[ICML 2024] SqueezeLLM: Dense-and-Sparse Quantization

[NeurIPS 2024] KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

OpenSSA: Small Specialist Agents based on Domain-Aware Neurosymbolic Agent (DANA) architecture for industrial problem-solving

[CVPR 2024] Asymmetric Masked Distillation for Pre-Training Small Foundation Models

This repository features a custom-built decoder-only language model (LLM) with a total of 37 million parameters 🔥. I train the model to be able to ask question from a given context

🤩 GreaterPrompt: A Python Toolkit for Prompt Optimization via Small Models

BobVLM – A 1.5B multimodal model built from scratch and pre-trained on a single P100 GPU capable of image descriptions and moderate question answering. 🤗🎉

Code for "On the Surprising Efficacy of Distillation as an Alternative to Pre-Training Small Models"

Overview of self-supervised learning of tiny models, including distillation-based methods (aks. self-supervised distillation) and non-distillation methods.

Help us define the Pareto front of small models for MNIST classification. Frugal AI.

Fine-tunes a T5-small model on the TellMeWhy dataset using context injection from a large language model (Gemini) to improve causal reasoning for “why” questions in narratives. Combines efficient training with human and automated evaluations to assess impact.

Phi-3-Vision model test - running locally