Update README.md

README.md

@@ -1,208 +1,100 @@
 ---
 library_name: peft
 base_model: declare-lab/flan-alpaca-base
+datasets:
+- knowrohit07/know_sql
+license: mit
+language:
+- en
+pipeline_tag: text2text-generation
+tags:
+- sql
+- query
+- database
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
+This model is based on the declare-lab/flan-alpaca-base model finetuned with knowrohit07/know_sql dataset.
 
-
-- **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]
+- **Developed by:** Jonathan Jordan
+- **Model type:** FLAN Alpaca
+- **Language(s) (NLP):** English
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Finetuned from model:** declare-lab/flan-alpaca-base
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
+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).
 
-[More Information Needed]
+#### 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""""
+```
 
-#### Training Hyperparameters
+### How to use
+Load model
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+```python
+from peft import get_peft_config, get_peft_model, TaskType
+from peft import PeftConfig, PeftModel
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-#### Speeds, Sizes, Times [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)
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+model = PeftModel.from_pretrained(model_, model_id)
+model = get_peft_model(model,config)
+```
 
-[More Information Needed]
+Model inference
 
-## Evaluation
+```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 )"
 
-<!-- This section describes the evaluation protocols and provides the results. -->
 
-### Testing Data, Factors & Metrics
 
-#### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
 
-[More Information Needed]
+test = f"""Question: {question}\nTable: {table}\nSQL: """
 
-#### Factors
+p = tokenizer(test, return_tensors='pt')
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-[More Information Needed]
 
-#### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+print("output :", tokenizer.batch_decode(model.to(device).generate(**p.to(device),max_new_tokens=50),skip_special_tokens=True)[0])
 
-[More Information Needed]
+```
 
-### Results
+## Performance
 
-[More Information Needed]
+### Speed Performance
+The model inference takes about 2-3 seconds to run with Google Colab Free Tier CPU
 
-#### Summary
 
 
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-
-
-## Training procedure
-
+### Downstream Use [optional]
 
 ### Framework versions
 
 
-- PEFT 0.6.2
+- PEFT 0.6.2