zechenzhangagi/ai-research-skills

ml-paper-writing skill from zechenzhangagi/ai-research-skills

Performs efficient semantic vector search and similarity matching using Qdrant's vector database for AI-powered information retrieval and recommendation systems.

Orchestrates seamless multi-cloud deployment and management of AI workloads across different cloud providers using SkyPilot's infrastructure automation capabilities.

Orchestrates multi-agent collaboration using CrewAI for complex research tasks, enabling specialized AI agents to work together systematically.

Enables AI agents to leverage LangChain's framework for building complex language model workflows and chaining together different AI components and tools.

Trains large language models using Megatron framework with advanced parallelism and optimization techniques for high-performance AI model development.

Visualizes and analyzes machine learning model performance, training metrics, and computational graphs using TensorBoard's interactive dashboard.

Logs and tracks machine learning experiments, model performance, and hyperparameters using Weights & Biases platform for comprehensive AI research visualization.

Generates and manages autonomous AI agents using AutoGPT's framework for executing complex, multi-step research and problem-solving tasks.

Enables distributed and efficient machine learning training across multiple GPUs or machines using Hugging Face's Accelerate library.

Enables reinforcement learning with human feedback (RLHF) training for large language models, facilitating model alignment and performance improvement through iterative feedback mechanisms.

Enables fine-tuning large language models using the Transformers Reinforcement Learning (TRL) library for efficient and customizable model adaptation.

Generates and manages fine-tuning configurations and workflows for Llama language models, streamlining the process of customizing and training large language models.

Performs Simple Preference Optimization (SIMPO) training for fine-tuning language models using a lightweight preference learning approach.

Accelerates large language model inference by predicting and pre-computing potential token sequences before final model verification, reducing computational latency.

Interprets and visualizes internal representations and activation patterns within transformer neural network models to understand their inner workings and decision-making processes.

Accelerates distributed machine learning training by enabling efficient parallel processing and memory optimization across multiple GPUs or machines using DeepSpeed.

Segments and extracts precise object boundaries from images using Meta AI's Segment Anything Model (SAM) for advanced computer vision tasks.

Performs knowledge distillation to transfer complex model insights into a more compact, efficient neural network model.

Optimizes and accelerates fine-tuning of large language models using memory-efficient techniques and unsloth's specialized training methods.

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Enables targeted neural network intervention and manipulation through a flexible Python library for probing and modifying model representations.

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Provisions and manages GPU cloud compute resources from Lambda Labs for AI research and machine learning workloads.

Enables seamless vector database interactions with Pinecone for efficient semantic search and retrieval of research-related embeddings.

Streamlines PyTorch deep learning model training by providing high-level abstractions for distributed training, logging, and experiment management.

Generates high-quality images using the Stable Diffusion AI model based on text prompts or image generation parameters.

Quantizes machine learning models using the bitsandbytes library to reduce model size and computational requirements while maintaining performance.

Retrieves, indexes, and enables semantic search across complex document collections using LlamaIndex for advanced AI-powered information retrieval and knowledge management.

Enables AI agents to effectively process, comprehend, and work with extremely long input contexts beyond typical token length limitations.

Trains a sparse autoencoder on neural network activations to discover and extract interpretable features from hidden layers.

Manages vector database operations using Chroma, enabling efficient semantic search and storage of embeddings for AI research tasks.

nemo-evaluator-sdk skill from zechenzhangagi/ai-research-skills

Implements safety guardrails for NVIDIA NeMo language models to prevent harmful or inappropriate AI responses during interactions.

Enables remote neural network interpretation and analysis through interactive exploration of model internals and representations.

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Manages machine learning experiment tracking, logging metrics, parameters, and models using MLflow's comprehensive tracking and versioning capabilities.

Tokenizes text using Hugging Face's advanced tokenization library, preparing input data for natural language processing and machine learning models.

Performs Activation-aware Weight Quantization (AWQ) to compress large language models by reducing model size and computational requirements while preserving performance.

Optimizes attention mechanisms in deep learning models using Flash Attention for improved computational efficiency and performance.

Analyzes, explains, and generates code implementations for the Mamba neural network architecture, focusing on its state space model design and potential machine learning applications.

