Data Science & ML Habilidades de Claude

HuggingFace Accelerate Distributed Training

Simplifies PyTorch distributed training across multiple GPUs, TPUs, and nodes with minimal code changes and a unified API.

SGLang Inference Serving

Optimizes LLM serving and structured data generation with RadixAttention prefix caching for high-performance agentic workflows.

HQQ Model Quantization

Quantizes Large Language Models to ultra-low bit precision without requiring calibration datasets for efficient inference and fine-tuning.

NeMo LLM Evaluator

Evaluates Large Language Models across 100+ industry-standard benchmarks using NVIDIA's enterprise-grade containerized architecture.

SAELens Mechanistic Interpretability

Decomposes neural network activations into interpretable, sparse features using SAELens for deep mechanistic interpretability research.

Model Pruning & LLM Compression

Reduces Large Language Model size and accelerates inference using advanced pruning techniques like Wanda and SparseGPT.

CrewAI Multi-Agent Orchestration

Orchestrates autonomous teams of specialized AI agents to collaborate on complex, multi-step tasks and production workflows.

SentencePiece Tokenization

Implements language-independent subword tokenization using BPE and Unigram algorithms for robust NLP model training and inference.

NeMo Guardrails Safety Alignment

Implements programmable safety rails and runtime validation for LLM applications using NVIDIA's NeMo Guardrails framework.

Stable Diffusion Image Generation

Generates high-quality images from text and performs advanced image-to-image transformations using the HuggingFace Diffusers library.

Instructor - Structured Data Extraction

Extracts and validates structured data from LLM responses using Pydantic for reliable, type-safe outputs and automatic retries.

OpenRLHF Model Training

Deploys and manages high-performance RLHF training pipelines for large-scale language models using Ray and vLLM acceleration.

Weights & Biases MLOps

Integrates Weights & Biases into your workflow to track machine learning experiments, visualize training metrics, and manage model artifacts in real-time.

Pyvene Causal Interventions

Facilitates causal interventions on PyTorch models using a declarative framework for mechanistic interpretability experiments.

LM Evaluation Harness

Evaluates Large Language Models across 60+ academic benchmarks using standardized prompts and metrics for reproducible research.

BLIP-2 Vision-Language

Implements state-of-the-art vision-language pre-training to enable high-quality image captioning and visual question answering within AI workflows.

Chroma Vector Database

Manages high-performance vector embeddings and metadata for RAG applications and semantic search using the open-source Chroma database.

NVIDIA TensorRT-LLM Optimization

Optimizes Large Language Model inference for maximum throughput and ultra-low latency on NVIDIA GPUs.

Ray Data Scalable Processing

Facilitates high-performance distributed data processing and streaming for large-scale machine learning workloads.

torchforge RL Training

Implements PyTorch-native agentic reinforcement learning workflows using Meta's torchforge library for scalable algorithm experimentation.

FAISS Vector Search

Implements efficient similarity search and clustering for dense vectors at scale using Facebook AI's high-performance library.

MLflow MLOps Manager

Manage the complete machine learning lifecycle including experiment tracking, model versioning, and deployment using the MLflow framework.

Sentence Transformers Embeddings

Generates high-quality sentence, text, and image embeddings for RAG, semantic search, and clustering using state-of-the-art transformer models.

RWKV Model Architecture

Implements and manages RWKV architectures for efficient, linear-time AI inference and long-context processing.

DSPy AI Programming & Optimization

Build and optimize complex AI systems using declarative programming instead of manual prompt engineering.

Mamba Architecture Guide

Implements and optimizes Selective State Space Models (SSM) for high-performance sequence modeling and long-context AI applications.

Slime RL Post-Training

Scales LLM post-training via reinforcement learning by integrating Megatron-LM training with high-throughput SGLang inference.

Pinecone Vector Database

Integrates Pinecone's managed vector database to power high-performance RAG, semantic search, and recommendation systems.

LlamaIndex RAG Framework

Builds sophisticated RAG applications by connecting LLMs to private data through advanced indexing and retrieval patterns.

Model Merging & Fusion

Combines multiple fine-tuned AI models into a single high-performance model without requiring additional training or expensive GPU resources.