🟢 Llama Models

Meta's Llama (Large Language Model Meta AI) family represents some of the most powerful open-source language models available, offering excellent performance across various tasks with the flexibility of self-hosting or cloud deployment.

🌟 Why Choose Llama?

Llama Advantages

📊 Available Models

Model	Parameters	Context Window	Best For	Deployment
Llama 3.1 8B	8B	8K tokens	Fast inference, resource-efficient	Local/Cloud
Llama 3.1 70B	70B	8K tokens	High-quality outputs	Cloud/High-end
Llama 3.1 405B	405B	8K tokens	Research, enterprise	Cloud only
Llama 2 7B	7B	4K tokens	Lightweight tasks	Local/Cloud
Llama 2 13B	13B	4K tokens	Balanced performance	Local/Cloud
Llama 2 70B	70B	4K tokens	High-quality outputs	Cloud/High-end

Source: Meta AI Llama Models

🚀 Getting Started

Option 1: Self-Hosting (Recommended)

Step 1: Choose Your Infrastructure

Local Deployment:

Hardware: 16GB+ RAM, GPU recommended
Software: Docker, Python 3.8+
Model: Llama 3.1 8B or Llama 2 7B

Cloud Deployment:

AWS: EC2 with GPU instances
Google Cloud: Compute Engine with GPUs
Azure: Virtual Machines with GPUs
VPS: DigitalOcean, Linode, etc.

Step 2: Install Ollama (Easiest Method)

bash

# macOS/Linux
curl -fsSL https://ollama.ai/install.sh | sh

# Windows
# Download from https://ollama.ai/download

Step 3: Pull Llama Model

bash

# Pull Llama 3.1 8B
ollama pull llama3.1:8b

# Or Llama 2 7B
ollama pull llama2:7b

Step 4: Start Ollama Server

bash

ollama serve

Step 5: Configure in MCP for WP

Go to MCP for WP > Settings
Set Provider to "Llama"
Set API Endpoint: http://localhost:11434
Set Model: llama3.1:8b
Click "Test Connection"
Save settings

Option 2: Cloud Providers

Using Together AI

Sign up at Together AI
Get API key
Configure in MCP for WP:
- API Endpoint: https://api.together.xyz
- API Key: Your Together AI key
- Model: meta-llama/Llama-3.1-8B-Instruct

Using Replicate

Sign up at Replicate
Get API key
Configure in MCP for WP:
- API Endpoint: https://api.replicate.com
- API Key: Your Replicate key
- Model: meta/llama-3.1-8b-instruct

⚙️ Model Configuration

Default Settings

json

{
  "model": "llama3.1:8b",
  "max_tokens": 2048,
  "temperature": 0.7,
  "top_p": 0.9,
  "top_k": 40
}

Parameter Guide

Model Selection

llama3.1:8b: Fast, efficient, good for most tasks
llama3.1:70b: High quality, requires more resources
llama2:7b: Lightweight, good for simple tasks
llama2:13b: Balanced performance and resources

Max Tokens

Range: 1 to 8192 (varies by model)
Recommendation: Start with 2048, adjust as needed

Temperature

Range: 0.0 to 1.0
0.0: Deterministic
0.7: Balanced
1.0: Creative

Top P

Range: 0.0 to 1.0
1.0: All tokens
0.9: Top 90% probability mass

Top K

Range: 1 to 100
40: Good balance
Lower: More focused
Higher: More diverse

💰 Pricing & Usage

Self-Hosting Costs

Model	RAM Required	GPU Required	Monthly Cost
Llama 3.1 8B	16GB	Optional	$0-50
Llama 3.1 70B	140GB	Required	$200-500
Llama 2 7B	14GB	Optional	$0-30
Llama 2 13B	26GB	Recommended	$50-150

Cloud Provider Pricing

Together AI

Llama 3.1 8B: $0.20/1M tokens
Llama 3.1 70B: $0.80/1M tokens
Free tier: $25 credit

Replicate

Llama 3.1 8B: $0.25/1M tokens
Llama 3.1 70B: $1.00/1M tokens
Free tier: $5 credit

Cost Optimization Tips

Use smaller models for simple tasks
Self-host for high-volume usage
Monitor token usage
Cache responses when possible

🔧 Advanced Configuration

System Instructions

json

{
  "system_instruction": "You are a helpful assistant."
}

Custom Prompts

json

{
  "prompt_template": "<|system|>\n{system}\n<|user|>\n{user}\n<|assistant|>\n{assistant}"
}

Function Calling

json

{
  "functions": [
    {
      "name": "get_weather",
      "description": "Get weather information",
      "parameters": {
        "type": "object",
        "properties": {
          "location": { "type": "string" }
        }
      }
    }
  ]
}

🛠️ Use Cases & Examples

Content Generation

Tool Configuration:

json

{
  "input_schema": {
    "type": "object",
    "properties": {
      "topic": { "type": "string", "description": "Topic to write about" },
      "style": { "type": "string", "enum": ["formal", "casual", "creative"] },
      "length": { "type": "string", "enum": ["short", "medium", "long"] }
    },
    "required": ["topic"]
  }
}

Recommended Settings:

Model: llama3.1:8b
Temperature: 0.8
Max Tokens: 1024

Code Generation

Tool Configuration:

json

{
  "input_schema": {
    "type": "object",
    "properties": {
      "language": { "type": "string", "description": "Programming language" },
      "task": { "type": "string", "description": "What to code" },
      "complexity": { "type": "string", "enum": ["simple", "medium", "complex"] }
    },
    "required": ["language", "task"]
  }
}

Recommended Settings:

Model: llama3.1:8b
Temperature: 0.3
Max Tokens: 2048

Analysis & Summarization

Tool Configuration:

json

{
  "input_schema": {
    "type": "object",
    "properties": {
      "text": { "type": "string", "description": "Text to analyze" },
      "analysis_type": { "type": "string", "enum": ["summarize", "extract", "classify"] }
    },
    "required": ["text", "analysis_type"]
  }
}

Recommended Settings:

Model: llama3.1:8b
Temperature: 0.3
Max Tokens: 1024

🔍 Troubleshooting

Common Issues

Connection Failed: Check if Ollama is running
Out of Memory: Use smaller model or add more RAM
Slow Performance: Enable GPU acceleration
Model Not Found: Pull the model with ollama pull

Debugging Tips

Check Ollama logs: ollama logs
Test with curl: curl -X POST http://localhost:11434/api/generate
Monitor system resources
Check model availability
Verify API endpoint

Performance Optimization

Use GPU acceleration when available
Choose appropriate model size
Optimize batch size
Use quantization for memory efficiency

📚 Additional Resources

🔐 Security Best Practices

Keep models updated
Monitor API usage
Use secure connections
Implement rate limiting
Regular security audits

📞 Support

Ready to get started? Configure your Llama integration or explore other providers!