Update README.md

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README.md

@@ -35,52 +35,6 @@ model = "arunb74/Luxeai-anu-1-bit-70M"
 tokenizer = AutoTokenizer.from_pretrained(model)
 model = AutoModelForCausalLM.from_pretrained(model)
 
-def activation_norm_quant(x):
-    x = RMSNorm(x)
-    scale = 127.0 / x.abs().max(dim=-1, keepdim=True).values.clamp_(min=1e-5)
-    y = (x * scale).round().clamp_(-128, 127)
-    y = y / scale
-    return y, scale
-
-def weight_quant(w):
-    scale = 1.0 / w.abs().mean().clamp_(min=1e-5)
-    u = (w * scale).round().clamp_(-1, 1)
-    u = u / scale
-    return u
-
-class BitNetInference(nn.Linear):
-    def forward(self, x):
-        w = self.weight # a weight tensor with shape [d, k]
-        x = x.to(w.device)
-        RMSNorm = LlamaRMSNorm(x.shape[-1]).to(w.device)
-        x_norm = RMSNorm(x)
-        # A trick for implementing Straight−Through−Estimator (STE) using detach()
-        x_quant = x_norm + (activation_norm_quant(x_norm) - x_norm).detach()
-        w_quant = w + (weight_quant(w) - w).detach()
-        y = F.linear(x_quant, w_quant)
-        return y
-
-
-def convert_to_bitnet(model, copy_weights):
-    for name, module in model.named_modules():
-        # Replace linear layers with BitNet
-        if isinstance(module, LlamaSdpaAttention) or isinstance(module, LlamaMLP):
-            for child_name, child_module in module.named_children():
-                if isinstance(child_module, nn.Linear):
-                    bitlinear = BitNetInference(child_module.in_features, child_module.out_features, child_module.bias is not None).to(device="cuda:0")
-                    if copy_weights:
-                        bitlinear.weight = child_module.weight
-                        if child_module.bias is not None:
-                            bitlinear.bias = child_module.bias
-                    setattr(module, child_name, bitlinear)
-        # Remove redundant input_layernorms
-        elif isinstance(module, LlamaDecoderLayer):
-            for child_name, child_module in module.named_children():
-                if isinstance(child_module, LlamaRMSNorm) and child_name == "input_layernorm":
-                    setattr(module, child_name, nn.Identity().to(device="cuda:0"))
-
-
-convert_to_bitnet(model, copy_weights=True)
 
 # Create a text generation pipeline
 pipe = pipeline(