Update README.md

README.md

@@ -1,47 +1,81 @@
 ---
 library_name: peft
 base_model: declare-lab/flan-alpaca-base
+datasets:
+- knowrohit07/know_sql
 ---
 
-# Model Card for Model ID
+## Model Details
 
-<!-- Provide a quick summary of what the model is/does. -->
+### Model Description
 
+This model is based on the declare-lab/flan-alpaca-base model finetuned with knowrohit07/know_sql dataset.
 
+- **Developed by:** Jonathan Jordan
+- **Model type:** [FLAN Alpaca]
+- **Language(s) (NLP):** [English]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [declare-lab/flan-alpaca-base]
 
-## Model Details
+## Uses
 
-### Model Description
+The model generates a string of SQL query based on a question and MySQL table schema. 
+You can modify the table schema to match MySQL table schema if you are using different type of SQL database (e.g. PostgreSQL, Oracle, etc).
+The generated SQL query can be run perfectly on the python SQL connection (e.g. psycopg2, mysql_connector, etc).
 
-<!-- Provide a longer summary of what this model is. -->
+#### Limitations
+1. The question MUST be in english
+2. Keep in mind about the difference in data type naming between MySQL and the other SQL databases
+3. The output always starts with SELECT *, you can't choose which columns to retrieve.
+4. Aggregation function is not supported
 
+### Input Example
+```python
+"""Question: what is What was the result of the election in the Florida 18 district?\nTable: table_1341598_10 (result VARCHAR, district VARCHAR)\nSQL: """
+```
+### Output Example
+```python
+"""SELECT * FROM table_1341598_10 WHERE district = "Florida 18""""
+```
 
+### How to use
+Load model
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+```python
+from peft import get_peft_config, get_peft_model, TaskType
+from peft import PeftConfig, PeftModel
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-### Model Sources [optional]
+model_id = "jonathanjordan21/flan-alpaca-base-finetuned-lora-knowSQL"
+config = PeftConfig.from_pretrained(model_id)
+model_ = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, return_dict=True)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
 
-<!-- Provide the basic links for the model. -->
+model = PeftModel.from_pretrained(model_, model_id)
+model = get_peft_model(model,config)
+```
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+Model inference
 
-## Uses
+```python
+question = "server of user id 11 with status active and server id 10"
+table = "table_name_77 ( user id INTEGER, status VARCHAR, server id INTEGER )"
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+test = f"""Question: {question}\nTable: {table}\nSQL: """
 
-### Direct Use
+p = tokenizer(test, return_tensors='pt')
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+print("output :", tokenizer.batch_decode(model.to(device).generate(**p.to(device),max_new_tokens=50),skip_special_tokens=True)[0])
+
+```
+
+## Performance
+
+### Speed Performance
+The model inference takes about 2-3 seconds to run with Google Colab Free Tier CPU
 
-[More Information Needed]
 
 ### Downstream Use [optional]
 
@@ -205,4 +239,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 ### Framework versions
 
 
-- PEFT 0.6.2
+- PEFT 0.6.2