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Model: prem-research/prem-1B-SQL Source: Original Platform
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---
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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---
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# Prem-1B-SQL (HuggingFace)
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- Read the blogpost [here](https://blog.premai.io/prem-1b-sql-fully-local-performant-slm-for-text-to-sql/)
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- PremSQL Library | [GitHub](https://github.com/premAI-io/premsql)
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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
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it easily fits on low GPU devices (and CPU devices when quantized). We believe that AI assisted data analysis should be a Local first
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approach. Because exposing Databases to third-party closed-source models can lead to data security breaches. We will be publishing some
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of the public benchmark results of this model very soon. We will also be iterating on this model for more better results.
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- **Developed by:** [Prem AI](https://www.premai.io/)
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- **License:** [MIT]
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## Results
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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:
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| Dataset | Execution Accuracy |
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| ------------------------ | ------------------ |
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| BirdBench (validation) | 46% |
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| BirdBench (private test) | 51.54% |
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| Spider | 85% |
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The BirdBench dataset is distributed across different difficulty levels. Here is a detailed view of the private results across different difficulty levels.
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| Difficulty | Count | EX | Soft F1 |
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| ----------- | ----- | ----- | ------- |
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| Simple | 949 | 60.70 | 61.48 |
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| Moderate | 555 | 47.39 | 49.06 |
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| Challenging | 285 | 29.12 | 31.83 |
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| Total | 1789 | 51.54 | 52.90 |
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Here is a more detailed comparison of popular closed- and open-source models.
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| Model | # Params (in Billion) | BirdBench Test Scores |
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| --------------------------------- | --------------------- | --------------------- |
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| AskData + GPT-4o (current winner) | NA | 72.39 |
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| DeepSeek coder 236B | 236 | 56.68 |
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| GPT-4 (2023) | NA | 54.89 |
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| **PremSQL 1B (ours)** | 1 | 51.4 |
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| Qwen 2.5 7B Instruct | 7 | 51.1 |
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| Claude 2 Base (2023) | NA | 49.02 |
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## How to use Prem-1B-SQL
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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
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as running normal LLMs. The reason lies in model input prompt formations which is tightly coupled with databases. So we have developed PremSQL,
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a fully open source library which is:
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- **Local-First**: Avoid third-party closed-source providers and keep your data secure.
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- **Customizable Datasets**: Create, fine-tune, and evaluate models with built-in or custom datasets.
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- **Robust Executors and Evaluators**: Easily connect to databases and assess model performance.
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- **Advanced Generators**: Convert natural language prompts into executable SQL queries.
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- **Error Handling and Self-Correction**: Automatically correct SQL queries during inference.
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- **Fine-Tuning Support**: Fine-tune models with LoRA, QLoRA, or full fine-tuning strategies.
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- **End-to-End Pipelines**: Seamlessly integrate all components for autonomous data analysis.
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To install PremSQL just create a new environment and type:
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```bash
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pip install -U premsql
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```
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Please [check out our documentation](https://docs.premai.io/premsql/introduction) to know about more details of the library usage.
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### Running Prem-1B-SQL using PremSQL BaseLine Agent
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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)
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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:
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```python
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from premsql.agents import BaseLineAgent
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from premsql.generators import Text2SQLGeneratorOllama
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from premsql.agents.tools import SimpleMatplotlibTool
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from premsql.executors import SQLiteExecutor
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text2_sqlmodel = Text2SQLGeneratorHF(
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model_or_name_or_path="prem-research/prem-1B-SQL",
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experiment_name="test_generators",
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device="cuda:0",
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type="test"
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)
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analyser_and_plotter = Text2SQLGeneratorHF(
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model_or_name_or_path="meta-llama/Llama-3.2-1B-Instruct",
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experiment_name="test_generators",
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device="cuda:0",
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type="test"
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)
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agent = BaseLineAgent(
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session_name="testing_hf",
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db_connection_uri="sqlite:////path/to/your/database.sqlite",
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specialized_model1=model,
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specialized_model2=model,
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plot_tool=SimpleMatplotlibTool(),
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executor=SQLiteExecutor()
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)
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response = agent(
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"/query what all tables are present inside the database"
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)
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response.show_dataframe()
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```
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Under the hood, it automatically connects with your Database and do all the heavy lifting like prompt creation, execution etc for you.
