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Offline Llama

offline llama

and-ray-m0 source installs☆ 0 Repository stars

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install and-ray-m/offline-llama

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Skill instructions

offline-llama

Autonomously manage and use local Ollama models for continuous operation without internet dependency. Includes model health monitoring, automatic fallback, and self-healing capabilities.

Overview

This skill enables autonomous operation with local Ollama models. It monitors model health, automatically switches between models when issues occur, and maintains functionality even without internet connectivity. The skill includes self-healing capabilities to restart services and clear resources when needed.

Core Features

Model Management
  • Health Monitoring: Continuously check model availability and performance
  • Automatic Fallback: Switch to alternative models when primary fails
  • Model Switching: Dynamically select best available model for task
Self-Healing
  • Service Restart: Automatically restart Ollama when models become unavailable
  • Resource Management: Clear cache and temporary files to free resources
  • Model Reinstallation: Reinstall problematic models automatically
Connectivity Awareness
  • Internet Detection: Monitor internet connectivity status
  • Smart Fallback: Switch to remote models when local models unavailable and internet is present
  • Offline Mode: Maintain full functionality without internet

Configuration

Models
  • Primary: llama-3.1-8b-instruct (general tasks)
  • Secondary: mistral-7b-instruct (faster responses)
  • Specialized: code-llama-7b (coding tasks)
Health Checks
  • Model Status: Monitor availability every 30 seconds
  • Latency Tracking: Monitor response times every minute
  • Resource Usage: Monitor GPU/CPU and memory every 5 minutes
Fallback Strategies
  1. Model Switching: Automatically switch to alternative local models
  2. Response Retry: Retry failed requests with exponential backoff
  3. Degraded Mode: Continue with limited functionality if all models unavailable

Usage

When Internet is Available
  • Use local models...

Source information

Source
github
Repository
openclaw/skills
Path
skills/and-ray-m/offline-llama
Version
—
Commit
908bb3305b44a1217a69135a5a2c4c30974b7b35
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# offline-llama

Autonomously manage and use local Ollama models for continuous operation without internet dependency. Includes model health monitoring, automatic fallback, and self-healing capabilities.

## Overview

This skill enables autonomous operation with local Ollama models. It monitors model health, automatically switches between models when issues occur, and maintains functionality even without internet connectivity. The skill includes self-healing capabilities to restart services and clear resources when needed.

## Core Features

### Model Management
- **Health Monitoring**: Continuously check model availability and performance
- **Automatic Fallback**: Switch to alternative models when primary fails
- **Model Switching**: Dynamically select best available model for task

### Self-Healing
- **Service Restart**: Automatically restart Ollama when models become unavailable
- **Resource Management**: Clear cache and temporary files to free resources
- **Model Reinstallation**: Reinstall problematic models automatically

### Connectivity Awareness
- **Internet Detection**: Monitor internet connectivity status
- **Smart Fallback**: Switch to remote models when local models unavailable and internet is present
- **Offline Mode**: Maintain full functionality without internet

## Configuration

### Models
- **Primary**: llama-3.1-8b-instruct (general tasks)
- **Secondary**: mistral-7b-instruct (faster responses)
- **Specialized**: code-llama-7b (coding tasks)

### Health Checks
- **Model Status**: Monitor availability every 30 seconds
- **Latency Tracking**: Monitor response times every minute
- **Resource Usage**: Monitor GPU/CPU and memory every 5 minutes

### Fallback Strategies
1. **Model Switching**: Automatically switch to alternative local models
2. **Response Retry**: Retry failed requests with exponential backoff
3. **Degraded Mode**: Continue with limited functionality if all models unavailable

## Usage

### When Internet is Available
- Use local models...

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