Update README.md

README.md

@@ -43,6 +43,34 @@ This adapter was created through **instruction tuning**.
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 To use this LoRA adapter, refer to the following code:
 
+### Load Apdater
+
+```
+from transformers import BitsAndBytesConfig
+
+def get_bnb_config(bit=8):
+    if bit == 8:
+        return BitsAndBytesConfig(load_in_8bit=True)
+    else:
+        print(f"You put {bit} bit in argument.\nWhatever the number you put in, if it is not 8 then 4bit config would be returned.")
+        return BitsAndBytesConfig(load_in_4bit=True)
+```
+
+```
+from unsloth import FastLanguageModel
+
+model_name = "unsloth/Qwen2.5-Coder-32B-Instruct"
+adapter_revision = "checkpoint-200" # checkpoint-100 ~ 350, main(which is checkpoint-384)
+
+bnb_config = get_bnb_config(bit=8)
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=model_name,
+    dtype=None,
+    quantization_config=bnb_config,
+)
+model.load_adapter("100suping/Qwen2.5-Coder-34B-Instruct-kosql-adapter", revision=adapter_revision)
+```
+
 ### Prompt
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
@@ -60,29 +88,6 @@ GENERATE_QUERY_INSTRUCTIONS = """
 """
 ```
 
-### Preprocess Functions
-
-```
-def get_conversation_data(examples):
-    questions = examples['question']
-    schemas =examples['schema']
-    sql_queries =examples['SQL']
-    convos = []
-    for question, schema, sql in zip(questions, schemas, sql_queries):
-        conv = [
-        {"role": "system", "content": GENERAL_QUERY_PREFIX.format(context=schema) + GENERATE_QUERY_INSTRUCTIONS},
-        {"role": "user", "content": question},
-        {"role": "assistant", "content": "```sql\n"+sql+";\n```"}
-        ]
-        convos.append(conv)
-    return {"conversation":convos,}
-
-def formatting_prompts_func(examples):
-    convos = examples["conversation"]
-    texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]
-    return { "text" : texts, }
-```
-
 ### Example input
 
 ```
@@ -164,9 +169,27 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 ```
 ```
 
-#### Preprocessing [optional]
+### Preprocess Functions
 
 ```
+def get_conversation_data(examples):
+    questions = examples['question']
+    schemas =examples['schema']
+    sql_queries =examples['SQL']
+    convos = []
+    for question, schema, sql in zip(questions, schemas, sql_queries):
+        conv = [
+        {"role": "system", "content": GENERAL_QUERY_PREFIX.format(context=schema) + GENERATE_QUERY_INSTRUCTIONS},
+        {"role": "user", "content": question},
+        {"role": "assistant", "content": "```sql\n"+sql+";\n```"}
+        ]
+        convos.append(conv)
+    return {"conversation":convos,}
+
+def formatting_prompts_func(examples):
+    convos = examples["conversation"]
+    texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]
+    return { "text" : texts, }
 ```
 
 #### Training Hyperparameters