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### Running Prem-1B-SQL using PremSQL Generators
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You can also run the model using PremSQL Generators. This is helpful when you want to do generations in
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bulk on some dataset. Here is an example:
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```python
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from premsql.generators import Text2SQLGeneratorHF
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from premsql.datasets import Text2SQLDataset
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# Define a dataset
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dataset = bird_dataset = Text2SQLDataset(
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dataset_name='bird', split="validation", force_download=False,
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dataset_folder="/path/to/dataset"
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).setup_dataset(num_rows=10, num_fewshot=3)
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# Define a generator
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generator = Text2SQLGeneratorHF(
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model_or_name_or_path="prem-research/prem-1B-SQL",
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experiment_name="test_generators",
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device="cuda:0",
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type="test"
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)
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# Generate on the full dataset
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responses = generator.generate_and_save_results(
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dataset=bird_dataset,
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temperature=0.1,
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max_new_tokens=256
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)
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print(responses)
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```
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### Using Execution guided Decoding
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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.
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```python
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from premsql.executors import SQLiteExecutor
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executor = SQLiteExecutor()
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response = generator.generate_and_save_results(
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dataset=bird_dataset,
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temperature=0.1,
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max_new_tokens=256,
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force=True,
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executor=executor,
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max_retries=5 # this is optional (default is already set to 5)
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)
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```
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You can also fine-tune Prem-1B-SQL using HuggingFace Transformers and with [PremSQL Tuners](https://docs.premai.io/premsql/tuners) as well.
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Please [check out our documentation](https://docs.premai.io/premsql/introduction) to know about more about PremSQL and all the features
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we provide.
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## Datasets used to train the model
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Prem-1B-SQL is trained using the following datasets:
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1. [BirdBench Training dataset](https://bird-bench.github.io/) | Uploaded on [PremSQL datasets on HF](https://huggingface.co/datasets/prem-research/birdbench)
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2. [Spider dataset](https://yale-lily.github.io/spider) | Uploaded on [PremSQL datasets on HF](https://huggingface.co/datasets/prem-research/spider)
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3. [Domain specialization dataset, gathered and uploaded to PremSQL datasets](https://huggingface.co/datasets/prem-research/domains)
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4. [Gretel AI synthetic dataset](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql?row=0)
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Additionally we made error handling datasets on top of these datasets to make the model learn from its errors and self correct them.
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## Evaluation results of Prem-1B-SQL
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The results of Prem-1B-SQL on some public benchmarks will be published soon.
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config.json
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{
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"_name_or_path": "output_deepseek_v0.0.2/checkpoint-2400",
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"architectures": [
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"LlamaForCausalLM"
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"attention_bias": false,
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"attention_dropout": 0.0,
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"rope_theta": 100000,
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"tie_word_embeddings": false,
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"use_cache": true,
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"vocab_size": 32256
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}
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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||||
"special": false
|
||||
},
|
||||
"32002": {
|
||||
"content": "Á",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32003": {
|
||||
"content": "ý",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32004": {
|
||||
"content": "À",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32005": {
|
||||
"content": "ÿ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32006": {
|
||||
"content": "ø",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32007": {
|
||||
"content": "ú",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32008": {
|
||||
"content": "þ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32009": {
|
||||
"content": "ü",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32010": {
|
||||
"content": "ù",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32011": {
|
||||
"content": "ö",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32012": {
|
||||
"content": "û",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32013": {
|
||||
"content": "<｜begin▁of▁sentence｜>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32014": {
|
||||
"content": "<｜end▁of▁sentence｜>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32015": {
|
||||
"content": "<｜fim▁hole｜>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32016": {
|
||||
"content": "<｜fim▁begin｜>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32017": {
|
||||
"content": "<｜fim▁end｜>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32018": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32019": {
|
||||
"content": "<|User|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32020": {
|
||||
"content": "<|Assistant|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32021": {
|
||||
"content": "<|EOT|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<｜begin▁of▁sentence｜>",
|
||||
"chat_template": "{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n {%- if message['role'] == 'system' -%}\n {%- set ns.found = true -%}\n {%- endif -%}\n{%- endfor -%}\n{{bos_token}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\n'}}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n {%- else %}\n {%- if message['role'] == 'user' %}\n{{'### Instruction:\\n' + message['content'] + '\\n'}}\n {%- else %}\n{{'### Response:\\n' + message['content'] + '\\n<|EOT|>\\n'}}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|EOT|>",
|
||||
"legacy": true,
|
||||
"max_length": null,
|
||||
"model_max_length": 16384,
|
||||
"pad_to_multiple_of": null,
|
||||
"pad_token": "<｜end▁of▁sentence｜>",
|
||||
"pad_token_type_id": 0,
|
||||
"padding_side": "left",
|
||||
"padding_size": "right",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": null,
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
1023
trainer_state.json
Normal file
1023
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user