'models' 검색 결과

Design product pricing based on customer value, competitor offerings, and willingness to pay. Compare charging models, define plan tiers and included features, and outline experiments to test prices and assumptions before introducing a new pricing structure.

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• Setting prices for a new product
• Comparing flat-rate and usage-based pricing
• Designing free and paid plan tiers

Evaluate a technology adoption question, a document, or proposed approaches against project evidence and constraints. Receive a supported position with tradeoffs and uncertainty, with an optional panel that consults other models and reconciles their views.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing whether to adopt an external technology
• Getting an overall assessment of a document
• Comparing a supplied set of approaches

Create and edit 3D parts and assemblies with adjustable dimensions using cadgen. Export STEP, STL, 3MF, or GLB files, inspect geometry, and open existing models in CAD Viewer for visual review.

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• Create a part from specified dimensions or a drawing
• Revise part dimensions and assembly placements
• Export models as STEP, STL, 3MF, or GLB

Turn 3D models into printer instructions called G-code with OrcaSlicer. The skill selects existing printer and material presets, slices the model, and checks dimensions and output settings before handing over the files.

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• Prepare an STL model for a specific printer
• Slice models using existing material presets
• Check model size and print settings

Inspect and manage model tuning jobs, which adapt models through additional training, in Agent Platform. List jobs, check their status and details, and cancel running jobs after reviewing the target and explicitly confirming the action.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• List running model tuning jobs
• Check the status and details of a specific job
• Cancel a long-running job after confirmation

Plan and troubleshoot large language and vision model training and inference. This guide uses hardware measurements to identify bottlenecks, address memory shortages and unstable training, and improve checkpointing, recovery, and performance across multiple GPUs and machines.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Diagnosing slow training and GPU memory shortages
• Investigating loss spikes and invalid numerical values
• Debugging communication hangs across GPUs and machines

Train sentence-transformers models for search, similarity, and reranking, which reorders search results by relevance. Choose a model type, prepare data, configure training, compare results against a baseline, and publish the trained model to Hugging Face Hub.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fine-tuning an embedding model for domain-specific search
• Training a model to rerank search results
• Training SPLADE or ColBERT retrieval models

Operate an opencodex proxy, which acts as an intermediary for AI service requests. Use CLI commands to inspect configuration, manage accounts and models, review request routing, and check usage and runtime health.

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• Check proxy readiness and diagnose connection problems
• Review provider settings and available models
• Inspect account selection and request routing

Research new models and API capabilities offered by providers behind Pipecat services. Produce dated reports for each service and local committed branches for clear updates, with review commands and separate publishing steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking providers for new models and API features
• Researching selected providers or service units
• Preparing local branches for clear updates

Import FBX 3D models into CARLA as props that can be placed through its Python API. Handles category tags, measured dimensions, asset registration, and spawn checks for individual files or folders of models.

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• Add custom benches and signs to CARLA
• Import a folder of models grouped by tag
• Check model dimensions and spawning

Configure LLM inference and serving on NVIDIA GPUs with TensorRT-LLM. Covers quantization, which reduces numerical precision, batching requests during generation, and distributing models across GPUs to improve processing capacity and response times.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up an LLM server on NVIDIA GPUs
• Configuring FP8 or INT4 quantization
• Distributing large models across multiple GPUs

Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate document embeddings for RAG
• Find sentences with similar meanings
• Group documents by topic

Manage machine learning experiments with MLflow by recording settings, metrics, files, and trained models. Covers experiment comparison, model version management, reproducible project configurations, and deployment workflows across local and cloud environments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording experiment settings and evaluation results
• Comparing model performance across experiments
• Managing model versions for production use

Guides fine-tuning and evaluation of OpenVLA-OFT robot models that turn images and language into actions. Covers LIBERO simulation, ALOHA deployment, LoRA adaptation that updates a small set of parameters, and troubleshooting action scaling or configuration mismatches.

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• Evaluate pretrained models in LIBERO
• Fine-tune robot action models with LoRA
• Deploy server-client inference for ALOHA

Guides experiments that inspect and modify internal values in PyTorch models to study their effects on predictions. Covers local analysis and remote experiments through NDIF for models too large for local GPUs.

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• Inspect internal values and attention patterns across layers
• Replace internal values to compare changes in predictions
• Run large-model experiments without local GPUs

Configure large language model training across multiple NVIDIA GPUs with Megatron-Core. The skill helps choose how to split models and workloads, set training parameters, monitor performance, and address memory shortages or low GPU utilization.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning distributed training for a LLaMA-style model
• Distributing MoE expert networks across GPUs
• Tuning GPU utilization and training throughput

Apply HQQ quantization, which stores language model weights using fewer bits without calibration examples. Covers precision settings, inference backends, saving compressed models, deployment with HuggingFace Transformers or vLLM, and further training with LoRA.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compressing a model without calibration data
• Comparing 4-bit and 2-bit quantization settings
• Running quantized models with HuggingFace Transformers or vLLM

Helps reduce GPU memory use by loading large language models in 4-bit or 8-bit formats, a technique called quantization. Covers bitsandbytes configuration, memory checks, QLoRA fine-tuning, and training with memory-saving optimizers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Load a larger model within available GPU memory
• Compare 4-bit and 8-bit quantization settings
• Fine-tune a quantized model with QLoRA

Guides you through setting up RWKV, generating text, processing long documents in chunks, and fine-tuning models. Explains how to carry context between steps and compare memory use with Transformer models.

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• Build streaming text generation
• Process long documents in chunks
• Plan RWKV fine-tuning

Set up reinforcement learning, training guided by rewards, for language models with slime. The skill covers Megatron-LM training, SGLang response generation, data preparation, asynchronous execution, and custom workflows for agents that use tools across multiple turns.

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• Prepare GRPO training for GLM or Qwen3 models
• Overlap response generation and model training
• Train agents with multi-turn tool use

Build machine learning workflows that connect data checks, training, evaluation, and deployment. The skill implements experiment tracking, model version management, and automated retraining, with reproducible environments and explicit validation thresholds before models enter production.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Automate model training and retraining workflows
• Record and compare machine learning experiments
• Build a feature store for model input data

Examine important choices and assess claims by rebuilding their reasoning from basic knowledge. Use mental models, reusable ways to understand problems, to compare conclusions, identify unknowns, and consider long-term consequences.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a direction with long-term consequences
• Assessing the reasoning behind an expert's claims
• Building judgment in an unfamiliar field

Read and summarize AI research papers from Hugging Face or arXiv links and identifiers. Retrieve paper content alongside structured details about authors, related models, datasets, Spaces, and implementation links to support further investigation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Summarizing a paper from its URL or arXiv ID
• Finding models, datasets, and Spaces linked to a paper
• Checking authors and implementation links

Register arXiv papers on Hugging Face Hub, connect them to models and datasets, and manage authorship claims and profile visibility. Create research articles from Markdown templates and convert them into HTML for sharing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Registering an arXiv paper on Hugging Face Hub
• Linking papers to models and datasets
• Managing authorship claims and profile visibility