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--- |
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license: llama2 |
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datasets: |
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- bigcode/the-stack |
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- NumbersStation/NSText2SQL |
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language: |
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- en |
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--- |
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# nova-nsql-Llama-2-70B |
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## Model Description |
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NSQL is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks. |
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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. |
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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 |
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### Basic Information |
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<!-- Provide the basic links for the model. --> |
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- **Blog Post**: [Link](TBA) |
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- **HF Hosting**: [Chat with me!](TBA) |
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## Training Data |
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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). |
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## Evaluation Data |
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We evaluate our models on three text-to-SQL benchmarks: Spider, Bird, and text2sql. |
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## Training Procedure |
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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. |
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### Hyperparameters |
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**Continous pretraining on Stack-SQL dataset** |
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- Hardware: SambaNova Reconfigurable Dataflow Unit (RDU) |
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- Optimizer: AdamW |
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- Epochs: 2 |
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- Global Batch size: 256 |
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- Batch tokens: 256 * 4096 = 1,048,576 tokens |
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- Learning Rate: 1e-5 |
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- Learning Rate Scheduler: Fixed |
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- Warmup Steps: 0 |
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- Weight decay: 0.1 |
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**Finetuning on NSText2SQL dataset** |
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- Hardware: SambaNova Reconfigurable Dataflow Unit (RDU) |
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- Optimizer: AdamW |
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- Epochs: 10 |
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- Global Batch size: 64 |
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- Batch tokens: 64 * 4096 = 262,144 tokens |
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- Learning Rate: 1e-5 |
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- Learning Rate Scheduler: Cosine Schedule with Warmup |
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- Warmup Steps: 0 |
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- End Learning Ratio: 0.1 |
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- Weight decay: 0.1 |
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## Intended Use and Limitations |
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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. |
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## How to Use |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/nova-nsql-Llama-2-70B") |
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model = AutoModelForCausalLM.from_pretrained("sambanovasystems/nova-nsql-Llama-2-70B", torch_dtype=torch.bfloat16) |
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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" |
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``` |
