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Model: NumbersStation/nsql-llama-2-7B
Source: Original Platform

.gitattributes

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README.md

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+---
+license: llama2
+inference:
+  parameters:
+    do_sample: false
+    max_length: 200
+widget:
+- 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"
+  example_title: "Number stadiums"
+- 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"
+  example_title: "Open work orders"
+- 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"
+  example_title: "Stadium capacity"
+---
+
+# NSQL-Llama-2-7B
+
+## 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-2-7B. 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.
+
+## 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 more than 20 public sources across the web from standard datasets. We hold out Spider and GeoQuery datasets for use in evaluation.
+
+## Evaluation Data
+
+We evaluate our models on two text-to-SQL benchmarks: Spider and GeoQuery.
+
+## Evaluation Results
+
+We evaluate our models on two text-to-SQL benchmarks: Spider and GeoQuery.
+
+### Spider Benchmark (Text-to-SQL Standard Evaluation)
+
+NSQL-llama-2-7B was evaluated on the Spider benchmark, the standard academic evaluation for Text-to-SQL systems.
+
+#### Overall Performance
+
+| Model | Size | Execution Accuracy | Matching Accuracy |
+|-------|------|-------------------|-------------------|
+| **NSQL-llama-2-7B** | 7B | 78.1% | **66.3%** |
+| GPT-4 | ~1.8T | 76.2% | 41.9% |
+| GPT-3.5 Chat | — | 72.8% | 44.2% |
+| Llama-2-7B (base) | 7B | 29.1% | 19.3% |
+| Llama-2-70B | 70B | 61.5% | 35.4% |
+
+#### Performance by Query Complexity
+
+| Query Type | NSQL-llama-2-7B | GPT-4 | NSQL Advantage |
+|------------|-----------------|-------|----------------|
+| **Join Queries** | **53.7%** | ~37.6% | **+43% relative** |
+| **Nested Queries** | **57.2%** | ~37.1% | **+54% relative** |
+| Simple Queries | 91.4% | Higher | GPT-4 advantage |
+
+#### Key Findings
+
+1. **Complex Query Performance:** NSQL-llama-2-7B significantly outperforms GPT-4 on complex queries:
+   - +43% improvement on Join queries
+   - +54% improvement on Nested queries
+
+2. **Matching Accuracy:** NSQL achieves 66.3% matching accuracy vs. GPT-4's 41.9% (+24.4 points), indicating more structurally correct SQL generation.
+
+3. **Efficiency:** NSQL achieves near-parity with GPT-4 on overall execution (78.10% vs 76.2%) while being ~250× smaller.
+
+4. **Local Deployment:** The 7B parameter size enables local deployment on commodity hardware, preserving data privacy.
+
+#### Why This Matters
+
+GPT-4 achieves marginally higher overall execution accuracy primarily through superior performance on simple single-table queries. However, enterprise SQL workloads typically involve:
+- Multiple table joins
+- Nested subqueries
+- Complex business logic
+
+On these complex query types, NSQL substantially outperforms GPT-4 while enabling privacy-preserving local deployment.
+
+### GeoQuery Benchmark
+
+| Model | Size | Execution Accuracy | Matching Accuracy |
+|-------|------|-------------------|-------------------|
+| NSQL-llama-2-7B | 7B | 26.5% | 30.4% |
+| GPT-4 | ~1.8T | 55.1% | 39.1% |
+
+*Note: GeoQuery is a narrower benchmark; Spider is the primary industry standard for Text-to-SQL evaluation.*
+
+### NSQL Model Family Comparison
+
+| Model | Size | Spider Exec | Spider Match |
+|-------|------|-------------|--------------|
+| NSQL-350M | 350M | 51.7% | 45.6% |
+| NSQL-2B | 2B | 59.3% | 53.2% |
+| NSQL-6B | 6B | 63.6% | 57.4% |
+| **NSQL-llama-2-7B** | **7B** | **78.1%** | **66.3%** |
+
+---
+
+## Evaluation Methodology
+
+- **Benchmark:** Spider (Yu et al., 2018)
+- **Metric - Execution Accuracy:** Percentage of queries returning correct results
+- **Metric - Matching Accuracy:** Percentage of queries structurally matching ground truth
+- **Query Type Breakdown:** Join, Nested, Simple categories per Spider schema
+
+
+## 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 80GB A100s, leveraging data and model parallelism. We pre-trained for 3 epochs and fine-tuned for 10 epochs.
+
+## 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("NumbersStation/nsql-llama-2-7B")
+model = AutoModelForCausalLM.from_pretrained("NumbersStation/nsql-llama-2-7B", torch_dtype=torch.bfloat16)
+
+text = """CREATE TABLE stadium (
+    stadium_id number,
+    location text,
+    name text,
+    capacity number,
+    highest number,
+    lowest number,
+    average number
+)
+
+CREATE TABLE singer (
+    singer_id number,
+    name text,
+    country text,
+    song_name text,
+    song_release_year text,
+    age number,
+    is_male others
+)
+
+CREATE TABLE concert (
+    concert_id number,
+    concert_name text,
+    theme text,
+    stadium_id text,
+    year text
+)
+
+CREATE TABLE singer_in_concert (
+    concert_id number,
+    singer_id text
+)
+
+-- Using valid SQLite, answer the following questions for the tables provided above.
+
+-- What is the maximum, the average, and the minimum capacity of stadiums ?
+
+SELECT"""
+
+input_ids = tokenizer(text, return_tensors="pt").input_ids
+
+generated_ids = model.generate(input_ids, max_length=500)
+print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
+```
+
+Example 2:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("NumbersStation/nsql-llama-2-7B")
+model = AutoModelForCausalLM.from_pretrained("NumbersStation/nsql-llama-2-7B", torch_dtype=torch.bfloat16)
+
+text = """CREATE TABLE stadium (
+    stadium_id number,
+    location text,
+    name text,
+    capacity number,
+)
+
+-- Using valid SQLite, answer the following questions for the tables provided above.
+
+-- how many stadiums in total?
+
+SELECT"""
+
+input_ids = tokenizer(text, return_tensors="pt").input_ids
+
+generated_ids = model.generate(input_ids, max_length=500)
+print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
+```
+
+Example 3:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("NumbersStation/nsql-llama-2-7B")
+model = AutoModelForCausalLM.from_pretrained("NumbersStation/nsql-llama-2-7B", torch_dtype=torch.bfloat16)
+
+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,
+)
+
+-- Using valid SQLite, answer the following questions for the tables provided above.
+
+-- how many work orders are open?
+
+SELECT"""
+
+input_ids = tokenizer(text, return_tensors="pt").input_ids
+
+generated_ids = model.generate(input_ids, max_length=500)
+print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
+```
+
+
+
+For more information (e.g., run with your local database), please find examples in [this repository](https://github.com/NumbersStationAI/NSQL).

config.json

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+{
+  "_name_or_path": "nsql-llama-2-7B",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pad_token_id": 2,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.31.0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+  "eos_token_id": 2,
+  "max_length": 4096,
+  "pad_token_id": 0,
+  "temperature": 0.9,
+  "top_p": 0.6,
+  "transformers_version": "4.31.0"
+}

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+}

special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,32 @@
+{
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
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+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "legacy": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}