diff --git a/examples/qualcomm/oss_scripts/llama/eval_llama_qnn.py b/examples/qualcomm/oss_scripts/llama/eval_llama_qnn.py
index 1105ac0ef82..4e092b71892 100644
--- a/examples/qualcomm/oss_scripts/llama/eval_llama_qnn.py
+++ b/examples/qualcomm/oss_scripts/llama/eval_llama_qnn.py
@@ -20,6 +20,14 @@
     annotate_matmul_16a8w,
 )
 
+from executorch.backends.qualcomm.quantizer.observers.per_channel_param_observer import (
+    PerChannelParamObserver,
+)
+from executorch.backends.qualcomm.quantizer.qconfig import (
+    _derived_bias_quant_spec,
+    QuantizationConfig,
+)
+
 from executorch.backends.qualcomm.quantizer.quantizer import QuantDtype
 from executorch.backends.qualcomm.utils.utils import convert_linear_to_conv2d
 
@@ -47,6 +55,8 @@
 
 from torchao.quantization.pt2e import MinMaxObserver
 from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e
+from torchao.quantization.pt2e.quantizer import QuantizationSpec
+
 
 sys.setrecursionlimit(4096)
 FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
@@ -78,6 +88,33 @@ def forward(
         return self.model.forward(tokens, self.atten_mask)
 
 
+def add_mse_weight_observer(quant_dtype, quantizer):
+    weight_dtype = (
+        torch.int4
+        if quant_dtype in (QuantDtype.use_16a4w, QuantDtype.use_16a4w_block)
+        else torch.int8
+    )
+    per_channel_q_config = quantizer.default_quant_config.quant_config
+    weight_qspec = QuantizationSpec(
+        dtype=torch.int8 if weight_dtype == torch.int4 else weight_dtype,
+        quant_min=(
+            -7 if weight_dtype == torch.int4 else torch.iinfo(weight_dtype).min + 1
+        ),
+        quant_max=(7 if weight_dtype == torch.int4 else torch.iinfo(weight_dtype).max),
+        qscheme=torch.per_channel_symmetric,
+        ch_axis=0,
+        observer_or_fake_quant_ctr=PerChannelParamObserver.with_args(
+            **{"steps": 200, "use_mse": True}
+        ),
+    )
+    quantizer.default_quant_config.per_channel_quant_config = QuantizationConfig(
+        input_activation=per_channel_q_config.input_activation,
+        output_activation=per_channel_q_config.output_activation,
+        weight=weight_qspec,
+        bias=_derived_bias_quant_spec,
+    )
+
+
 def gen_eval_wrapper(model_name, args):
     tokenizer = get_tokenizer(args.tokenizer_path)
     with open(args.params) as f:
@@ -142,13 +179,13 @@ def permute(w, heads):
         if getattr(layer.feed_forward, "prepare_feedfoward_conv", None):
             layer.feed_forward.prepare_feedfoward_conv()
 
-    model.to(dtype=torch.bfloat16)
+    model.to(dtype=torch.float)
     model.to(device=args.device)
 
     tokens, atten_mask = model.get_example_inputs(use_kv_cache=False)
     tokens = tokens.to(device=args.device)
     atten_mask = atten_mask.to(device=args.device)
-    atten_mask = atten_mask.to(dtype=torch.bfloat16)
+    atten_mask = atten_mask.to(dtype=torch.float)
     inputs = (tokens, atten_mask)
 
     if args.embedding_quantize:
@@ -174,7 +211,8 @@ def permute(w, heads):
         )
         quantizer.add_custom_quant_annotations(custom_annotations)
 
-        model.has_quant_io = True
+        if args.range_setting == "mse_weight":
+            add_mse_weight_observer(quant_dtype, quantizer)
 
         with torch.no_grad():
             model = torch.export.export(model, inputs, strict=True).module()
@@ -245,6 +283,23 @@ def main() -> None:
     torch.manual_seed(seed)
     modelname = "llama2"
     parser = build_args_parser()
+    parser.add_argument(
+        "-P",
+        "--ptq",
+        help="If specified, will do PTQ quantization. default is 16bits activation and 4bits weight. Support 8a8w, 16a4w and 16a4w_block.",
+        type=str,
+    )
+    parser.add_argument(
+        "--range_setting",
+        help="Choose which range setting method (e.g. mse_weight). If not specified, will do minmax for weights and activations",
+        type=str,
+    )
+    parser.add_argument(
+        "--limit",
+        help="the number of examples per task (only use this for testing), If <1, limit is a percentage of the total number of examples",
+        type=str,
+    )
+
     args = parser.parse_args()
     args.llama_model = "llama3_2"
     # Overrides this arg, because evaluation requires full logits.
@@ -257,15 +312,9 @@ def main() -> None:
     args.use_kv_cache = False
     args.prefill_ar_len = args.max_seq_length
 
-    # To do fewer samples for faster evaluation
-    args.limit = 0.1
-    # args.samples = {'wikitext': list(range(1))}
-
     args.device = "cuda" if torch.cuda.is_available() else "cpu"
     torch.set_default_device(args.device)
 
-    args.ptq = "8a8w"
-
     eval_llama(modelname, args)