[ICCVW 2025] LLaVA-MORE: A Comparative Study of LLMs and Visual Backbones for Enhanced Visual Instruction Tuning
Domain-specialized Gemma 2 27B + Gemma 4 31B for SEC filings — fine-tuned on TPU v6e-8 with PyTorch/XLA FSDPv2, plus a Vertex AI Vector Search RAG demo (69 tickers × 381 filings). Same LoRA recipe, +3.5% / +5.8% BERTScore F1.
Fine-tuning of Gemma 2 model in Google Competition using a dataset of Chinese poetry. The goal is to adapt the model to generate Chinese poetry in a classical style by training it on a subset of poems. The fine-tuning process leverages LoRA (Low-Rank Adaptation) for efficient model adaptation.
Zero-shot, unsupervised latent phase diagnostics and Format Dominance analysis in LLMs.
Living medical intelligence engine with real-time streaming AI consults, tri-paradigm reasoning, and FHIR R4 care plans powered by Angular 22 & Google Gemini
A production-ready Multi-Agent RAG system with 4 specialized agents (Retriever, Planner, Reasoner, Answer), Redis cache, SSE streaming, conversation memory, and comprehensive evaluation. Supports Persian/English queries with local LLM (Gemma 2) and free API (Nemotron 3 Ultra). Built with FastAPI, LangChain, ChromaDB, and BGE-M3 embeddings.
Packet-Switched Attention for stable 2-bit quantized MoE inference, with variance-aware routing and Protocol C benchmarks.
Do language models show non-verbal signs of adverse treatment, or are we reading decoder noise? A preregistered stress test of answer-margin, resample and revision markers under false-failure feedback and hostile tone (Gemma, Qwen, Llama), with probing, DPO suppression and robustness checks.
⚗️ Gemma 2 9B model instruct repository
Zero-cloud on-device Korean MCQ generator Android SDK & CoT fine-tuning pipeline based on Google Gemma-2-2B (QuizScore 0.985)
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