Discover Agent Skills for data science & ml. Browse 53skills for Claude, ChatGPT & Codex.
Implements and optimizes Mamba-based Selective State Space Models for high-efficiency sequence modeling and long-context AI research.
Quantizes Large Language Models to 4-bit or 8-bit formats to reduce GPU memory usage by up to 75% with minimal accuracy loss.
Implements Anthropic's Constitutional AI method to train harmless, helpful models through self-critique and automated AI feedback.
Optimizes large-scale AI model training using PyTorch Fully Sharded Data Parallelism for efficient memory management and scaling.
Implements language-independent subword tokenization using BPE and Unigram algorithms for advanced AI model development.
Implements and trains minimalist GPT architectures for educational and research purposes using Andrej Karpathy's clean, hackable codebase.
Transcribes audio, translates speech to English, and automates multilingual audio processing using OpenAI's Whisper models.
Serves Large Language Models with maximum throughput and efficiency using vLLM's PagedAttention and continuous batching.
Deploys and optimizes LLM inference on CPU, Apple Silicon, and consumer hardware using GGUF quantization.
Facilitates mechanistic interpretability research by providing tools to inspect, cache, and manipulate transformer model activations via HookPoints.
Processes large-scale datasets for machine learning workloads using distributed streaming execution across CPU and GPU clusters.
Provides high-performance, Rust-optimized text tokenization for NLP research and production-grade machine learning pipelines.
Simplifies large language model alignment using reference-free preference optimization to improve model performance without the overhead of PPO or DPO.
Streamlines the fine-tuning process for over 100 large language models using the LLaMA-Factory framework and QLoRA techniques.
Optimizes LLM serving and structured generation using RadixAttention prefix caching for high-performance agentic workflows.
Tracks machine learning experiments and manages model lifecycles with real-time visualization and collaborative tools.
Accelerates LLM inference speeds by up to 3.6x using advanced decoding techniques like Medusa heads and lookahead decoding.
Optimizes AI models for efficient local inference using the GGUF format and llama.cpp quantization techniques.
Implements Meta AI's foundation model for high-precision zero-shot image segmentation using points, boxes, and masks.
Builds complex AI systems using Stanford's declarative programming framework to optimize prompts and create modular RAG systems automatically.
Enables zero-shot image classification and semantic image search by connecting visual concepts with natural language.
Decomposes complex neural network activations into sparse, interpretable features to understand and steer model behavior.
Accelerates LLM fine-tuning workflows with Unsloth to achieve up to 5x faster training speeds and 80% reduced memory consumption.
Performs declarative causal interventions and mechanistic interpretability experiments on PyTorch models.
Connects LLMs to private data sources through advanced document ingestion, vector indexing, and retrieval-augmented generation (RAG) pipelines.
Integrates Salesforce's BLIP-2 framework to enable advanced image captioning, visual question answering, and multimodal reasoning within AI workflows.
Simplifies PyTorch distributed training by providing a unified API for DDP, DeepSpeed, and FSDP with minimal code changes.
Generates high-quality images and performs advanced image transformations using Stable Diffusion models and the HuggingFace Diffusers library.
Accelerates genomic interval analysis and machine learning preprocessing using a high-performance Rust toolkit with Python bindings.
Performs comprehensive bioinformatics analysis including sequence manipulation, phylogenetics, and microbial ecology statistics within Python.
Scroll for more results...