README.md

---
license: mit
datasets:
- b-mc2/sql-create-context
language:
- en
metrics:
- accuracy
- code_eval
library_name: transformers
pipeline_tag: text-generation
tags:
- peft
- nl2sql
widget:
- text: "### Task\nGenerate a SQL query to answer the following question:\n`How many heads of the departments are older than 56?`\n\n### Database Schema\nThe query will run on a database with the following schema:\nCREATE TABLE head (age INTEGER)\n\n### Answer\nGiven the database schema, here is the SQL query that answers `How many heads of the departments are older than 56?`:\n```sql"
---

# Update: 14-03-2024 - The model card is still updating. Thanks for being patient! 💜💜

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

A fine-tuned version of Phi-2 for the NL2SQL usecase on `b-mc2/sql-create-context` dataset. 

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
This model has been finetuned with `b-mc2/sql-create-context` on `microsoft/phi-2`. This performed better than `defog/sqlcoder-7b-2` in terms of inference time and accuracy on the holdback dataset. The evaluation is done on `.gguf` models on CPU machine with limited RAM. The average inference times of the Phi-2, and SQLCoder are 24 secs, and 41 secs respectively. That is 41% faster on average. This is due to its smaller size. The Finetuned Phi-2 is 29% better than the SQLCoder based on execution success. The major drawback is its context window of 2048 tokens which requires additional input engineering to get results.

- **Developed by:** pavankumarbalijepalli
- **Model type:** CASUAL_LM
- **Language(s) (NLP):** English, SQL
- **License:** MIT
- **Finetuned from model [optional]:** [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [pavankumarbalijepalli/pr-phi2-vs-defog](https://github.com/pavankumarbalijepalli/pr-phi2-vs-defog/)
- **Paper [optional]:** [BITS Project Paper](https://github.com/pavankumarbalijepalli/pr-phi2-vs-defog/blob/main/2021SC04115%20-%20Final.pdf)

## 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. -->

Model is supposed to be used for the cases where you have a natural language question, database schema which is relevant the question to retrieve a SQL query which answers the question. The context should be below 2048 tokens. The output will be generated in postgresql.

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

```python
# SAME TEMPLATE AS DEFOG MODEL
prompt = f"""### Task
Generate a SQL query to answer the following question:
`{data_point['question']}`

### Database Schema
The query will run on a database with the following schema:
{data_point['context']}

### Answer
Given the database schema, here is the SQL query that answers `{data_point['question']}`:
```sql"""
```

```python
# USING ON CPU MACHINE
from llama_cpp import Llama

phi2 = Llama(model_path=f"{path_to_model}/phi2_sqlcoder_f16.gguf")

response = phi2(prompt=prompt, max_tokens = 200, temperature = 0.2, stop = ['```'])

print(response['choices'][0]['text'].strip())
```

### Downstream Use

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig

model_name = "microsoft/phi-2"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
inputs = tokenizer(new_prompt, return_tensors="pt", padding=True, truncation=True)
inputs.to('cuda')

model_id = "pavankumarbalijepalli/phi2-sqlcoder"
trained_model = PeftModel.from_pretrained(model, model_id)
outputs = trained_model.generate(**inputs, max_length=1000)
text = tokenizer.batch_decode(outputs,skip_special_tokens=True)[0]
print(text)
```

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

__Generating Unintended Code:__

While the model can translate natural language into SQL queries, it may not be robust enough to handle complex logic or edge cases. Using it to generate critical production code could lead to errors or unexpected behavior in databases.

__Security Risks:__

NL2SQL models can be susceptible to adversarial attacks where malicious users input natural language designed to trick the model into generating SQL code with security vulnerabilities, like SQL injection attacks.

__Beyond its Training Scope:__

The model is trained on a specific SQL Language (e.g., PostgreSQL). Using it for a different SQL Syntax (e.g., MS SQL Server) could lead to inaccurate or nonsensical SQL queries.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

__Bias and Fairness:__

The model's training data may contain biases that are reflected in the generated SQL queries. This could lead to unfair or discriminatory outcomes, especially if the data is not carefully curated.

__Interpretability and Explainability:__

NL2SQL models are often "black boxes" where it's difficult to understand how they translate natural language to SQL. This lack of interpretability makes it challenging to debug errors or ensure the generated queries are safe and efficient.

__Replacing Human Expertise:__

While the model can automate some SQL query generation tasks, it shouldn't be a complete replacement for human database administrators or analysts. Understanding the data schema and database design is crucial for writing efficient and secure SQL queries.


### 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]
```
@misc{b-mc2_2023_sql-create-context,
  title   = {sql-create-context Dataset},
  author  = {b-mc2}, 
  year    = {2023},
  url     = {https://huggingface.co/datasets/b-mc2/sql-create-context},
  note    = {This dataset was created by modifying data from the following sources: \cite{zhongSeq2SQL2017, yu2018spider}.},
}
```

### 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]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- 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]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### 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:**

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**APA:**

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## 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]

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## Model Card Authors [optional]

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## Model Card Contact

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