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Model Description
<The RAFT-based QLoRA fine-tuned Llama-2-7B model accepts legal contract documents as input and extracts the textual content for further processing.
The extracted text is divided into meaningful chunks, converted into embeddings, and stored in a FAISS vector database.
When a user submits a legal question, the system retrieves the most relevant contract chunks through semantic similarity search.
These retrieved chunks are provided as contextual input to the quantized Llama-2-7B model with LoRA adapters, enabling the model to generate accurate, context-aware responses. Finally, the system produces a structured summary of the legal contract in the form of question–answer pairs, making contract analysis faster and easier to understand.>
- Developed by: Harsha Deep Joga
- Funded by [optional]: Self Project
- Shared by [optional]: HarshaDeep2006
- Model type: Llama-2-7B QLoRa with RAFT
- Language(s) (NLP): English
- License: MIT
- Finetuned from model [optional]: meta-llama/Llama-2-7b-hf
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