Update README.md

README.md

@@ -1,19 +1,19 @@
----
-license: apache-2.0
-library_name: peft
-tags:
-- trl
-- sft
-- generated_from_trainer
-base_model: mistralai/Mistral-7B-Instruct-v0.3
-model-index:
-- name: mistral-v0.3-alpaca-vi-v1
-  results: []
-language:
-- vi
-- en
-pipeline_tag: text-generation
----
+---
+license: apache-2.0
+library_name: peft
+tags:
+- trl
+- sft
+- generated_from_trainer
+base_model: mistralai/Mistral-7B-Instruct-v0.3
+model-index:
+- name: mistral-v0.3-vi-alpaca
+  results: []
+language:
+- vi
+- en
+pipeline_tag: text-generation
+---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
@@ -32,9 +32,12 @@ It achieves the following results on the evaluation set:
 ## Usage
 
 ```python
-model_name = "mistralai/Mistral-7B-Instruct-v0.3"
-from transformers import AutoModelForCausalLM, BitsAndBytesConfig
+from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer
 import torch
+
+model_name = "mistralai/Mistral-7B-Instruct-v0.3"
+peft_model_id = "date3k2/mistral-v0.3-alpaca-vi-v1"
+
 bnb_config = BitsAndBytesConfig(  
     load_in_8bit= True
 )
@@ -44,16 +47,16 @@ model = AutoModelForCausalLM.from_pretrained(
         device_map="auto",
         trust_remote_code=True,
 )
-from transformers import AutoModelForCausalLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-device = "cuda"
 
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+model.load_adapter(peft_model_id)
+device = "cuda"
 
 messages = [
     {"role": "user", "content": "Bạn là ai?"},
     {"role": "assistant", "content": "Tôi là Dastral, một trợ lý AI đắc lực"},
-    {"role": "user", "content": "Viết công thức để nấu một món bánh mì thịt heo."}
+    {"role": "user", "content": "Viết công thức để nấu một món ngon từ thịt bò."}
 ]
 
 encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")