An innovative library for efficient LLM inference via low-bit quantization

Collection of resources for finetuning Large Language Models (LLMs).

synthetic dataset generation workflow using local file resources for finetuning llms.

Sustain-LC is a benchmarking environment for traditional and reinforcement learning based controls as well as LLM based control

Distributed Reinforcement Learning for LLM Fine-Tuning with multi-GPU utilization

The Personal Knowledge Graph You Didn’t Know You Already Wrote

Fine tune LLM with HuggingFace

This repository contains code associated with Neuro-LIFT: A Neuromorphic, LLM-based Interactive Framework for Autonomous Drone FlighT at the Edge

A sacred space for heartfelt conversations, where wisdom flows freely and memories gently fade like whispers at sunset.

Análise Avançada de Dados com Causalidade e Aprendizado por Reforço

Experiments in Latin dactylic hexameter generation with transformers: A hybrid post hoc feedback framework

The course teaches how to fine-tune LLMs using Group Relative Policy Optimization (GRPO)—a reinforcement learning method that improves model reasoning with minimal data. Learn RFT concepts, reward design, LLM-as-a-judge evaluation, and deploy jobs on the Predibase platform.

CLI tool for generating high-quality synthetic datasets for LLM fine-tuning.

Flipper Zero Sub-GHz RF dataset (280-1100 MHz, 9 countries) with 1500 Q&A pairs for LLM fine-tuning, fact-checked allocations, and a GPU-accelerated validation pipeline (Ollama Qwen 32B + DeBERTa NLI).

Fully Connected Neural Networks, Multilayer Neural Networks, MAdaline, CNNs, Segmentation, Detection, RNNs, CNN-LSTM, LSTM, Bi-LSTM, GRU, Transformers, Huber Loss, ViT, DGMs, Triplet VAE, AdvGAN, Image Caption Generation, attention, LLM Fine-Tuning, Soft Prompting, LoRA, Layer Freezing, SlimOrca

No-code desktop app for generating high-quality synthetic datasets to fine-tune LLMs — plan-then-execute pipeline, LLM-as-judge, HuggingFace upload.

FlowerTune LLM on Coding Dataset

High-performance Rust extensions for Axolotl (no OOM for large datasets) - drop-in acceleration for existing installations.

Process-supervised RL for a multi-step reasoning agent — DAPO + a learned Process Reward Model (PRM) training a Qwen3-8B Planner. A modern, from-scratch rebuild of the AgentFlow paper (ICLR 2026).

ARC-Test-Time-Training (ARC-TTT)