grpo-rl-training

21.8k

davila7

Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training

transformers

21.8k

davila7

This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.

peft-fine-tuning

21.8k

davila7

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train less than 1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

senior-ml-engineer

21.8k

davila7

World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.

llama-factory

21.8k

davila7

Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support

gptq

21.8k

davila7

Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.

unsloth

21.8k

davila7

Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization

clip

21.8k

davila7

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

implementing-llms-litgpt

21.8k

davila7

Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when needing clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.

fine-tuning-with-trl

21.8k

davila7

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

axolotl

21.8k

davila7

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

transformers

10.8k

K-Dense-AI

This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.

fine-tuning-expert

4.1k

Jeffallan

Use when fine-tuning LLMs, training custom models, or optimizing model performance for specific tasks. Invoke for parameter-efficient methods, dataset preparation, or model adaptation.

gpt

1.8k

openclaw

OpenAI GPT integration. Chat completions, image generation, embeddings, and fine-tuning via OpenAI API.

adapting-transfer-learning-models

1.5k

jeremylongshore

This skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. It is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing transfer learning. It analyzes the user's requirements, generates code for adapting the model, includes data validation and error handling, provides performance metrics, and saves artifacts with documentation. Use this skill when you need to leverage existing models for new tasks or datasets, optimizing for performance and efficiency.

skills-manager

451

anyproto

Context-aware routing to skills and hooks management. Use when troubleshooting skill activation, fine-tuning keywords, or managing the automated documentation system.

huggingface-classifier

376

a5c-ai

Hugging Face transformer model fine-tuning and inference for intent classification

grpo-finetuning

158

aws-solutions-library-samples

Implement GRPO (Group Relative Policy Optimization) fine-tuning for vision-language models on small datasets. Use when SFT underperforms or training data is limited (<1000 examples).

tinker

144

sundial-org

Fine-tune LLMs using the Tinker API. Covers supervised fine-tuning, reinforcement learning, LoRA training, vision-language models, and both high-level Cookbook patterns and low-level API usage.

tinker-training-cost

144

sundial-org

Calculate training costs for Tinker fine-tuning jobs. Use when estimating costs for Tinker LLM training, counting tokens in datasets, or comparing Tinker model training prices. Tokenizes datasets using the correct model tokenizer and provides accurate cost estimates.

training-data-curation

144

sundial-org

Guidelines for creating high-quality datasets for LLM post-training (SFT/DPO/RLHF). Use when preparing data for fine-tuning, evaluating data quality, or designing data collection strategies.

ai-llm-engineering

132

Microck

Operational skill hub for LLM system architecture, evaluation, deployment, and optimization (modern production standards). Links to specialized skills for prompts, RAG, agents, and safety. Integrates recent advances: PEFT/LoRA fine-tuning, hybrid RAG handoff (see dedicated skill), vLLM 24x throughput, multi-layered security (90%+ bypass for single-layer), automated drift detection (18-second response), and CI/CD-aligned evaluation.

reviewing-ai-papers

107

oaustegard

Analyze AI/ML technical content (papers, articles, blog posts) and extract actionable insights filtered through enterprise AI engineering lens. Use when user provides URL/document for AI/ML content analysis, asks to "review this paper", or mentions technical content in domains like RAG, embeddings, fine-tuning, prompt engineering, LLM deployment.

flux-image

76

inference-sh

Generate images with FLUX models (Black Forest Labs) via inference.sh CLI. Models: FLUX Dev LoRA, FLUX.2 Klein LoRA with custom style adaptation. Capabilities: text-to-image, image-to-image, LoRA fine-tuning, custom styles. Triggers: flux, flux.2, flux dev, flux schnell, flux pro, black forest labs, flux image, flux ai, flux model, flux lora

discover-ml

76

rand

Automatically discover machine learning and AI skills when working with machine learning, PyTorch, training, inference, RAG, embeddings, fine-tuning, LLM, DSPy, HuggingFace, or diffusion models. Activates for ML development tasks.

fts-development-branch-version-upgrade

70

speediedan

Automates Fine-Tuning Scheduler development branch version upgrade process including basic metadata oriented code changes, documentation updates, environment setup, testing, and coverage collection. Use when starting development on a new FTS minor release (e.g., 2.10.0 -> 2.11.0).

dspy-finetune-bootstrap

46

OmidZamani

This skill should be used when the user asks to "fine-tune a DSPy model", "distill a program into weights", "use BootstrapFinetune", "create a student model", "reduce inference costs with fine-tuning", mentions "model distillation", "teacher-student training", or wants to deploy a DSPy program as fine-tuned weights for production efficiency.

