“local models”的搜索结果

Find and run local AI models with llama.cpp. Choose GGUF model files and quantization levels that fit available memory, launch a command line session or local server, and test an OpenAI-compatible API on your computer.

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• Find models to run on a local computer
• Choose quantization for available memory
• Launch and test a local OpenAI-compatible API

Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.

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• Extracting company or profile details into JSON
• Classifying text using a fixed set of labels
• Generating text that matches a specified pattern

Connect a container agent to local Ollama models through MCP, a protocol for agent tools. The agent can list models and generate responses, with optional tools to download, delete, inspect, and check loaded models.

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• Delegate response generation to a local model
• Check which Ollama models are installed
• Download or delete models through the agent

Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.

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• Extracting company or profile details into JSON
• Classifying text using a fixed set of labels
• Generating text that matches a specified pattern

Let a NanoClaw container agent call local models running in Atomic Chat. The skill adds an MCP server, a connection that exposes model listing and text generation as tools, while Claude continues to coordinate the work.

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• Delegate part of Claude's work to a local model
• List models available in Atomic Chat
• Generate an answer with a specified local model

Find AI models for a specific task using official Hugging Face benchmark leaderboards. Compare scores, model sizes, and licenses, with memory-based estimates of device suitability and flags for models requiring quantization or API access.

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• Compare coding or reasoning models by benchmark scores
• Find model candidates for a laptop's available memory
• Choose models for OCR or speech recognition

Train or fine-tune language and vision models on Hugging Face Jobs without a local GPU. Covers dataset checks, hardware and cost planning, training monitoring, Hub storage, and GGUF conversion for local use.

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• Fine-tune a language model without a local GPU
• Validate preference data before DPO training
• Estimate training costs and choose GPU hardware

Expert knowledge for Microsoft Foundry Local (aka Azure AI Foundry Local) development including best practices, decision making, configuration, and integrations & coding patterns. Use when compiling HF models with Olive, using Foundry Local CLI, embeddings/chat/audio APIs, or OpenAI-style integrations, and other Microsoft Foundry Local related development tasks. Not for Microsoft Foundry (use microsoft-foundry), Microsoft Foundry Classic (use microsoft-foundry-classic), Azure Local (use azure-local), Azure Content Understanding in Foundry Tools (use azure-content-understanding).

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Use the hf command-line tool to find, download, and upload models and datasets on Hugging Face Hub. The guide also covers repository management, hosted computing resources, local cache maintenance, and automation options.

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• Find and download models or datasets
• Upload local files to the Hub
• Create repositories and manage branches or tags

Configure Hermes to run AI models through Actual Computer on your own hardware. Set up either the hosted relay or a local connection, select models, verify responses, and troubleshoot request errors or empty output.

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• Connecting Hermes with an Actual inference key
• Running models through a local Actual daemon
• Downloading and loading local models

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

Manage Hugging Face Hub through the hf command line tool. Download and upload models or datasets, organize cloud storage, manage authentication and local caches, run scheduled jobs, and deploy or maintain model inference services.

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• Download or publish models and datasets
• Sync training checkpoints to cloud storage
• Inspect and clean the local cache

Use this skill when running local AI models with Docker Model Runner — the `docker model` CLI — e.g. "run an LLM locally with Docker", "pull a model from the ai/ namespace", "connect my app to a local model", "use a local model as backend for the Drupal AI module", or when wiring the `models:` top-level element into a compose.yaml. Covers pulling/running models, OpenAI-compatible endpoints, and Compose integration.

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Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.

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Find language models on Hugging Face Hub and run them locally with llama.cpp. The skill helps compare GGUF model files, choose compressed versions that fit available memory, and prepare commands for a local chat server or command-line session.

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• Find a model that fits local RAM or GPU memory
• Compare available GGUF files and sizes
• Prepare a local chat server command

Review estimated spending, token usage, and cache hit ratios across Claude Code, Codex, and Grok using local logs. Filter by time, project, or provider, while keeping currencies separate and identifying models without pricing.

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• Reviewing estimated spending for the past seven days
• Comparing usage across providers and models
• Checking costs for a specific project or time range

Switch a NanoClaw agent group to a local Ollama model by configuring environment variables and model settings. The skill covers setup checks, optional blocking of Anthropic API access, verification, troubleshooting, and switching back to Claude.

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• Running a NanoClaw agent with a local model
• Reducing use of the Anthropic API
• Trying open-weight models through Ollama

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.

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

Set up, configure, update, and troubleshoot persistent memory for OpenClaw agents. The plugin stores conversations locally and supports automatic recall, task summaries, reusable skills derived from successful tasks, and a visual memory manager.

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• Setting up persistent memory for OpenClaw
• Updating or troubleshooting the memory plugin
• Choosing local or external memory search models

Evaluate Hugging Face Hub models using inspect-ai or lighteval, with local inference or a provider-backed option. Choose an inference engine, start with a small test, and switch engines when model compatibility requires it.

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• Run benchmark evaluations on a local GPU
• Choose an evaluation framework and inference engine
• Check a small sample before a full evaluation

Safely auto-detect the current machine's hardware (GPU, VRAM, system RAM, OS) with read-only commands, then recommend the top 3 local AI models to run — most powerful that fits, best for coding, and best small-and-fast — each with download links. Use when someone asks "what AI models can my computer run" or "what's the best local model for my rig".

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Set up and optimize a complete local AI infrastructure — Ollama, Open WebUI, local RAG pipelines, and private model serving. Use when running models locally for privacy, compliance, offline use, or cost, or setting up GPU-accelerated local inference and RAG.

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Search local notes, documents, and meeting transcripts using keywords and meaning. Combine both methods and use local AI models to reorder results by relevance, then retrieve full documents or selected passages without cloud processing.

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• Find decisions recorded in meeting transcripts
• Search notes by concept rather than exact wording
• Look up names and identifiers in project documentation

Train and evaluate RuView models using Wi-Fi measurements for pose estimation, retrieval, and adaptation to different environments. Follow laptop or GCloud GPU workflows, then package and publish trained models to Hugging Face.

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• Train pose estimation without cameras or labels
• Train pose estimation with paired camera and Wi-Fi data
• Adapt models to different rooms and environments