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Search Model Training , found 43 results

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GPTQ LLM Quantization

Orchestra-Research/AI-Research-SKILLs

Post-training 4-bit quantization for LLMs that trims memory by 4× with minimal perplexity loss, enabling 70B+ models to run faster on consumer GPUs while integrating with transformers, PEFT, and QLoRA workflows.

GRPO RL Training

grpo-rl-training

Orchestra-Research/AI-Research-SKILLs

Provides expert guidance for implementing GRPO with TRL to fine-tune reasoning models, enforce structured outputs, and optimize custom reward functions for verifiable and multi-objective tasks.

Hyperparameter Tuner Assistant

hyperparameter-tuner

jeremylongshore/claude-code-plugins-plus-skills

Automated guidance for hyperparameter tuning within ML training workflows, offering best practices, production-ready code, and validation across modeling stages like data prep, training, and experiment tracking.

Enterprise Miles RL

miles-rl-training

Orchestra-Research/AI-Research-SKILLs

Guidance for training large multimodal MoE models with miles, covering FP8/INT4 quantization-aware regimes, train-inference alignment, speculative RL throughput tricks, and enterprise stability practices for production deployments.

Mixed Precision Trainer

mixed-precision-trainer

jeremylongshore/claude-code-plugins-plus-skills

Automates mixed precision ML training workflows by surfacing best practices, generating runnable code/configurations, and validating setups across data prep, model tuning, and experiment tracking.

Production ML Pipeline

Jeffallan/claude-skills

Designs production-grade ML pipeline infrastructure covering experiment tracking, feature stores, DAG orchestration, and automated retraining so teams can reliably version data, tune hyperparameters, and deploy models with tools like MLflow, Kubeflow, Airflow, or Feast.

ML Pipeline Workflow

ml-pipeline-workflow

sickn33/antigravity-awesome-skills

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating mod...

Model Checkpoint Manager

model-checkpoint-manager

jeremylongshore/claude-code-plugins-plus-skills

Automated guidance for managing model checkpoints in ML training workflows, offering best practices, sample code, and validation to keep experiments stable and reproducible.

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.

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