Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- PipableAI/pip-txt-to-sql-spider-bird-dataset
+language:
+- en
+metrics:
+- accuracy
+tags:
+- sql
+- code
+- text2sql
+- instruction_tuned
+- basemodel
+- jax
+- pytorch
+- tensorflow
+- text-generation-inference
+library_name: transformers
+pipeline_tag: text-generation
+---
+# pipSQL-1.3b
+[pipableAi](https://www.linkedin.com/company/pipable.ai/about/)
+
+## What have we built?
+A 1.3 bn SQL model that outperforms most SQL expert models and chatgpt on popular benchmarks.
+This is a distilled model built on the deepseek base model.
+
+## How we built it?
+
+We used softmax cross entropy and a modified form of policy grad along with Q loss, optimized in an EM set up.
+
+## Benchmarking :
+For benchmarking purposes we are using Semantic Evaluation for Text-to-SQL with 
+Distilled Test Suites, an officially accepted evaluation framework for Spider, SParC, and CoSQL which was proposed by a research team of Yale and Berkeley. 
+The benchmark contains 2200 test data points
+Here is the link to run the evaluation:
+
+
+[Test Suite SQL Eval](https://github.com/taoyds/test-suite-sql-eval)
+
+|model|easy|medium|hard|extra|
+|-----|----|------|----|-----|
+|sqlcoder-7b-2|72.0|58.0|40.6|37.3|
+|pip-sql-1b-Qstar|74.0|54.0|36.5|30.0|
+|pipSQL-7b|63.0|40.0|30.2|25.0|
+|sqlcoder-7b|60.6|48.2|28.3|20.4|
+|gpt-3.5|58.8|44.7|31.0|28.4|
+
+We have also benchmarked it on defog eval.
+It contains 200 test data points handpicked by defog team.
+Here is the link to it:
+
+
+[Defog SQL-Eval](https://github.com/defog-ai/sql-eval)
+These are the results -
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d32c6b921678fdc9de3302/ZU_YeKLEtHg7ImXI5LrXo.png)
+
+## License
+The model is open source under apache 2.0. License
+
+## Usage
+
+### Installation
+
+```bash
+pip install transformers
+```
+
+### Prompt
+```python
+prompt = f"""<schema>{schema}</schema>
+<question>{question}</question>
+<sql>"""
+```
+
+### PyTorch
+```python
+from transformers import AutoModelForCasualLM, AutoTokenizer
+device = "cuda"
+model = AutoModelForCausalLM.from_pretrained("PipableAI/pipSQL-1.3b")
+tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL-1.3b")
+
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=200)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])
+```
+
+### Flax
+```python
+from transfomers import FlaxAutoModelForCausalLM, AutoTokenizer
+device = "cuda"
+model = FlaxAutoModelForCausalLM.from_pretrained("PipableAI/pipSQL-1.3b")
+tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL-1.3b")
+
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=200)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])
+```
+
+### TensorFlow
+```python
+from transfomers import TFAutoModelForCausalLM, AutoTokenizer
+device = "cuda"
+model = TFAutoModelForCausalLM.from_pretrained("PipableAI/pipSQL-1.3b")
+tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL-1.3b")
+
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=200)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])
+```