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README.md

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+---
+library_name: transformers
+datasets:
+- premai-io/spider
+- premai-io/domains
+- premai-io/birdbench
+- gretelai/synthetic_text_to_sql
+
+metrics:
+- accuracy
+base_model:
+- deepseek-ai/deepseek-coder-1.3b-instruct
+pipeline_tag: text2text-generation
+---
+
+# Prem-1B-SQL
+
+- Read the blogpost [here](https://blog.premai.io/prem-1b-sql-fully-local-performant-slm-for-text-to-sql/)
+- PremSQL Library | [GitHub](https://github.com/premAI-io/premsql)
+
+Prem-1B-SQL is one of the very first series of fully local Text-to-SQL models developed by Prem AI. Being a 1B parameter model
+it easily fits on low GPU devices (and CPU devices when quantized). We believe that AI assisted data analysis should be a Local first
+approach. Because exposing Databases to third-party closed-source models can lead to data security breaches. We will be publishing some
+of the public benchmark results of this model very soon. We will also be iterating on this model for more better results. 
+
+- **Developed by:** [Prem AI](https://www.premai.io/)
+- **License:** [MIT]
+
+## Results
+
+We evaluated our model on two popular benchmark datasets: BirdBench and Spider. BirdBench consists of a public validation dataset (with 1534 data points) and a private test dataset. Spider comes up with only a public validation dataset. Here are the results:
+
+| Dataset                  | Execution Accuracy |
+|--------------------------|--------------------|
+| BirdBench (validation)    | 46%                |
+| BirdBench (private test)  | 51.54%             |
+| Spider                   | 85%                |
+
+ The BirdBench dataset is distributed across different difficulty levels. Here is a detailed view of the private results across different difficulty levels.
+
+ | Difficulty  | Count |    EX   | Soft F1 |
+|-------------|-------|---------|---------|
+| Simple      |  949  |  60.70  |  61.48  |
+| Moderate    |  555  |  47.39  |  49.06  |
+| Challenging |  285  |  29.12  |  31.83  |
+| Total       | 1789  |  51.54  |  52.90  |
+
+
+Here is a more detailed comparison of popular closed- and open-source models. 
+
+| Model                         | # Params (in Billion) | BirdBench Test Scores |
+|-------------------------------|-----------------------|-----------------------|
+| AskData + GPT-4o (current winner) | NA                    | 72.39                 |
+| DeepSeek coder 236B            | 236                   | 56.68                 |
+| GPT-4 (2023)                   | NA                    | 54.89                 |
+| **PremSQL 1B (ours)**              | 1                     | 51.4                  |
+| Qwen 2.5 7B Instruct           | 7                     | 51.1                  |
+| Claude 2 Base (2023)           | NA                    | 49.02                 |
+
+
+## How to use Prem-1B-SQL
+
+Since it is a model built upon transformers, so it can be directly used with transformers. However running Text-to-SQL is not as simple
+as running normal LLMs. The reason lies in model input prompt formations which is tightly coupled with databases. So we have developed PremSQL,
+a fully open source library which is:
+
+- **Local-First**: Avoid third-party closed-source providers and keep your data secure.
+- **Customizable Datasets**: Create, fine-tune, and evaluate models with built-in or custom datasets.
+- **Robust Executors and Evaluators**: Easily connect to databases and assess model performance.
+- **Advanced Generators**: Convert natural language prompts into executable SQL queries.
+- **Error Handling and Self-Correction**: Automatically correct SQL queries during inference.
+- **Fine-Tuning Support**: Fine-tune models with LoRA, QLoRA, or full fine-tuning strategies.
+- **End-to-End Pipelines**: Seamlessly integrate all components for autonomous data analysis.
+
+To install PremSQL just create a new environment and type:
+
+```bash
+pip install -U premsql
+```
+
+Please [check out our documentation](https://docs.premai.io/premsql/introduction) to know about more details of the library usage. 
