'code models' 검색 결과

Develop Azure Functions with guidance for .NET, Python, and Node.js. Covers asynchronous processing, Durable Functions for coordinating tasks, startup delay optimization, and production concerns such as timeouts, HTTP client reuse, and monitoring configuration.

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• Choose an execution model for a new .NET function
• Coordinate asynchronous tasks with Durable Functions
• Review startup delays and timeout settings

Generate or modify images through the Luma AI API using Photon models. The skill gathers image descriptions and options, supports reference images for visual guidance, and uses a bundled Python script to wait for completion and download results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a landscape image for a website
• Generating square product images
• Guiding image style or composition with a reference

Set up reinforcement learning, which trains models using rewards, with verl. Follow workflows for preparing data, configuring GRPO or PPO training, scaling across GPUs, and addressing memory errors or unstable training.

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• Training a math reasoning model with GRPO
• Configuring PPO training with a critic model
• Scaling large model training across multiple nodes

Design RAG systems that retrieve relevant documents to support LLM answers. The skill guides document splitting, meaning-based and keyword search, result reranking, and separate retrieval evaluation to improve the context passed to the model.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing document retrieval for LLM answers
• Reviewing document splitting strategies
• Combining keyword and meaning-based search

Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train a model on instruction and response examples
• Align responses using preferred and rejected answer pairs
• Build a training pipeline using human feedback

Set up model training with torchforge for reinforcement learning, where rewards guide improvement. Covers training configurations, custom reward and loss functions, and distributed execution with Monarch and TorchTitan, keeping algorithm experiments separate from infrastructure concerns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train mathematical reasoning models with GRPO
• Experiment with custom reward and loss functions
• Scale training across multiple GPUs or nodes

Investigate how transformer language models produce predictions with TransformerLens. This skill guides inspection of intermediate values and attention patterns, plus experiments that replace internal values to test their effects and identify components involved in model behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspecting attention patterns and intermediate values
• Testing output changes by replacing internal activations
• Analyzing attention head contributions to predictions

Use TensorBoard to visualize training loss and accuracy, compare experiments, and inspect model structure. Includes PyTorch and TensorFlow logging examples for examining weight distributions, predictions, and performance bottlenecks during machine learning training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing loss and accuracy across training runs
• Inspecting weight and gradient distributions
• Exploring embeddings in two or three dimensions

Configure large language model pretraining with torchtitan across multiple GPUs. Covers ways to split training work, launch jobs on computing clusters, enable Float8 numerical processing, and save or resume training through distributed checkpoints.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Pretraining Llama 3.1 8B on eight GPUs
• Configuring multi-node training with SLURM
• Enabling Float8 training on H100 GPUs

Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating images from text descriptions
• Changing the style of an existing image
• Replacing a selected region of an image

Investigate why a PyTorch model behaves a certain way by changing its internal values with pyvene. The skill guides experiments that swap or remove activations, train interventions, compare outputs, and save or share intervention configurations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Locating model components that affect an output
• Swapping activations to test behavioral hypotheses
• Training interventions to investigate causal structure

Run machine learning workloads on Modal's cloud GPUs without managing servers. Guides Python configuration for training, inference, batch processing, and API deployment, including automatic scaling, persistent storage, scheduled jobs, and troubleshooting.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running GPU inference without managing servers
• Deploying a model as an automatically scaling API
• Processing large batches in parallel

Guides the use of RWKV, a language model architecture that carries information forward in a compact state. Covers streaming text generation, long document processing, fine-tuning, and memory comparisons with Transformers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build streaming text generation
• Process long documents in chunks
• Compare memory needs with Transformers

Integrate Flash Attention into transformer models to reduce processing time and GPU memory use for long sequences. Covers PyTorch integration, advanced attention options, and benchmarks that compare speed, memory use, and output accuracy.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Speeding up transformer training on long sequences
• Reducing GPU memory use during attention
• Adding Flash Attention to a PyTorch model

Reduce LLM weight usage through pruning, which removes selected model parameters. Explore Wanda and SparseGPT for compression without retraining, choose hardware-friendly patterns for faster inference, and compare the compressed model's accuracy with the original.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compressing an LLM without retraining
• Comparing pruning methods
• Exploring faster inference on supported hardware

Build AI applications with LangChain, including chatbots with conversation history and systems that search documents before answering. The skill provides examples for connecting models, calling tools, retrieving information, and tracing application runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a chatbot that uses conversation history
• Creating a question-answering system for documents
• Developing an agent that calls external tools

Analyze business data, test hypotheses, and build predictive models with statistical and machine learning methods. Get support across data exploration, experiment design, visualization, result validation, and planning model deployment and monitoring.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Analyzing customer churn and predicting customers at risk
• Designing an A/B test and evaluating its results
• Forecasting demand for inventory planning

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Build formula-driven Excel financial models for linked financial statements, business valuation, and fundraising forecasts. Separate assumptions, calculations, and results, compare scenarios, and check that statement balances and saved calculation results are consistent.

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• Build linked income, balance sheet, and cash flow forecasts
• Estimate business value with discounted cash flows
• Compare results under different assumptions

Find CAD models of off-the-shelf parts on step.parts using model names, standards, aliases, and dimensions. Compare matching records, download STEP files when needed, and check file integrity against a supplied checksum.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find a servo or motor by model number and aliases
• Compare screws and bearings by standard and dimensions
• Download a selected STEP file and check its integrity

Choose a Google Cloud database by clarifying data needs, workload, and scale. The skill explains its recommendation and prepares validated Terraform configurations or provisioning scripts for review before infrastructure changes are made.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a database for a new Google Cloud application
• Clarifying data models, workloads, and scale requirements
• Preparing and validating database provisioning code for review

Manage LangBot bots and processing workflows through MCP, a standard for connecting agents to tools. Configure authenticated access, inspect resources, update supported settings, and investigate execution histories, logs, and model request diagnostics.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connect an MCP client to LangBot
• Create or update bots and processing workflows
• Investigate execution logs and errors

Plan and improve AI agent systems by managing the information models receive. This collection covers memory, agent coordination, tool design, context compression, evaluation, and the operating controls needed for reliable autonomous workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning information management for a new AI agent
• Investigating failures in long agent conversations
• Designing agent memory and multi-agent coordination

Build image conversations with LLaVA, a model that connects image recognition with language. This skill guides setup, image descriptions, visual questions, and follow-up conversations, with examples for Python, command-line use, and a Gradio interface.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a chatbot that answers questions about images
• Write detailed descriptions of photos
• Ask follow-up questions about the same image