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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 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

Training Data

The general SQL queries are the SQL subset from 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:

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)