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Dataset Loader Creator

dataset-loader-creator

jeremylongshore/claude-code-plugins-plus-skills

Automates dataset loader creation within ML Training workflows, offering best-practice guidance, validation checks, and production-ready code to accelerate data prep and model tuning.

TorchTitan Distributed Pretraining

distributed-llm-pretraining-torchtitan

Orchestra-Research/AI-Research-SKILLs

Orchestrates PyTorch-native TorchTitan 4D parallelism (FSDP2, TP, PP, CP) for large-scale LLM pretraining, scaling from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing to boot Llama 3.1, DeepSeek V3, or custom models.

Distributed Training Setup

distributed-training-setup

jeremylongshore/claude-code-plugins-plus-skills

Guides automated distributed training setups with step-by-step practices, production-ready configs, and validation checks for ML training workflows.

LLM Evaluation Harness

evaluating-llms-harness

Orchestra-Research/AI-Research-SKILLs

Evaluates large language models across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag) with standardized prompts and metrics, making it easy to benchmark HuggingFace or API models, compare releases, and track training progress.

Feature Engineering Helper

feature-engineering-helper

jeremylongshore/claude-code-plugins-plus-skills

Automatically activates when you mention feature engineering helper, offering guidance on data preparation, model training, hyperparameter tuning, and experiment tracking for ML projects.

LLM Fine-Tuning Guide

fine-tuning-expert

Jeffallan/claude-skills

Guides ML engineers through dataset prep, PEFT strategy selection, LoRA/QLoRA adapter setup, training monitoring, and deployment checks for practical LLM fine-tuning, quantization, and adapter merge workflows.

TRL RLHF Pipeline

fine-tuning-with-trl

Orchestra-Research/AI-Research-SKILLs

Fine-tune LLMs with TRL’s RLHF toolkit, covering SFT, DPO, PPO/GRPO and reward model training so you can align HuggingFace Transformers with human preferences or feedback in post-training workflows.

GPTQ Quantization Guide

Orchestra-Research/AI-Research-SKILLs

GPTQ enables post-training 4-bit quantization for large LLMs, delivering up to 4× memory reduction and 3-4× faster inference with under 2% perplexity loss on consumer GPUs through AutoGPTQ, PEFT, and Transformer integrations.

Gradient Clipping Helper

gradient-clipping-helper

jeremylongshore/claude-code-plugins-plus-skills

Gradient Clipping Helper automates ML training work by guiding gradient clipping workflows, generating production-ready code and configs, and validating outputs for PyTorch, TensorFlow, and related pipelines.

GRPO RL Fine-Tuning

grpo-rl-training

Orchestra-Research/AI-Research-SKILLs

Provides expert guidance for GRPO training with TRL, covering dataset prep, reward engineering, structured outputs, and multi-objective alignment when preference pairs are scarce.

Run Workloads on Hugging Face Jobs

hugging-face-jobs

sickn33/antigravity-awesome-skills

Execute complex and compute-intensive workloads directly on fully managed Hugging Face cloud infrastructure. This skill eliminates the need for local setup, allowing users to run jobs using cloud CPUs, GPUs, or TPUs. Ideal for large-scale data processing, batch inference, reproducible model training, and scheduled automation tasks, while persisting results to the Hugging Face Hub.

HuggingFace Tokenizer Suite

huggingface-tokenizers

Orchestra-Research/AI-Research-SKILLs

High-performance Rust-backed tokenization toolkit with Python bindings, offering BPE/WordPiece/Unigram, custom vocabulary training, alignment tracking, and production-ready integrations for NLP pipelines.

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