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48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
"""
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Gpt5 model implementation
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Parameters: {
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"unlimited_context": true,
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"n_head": 300,
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"active_experts": 16,
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"n_embd": 22528,
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"params": "1000B",
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"n_layer": 300,
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"experts": 300,
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"block_size": 1000000000,
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"moe": true
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}
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"""
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import torch
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import torch.nn as nn
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from configs.gpt5_config import Gpt5Config
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# Advanced architecture with unlimited context and MoE
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from dynamic_context import DynamicContextManager, DynamicContextConfig
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class Gpt5AdvancedTransformer(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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# Advanced architecture
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self.num_experts = config.num_experts
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self.num_active_experts = config.num_active_experts
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# Initialize dynamic context manager
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context_config = DynamicContextConfig(
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max_memory_mb=0, # Auto-detect
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begin_tokens_to_preserve=config.preserve_begin_tokens,
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end_tokens_to_preserve=config.preserve_end_tokens,
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)
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self.context_manager = DynamicContextManager(context_config)
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print("Initializing {} model with {} parameters".format(
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"Gpt5", config.parameter_count))
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print("Using {} experts with {} active per token".format(
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self.num_experts, self.num_active_experts))
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print("Unlimited context enabled with dynamic token management")
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def forward(self, x, **kwargs):
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# This implementation would handle the full model forward pass
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# with dynamic context management and MoE routing
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pass |