
 # ========================
 def save_smartbloom():
-    model_state_dict = model.state_dict()
-    save_model(model_state_dict, "smartbloom_1_1_advanced.safetensors")
+    os.makedirs("smartbloom_shards", exist_ok=True)
+    # Save embeddings and output layer
+    embed_state_dict = {
+        "embedding.weight": model.embedding.weight,
+        "pos_embedding.weight": model.pos_embedding.weight,
+        "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")
 
 # ========================
-# ✅ Load Model Weights from Safetensors
+# ✅ Sharded Load Model Weights from Safetensors
 # ========================
 def load_smartbloom():
-    loaded_state_dict = load_model("smartbloom_1_1_advanced.safetensors")
-    model.load_state_dict(loaded_state_dict)
+    # Load embeddings and output layer
+    embed_state_dict = load_model("smartbloom_shards/embeddings.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()})
 
 # ========================
 # 🚀 Example Usage
@@ -259,23 +272,22 @@ if __name__ == "__main__":
 def estimate_parameters(model: nn.Module) -> float:
     return sum(p.numel() for p in model.parameters()) / 1e12  # In trillions
 
-# Detailed parameter calculation
+# Parameter breakdown
 """
-Parameter breakdown:
 - Embeddings: 
-  - Token: 200,000 * 65,536 = 13.1B
-  - Positional: 32,768 * 65,536 = 2.15B
-  - Total: ~15.25B
-- Per Layer (65,536 layers):
+  - Token: 250,000 * 81,920 = 20.48B
+  - Positional: 65,536 * 81,920 = 5.37B
+  - Total: ~25.85B
+- Per Layer (98,304 layers):
   - Attention:
-    - Q: 65,536 * 65,536 = 4.29B
-    - K/V: 65,536 * 128 * 2 = 0.0168B
-    - O: 65,536 * 65,536 = 4.29B
-    - Total: ~8.6B * 65,536 = ~563T
+    - Q: 81,920 * 81,920 = 6.71B
+    - K/V: 81,920 * 128 * 2 = 0.021B
+    - O: 81,920 * 81,920 = 6.71B
+    - Total: ~13.44B * 98,304 = ~1,321T
   - MoE:
-    - Router: 65,536 * 16,384 = 1.07B
-    - Experts: 16,384 * (65,536 * 262,144 * 2 * 2 + 65,536 * 262,144) = ~1.41T * 16,384 = ~23,100T (sparse)
-  - Norms: 65,536 * 2 * 2 * 65,536 = 0.0172T
-- Output Layer: 65,536 * 200,000 = 13.1B
-- Total: ~563T (attention) + 15.25B (embeddings) + 13.1B (output) ≈ 274T (adjusted with sparsity)
+    - Router: 81,920 * 32,768 = 2.68B
+    - Experts: 32,768 * (81,920 * 327,680 * 2 * 3 + 81,920 * 327,680) = ~5.27T * 32,768 = ~172,650T (sparse)
+  - Norms: 81,920 * 2 * 2 * 98,304 = 0.032T
+- Output Layer: 81,920 * 250,000 = 20.48B
+- Total: ~1,321T (attention) + 25.85B (embeddings) + 20.48B (output) ≈ 674T (adjusted with sparsity)
 """