Update README.md

Add use case info, example code for fine-tuning Mystral 7b Instruct.

README.md

@@ -8,4 +8,84 @@ tags:
 pretty_name: Presto/Athena Text to SQL Dataset
 size_categories:
 - 1K<n<10K
----
+---
+
+I created this dataset using [sqlglot](https://github.com/tobymao/sqlglot) to auto-convert the Spider and Wikisql datasets to Presto syntax, along with running some regex's for additional cleanup.
+
+An example use case is fine-tuning an existing model to respond with Presto/Athena text-to-sql, if it performs well at standard SQL syntax used by the major text to sql training datasets.
+
+Example of fine-tuning using this dataset (in this case for Mystral 7b Instruct):
+
+```
+import json
+import pandas as pd
+from datasets import Dataset
+
+def read_jsonl(file_path):
+    data = []
+    with open(file_path, 'r', encoding='utf-8') as file:
+        for line in file:
+            json_data = json.loads(line.strip())
+            data.append(json_data)
+    return data
+
+# Read the train and validation files
+train_data = read_jsonl('training_data/train.jsonl') # use your own path to the training/validation data here
+valid_data = read_jsonl('training_data/valid.jsonl')
+
+# Convert to pandas DataFrame
+train_df = pd.DataFrame(train_data)
+valid_df = pd.DataFrame(valid_data)
+
+# Convert DataFrame to Huggingface Dataset
+train_dataset = Dataset.from_pandas(train_df)
+valid_dataset = Dataset.from_pandas(valid_df)
+
+# Example of processing
+# train_texts = [example['text'] for example in train_dataset]
+# valid_texts = [example['text'] for example in valid_dataset]
+
+instruct_tune_dataset = {
+    "train": train_dataset,
+    "test": valid_dataset
+}
+
+...
+
+def create_prompt(sample):
+  """
+  Update the prompt template:
+  Combine both the prompt and input into a single column.
+
+  """
+  bos_token = "<s>"
+  original_system_message = "table:"
+  system_message = "Use the provided input to create an instruction that could have been used to generate the response with an LLM. The query dialect is Athena/Presto. The database table used is: "
+  question_and_response = sample["text"].replace(original_system_message, "").replace("Q:", "\n\n### Input:\n").replace("A:","\n### Response:\n").strip()
+  eos_token = "</s>"
+
+  full_prompt = ""
+  full_prompt += bos_token
+  full_prompt += "### Instruction:"
+  full_prompt += "\n" + system_message
+  full_prompt += "\n" + question_and_response
+  full_prompt += eos_token
+
+  return full_prompt
+
+...
+
+from trl import SFTTrainer
+
+trainer = SFTTrainer(
+  model=model,
+  peft_config=peft_config,
+  max_seq_length=max_seq_length,
+  tokenizer=tokenizer,
+  packing=True,
+  formatting_func=create_prompt, # this will apply the create_prompt mapping to all training and test dataset
+  args=args,
+  train_dataset=instruct_tune_dataset["train"],
+  eval_dataset=instruct_tune_dataset["test"]
+)
+```