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Model: NumbersStation/nsql-llama-2-7B Source: Original Platform
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README.md
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---
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license: llama2
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inference:
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parameters:
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do_sample: false
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max_length: 200
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widget:
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- text: "CREATE TABLE stadium (\n stadium_id number,\n location text,\n name text,\n capacity number,\n)\n\n-- Using valid SQLite, answer the following questions for the tables provided above.\n\n-- how many stadiums in total?\n\nSELECT"
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example_title: "Number stadiums"
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- text: "CREATE TABLE work_orders ( ID NUMBER, CREATED_AT TEXT, COST FLOAT, INVOICE_AMOUNT FLOAT, IS_DUE BOOLEAN, IS_OPEN BOOLEAN, IS_OVERDUE BOOLEAN, COUNTRY_NAME TEXT, )\n\n-- Using valid SQLite, answer the following questions for the tables provided above.\n\n-- how many work orders are open?\n\nSELECT"
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example_title: "Open work orders"
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- text: "CREATE TABLE stadium ( stadium_id number, location text, name text, capacity number, highest number, lowest number, average number )\n\nCREATE TABLE singer ( singer_id number, name text, country text, song_name text, song_release_year text, age number, is_male others )\n\nCREATE TABLE concert ( concert_id number, concert_name text, theme text, stadium_id text, year text )\n\nCREATE TABLE singer_in_concert ( concert_id number, singer_id text )\n\n-- Using valid SQLite, answer the following questions for the tables provided above.\n\n-- What is the maximum, the average, and the minimum capacity of stadiums ?\n\nSELECT"
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example_title: "Stadium capacity"
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---
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# NSQL-Llama-2-7B
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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-2-7B. It's based on Meta's original [Llama-2 7B model](https://huggingface.co/meta-llama/Llama-2-7b) 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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## 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 more than 20 public sources across the web from standard datasets. We hold out Spider and GeoQuery datasets for use in evaluation.
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## Evaluation Data
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We evaluate our models on two text-to-SQL benchmarks: Spider and GeoQuery.
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## Evaluation Results
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We evaluate our models on two text-to-SQL benchmarks: Spider and GeoQuery.
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### Spider Benchmark (Text-to-SQL Standard Evaluation)
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NSQL-llama-2-7B was evaluated on the Spider benchmark, the standard academic evaluation for Text-to-SQL systems.
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#### Overall Performance
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| Model | Size | Execution Accuracy | Matching Accuracy |
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|-------|------|-------------------|-------------------|
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| **NSQL-llama-2-7B** | 7B | 78.1% | **66.3%** |
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| GPT-4 | ~1.8T | 76.2% | 41.9% |
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| GPT-3.5 Chat | — | 72.8% | 44.2% |
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| Llama-2-7B (base) | 7B | 29.1% | 19.3% |
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| Llama-2-70B | 70B | 61.5% | 35.4% |
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#### Performance by Query Complexity
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| Query Type | NSQL-llama-2-7B | GPT-4 | NSQL Advantage |
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|------------|-----------------|-------|----------------|
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| **Join Queries** | **53.7%** | ~37.6% | **+43% relative** |
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| **Nested Queries** | **57.2%** | ~37.1% | **+54% relative** |
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| Simple Queries | 91.4% | Higher | GPT-4 advantage |
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#### Key Findings
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1. **Complex Query Performance:** NSQL-llama-2-7B significantly outperforms GPT-4 on complex queries:
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- +43% improvement on Join queries
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- +54% improvement on Nested queries
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2. **Matching Accuracy:** NSQL achieves 66.3% matching accuracy vs. GPT-4's 41.9% (+24.4 points), indicating more structurally correct SQL generation.
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3. **Efficiency:** NSQL achieves near-parity with GPT-4 on overall execution (78.10% vs 76.2%) while being ~250× smaller.
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4. **Local Deployment:** The 7B parameter size enables local deployment on commodity hardware, preserving data privacy.
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#### Why This Matters
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GPT-4 achieves marginally higher overall execution accuracy primarily through superior performance on simple single-table queries. However, enterprise SQL workloads typically involve:
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- Multiple table joins
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- Nested subqueries
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- Complex business logic
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On these complex query types, NSQL substantially outperforms GPT-4 while enabling privacy-preserving local deployment.
