Update README.md

README.md

@@ -10,6 +10,8 @@ tags:
 - gemma2
 - trl
 - sft
+datasets:
+- Clinton/Text-to-sql-v1
 ---
 
 # Uploaded  model
@@ -21,3 +23,47 @@ tags:
 This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+
+
+
+
+
+## Model Information
+
+Summary description and brief definition of inputs and outputs.
+
+### Description
+
+This model is based on Gemma2 and is fine-tuned to generate SQL from Natural Language.
+
+### Usage
+
+Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with:
+```sh
+pip install -U transformers
+...
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("circlelee/gemma-2-2b-it-nl2sql")
+tokenizer = AutoTokenizer.from_pretrained("circlelee/gemma-2-2b-it-nl2sql", trust_remote_code=True)
+
+table_schemas = "CREATE TABLE person ( name VARCHAR, age INTEGER, address VARCHAR )"
+user_query = "people whoes ages are older than 27 and name starts with letter 'k'"
+messages = [
+        {"role": "user", "content": f"""Use the below SQL tables schemas paired with instruction that describes a task. make SQL query that appropriately completes the request for the provided tables. And make SQL query according the steps.
+{table_schemas}
+step 1. check columns that I want.
+step 2. check condition that I want.
+step 3. make SQL query to get every information that I want.
+
+{user_query}
+"""}
+]
+
+formated_messages = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, return_tensors="pt")
+input_ids = tokenizer(formated_messages, return_tensors="pt")
+
+outputs = model.generate(**input_ids, max_new_tokens=64)
+print(tokenizer.decode(outputs[0]))
+```