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README.md

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 # Running Open-Source LLMs on CPU Inference for Document Q&A
 
 ### Clearly explained step-by-step guide on using C Transformers, GGML, and LangChain for running LLM Python applications on CPU instances
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+**Link to TowardsDataScience article**: *Coming Soon*
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 ## Context
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+- Third-party commercial large language model (LLM) providers like OpenAI's GPT4 have democratized LLM use via simple API calls. 
+- However, there are instances where teams would require self-managed or private model deployment for reasons like data privacy and residency rules.
+- The proliferation of open-source LLMs has opened up a vast range of options for us, thus reducing our reliance on these third-party providers. 
+- When we host open-source LLMs locally on-premise or in the cloud, the dedicated compute capacity becomes a key issue. While GPU instances may seem the obvious choice, the costs can easily skyrocket beyond budget.
+- In this project, we will discover how to run quantized versions of open-source LLMs on local CPU inference for document question-and-answer (Q&A).
+<br><br>
+![Alt text](assets/document_qa_flowchart.png)
 
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 ## Tools
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+- LangChain: 
+- C Transformers: Python
+- FAISS:
+- Sentence-Transformers (all-MiniLM-L6-v2): 
+- MPT-7B-Instruct (LLM): 
 
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 ## Files