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### GeoQuery Benchmark
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| Model | Size | Execution Accuracy | Matching Accuracy |
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|-------|------|-------------------|-------------------|
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| NSQL-llama-2-7B | 7B | 26.5% | 30.4% |
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| GPT-4 | ~1.8T | 55.1% | 39.1% |
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*Note: GeoQuery is a narrower benchmark; Spider is the primary industry standard for Text-to-SQL evaluation.*
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### NSQL Model Family Comparison
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| Model | Size | Spider Exec | Spider Match |
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|-------|------|-------------|--------------|
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| NSQL-350M | 350M | 51.7% | 45.6% |
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| NSQL-2B | 2B | 59.3% | 53.2% |
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| NSQL-6B | 6B | 63.6% | 57.4% |
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| **NSQL-llama-2-7B** | **7B** | **78.1%** | **66.3%** |
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---
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## Evaluation Methodology
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- **Benchmark:** Spider (Yu et al., 2018)
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- **Metric - Execution Accuracy:** Percentage of queries returning correct results
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- **Metric - Matching Accuracy:** Percentage of queries structurally matching ground truth
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- **Query Type Breakdown:** Join, Nested, Simple categories per Spider schema
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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 80GB A100s, leveraging data and model parallelism. We pre-trained for 3 epochs and fine-tuned for 10 epochs.
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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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Example 1:
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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("NumbersStation/nsql-llama-2-7B")
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model = AutoModelForCausalLM.from_pretrained("NumbersStation/nsql-llama-2-7B", torch_dtype=torch.bfloat16)
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text = """CREATE TABLE stadium (
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stadium_id number,
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location text,
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name text,
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capacity number,
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highest number,
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lowest number,
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average number
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)
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CREATE TABLE singer (
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singer_id number,
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name text,
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country text,
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song_name text,
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song_release_year text,
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age number,
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is_male others
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)
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CREATE TABLE concert (
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concert_id number,
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concert_name text,
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theme text,
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stadium_id text,
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year text
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)
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CREATE TABLE singer_in_concert (
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concert_id number,
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singer_id text
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)
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-- Using valid SQLite, answer the following questions for the tables provided above.
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-- What is the maximum, the average, and the minimum capacity of stadiums ?
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SELECT"""
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input_ids = tokenizer(text, return_tensors="pt").input_ids
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generated_ids = model.generate(input_ids, max_length=500)
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print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
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```
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Example 2:
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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("NumbersStation/nsql-llama-2-7B")
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model = AutoModelForCausalLM.from_pretrained("NumbersStation/nsql-llama-2-7B", torch_dtype=torch.bfloat16)
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text = """CREATE TABLE stadium (
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stadium_id number,
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location text,
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name text,
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capacity number,
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)
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-- Using valid SQLite, answer the following questions for the tables provided above.
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-- how many stadiums in total?
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SELECT"""
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input_ids = tokenizer(text, return_tensors="pt").input_ids
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generated_ids = model.generate(input_ids, max_length=500)
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print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
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```
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Example 3:
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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("NumbersStation/nsql-llama-2-7B")
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model = AutoModelForCausalLM.from_pretrained("NumbersStation/nsql-llama-2-7B", torch_dtype=torch.bfloat16)
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text = """CREATE TABLE work_orders (
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ID NUMBER,
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CREATED_AT TEXT,
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COST FLOAT,
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INVOICE_AMOUNT FLOAT,
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IS_DUE BOOLEAN,
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IS_OPEN BOOLEAN,
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IS_OVERDUE BOOLEAN,
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COUNTRY_NAME TEXT,
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)
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-- Using valid SQLite, answer the following questions for the tables provided above.
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-- how many work orders are open?
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SELECT"""
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input_ids = tokenizer(text, return_tensors="pt").input_ids
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generated_ids = model.generate(input_ids, max_length=500)
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print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
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```
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For more information (e.g., run with your local database), please find examples in [this repository](https://github.com/NumbersStationAI/NSQL).
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config.json
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"LlamaForCausalLM"
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],
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"hidden_act": "silu",
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"model_type": "llama",
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||||
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||||
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||||
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||||
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||||
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|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
}
|
||||
93391
tokenizer.json
Normal file
93391
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
32
tokenizer_config.json
Normal file
32
tokenizer_config.json
Normal file
@@ -0,0 +1,32 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"__type": "AddedToken",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
"eos_token": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"legacy": false,
|
||||
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|
||||
"pad_token": null,
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<unk>",
|
||||
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|
||||
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|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user