diff --git a/quantize/awq_quantize.py b/quantize/awq_quantize.py
index 38afbc9..0a1543b 100644
--- a/quantize/awq_quantize.py
+++ b/quantize/awq_quantize.py
@@ -7,10 +7,11 @@ import os
 
 model_path = '/root/ld/ld_model_pretrained/MiniCPM-1B-sft-bf16' # model_path or model_id
 quant_path = '/root/ld/ld_project/pull_request/MiniCPM/quantize/awq_cpm_1b_4bit' # quant_save_path
-quant_data_path='/root/ld/ld_project/pull_request/MiniCPM/quantize/quantize_data/wikitext'
+quant_data_path='/root/ld/ld_project/pull_request/MiniCPM/quantize/quantize_data/wikitext'# 写入自带
 quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" } # "w_bit":4 or 8
 quant_samples=512 # how many samples to use for calibration
-
+custom_data=[{'question':'你叫什么名字。','answer':'我是openmbmb开源的小钢炮minicpm。'},
+                 {'question':'你有什么特色。','answer':'我很小，但是我很强。'}]
 # Load model
 model = AutoAWQForCausalLM.from_pretrained(model_path)
 tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True,device_map={"": "cuda:0"})
@@ -21,7 +22,7 @@ def load_alpaca(quant_data_path):
 
     # concatenate data
     def concatenate_data(x):
-        return {"text": '<s><用户>'+x['instruction'] + '<AI>' + x['input'] + '\n' + x['output']}
+        return {"text": '<s><用户>'+x['instruction']  + x['input'] + '<AI>' + '\n' + x['output']}
     
     concatenated = data.map(concatenate_data)[:quant_samples]
     return [text for text in concatenated["text"]]
@@ -30,6 +31,9 @@ def load_wikitext(quant_data_path):
     data = load_dataset(quant_data_path,  split="train")
     return [text for text in data["text"] if text.strip() != '' and len(text.split(' ')) > 20][:quant_samples]
 
+def load_cust_data(custom_data):
+    quant_data=['<s><用户>'+i['question'] + '<AI>'  + i['answer'] + '<s>' for i in custom_data]
+    return quant_data[:quant_samples]
 # Quantize
 model.quantize(tokenizer, quant_config=quant_config, calib_data=load_wikitext(quant_data_path=quant_data_path))