Enables efficient similarity search and clustering of high-dimensional vectors using Facebook AI's FAISS library for fast nearest neighbor retrieval.

Reduces the size and computational requirements of large language models by converting model weights to lower-precision quantized GGUF (GPT-Generated Unified Format) representations.

Quantizes and compresses large language models using GPTQ (GPT Quantization) technique to reduce model size and improve inference efficiency.

Enables efficient local inference and interaction with Llama language models using the lightweight C++ implementation of the Llama model.

Tokenizes and preprocesses text using SentencePiece, enabling efficient subword-level text segmentation for natural language processing tasks.

Evaluates large language models using the EleutherAI/lm-evaluation-harness framework to systematically assess model performance across multiple benchmarks and tasks.

Evaluates the performance, quality, and capabilities of code generation AI models through comprehensive benchmarking and systematic assessment techniques.

Trains reinforcement learning models using Group-based Policy Optimization (GRPO) techniques for AI research and development.

Enables tracking, logging, and monitoring of AI research workflows and experiments using LangSmith's observability tools for enhanced debugging and performance analysis.

Enhances AI research workflows by providing advanced programmatic optimization techniques for language model prompting and retrieval-augmented generation (RAG) using DSPy framework.

Analyzes and explains the architectural design and implementation details of the RWKV (Receptance Weighted Key Value) neural network architecture.

Provides structured, step-by-step guidance and recommendations for AI research tasks, helping researchers navigate complex workflows and methodological decisions.

Enables parameter-efficient fine-tuning of large language models using techniques like LoRA to adapt models with minimal computational resources.

Transforms text sentences into dense vector representations, enabling semantic similarity comparisons and advanced natural language understanding tasks.

Serves large language models efficiently using vLLM, enabling high-performance and scalable model inference with optimized resource utilization.

Optimizes large language model inference performance by leveraging NVIDIA TensorRT-LLM for accelerated GPU-based model deployment and execution.

Trains Mixture of Experts (MoE) machine learning models by dynamically routing inputs to specialized expert neural network submodules during training.

Implements a compact GPT language model training and generation pipeline, allowing quick prototyping and experimentation with neural network architectures.

Prunes and reduces machine learning model complexity by removing less important parameters while preserving performance.

Processes and analyzes multimodal visual-language inputs using the LLaVA (Large Language and Vision Assistant) model for advanced image understanding tasks.

Filters and prevents potentially harmful or unsafe language model outputs using Meta's LlamaGuard safety model to ensure responsible AI interactions.

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Enables efficient and flexible language model inference by providing a high-performance programming interface for defining and executing complex LLM generation workflows.

Enables AI agents to analyze and understand images by leveraging the BLIP-2 vision-language model for multimodal perception and reasoning tasks.

Generates structured research paper outlines by systematically organizing research topics, key sections, and potential content flow for academic writing.

Merges multiple machine learning models together to create a unified, potentially more powerful model with combined capabilities.

Generates high-quality audio and music using Meta's AudioCraft AI model, enabling AI agents to create custom audio samples programmatically.

Manages and streamlines fine-tuning of large language models using configuration-driven approaches and efficient training techniques.

Performs high-quality quantization of machine learning models, reducing model size and computational complexity while preserving performance.

Monitors and tracks performance metrics, logs, and traces for AI research infrastructure using Phoenix observability framework.

Enables distributed training of large PyTorch models across multiple GPUs using Fully Sharded Data Parallel (FSDP) technique for efficient memory and computational scaling.

Implements Large Language Model (LLM) training and fine-tuning workflows using the LitGPT library, enabling efficient model development and customization.

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Trains reinforcement learning agents in a slime-based environment using advanced machine learning techniques and custom simulation parameters.

Enables distributed large language model pre-training using PyTorch Titan, facilitating efficient multi-node and multi-GPU training of large neural network models.

Trains reinforcement learning models using the Miles algorithm, optimizing agent performance through iterative policy and value function updates.

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Enables reinforcement learning training workflows using PyTorch, facilitating advanced RL model configuration, environment setup, and training pipeline management.

Enables distributed machine learning training using Ray, facilitating parallel and scalable model training across multiple compute resources.

Trains reinforcement learning models using Vector-based Efficient Reinforcement Learning (VERL) techniques for AI research and development.