6 papers
MINCE: Shrinking LLM Evaluation Datasets via Few-Model Monte Carlo Calibration
Devleena Das, Rajeev Patwari, Vikram Kumar Bukka +3
Evaluating LLMs across many model variants -- quantized, fine-tuned, or deployment-specific -- requires running large benchmarks repeatedly, a process that can take tens of hours p…
Recover-LoRA for Aggressive Quantization: Reclaiming Accuracy in 2-Bit Language Models via Low-Rank Adaptation with Knowledge Distillation on Synthetic Data
Devleena Das, Rajeev Patwari, Elliott Delaye +1
Aggressive weight quantization to 2-bit precision offers substantial throughput and memory gains for large language model (LLM) inference, but typically incurs severe accuracy degr…
KV Pareto: Systems-Level Optimization of KV Cache and Model Compression for Long Context Inference
Sai Gokhale, Devleena Das, Rajeev Patwari +2
Long-context Large Language Models (LLMs) face significant memory bottlenecks during inference due to the linear growth of key-value (KV) cache with sequence length. While individu…
Recover-LoRA: Data-Free Accuracy Recovery of Degraded Language Models via Low-Rank Adaptation
Devleena Das, Rajeev Patwari, Ashish Sirasao
Inference optimizations such as quantization, pruning, format and datatype conversion, model export, and serialization can lead to functional degradations in language model task pe…
ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way
Rajarshi Roy, Devleena Das, Ankesh Banerjee +3
We introduce ByDeWay, a training-free framework designed to enhance the performance of Multimodal Large Language Models (MLLMs). ByDeWay uses a novel prompting strategy called Laye…
Forecasting LLM Inference Performance via Hardware-Agnostic Analytical Modeling
Rajeev Patwari, Ashish Sirasao, Devleena Das
Large language models (LLMs) have been increasingly deployed as local agents on personal devices with CPUs, NPUs and integrated GPUs. However, forecasting inference performance on…