model-debugging

Monitoring platform for ML teams building real‑world AI at scale.

XAI-driven augmentation & diagnostics for PyTorch vision - find model failures, fix with saliency-guided augmentation (ICD/AICD), prove with auditable reports.

Codes for the EMNLP 2020 paper -- "FIND: Human-in-the-loop Debugging Deep Text Classifiers"

Autonomous failure investigation, root-cause analysis, and validated interventions for LLMs, VLMs, and agents.

Custom ML tracking experiment and debugging tools.

ML is a tool for diagnosing model errors: segments, fairness, and calibration

PyTorch NaNs are silent killers. This hook catches them at the exact layer and batch — with ~3 ms overhead vs ~7 ms for set_detect_anomaly.

Neural network visual debugger for model graph inspection, activation health, and gradient diagnostics.

Compare saved classifier predictions, check release requirements and inspect changed cases in local HTML reports.

Dog Breed Classification using Transfer Learning in AWS Sagemaker

file(1) of the ternary age — balanced-ternary-aware GGUF inspector and debugger in Rust

Local PyTorch model debugger for mechanistic interpretability: inspect activation vectors, compare prompts, and run controlled interventions on supported transformer models.

Behavioral debugging for neural networks: trace checkpoint changes to representations, mechanisms, and influential training data.

Mechanistic interpretability that ships: MAIR-backed evidence bundles, receipts, and comparison packets.

Easy-to-use UI based tool that visualizes the internal layers and activations of any Pytorch network that takes image as input , built using PyQt

Model Debugging

BRACE - BetteR Accuracy from Concept-based Explanation

Stop guessing why your MLX model outputs garbage. Triage in 30 seconds — no model load required.