huggingface-transformers

44

applied-artificial-intelligence

Hugging Face Transformers best practices including model loading, tokenization, fine-tuning workflows, and inference optimization. Use when working with transformer models, fine-tuning LLMs, implementing NLP tasks, or optimizing transformer inference.

huggingface_transformers

39

vuralserhat86

Hugging Face Transformers best practices including model loading, tokenization, fine-tuning workflows, and inference optimization. Use when working with transformer models, fine-tuning LLMs, implementing NLP tasks, or optimizing transformer inference.

langchain_patterns

39

vuralserhat86

Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use when creating question-answering systems over document collections or AI assistants with external knowledge bases.

model_finetuning

39

vuralserhat86

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

llm-architect

36

zenobi-us

Expert LLM architect specializing in large language model architecture, deployment, and optimization. Masters LLM system design, fine-tuning strategies, and production serving with focus on building scalable, efficient, and safe LLM applications.

ai-llm

34

vasilyu1983

Production LLM engineering skill. Covers strategy selection (prompting vs RAG vs fine-tuning), dataset design, PEFT/LoRA, evaluation workflows, deployment handoff to inference serving, and lifecycle operations with cost/safety controls.

LLM fine-tuning

29

omer-metin

Use when adapting large language models to specific tasks, domains, or behaviors — covers LoRA, QLoRA, PEFT, instruction tuning, and full fine-tuning strategies. Use when ", " mentioned.

senior-ml-engineer

28

Hainamchung Agent Assistant Senior Ml Engineer

World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.

replicate-cli

25

rawveg

This skill provides comprehensive guidance for using the Replicate CLI to run AI models, create predictions, manage deployments, and fine-tune models. Use this skill when the user wants to interact with Replicate's AI model platform via command line, including running image generation models, language models, or any ML model hosted on Replicate. This skill should be used when users ask about running models on Replicate, creating predictions, managing deployments, fine-tuning models, or working with the Replicate API through the CLI.

llm

23

Mindrally

Large Language Model development, training, fine-tuning, and deployment best practices.

ai-eval-design-and-iteration

19

samarv

Develop "quizzes" (evals) to measure model performance on specific tasks. Use these benchmarks to guide fine-tuning, determine product UX patterns, and track performance improvements over time. Use this when launching a new AI feature, switching between model versions, or optimizing for high-stakes accuracy.

tuning-panel

18

petekp

Create visual parameter tuning panels for iterative adjustment of animations, layouts, colors, typography, physics, or any numeric/visual values. Use when the user asks to "create a tuning panel", "add parameter controls", "build a debug panel", "tweak parameters visually", "fine-tune values", "dial in the settings", or "adjust parameters interactively". Also triggers on mentions of "leva", "dat.GUI", or "tweakpane".

ai-ml-development

16

travisjneuman

AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.

transformers

15

oimiragieo

This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.

transformers

14

itsmostafa

Loading and using pretrained models with Hugging Face Transformers. Use when working with pretrained models from the Hub, running inference with Pipeline API, fine-tuning models with Trainer, or handling text, vision, audio, and multimodal tasks.

qlora

14

itsmostafa

Memory-efficient fine-tuning with 4-bit quantization and LoRA adapters. Use when fine-tuning large models (7B+) on consumer GPUs, when VRAM is limited, or when standard LoRA still exceeds memory. Builds on the lora skill.

mlx

14

itsmostafa

Running and fine-tuning LLMs on Apple Silicon with MLX. Use when working with models locally on Mac, converting Hugging Face models to MLX format, fine-tuning with LoRA/QLoRA on Apple Silicon, or serving models via HTTP API.

lora

14

itsmostafa

Parameter-efficient fine-tuning with Low-Rank Adaptation (LoRA). Use when fine-tuning large language models with limited GPU memory, creating task-specific adapters, or when you need to train multiple specialized models from a single base.

llm-fine-tuning

10

BagelHole

Set up infrastructure for fine-tuning LLMs with QLoRA, LoRA, and full fine-tuning using Hugging Face TRL, Axolotl, and distributed training with DeepSpeed or FSDP. Covers dataset prep, training runs, and model export.

using-llm-specialist

10

tachyon-beep

LLM specialist router to prompt engineering, fine-tuning, RAG, evaluation, and safety skills.

huggingface

9

TerminalSkills

Work with Hugging Face's ecosystem for machine learning — transformers library, model hub, tokenizers, inference pipelines, and fine-tuning. Covers downloading pre-trained models, running inference, training custom models, and publishing to the Hub.