'models' 검색 결과

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

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Integrate on-device AI using Foundation Models framework, Core ML, and open-source LLM runtimes on Apple Silicon. Covers Foundation Models (LanguageModelSession, @Generable, @Guide, SystemLanguageModel, structured output, tool calling), Core ML (coremltools, model conversion, quantization, palettization, pruning, Neural Engine, MLTensor), MLX Swift (transformer inference, unified memory), and llama.cpp (GGUF, cross-platform LLM). Use when building tool-calling AI features, working with guided generation schemas, converting models, or running on-device inference.

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Guide for choosing models in Tinker — available model families, model types (base, instruction, reasoning, hybrid, vision), architecture (dense vs MoE), and how to match renderers to models. Use when the user asks which model to use, what models are available, or how to pick a model for their task.

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Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.

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Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.

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Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

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Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.

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Cross-model benchmark for gstack skills. Runs the same prompt through Claude, GPT (via Codex CLI), and Gemini side-by-side — compares latency, tokens, cost, and optionally quality via LLM judge. Answers "which model is actually best for this skill?" with data instead of vibes. Separate from /benchmark, which measures web page performance. Use when: "benchmark models", "compare models", "which model is best for X", "cross-model comparison", "model shootout". (gstack) Voice triggers (speech-to-text aliases): "compare models", "model shootout", "which model is best".

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가중치가 공개된 언어 모델의 응답 거부 원리를 분석하고 재학습 없이 가중치를 수정하는 실험을 돕습니다. 환경에 맞는 모델과 방법을 선택하고 수정 전후의 거부율과 답변 품질을 비교합니다.

• 언어 모델의 응답 거부 원리 연구
• 재학습 없이 가중치 수정 실험
• 계산 환경에 맞는 모델과 방법 선택

fal.ai에서 글이나 이미지를 바탕으로 3D 모델을 생성하는 스킬입니다. 게임 소재, 증강 현실 미리보기, 제품 시제품 이미지와 입체 조형 구상에 활용합니다.

• 게임용 3D 소재 생성
• 증강 현실 미리보기용 모델 제작
• 제품 시제품 이미지 제작

실행 중인 DeerFlow에 조사와 분석을 요청하고 답변 조회, 대화 이어가기, 자료 업로드를 수행합니다. 모델과 스킬 목록 및 실행 상태도 확인합니다.

• DeerFlow에 조사와 분석을 맡깁니다
• 대화에 자료를 첨부합니다
• 이전 대화를 이어갑니다

시장 데이터로 예측 모델의 학습·예측 명령을 실행하고 결과를 저장하는 절차를 안내합니다. 실제 학습 없이 임시 출력만 생성하는 버전의 제약도 확인합니다.

• 시장 예측 모델 명령 준비
• 학습 결과가 임시 출력인지 확인
• 모델 실행 기록 정리

Get guidance on Codex, OpenAI APIs, and ChatGPT Work using official documentation with citations. Use it to investigate settings and errors, compare models, plan migrations, or work on prompts and API integrations.

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• Check Codex settings and troubleshoot errors
• Compare models for a particular task
• Plan a model migration and prompt changes

Train and run Uni-Mol Tools models to predict molecular properties from CSV data. Organize experiments into projects, compare model performance, rank results, and manage storage through interactive commands or machine-readable JSON output.

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• Train molecular property models from CSV data
• Predict properties with a trained model
• Organize experiments and compare model performance

Manage ComfyUI image generation from the command line. Validate workflows, which define generation steps, submit them for execution, monitor queued jobs, inspect available models, and download images from completed runs.

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• Validating and submitting image generation workflows
• Checking or interrupting queued generation jobs
• Listing available models and node types

Build financial models for valuation and investment analysis using discounted future cash flows, assumption testing, and probability simulations. Outputs include Excel models, comparison tables, charts, and valuation ranges to examine possible outcomes and risks.

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• Build a company valuation model from financial statements
• Test how growth and discount rates affect valuation
• Simulate acquisition valuation under uncertain assumptions

Build and troubleshoot TorchDrug 0.2.1 workflows for molecular property prediction, molecule generation, protein modeling, and knowledge graph reasoning. Connect datasets, models, training tasks, and evaluation while checking version compatibility and common implementation errors.

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• Training and evaluating molecular property prediction models
• Building molecule generation workflows
• Learning representations of protein sequences and structures

Build and evaluate time-to-event models with scikit-survival, including records whose events remain unobserved when follow-up ends. Keep preprocessing separate from evaluation data, compare models, predict survival probabilities, and summarize censoring-aware metrics.

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• Validate event indicators and follow-up times in local CSV data
• Train survival models without leaking evaluation data
• Evaluate survival probabilities with censoring-aware metrics

Simulate concentration changes over time from biochemical models in SBML or Antimony, and compare fixed parameter conditions. Checks model units and exports CSV results plus COMBINE archives containing models and experiment settings, then replays them to compare results.

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• Calculate concentration changes in reaction networks
• Compare baseline and altered kinetic parameters
• Inspect model units and consistency warnings

Use Hugging Face Transformers to load pretrained models, generate text, and classify text, images, or audio. Prepare model inputs, control generation settings, and fine-tune models on custom datasets with the Trainer API.

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• Prototype text, image, or audio classification
• Configure text generation and conversational responses
• Fine-tune a model on a custom dataset

Build and optimize quantum circuits with PennyLane, including models that combine quantum circuits with PyTorch or JAX. Explore molecular energy estimation and optimization problems, validate values and gradients in simulation, and prepare tested circuits for provider plugins.

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• Check quantum circuit values and gradients in simulation
• Train hybrid quantum and PyTorch or JAX models
• Estimate molecular energies with VQE

Analyze drug concentration and response data to calculate exposure, fit models of drug movement and effects, and simulate dosing schedules. Produces exploratory research results with explicit assumptions and diagnostic checks.

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• Calculate exposure and half-life from concentration data
• Compare models of drug concentration over time
• Simulate dosing schedules for research

Find scientific datasets, AI models, methodology posts, and interactive demos through the Hugging Science catalog. Compare candidates for a research task and verify their licenses, input formats, access requirements, and supported ways to run them.

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• Finding models for protein or genome analysis
• Selecting datasets for scientific machine learning
• Comparing resource licenses and runtime requirements

Design custom laboratory parts as adjustable build123d models and export STEP, STL, and DXF fabrication files. Check mating dimensions against standards, test geometry, and visually review holders, mounts, microfluidic parts, and other research fixtures before fabrication.

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• Designing holders for microplates, cuvettes, and tubes
• Creating mounts and adapters for optical tables
• Modeling microfluidic chips and molds