Reward-Modeling-RLHF-Finetune-and-RAG

Finetune Llama 3.3, DeepSeek-R1 & Reasoning LLMs 2x faster with 70% less memory! ?

:fire: Android developers should collect the following utils(updating).

Redisson - Valkey and Redis Java client. Real-Time Data Platform. Sync/Async/RxJava/Reactive API. Over 50 Valkey and Redis based Java objects and services: Set, Multimap, SortedSet, Map, List, Queue, Deque, Semaphore, Lock, AtomicLong, Map Reduce, Bloom filter, Spring, Tomcat, Scheduler, JCache API, Hibernate, RPC, local cache..

Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama model family and using them on various provider services

? Like builtins, but boltons. 250+ constructs, recipes, and snippets which extend (and rely on nothing but) the Python standard library. Nothing like Michael Bolton.

HAProxy Load Balancer's development branch (mirror of git.haproxy.org)

Efficient Triton Kernels for LLM Training

H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://docs.h2o.ai/h2o-llmstudio/

Interact with your SQL database, Natural Language to SQL using LLMs

A PyTorch Library for Meta-learning Research