Update README.md

README.md

@@ -30,7 +30,8 @@ widget:
 ## What have we built?
 
 A 1.3 bn code documentation model that outperforms most models on documenting codes and making your in-house libs ready for LLM and RAG pipelines.
-We have also open sourced a [parsing lib](https://github.com/PipableAI/pip-library-parser) for the same, together the lib and model can turn your codebase to functional parse tree ready to be consumed by LLMs to execute complex tasks.
+We have also open sourced a [pip library_etl](https://github.com/PipableAI/pip-library-etl.git) for the same, together the lib and model can turn your codebase to functional parse tree ready to be consumed by LLMs to execute complex tasks.
+This model is also capable of generating SQL queries with accuracies on par with those of [pip-sql-1.3b](https://huggingface.co/PipableAI/pip-sql-1.3b), with additional capabilities of providing extra examples, instructions ,and column descriptions as context.
 This is a further trained version of pip-sql-1.3b.
 
 ## How we built it?
@@ -46,42 +47,9 @@ The model is open source under apache 2.0. License
 
 
 ### Library use
-```python
-!pip3 install git+https://github.com/PipableAI/pip-library-parser
-!pip3 install atlassian-python-api
-
-
-from pip_library_parser import CodeToDocGenerator
-
-# Replace 'your_module' and 'YourModule' with the actual module and module name
-module_name = 'your_module'
-module = __import__(module_name)
-
-# Instantiate the CodeToDocGenerator
-generator = CodeToDocGenerator()
-
-# Generate docstrings for the module's functions and methods
-docs = generator.generate_module_docs(module, module_name)
-
-# 'docs' now contains a dictionary mapping function/method names to their generated docstrings
-
-```
 
-```python
-from pip_library_parser import CodeToDocGenerator
-
-# Instantiate the CodeToDocGenerator
-generator = CodeToDocGenerator()
-
-code_snippet = """
-def example_function(x):
-    return x * 2
-"""
-
-docstring = generator.generate_docstring_from_pip_model(code_snippet)
-print("Generated Docstring:")
-print(docstring)
-```
+For directly using the capabilities of model without putting extra efforts on schems and prompts try to use [pip library_etl](https://github.com/PipableAI/pip-library-etl.git).
+For detaied usage refer to the [colab_notebook](https://colab.research.google.com/drive/17PyMU_3QN9LROy7x-jmaema0cuLRzBvc?usp=sharing)
 
 ### Installation
 
@@ -104,11 +72,11 @@ prompt = f"""<example_response>{example of some  --question: , --query}</example
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 device = "cuda"
-model = AutoModelForCausalLM.from_pretrained("PipableAI/pip-code-to-doc-1.3b").to(device)
-tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-code-to-doc-1.3b")
+model = AutoModelForCausalLM.from_pretrained("PipableAI/pip-library-etl-1.3b ").to(device)
+tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-library-etl-1.3b b")
 prompt = f"""<example_response>
 --code:def function_2(x): return x / 2
---question:Document the code
+--question:Document the python code above giving function description ,parameters and return type and example how to call the function.
 --doc:
     Description:This function takes a number and divides it by 2.
     Parameters:
@@ -125,8 +93,19 @@ def example_function(x):
 <question>Document the python code above giving function description ,parameters and return type and example how to call the function.</question>
 <doc>"""
 inputs = tokenizer(prompt, return_tensors="pt")
-outputs = model.generate(**inputs, max_new_tokens=300)
-tokenizer.decode(outputs[0], skip_special_tokens=True).split('<doc>')[-1].split('</doc>')[0]
+outputs = model.generate(**inputs, max_new_tokens=450)
+doc = (
+    self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+    .split("<doc>")[-1]
+    .split("</doc>")[0]
+)
+doc = (
+    doc.replace("<p>", "")
+    .replace("</p>", "")
+    .replace("<function_description>", "")
+    .replace("</function_description>", "")
+)
+print(doc)
 ```
 
 
@@ -138,7 +117,7 @@ tokenizer.decode(outputs[0], skip_special_tokens=True).split('<doc>')[-1].split(
 ```python
 text=''' <example_response>
 --code:def function_2(x): return x / 2
---question:Document the code
+--question:Document the python code above giving function description ,parameters and return type and example how to call the function.
 --doc:
     Description:This function takes a number and divides it by 2.
     Parameters: