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Search Model Training , found 26 results

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Model Explainability Assistant

model-explainability-tool

jeremylongshore/claude-code-plugins-plus-skills

Automated guidance for model explainability tooling within ML training workflows, giving step-by-step best practices, production-ready code, and validation for experiments whenever users mention explainability tool needs.

Model Merging Techniques

Orchestra-Research/AI-Research-SKILLs

Utilize Mergekit to merge multiple fine-tuned models via SLERP, TIES, DARE, Task Arithmetic, and linear merging; perfect for preserving diverse domain skills, improving benchmark performance, and experimenting with new combinations without retraining.

LLM Model Pruning

Orchestra-Research/AI-Research-SKILLs

Model Pruning skill applies Wanda, SparseGPT, structured, unstructured, and N:M sparsity to trim LLMs by around 50% and boost inference on accelerators or edge devices without retraining.

MoE Training Guide

Orchestra-Research/AI-Research-SKILLs

Guide to training Mixture of Experts models with DeepSpeed or HuggingFace for large-scale sparse architectures like Mixtral 8x7B, focusing on routing, load balancing, and cost-efficient scaling.

OpenRLHF High Performance Training

openrlhf-training

Orchestra-Research/AI-Research-SKILLs

OpenRLHF is a Ray-based RLHF framework that accelerates large-model PPO, GRPO, RLOO, and DPO training with vLLM inference and ZeRO-3 resource sharing, targeting distributed GPU clusters handling 7B-70B+ parameter models.

Pytorch Model Trainer

pytorch-model-trainer

jeremylongshore/claude-code-plugins-plus-skills

Automates PyTorch training workflows by guiding data preparation, model configuration, hyperparameter tuning, and experiment tracking while following industry best practices for production readiness.

SimPO Preference Optimization

Orchestra-Research/AI-Research-SKILLs

SimPO offers a reference-free preference optimization workflow to align LLMs more efficiently than DPO or PPO, requiring no reference model and suited for compute-constrained single-node training with proven AlpacaEval gains.

SAELens Sparse Autoencoder Tool

sparse-autoencoder-training

Orchestra-Research/AI-Research-SKILLs

Guides researchers through training and analyzing Sparse Autoencoders with SAELens to decompose neural activations into interpretable concepts, understand superposition, and steer or audit transformer models with sparse feature math.

TensorBoard Visualization Toolkit

Orchestra-Research/AI-Research-SKILLs

TensorBoard is Google’s ML visualization toolkit for tracking training metrics, comparing experiments, inspecting model graphs, projecting embeddings, and profiling performance in PyTorch and TensorFlow workflows.

Weights And Biases Platform

weights-and-biases

Orchestra-Research/AI-Research-SKILLs

Weights & Biases streamlines MLOps by logging metrics, configs, and artifacts, offering dashboards to monitor training, compare runs, optimize sweeps, and share models with teams.

PyHealth Clinical AI Toolkit

K-Dense-AI/claude-scientific-skills

PyHealth is a Python healthcare AI toolkit for building, training, evaluating, and deploying clinical prediction models using EHR, signal, imaging, and coding data while optimizing pipelines, calibration, and interpretability.

Lightning Training Workflow

pytorch-lightning

K-Dense-AI/claude-scientific-skills

PyTorch Lightning organizes models into LightningModule, data modules, callbacks, logging, and Trainer orchestration for scalable multi-GPU/TPU training with distributed strategies and experiment tracking.

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