Update README.md

README.md

@@ -1,3 +1,55 @@
 ---
 license: mit
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-generation
+tags:
+- code
+- sql
+- text2sql
+- instruction_tuned
+- basemodel
+- jax
+- pytorch
+datasets:
+- PipableAI/spider-bird
 ---
+# Pipable’s pipSQL
+
+Pipable’s pipSQL is a model distilled from llama 1b to generate sql queries given prompt and schema. 
+We used a unique pipeline which involved the model working on two objectives alternatively ----
+1. Maximizing the log prob of all tokens in the sequence (including the prompt tokens) 
+2. Minimizng the difference between the true value and the predicted maximum value of the output tokens i.e generated tokens for the sql query slice of the entire sequence.
+
+
+
+
+
+## License
+
+The model's new weights along with all other assets involved with it are open sourced under mit license.
+
+## How to Use
+
+```python
+text = """<schema>{schema}</schema>
+<question>{question}</question>
+<sql>"""
+```
+
+```python
+from transformers import AutoModelForCasualLM, AutoTokenizer
+device = "cuda"
+model = AutoModelForCausalLM.from_pretrained("PipableAI/pipSQL")
+tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL")
+
+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])
+```
+
+## The PipableAI team
+
+Avi Kothari, Pratham Gupta, Ritvik Aryan Kalra, Rohan Bhatial, Soham Acharya , Gyan Ranjan