+
+### Running Prem-1B-SQL using PremSQL Pipelines
+
+The easiest way to use this model is through PremSQL pipelines. All you need to do is provide the database path (in case of SQLite databases) 
+or provide the DB connection URI. After this, all you need to do is, connect it with the model. Here is how you do that:
+
+```python
+from premsql.pipelines import SimpleText2SQLAgent
+from premsql.generators import Text2SQLGeneratorHF
+from premsql.executors import SQLiteExecutor
+
+# Provide a SQLite file here or see documentation for more customization
+dsn_or_db_path = "./data/db/california_schools.sqlite"
+
+agent = SimpleText2SQLAgent(
+    dsn_or_db_path=dsn_or_db_path,
+    generator=Text2SQLGeneratorHF(
+        model_or_name_or_path="premai-io/prem-1B-SQL",
+        experiment_name="simple_pipeline",
+        device="cuda:0",
+        type="test"
+    ),
+)
+
+question = "please list the phone numbers of the direct charter-funded schools that are opened after 2000/1/1"
+
+response = agent.query(question)
+response["table"]
+```
+
+Under the hood, it automatically connects with your Database and do all the heavy lifting like prompt creation, execution etc for you. 
+
+
+### Running Prem-1B-SQL using PremSQL Generators
+
+You can also run the model using PremSQL Generators. This is helpful when you want to do generations in 
+bulk on some dataset. Here is an example: 
+
+```python
+from premsql.generators import Text2SQLGeneratorHF
+from premsql.datasets import Text2SQLDataset
+
+# Define a dataset
+dataset = bird_dataset = Text2SQLDataset(
+    dataset_name='bird', split="validation", force_download=False,
+    dataset_folder="/path/to/dataset"
+).setup_dataset(num_rows=10, num_fewshot=3)
+
+# Define a generator 
+generator = Text2SQLGeneratorHF(
+    model_or_name_or_path="premai-io/prem-1B-SQL",
+    experiment_name="test_generators",
+    device="cuda:0",
+    type="test"
+)
+
+# Generate on the full dataset
+responses = generator.generate_and_save_results(
+    dataset=bird_dataset,
+    temperature=0.1,
+    max_new_tokens=256
+)
+
+print(responses)
+```
+
+### Using Execution guided Decoding
+
+This strategy executes the generated SQL against the DB and, if it fails, uses the error message for correction, repeating until it gets a valid result or the retries run out.
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/637b0075806b18943e4ba357/_5rdIQZwyaUFb84xKW_AV.png)
+
+```python
+from premsql.executors import SQLiteExecutor
+
+executor = SQLiteExecutor()
+response = generator.generate_and_save_results(
+    dataset=bird_dataset,
+    temperature=0.1,
+    max_new_tokens=256,
+    force=True,
+    executor=executor,
+    max_retries=5 # this is optional (default is already set to 5)
+)
+```
+
+
+You can also fine-tune Prem-1B-SQL using HuggingFace Transformers and with [PremSQL Tuners](https://docs.premai.io/premsql/tuners)  as well.
+Please [check out our documentation](https://docs.premai.io/premsql/introduction) to know about more about PremSQL and all the features
+we provide.
+
+
+## Datasets used to train the model
+
+Prem-1B-SQL is trained using the following datasets:
+
+1. [BirdBench Training dataset](https://bird-bench.github.io/) | Uploaded on [PremSQL datasets on HF](https://huggingface.co/datasets/premai-io/birdbench)
+2. [Spider dataset](https://yale-lily.github.io/spider) | Uploaded on [PremSQL datasets on HF](https://huggingface.co/datasets/premai-io/spider)
+3. [Domain specialization dataset, gathered and uploaded to PremSQL datasets](https://huggingface.co/datasets/premai-io/domains)
+4. [Gretel AI synthetic dataset](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql?row=0)
+
+Additionally we made error handling datasets on top of these datasets to make the model learn from its errors and self correct them. 
+
+
+## Evaluation results of Prem-1B-SQL
+
+The results of Prem-1B-SQL on some public benchmarks will be published soon.

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