Technical Approach
An overview of the LLM training strategy, data sources, technology stack, and deployment architecture.
System Architecture
A modular architecture combining fine-tuned LLM capabilities with retrieval-augmented generation.
Base Model
Open-weight LLM (7B-13B parameters)
Fine-Tuning
LoRA / QLoRA on cloud GPUs
RAG System
Vector DB + retrieval pipeline
Frontend
Web-based chat interface
Infrastructure
Cloud GPU + on-prem option
Security
SCSU auth integration
Training Data Sources
Curated academic datasets providing comprehensive coverage across disciplines.
ArXiv
Open-access preprint repository with over 2 million scholarly articles across physics, mathematics, computer science, and more.
Semantic Scholar
AI-powered research tool providing access to 200+ million academic papers with rich metadata and citation graphs.
PubMed
Comprehensive biomedical literature database from the National Library of Medicine covering life sciences and health.
Training Pipeline
Six-stage process from data collection to production deployment.
Data Collection
Gather and curate academic papers from ArXiv, Semantic Scholar, and PubMed APIs.
Preprocessing
Clean, tokenize, and structure data into training-ready formats with quality filters.
Fine-Tuning
Apply LoRA/QLoRA techniques to adapt a base LLM for academic research tasks.
RAG Integration
Build retrieval-augmented generation pipeline with vector database for real-time knowledge.
Evaluation
Benchmark against commercial LLMs on academic tasks, safety, and accuracy metrics.
Deployment
Deploy web interface and package downloadable model for local installation.
LLM Training Strategy
The project adopts a parameter-efficient fine-tuning approach using LoRA (Low-Rank Adaptation) or QLoRA (Quantized LoRA). This allows us to adapt a capable open-weight base model (7B–13B parameters) to academic research tasks within a lean compute budget.
The training process combines supervised fine-tuning on curated academic Q&A pairs with retrieval-augmented generation (RAG) for real-time knowledge access. This hybrid approach ensures the model produces accurate, up-to-date responses grounded in actual academic literature.
Key optimization strategies include mixed-precision training, gradient checkpointing, and efficient batching to maximize GPU utilization. The model will be evaluated against established benchmarks for academic writing quality, citation accuracy, and factual consistency.
