README.md

---
library_name: transformers
tags:
- text-to-sql
datasets:
- kristiannordby/function_call_sql
- kristiannordby/claude_question-sql_pairs
base_model:
- defog/sqlcoder-7b-2
---

# Model Card for Model ID

This text-to-sql model was finetuned on army-aviation-specific data.



## Model Details

To load the model:
```
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kristiannordby/llama3-sqlcoder-ft")
model = AutoModelForCausalLM.from_pretrained("kristiannordby/llama3-sqlcoder-ft")
```
Prompt:
```
### Task
Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION]

### Database Schema
The query will run on a database with the following schema:
{table_metadata_string_DDL_statements}

### Answer
Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION]
[SQL]
```

To prompt the model for generation:
```
def build_prompt(user_question, create_table_statements):
    return (
        "### Task\n"
        f"Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION]\n\n"
        "### Database Schema\n"
        "The query will run on a database with the following schema:\n"
        f"{create_table_statements}\n\n"
        "### Answer\n"
        f"Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION]\n"
        "[SQL]\n"
    )

def build_output(sql):
    # Add a newline at end; if the data has a closing "[/SQL]", add it here!
    return f"{sql.strip()}\n"

create_table_statements = "YOUR TABLE SCHEMA HERE"

def sqllamma(question):
    input_ids = tokenizer(build_prompt(question, create_table_statements), return_tensors="pt", padding = True, truncation = True, max_length = 512).input_ids.to(model.device)
    outputs = model.generate(input_ids, max_new_tokens=100)
    output = tokenizer.decode(outputs[0])
    sql = output.split("###")[3].split("[SQL]")[1].strip()    
    return sql

sqllama("YOUR QUESTION HERE")
```



### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** @kristiannordby
- **Funded by [optional]:** AI2C, USMA D/MATH
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [Text-to-SQL]
- **Language(s) (NLP):** English, SQL
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** Defog/sqlcoder-7b-2

### Model Sources [optional]

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

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## 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. -->
This model was finetuned on an Army Aviation Question-SQL dataset.

### 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

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[More Information Needed]

### Recommendations

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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

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[More Information Needed]

### Training Procedure

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#### 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

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[More Information Needed]

#### Factors

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

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#### Metrics

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

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### Results

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#### 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

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#### Hardware

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#### Software

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## 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. -->

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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