data science & ml Claude 스킬을 발견하세요. 71개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Transcribes audio files into text or JSON format using OpenAI's state-of-the-art Whisper API.
Builds LLM-powered applications using agents, retrieval-augmented generation (RAG), and modular chains.
Aligns Large Language Models with human preferences using advanced reinforcement learning techniques including SFT, DPO, PPO, and GRPO.
Optimizes Large Language Models using 4-bit post-training quantization to reduce memory usage and accelerate inference on consumer GPUs.
Quantizes Large Language Models to 4/3/2-bit precision without calibration data for faster inference and reduced memory footprint.
Streamlines the fine-tuning of large language models using Axolotl through expert YAML configuration guidance and advanced training techniques.
Simplifies large language model alignment using reference-free preference optimization to improve model performance without the overhead of PPO or DPO.
Fine-tunes large language models using LoRA, QLoRA, and other parameter-efficient methods to drastically reduce memory and compute requirements.
Compresses large language models using teacher-student learning techniques to reduce inference costs while maintaining high performance.
Generates state-of-the-art text and image embeddings for RAG, semantic search, and clustering tasks.
Evaluates AI code generation models across multiple programming languages and benchmarks using standardized pass@k metrics.
Implements and optimizes Mixture of Experts (MoE) architectures to scale model capacity while reducing training and inference costs.
Optimizes Large Language Models using activation-aware 4-bit quantization to achieve 3x inference speedups and significant memory reduction with minimal accuracy loss.
Extends transformer context windows using RoPE, YaRN, and ALiBi techniques to process documents exceeding 128k tokens.
Optimizes LLM serving and structured generation using RadixAttention prefix caching for high-performance agentic workflows.
Deploys and optimizes LLM inference on CPU, Apple Silicon, and consumer hardware using GGUF quantization.
Optimizes Transformer models using Flash Attention to achieve significant speedups and memory reductions during training and inference.
Guarantees valid, type-safe JSON and structured outputs from Large Language Models using grammar-based constraints.
Streamlines the fine-tuning process for over 100 large language models using the LLaMA-Factory framework and QLoRA techniques.
Generates high-fidelity music and sound effects from text descriptions using Meta's AudioCraft framework.
Extracts structured, type-safe data from Large Language Models using Pydantic validation and automatic retries.
Simplifies PyTorch distributed training by providing a unified API for DDP, DeepSpeed, and FSDP with minimal code changes.
Deploys high-performance Reinforcement Learning from Human Feedback (RLHF) workflows using Ray and vLLM acceleration for large-scale model alignment.
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.
Transcribes audio, translates speech to English, and automates multilingual audio processing using OpenAI's Whisper models.
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.
Connects LLMs to private data sources through advanced document ingestion, vector indexing, and retrieval-augmented generation (RAG) pipelines.
Optimizes large-scale AI model training using PyTorch Fully Sharded Data Parallelism for efficient memory management and scaling.
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