Llm + Fine Tuning + Rag

23.0

Develop a framework for integrating dense geospatial embeddings directly into LLM reasoning loops. This enables a new class of agents capable of 'thinking' spatially about population dynamics and mobility data.

emergingimplementation gap

fine-tuningdistillationllmraggeospatialmemory-efficientinferenceinterface

Signals (4)

arXiv14h ago

Cross-Tokenizer LLM Distillation through a Byte-Level Interface

arXiv14h ago

GRASS: Gradient-based Adaptive Layer-wise Importance Sampling for Memory-efficient Large Language Model Fine-tuning

arXiv14h ago

Enabling Intrinsic Reasoning over Dense Geospatial Embeddings with DFR-Gemma

GitHub16h ago

Llm + Fine Tuning + Rag

Signals (4)

Cross-Tokenizer LLM Distillation through a Byte-Level Interface

GRASS: Gradient-based Adaptive Layer-wise Importance Sampling for Memory-efficient Large Language Model Fine-tuning

Enabling Intrinsic Reasoning over Dense Geospatial Embeddings with DFR-Gemma

open-webui/open-webui