# NSQL-Llama-2-70B

## Model Description

NSQL is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks.

In this repository we are introducing a new member of NSQL, NSQL-Llama-2-70B. It's based on Meta's original [Llama-2 70B model](https://huggingface.co/meta-llama/Llama-2-70b) and further pre-trained on a dataset of general SQL queries and then fine-tuned on a dataset composed of text-to-SQL pairs.

### Basic Information

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- **Blog Post**: [Link](TBA)
- **Discord**: [Link](TBA)
- **HF Hosting**: [Chat with me!](TBA)

## Training Data

The general SQL queries are the SQL subset from [The Stack](https://huggingface.co/datasets/bigcode/the-stack), containing 1M training samples. The labeled text-to-SQL pairs come from the NSText2SQL dataset (https://huggingface.co/datasets/NumbersStation/NSText2SQL).

## Evaluation Data

We evaluate our models on three text-to-SQL benchmarks: Spider, Bird, and text2sql.

## Training Procedure

NSQL was trained using cross-entropy loss to maximize the likelihood of sequential inputs. For finetuning on text-to-SQL pairs, we only compute the loss over the SQL portion of the pair. The model is trained using SambaNova's in-house Reconfigurable Dataflow Unit (RDU), leveraging data and model parallelism. We pre-trained for 2 epochs and fine-tuned for 10 epochs.

### Hyperparameters

**Continous pretraining on Stack-SQL dataset**

- Hardware: SambaNova Reconfigurable Dataflow Unit (RDU)
- Optimizer: AdamW
- Epochs: 2
- Global Batch size: 256
- Batch tokens: 256 * 4096 = 1,048,576 tokens
- Learning Rate: 1e-5
- Learning Rate Scheduler: Fixed
- Warmup Steps: 0
- Weight decay: 0.1

**Finetuning on NSText2SQL dataset**

- Hardware: SambaNova Reconfigurable Dataflow Unit (RDU)
- Optimizer: AdamW
- Epochs: 10
- Global Batch size: 64
- Batch tokens: 64 * 4096 = 262,144 tokens
- Learning Rate: 1e-5
- Learning Rate Scheduler: Cosine Schedule with Warmup
- Warmup Steps: 0
- End Learning Ratio: 0.1
- Weight decay: 0.1
## Intended Use and Limitations

The model was designed for text-to-SQL generation tasks from given table schema and natural language prompts. The model works best with the prompt format defined below and outputting `SELECT` queries.

## How to Use

Example 1:

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/nsql-Llama-2-70B")
model = AutoModelForCausalLM.from_pretrained("sambanovasystems/nsql-Llama-2-70B", torch_dtype=torch.bfloat16)

```