Data Science & ML Agent Skills
Discover Agent Skills for data science & ml. Browse 61skills for Claude, ChatGPT & Codex.
RWKV Model Architecture
Implements and optimizes RWKV architectures, a hybrid RNN-Transformer model offering linear-time inference and infinite context windows.
PyTorch Lightning Training & Scaling
Streamlines deep learning development by decoupling research code from engineering boilerplate for automated distributed training and hardware scaling.
LLM Evaluation Harness
Evaluates Large Language Models across 60+ academic benchmarks to measure reasoning, coding, and mathematical capabilities using industry-standard metrics.
LLM Guidance & Constrained Generation
Enforces structured LLM outputs using regex and grammars to guarantee valid JSON, XML, and code generation.
CrewAI Multi-Agent Orchestration
Orchestrates teams of autonomous AI agents to collaborate on complex tasks through role-based delegation and memory.
Segment Anything Model (SAM)
Implements Meta AI's foundation model for high-precision zero-shot image segmentation using points, boxes, and masks.
nnsight Model Interpretability
Interprets and manipulates neural network internals for any PyTorch model, including massive foundation models via remote execution.
Long Context Transformer Extension
Extends transformer context windows using RoPE, YaRN, and ALiBi techniques to process documents exceeding 128k tokens.
FAISS Vector Similarity Search
Accelerates large-scale similarity search and clustering for dense vectors using Facebook AI's high-performance library.
Axolotl LLM Fine-Tuning
Streamlines the fine-tuning of large language models using Axolotl through expert YAML configuration guidance and advanced training techniques.
SAELens: Mechanistic Interpretability
Decomposes complex neural network activations into sparse, interpretable features to understand and steer model behavior.
Sentence Transformers
Generates state-of-the-art text and image embeddings for RAG, semantic search, and clustering tasks.
PyTorch FSDP Expert
Optimizes large-scale AI model training using PyTorch Fully Sharded Data Parallelism for efficient memory management and scaling.
LLM Model Pruning
Compresses Large Language Models using advanced techniques like Wanda and SparseGPT to reduce memory footprint and accelerate inference speeds.
Mamba Architecture & SSM Implementation
Implements and optimizes Mamba-based Selective State Space Models for high-efficiency sequence modeling and long-context AI research.
bitsandbytes LLM Quantization
Quantizes Large Language Models to 4-bit or 8-bit formats to reduce GPU memory usage by up to 75% with minimal accuracy loss.
DeepSpeed Distributed Training
Optimizes large-scale AI model training using DeepSpeed's ZeRO, pipeline parallelism, and high-performance DeepNVMe I/O handling.
HuggingFace Tokenizers
Provides high-performance, Rust-optimized text tokenization for NLP research and production-grade machine learning pipelines.
Weights & Biases MLOps
Tracks machine learning experiments and manages model lifecycles with real-time visualization and collaborative tools.
Stable Diffusion Image Generation
Generates high-quality images and performs advanced image transformations using Stable Diffusion models and the HuggingFace Diffusers library.
HuggingFace Accelerate Distributed Training
Simplifies PyTorch distributed training by providing a unified API for DDP, DeepSpeed, and FSDP with minimal code changes.
AWQ LLM Quantization
Optimizes Large Language Models using activation-aware 4-bit quantization to achieve 3x inference speedups and significant memory reduction with minimal accuracy loss.
BigCode Evaluation Harness
Evaluates AI code generation models across multiple programming languages and benchmarks using standardized pass@k metrics.
AudioCraft AI Generation
Generates high-fidelity music and sound effects from text descriptions using Meta's AudioCraft framework.
Flash Attention Optimization
Optimizes Transformer models using Flash Attention to achieve significant speedups and memory reductions during training and inference.
LLaVA Multimodal Assistant
Enables advanced vision-language capabilities for image understanding, multi-turn visual conversations, and document analysis.
vLLM High-Performance Inference Serving
Serves Large Language Models with maximum throughput and efficiency using vLLM's PagedAttention and continuous batching.
Knowledge Distillation for LLMs
Compresses large language models using teacher-student learning techniques to reduce inference costs while maintaining high performance.
Speculative Decoding LLM Accelerator
Accelerates LLM inference speeds by up to 3.6x using advanced decoding techniques like Medusa heads and lookahead decoding.
pyvene Causal Interventions
Performs declarative causal interventions and mechanistic interpretability experiments on PyTorch models.
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