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---
license: llama2
datasets:
- bigcode/the-stack
- NumbersStation/NSText2SQL
language:
- en
---
# nova-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.
Use of this model is governed by the Meta’s Llama 2 Community License Agreement. Please review and accept the license before downloading the model weights and tokenizer
### Basic Information
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- **Blog Post**: [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
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/nova-nsql-Llama-2-70B")
model = AutoModelForCausalLM.from_pretrained("sambanovasystems/nova-nsql-Llama-2-70B", torch_dtype=torch.bfloat16)
text = "CREATE TABLE stadium (\n stadium_id number,\n location text,\n name text,\n capacity number,\n highest number,\n lowest number,\n average number\n)\n\nCREATE TABLE singer (\n singer_id number,\n name text,\n country text,\n song_name text,\n song_release_year text,\n age number,\n is_male others\n)\n\nCREATE TABLE concert (\n concert_id number,\n concert_name text,\n theme text,\n stadium_id text,\n year text\n)\n\nCREATE TABLE singer_in_concert (\n concert_id number,\n singer_id text\n)\n\n\n-- Using valid SQLite, answer the following questions for the tables provided above.\n\n-- What is the average, minimum, and maximum age of all singers from France?\nSELECT"
``` |