Update README.md

README.md

@@ -21,7 +21,8 @@ Users can ask questions about the provided documents.
 The virtual assistant provides answers based on the retrieved documents and a powerful, yet environmentally friendly, large language model (LLM).
 Technical Details
 
-####  Sentence Similarity: The application utilizes the Alibaba-NLP/gte-base-en-v1.5 model for efficient sentence embedding, allowing for semantic similarity comparisons between user queries and the legal documents.
+####  Sentence Similarity: 
+The application utilizes the Alibaba-NLP/gte-base-en-v1.5 model for efficient sentence embedding, allowing for semantic similarity comparisons between user queries and the legal documents.
 Local Vector Store: chroma acts as a local vector store, efficiently storing and managing the document embeddings for fast retrieval.
 RAG Chain with Quantized LLM: A Retrieval-Augmented Generation (RAG) chain is implemented to process user queries. This chain integrates two key components:
 Lightweight LLM: To ensure local operation, the application employs a compact LLM, specifically JCHAVEROT_Qwen2-0.5B-Chat_SFT_DPO.Q8_gguf, with only 0.5 billion parameters. This LLM is specifically designed for question answering tasks.
@@ -29,11 +30,13 @@ Quantization: This Qwen2 model leverages a technique called quantization, which
 CPU-based Processing: The entire application is currently implemented to function entirely on your CPU. While utilizing a GPU could significantly improve processing speed, this CPU-based approach allows the application to run effectively on a wider range of devices.
 Benefits of Compact Design
 
-####  Local Processing: The compact size of the LLM and the application itself enable local processing on your device, reducing reliance on cloud-based resources and associated environmental impact.
+####  Local Processing: 
+The compact size of the LLM and the application itself enable local processing on your device, reducing reliance on cloud-based resources and associated environmental impact.
 Mobile Potential: Due to its small footprint, this application has the potential to be adapted for mobile devices, bringing legal information access to a wider audience.
 Adaptability of Qwen2 0.5B
 
-####  Fine-tuning: While the Qwen2 0.5B model is powerful for its size, it can be further enhanced through fine-tuning on specific legal datasets or domains, potentially improving its understanding of Lithuanian legal terminology and nuances.
+####  Fine-tuning: 
+While the Qwen2 0.5B model is powerful for its size, it can be further enhanced through fine-tuning on specific legal datasets or domains, potentially improving its understanding of Lithuanian legal terminology and nuances.
 Conversation Style: Depending on user needs and desired conversation style, alternative pre-trained models could be explored, potentially offering a trade-off between model size and specific capabilities.
 ####  Requirements