chewwt/dm_qwen4b_emulator

import torch
import torch.nn as nn
from safetensors.torch import load_file
from huggingface_hub import hf_hub_download
 
class MLP(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, output_dim),
        )
    def forward(self, x):
        return self.mlp(x)
 
path = hf_hub_download("chewwt/dm_qwen4b_emulator", "model.safetensors")
model = MLP(input_dim=6, hidden_dim=512, output_dim=3)
model.load_state_dict(load_file(path))
model.eval()
 
with torch.no_grad():
    out = model(torch.randn(1, 6))