shap / shap
A game theoretic approach to explain the output of any machine learning model.
shap / shap
EthicalML / awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
interpretml / interpret
Fit interpretable models. Explain blackbox machine learning.
kserve / kserve
Standardized Distributed Generative and Predictive AI Inference Platform for Scalable, Multi-Framework Deployment on Kubernetes
meta-pytorch / captum
Model interpretability and understanding for PyTorch
TransformerLensOrg / TransformerLens
A library for mechanistic interpretability of GPT-style language models
PAIR-code / lit
The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.
MAIF / shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
frgfm / torch-cam
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM, Finer-CAM, LeGrad, RefineCAM)
anthropics / jacobian-lens
Companion code for the global workspace interpretability paper
microsoft / responsible-ai-toolbox
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
JShollaj / awesome-llm-interpretability
A curated list of Large Language Model (LLM) Interpretability resources.
csinva / imodels
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
kitops-ml / kitops
An open source DevOps tool from the CNCF for packaging and versioning AI/ML models, datasets, code, and configuration into an OCI Artifact.
hijohnnylin / neuronpedia
open source interpretability platform 🧠
ndif-team / nnsight
The nnsight package enables interpreting and manipulating the internals of deep learned models.
mmschlk / shapiq
Shapley Values and Shapley Interactions for Machine Learning
deel-ai / xplique
👋 Xplique is a Neural Networks Explainability Toolbox
google / yggdrasil-decision-forests
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
kochlisGit / ProphitBet-Soccer-Bets-Predictor
ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.
zengxiao-he / tessera
From teacher to tiles — a from-scratch LLM distillation & serving engine: custom Triton/CUDA kernels, FSDP distillation, paged-KV continuous batching, speculative decoding, a Rust gateway, a JAX oracle, and interpretability tooling.
lordx64 / phantom-kv
Refusal removal as a loadable KV-cache graft — zero weight modification, no refusal-direction projection, fully reversible.
MirroS-Lab / S-Space
S-Space: Exploring Spatial Workspace in Multimodal Models
canivel / dynolab
Dyno Lab — an AI safety, alignment and interpretability research workbench for Apple Silicon. Explore activations, probes, SAEs and interventions with local inference, a Python SDK, APIs and MCP.
Jize-W / Awesome-World-Model-Interpretability
[ArXiv 2026] Methods for world model interpretability
alesha-pro / atlas
Interactive canvas for taking an LLM apart tensor by tensor: measured INT8/INT4/FP8 error, distributions, spectra and outlier channels for every weight tensor
fulvian / engraft-ngram
ENGRAFT: write facts into an LLM's n-gram (Engram) memory table as a removable overlay: 100 facts in a 9 MB file, no weights touched, verified on llama.cpp GGUF (Qwen3.8-Flash-Next).
decoderesearch / interp-engine
Fast, standardized, and easy-to-use interpretability engine.
aimo-interp / getting-started
A minimal submission bundle for AIMO Interpretability Challenge
lvsir18 / CS224n-Winter-2026-Learning-Notes
这是一套围绕 Stanford CS224N: Natural Language Processing with Deep Learning 整理的系统课程笔记。仓库以课程 19 节 Lecture 为主线，从 Word Vectors、RNN、Attention、Transformer 一路延伸到 Pretraining、Post-training、PEFT、RAG、Agents、Reasoning、Multilinguality、Interpretability 与 Multimodality。