diff --git a/egs/librispeech/ASR/zipformer/export-onnx-streaming.py b/egs/librispeech/ASR/zipformer/export-onnx-streaming.py
index 6691c88b7..b32609122 100755
--- a/egs/librispeech/ASR/zipformer/export-onnx-streaming.py
+++ b/egs/librispeech/ASR/zipformer/export-onnx-streaming.py
@@ -343,7 +343,6 @@ def export_encoder_model_onnx(
     encoder_filename: str,
     opset_version: int = 11,
     feature_dim: int = 80,
-    fp16: bool = False,
 ) -> None:
     encoder_model.encoder.__class__.forward = (
         encoder_model.encoder.__class__.streaming_forward
@@ -489,12 +488,6 @@ def export_encoder_model_onnx(
 
     add_meta_data(filename=encoder_filename, meta_data=meta_data)
 
-    if(fp16) :
-        logging.info("Exporting Encoder model in fp16")
-        encoder = onnx.load(encoder_filename)
-        encoder_fp16 = float16.convert_float_to_float16(encoder, keep_io_types=True)
-        onnx.save(encoder_fp16,encoder_filename)
-
 def export_decoder_model_onnx(
     decoder_model: OnnxDecoder,
     decoder_filename: str,
@@ -741,7 +734,6 @@ def main():
         encoder_filename,
         opset_version=opset_version,
         feature_dim=params.feature_dim,
-        fp16=params.fp16,
     )
     logging.info(f"Exported encoder to {encoder_filename}")
 
@@ -766,8 +758,27 @@ def main():
     # Generate int8 quantization models
     # See https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html#data-type-selection
 
-    logging.info("Generate int8 quantization models")
 
+    if(params.fp16) :
+        logging.info("Exporting models in fp16")
+
+        encoder = onnx.load(encoder_filename)
+        encoder_fp16 = float16.convert_float_to_float16(encoder, keep_io_types=True)
+        encoder_filename_fp16 = params.exp_dir / f"encoder-{suffix}.fp16.onnx"
+        onnx.save(encoder_fp16,encoder_filename_fp16)
+
+        decoder = onnx.load(decoder_filename)
+        decoder_fp16 = float16.convert_float_to_float16(decoder, keep_io_types=True)
+        decoder_filename_fp16 = params.exp_dir / f"decoder-{suffix}.fp16.onnx"
+        onnx.save(decoder_fp16,decoder_filename_fp16)
+
+        joiner = onnx.load(joiner_filename)
+        joiner_fp16 = float16.convert_float_to_float16(joiner, keep_io_types=True)
+        joiner_filename_fp16 = params.exp_dir / f"joiner-{suffix}.fp16.onnx"
+        onnx.save(joiner_fp16,joiner_filename_fp16)
+
+    logging.info("Generate int8 quantization models")
+    
     encoder_filename_int8 = params.exp_dir / f"encoder-{suffix}.int8.onnx"
     quantize_dynamic(
         model_input=encoder_filename,