Update README.md

README.md

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 # pip-library-etl-1.3b
 
-[pipableAi](https://www.linkedin.com/company/pipable.ai/about/)
+[pipableAi](https://www.pipable.ai/)
 
-[colab_notebook](https://colab.research.google.com/drive/17PyMU_3QN9LROy7x-jmaema0cuLRzBvc?usp=sharing)
+[colab_notebook](https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing)
 
-[pip library_etl](https://github.com/PipableAI/pip-library-etl.git)
+[pip flow]()
 
-[linkedin_post](https://www.linkedin.com/posts/pipable%2Eai_github-pipableaipip-library-etl-this-activity-7179111129678327809-Pgxy?utm_source=share&utm_medium=member_desktop)
+[linkedin_post]()
 
-## What have we built?
+[reddit_post]()
+
+## Model attributes
+
+```javascript
+-- number of params ~ 1.3b [2.9 Gb GPU memory footprint]
+-- sequence length ~ 16.3k [Can go higher but will show performance degradation]
+-- license - apache 2.0
+-- tasks:
+1. complex planning of sequential function calls with right params to accomplish a goal | a list of callables
+2. function calling | doc or code and goal
+3. code generation | plan and goal
+4. code generation | goal
+5. doc generation | code
+6. code generation | doc
+7. file recreated in json | any raw data
+8. corrected generation | new instruction with error
+-- instruction following , RL tuned.
 
-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 [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 and performance comparable to GPT.
 
 
 ## How we built it?