Notes about LLaMA 2 model

This repository contains an implementation of the LLaMA 2 (Large Language Model Meta AI) model, a Generative Pretrained Transformer (GPT) variant. The implementation focuses on the model architecture and the inference process. The code is restructured and heavily commented to facilitate easy understanding of the key parts of the architecture.

This a minimal implementation of a GPT model but it has some advanced features such as temperature/ top-k/ top-p sampling, and KV Cache.

Easy control for Key-Value Constrained Generative LLM Inference(https://arxiv.org/abs/2402.06262)

Fine-Tuned Mistral 7B Persian Large Language Model LLM / Persian Mistral 7B

EXPRESS REST API CACHING + RATE LIMITING + KV-STORE

SCAC strategy for efficient and effective KV cache eviction in LLMs

Mistral and Mixtral (MoE) from scratch

Java-based caching solution designed to temporarily store key-value pairs with a specified time-to-live (TTL) duration.

Image Captioning With MobileNet-LLaMA 3

[NeurIPS'23] H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models.

A Golang implemented Redis Server and Cluster. Go 语言实现的 Redis 服务器和分布式集群

Completion After Prompt Probability. Make your LLM make a choice

Awesome-LLM-KV-Cache: A curated list of 📙Awesome LLM KV Cache Papers with Codes.

LLM KV cache compression made easy

Simple and easy to understand PyTorch implementation of Large Language Model (LLM) GPT and LLAMA from scratch with detailed steps. Implemented: Byte-Pair Tokenizer, Rotational Positional Embedding (RoPe), SwishGLU, RMSNorm, Mixture of Experts (MOE). Tested on Taylor Swift song lyrics dataset.

The Official Implementation of PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling