'local models' 검색 결과

Convert LLMs to GGUF and reduce model size through quantization, which stores weights at lower numerical precision. Prepare models for CPU or Apple Silicon inference, compare compression levels, and integrate them with local AI tools.

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• Preparing an LLM for CPU-only use
• Running a local model on Apple Silicon
• Comparing model size and quality across quantization levels

Evaluate language models with over 100 benchmark tests covering knowledge, math, coding, safety, and image understanding. Configure runs on Docker, Slurm computing clusters, or cloud infrastructure, compare models, and export results.

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• Compare models using the same benchmark tests
• Run large evaluations on a Slurm cluster
• Evaluate safety and image understanding

Set up access to 9Router, a shared gateway for multiple AI providers, and discover available models. Find the relevant capability skills for chat, images, speech, embeddings, web search, and webpage retrieval.

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• Configure a local or remote 9Router connection
• Check available models by capability
• Find instructions for image or speech tasks

Combine code from local directories or GitHub into one structured file for AI analysis. Examine project structure, search for related code, and check estimated tokens, the units AI models use to process text.

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• Preparing a single codebase file for AI analysis
• Reviewing a project's directory structure
• Finding authentication code and database 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

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.

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

Evaluate language models on knowledge, math, coding, safety, and image understanding benchmarks. The skill guides reproducible runs in containers using local Docker, Slurm clusters, or cloud execution, with shared configurations for model comparison and result export.

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• Comparing models on the same benchmark tasks
• Running a small evaluation sample before a full benchmark
• Running large evaluations on a Slurm cluster

Select technologies for AI and machine learning features in .NET 8+ applications. Get task-specific plans or implementation guidance for structured predictions, text generation, document retrieval, tool-using agents, and running pretrained models locally or in production.

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• Choosing libraries for classification or prediction in .NET
• Adding text generation or summarization to an application
• Designing document retrieval with source-backed AI answers

Build, refactor, or review macOS menubar apps using Tuist and SwiftUI. Separate data models, networking, state management, and views, then align project definitions and launch scripts for consistent local builds and restarts.

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• Creating a macOS menubar utility
• Separating networking and state management from views
• Organizing Tuist project definitions

Apply statsmodels in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses statsmodels or its strengths fit the task.

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Continue is an open-source AI code assistant for VS Code, JetBrains and the terminal (the cn CLI) that works with any LLM: Claude, GPT, Gemini, Ollama and other local models. Use when a user asks to configure Continue with config.yaml, add chat or tab autocomplete models, write rules or custom prompts, connect MCP servers, run cn headless in scripts, or migrate from config.json. Continue was acquired by Cursor in June 2026: the release from that month is final and the Hub is offline, so the skill covers local, self-configured use.

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Expert guide for Local Small Language Models (SLMs) and Edge AI execution — WebLLM, Transformers.js v3, ONNX Runtime Web, WebGPU, and zero-latency local embeddings / Panduan ahli SLM lokal dan AI edge di browser.

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Open a ready-to-use local 3D model composition workbench for GLB or GLTF files. Use when the user says "打开模型工作台", asks to place, rotate, scale, arrange, or frame one or more 3D models, or needs a clean composition screenshot before website or Image 2 design. If the user has not supplied a model, request GLB/GLTF files first; after models are available, generate the workspace, start its localhost server, and open it automatically.

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Protein language models through the EvolutionaryScale `esm` Python SDK. Generate and embed sequences with ESM3 (multimodal sequence, structure and function prompting), extract per-residue and mean-pooled embeddings with ESM C, fold sequences with ESMFold2, and run inference locally or against the Forge and Biohub hosted clients. Use this skill for protein representation learning, variant effect and mutational scanning from likelihoods, sequence generation and inpainting, structure prediction from sequence alone, and embedding features for downstream models. Also trigger on esm, ESM3, ESMC, ESM Cambrian, ESMFold2, `from esm.models`, ESMProtein, GenerationConfig, forge.evolutionaryscale.ai, biohub.ai, or ESM_API_KEY.

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Set up and run local LLMs with Harbor. Use when the user wants to run models locally, install Harbor, start Open WebUI, llama.cpp, Ollama, vLLM, Docker Model Runner, MLX, or oMLX, pull GGUF or HuggingFace models, add SearXNG web search, Speaches TTS/STT, or Open Terminal code execution, launch Codex/Claude/Grok/OpenCode against a Harbor backend, or troubleshoot GPU, VRAM, and service startup.

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Use when integrating an app with local AI models through Ollama — connection checks, model listing/selection, local-only mode, cloud-fallback consent, visible run states, and recovering from Ollama errors.

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Apple FoundationModelsフレームワークによるオンデバイスLLM — テキスト生成、@Generableによるガイド生成、ツール呼び出し、iOS 26+でのスナップショットストリーミング。

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[BETA] Detect, review, and fix code-quality and correctness issues in an AEM as a Cloud Service project — locally, with no external services or network calls. Use whenever a user wants to check, review, assess, audit, scan, modernize, upgrade, or fix AEM Java, Sling Models, OSGi, or Maven code — for example: "check my Sling Models are implemented correctly", "review my @Inject usage", "are my Maven dependencies up to date", "scan this AEM project for issues", "modernize my Sling Models", or "fix code-quality problems". Name the files to assess, or ask it to scan the repo; it detects issues, plans, and — only when you ask — applies surgical edits on a branch or in place, then verifies with mvn compile. It recognises the intent and handles each issue type itself, reporting anything it cannot yet fix. This skill is in beta. Verify all outputs before applying them to production projects.

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OpenClaw의 품질 검증 시나리오를 모의 환경이나 실제 서비스에서 실행하고, 실패 원인 수정과 보고서 확인, 여러 모델의 응답 스타일 비교를 지원합니다.

• 모의 환경이나 실제 서비스에서 품질 검증
• 실패 시나리오 원인 조사와 재검증
• 여러 모델의 캐릭터 표현 비교

음성 파일을 로컬에서 받아쓰기하여 텍스트나 번역 자막으로 저장합니다. API 키 없이 모델 크기를 선택해 처리 속도와 정확도를 조절할 수 있습니다.

• 녹음한 음성을 텍스트로 변환
• 음성 파일로 번역 자막 제작
• 속도와 정확도에 맞는 모델 선택

가사와 곡 분위기 및 악기 태그로 로컬에서 음악을 생성합니다. 설치와 입력 준비, GPU 메모리 절약 설정부터 MP3 저장까지 안내합니다.

• 직접 쓴 가사를 곡으로 만들기
• 태그로 곡 분위기와 악기 시험하기
• 로컬 환경에서 음악 생성하기

ComfyUI 워크플로로 이미지, 영상, 음성, 3D 콘텐츠를 생성합니다. 모델과 추가 노드 준비, 조건을 바꾼 일괄 생성, 진행 상황 확인도 다룹니다.

• Stable Diffusion이나 Flux로 이미지 생성
• 기존 이미지 변환과 지정 영역 다시 그리기
• 생성 조건을 바꿔 여러 후보 일괄 제작

Train, deploy, and manage neural networks with Flow Nexus in isolated E2B environments. Use custom architectures or ready-made templates, run predictions, monitor distributed training across multiple environments, and evaluate models before sharing them.

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• Train an image classification model
• Forecast time series with LSTM
• Deploy a sentiment analysis template

Search local session histories across Codex, Claude Code, OpenCode, and other coding agents. Find earlier requests, inspect transcripts and child sessions, and reconstruct past work using recorded evidence, with filters for dates, projects, platforms, and models.

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• Finding work done a few days ago
• Checking whether a migration was already completed
• Reviewing tasks delegated to child agents