Instructions to use replicate/moe-new-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use replicate/moe-new-models with Kernels:
# !pip install kernels from kernels import get_kernel # a version (or an explicit revision) is required; see the "Files and versions" tab for the available ones kernel = get_kernel("replicate/moe-new-models", version=1) - Notebooks
- Google Colab
- Kaggle
Fix Llama expert layout
Browse files- torch-ext/moe/layers.py +10 -0
torch-ext/moe/layers.py
CHANGED
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@@ -16,6 +16,14 @@ def _llama4_topk(
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return topk_weights, topk_ids
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class Llama4TextMoe(nn.Module):
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experts: nn.Module
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router: nn.Linear
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@@ -25,6 +33,8 @@ class Llama4TextMoe(nn.Module):
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = hidden_states.reshape(-1, hidden_states.shape[-1])
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router_logits = self.router(hidden_states)
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out = fused_moe(
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hidden_states,
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return topk_weights, topk_ids
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def _fix_llama4_experts(hidden_states: torch.Tensor, experts: nn.Module):
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if experts.gate_up_proj.shape[-1] != hidden_states.shape[-1]:
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experts.gate_up_proj = nn.Parameter(
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experts.gate_up_proj.transpose(1, 2).contiguous()
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)
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experts.down_proj = nn.Parameter(experts.down_proj.transpose(1, 2).contiguous())
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class Llama4TextMoe(nn.Module):
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experts: nn.Module
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router: nn.Linear
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = hidden_states.reshape(-1, hidden_states.shape[-1])
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_fix_llama4_experts(hidden_states, self.experts)
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router_logits = self.router(hidden_states)
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out = fused_moe(
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hidden_states,
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