1.rohitg00 / ai-engineering-from-scratch
Learn AI Engineering! Learn it. Build it. Ship it for others.
Ranked by Attention Score: stars gained over the last 7 complete days
18 repositories by Attention Score · Oct 27, 2026 – Nov 2, 2026 (UTC)
Learn AI Engineering! Learn it. Build it. Ship it for others.
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
Turn any LLM into a Jev-style decision model: typed decisions, real probabilities, no training. (continue updating, welcome any issue and PR request)
Machine Learning Engineering Open Book
Ongoing research training transformer models at scale
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
Open-source offline translation library written in Python
💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows
Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
A PyTorch-based Speech Toolkit
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.
Easy-to-use and powerful LLM and SLM library with awesome model zoo.
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, DeepSeek, Mixtral, Gemma, Phi, MiniCPM, Qwen-VL, MiniCPM-V, etc.) on Intel XPU (e.g., local PC with iGPU and NPU, discrete GPU such as Arc, Flex and Max); seamlessly integrate with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, vLLM, DeepSpeed, Axolotl, etc.
Superduper: End-to-end framework for building custom AI applications and agents.
Multilingual Automatic Speech Recognition with word-level timestamps and confidence