diff --git a/finetune/adapter_v2.py b/finetune/adapter_v2.py
index 2dd3c0bd..3db0e185 100644
--- a/finetune/adapter_v2.py
+++ b/finetune/adapter_v2.py
@@ -71,7 +71,7 @@ def main(
 
     fabric = L.Fabric(
         accelerator="cuda",
-        devices=1,
+        devices=devices,
         strategy=(DeepSpeedStrategy(config=ds_config) if devices > 1 else "auto"),
         precision="bf16-true",
     )
diff --git a/generate/adapter_v2.py b/generate/adapter_v2.py
index 95ae38e3..d6d9ef6a 100644
--- a/generate/adapter_v2.py
+++ b/generate/adapter_v2.py
@@ -65,7 +65,7 @@ def main(
                 device=fabric.device, dtype=dtype, quantization_mode=quantize
         ):
             model = LLaMA.from_name(name)
-            add_adapter_v2_parameters_to_linear_layers(model)
+            add_adapter_v2_parameters_to_linear_layers(model, dtype=dtype, quantize=quantize)
 
         # 1. Load the pretrained weights
         model.load_state_dict(pretrained_checkpoint, strict=False)
@@ -84,10 +84,18 @@ def main(
     prompt_length = encoded.size(0)
 
     t0 = time.perf_counter()
-    y = generate(model, encoded, max_new_tokens, temperature=temperature, top_k=top_k, eos_id=tokenizer.eos_id)
+    y = generate(
+        model,
+        idx=encoded,
+        max_seq_length=max_new_tokens,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        top_k=top_k,
+        eos_id=tokenizer.eos_id
+    )
     t = time.perf_counter() - t0
 
-    model.reset_cache()
+    model.reset_cache()  # technically only needed if max_seq_length varies
     output = tokenizer.decode(y)
     output = output.split("### Response:")[1].strip()
     print(output)
diff --git a/lit_llama/adapter_v2.py b/lit_llama/adapter_v2.py
index 368e695f..3afc7ceb 100644
--- a/lit_llama/adapter_v2.py
+++ b/lit_llama/adapter_v2.py
@@ -26,20 +26,34 @@ def adapter_v2_state_from_state_dict(state_dict: dict) -> dict:
 
 
 def adapter_v2_new_forward(self, input: Tensor) -> Tensor:
+    weight = self.weight
+
+    try:
+        from lit_llama.quantization import Linear8bitLt
+        if isinstance(self, Linear8bitLt):
+            weight = self.dequantize(input.dtype)
+    except:
+        None
     return self.adapter_scale * (
-        F.linear(input, self.weight, self.bias) + self.adapter_bias
+        F.linear(input, weight, self.bias) + self.adapter_bias
     )
 
 
-def adapter_v2_linear_with_bias_and_scale(layer):
-    layer.adapter_bias = torch.nn.Parameter(torch.zeros(layer.weight.shape[0]), requires_grad=True)
-    layer.adapter_scale = torch.nn.Parameter(torch.ones(layer.weight.shape[0]), requires_grad=True)
+def adapter_v2_linear_with_bias_and_scale(layer, dtype=None, quantize=False):
+    weight = layer.weight
+
+    if dtype is not None and quantize:
+        from lit_llama.quantization import Linear8bitLt
+        if isinstance(layer, Linear8bitLt):
+            weight = layer.dequantize(dtype)
+    layer.adapter_bias = torch.nn.Parameter(torch.zeros(weight.shape[0]), requires_grad=True)
+    layer.adapter_scale = torch.nn.Parameter(torch.ones(weight.shape[0]), requires_grad=True)
     bound_method = adapter_v2_new_forward.__get__(layer, layer.__class__)
     setattr(layer, 'forward', bound_method)
     return layer
 
 
-def add_adapter_v2_parameters_to_linear_layers(model):
+def add_adapter_v2_parameters_to_linear_layers(model, dtype=None, quantize=False):
     for module in model.modules():
         if isinstance(module, nn.Linear):
-            adapter_v2_linear_with_bias_and_scale(module)
+            adapter_v2_linear_with_bias_and_scale(module, dtype=dtype, quantize=quantize)
diff --git a/lit_llama/quantization.py b/lit_llama/quantization.py
index 668a39f4..ba1df00c 100644
--- a/lit_llama/quantization.py
+++ b/lit_llama/quantization.py
@@ -74,6 +74,17 @@ def _quantize_weight(self, weight: torch.Tensor) -> None:
             setattr(self.weight, "CB", CB)
             setattr(self.weight, "SCB", SCB)
 
+        def dequantize(self, dtype):
+            if dtype not in [torch.bfloat16, torch.float16, torch.float32]:
+                raise ValueError(f"Invalid dtype: {dtype}. Allowed dtypes are: bfloat16, float16, float32")
+            weight_CB = self.weight.CB
+            weight_SCB = self.weight.SCB
+            # Modify SBC shape if it doesn't match CB
+            if weight_CB.shape[1] != weight_SCB.shape[0]:
+                weight_SCB = weight_SCB.view(weight_SCB.shape[0], 1)
+            result = (weight_CB * weight_SCB) / 127
+            result = result.to(dtype)
+            return result
 
 if triton is not None:
     # This is adapted from the OpenAI Triton matmul example.