Meta Llama

Meta Llama is the most popular open-source model family — excellent for local development, fine-tuning, and self-hosted deployments with strong coding capabilities.

Why Vibe Coders Use It

• Industry-standard open model — Llama 3.3 70B rivals GPT for many tasks
• Self-hostable — run locally or on your own infrastructure for complete control
• Fine-tunable — customize for your specific domain or use case
• Strong code generation — competitive with Claude and GPT at coding tasks
• Massive ecosystem — available everywhere (HuggingFace, all inference platforms, Ollama, vLLM)

Key Specs

Getting Started

Option 1: Via API (Fireworks AI, Fastest)

npm install @ai-sdk/fireworks

import { fireworks } from '@ai-sdk/fireworks';
import { generateText } from 'ai';

const { text } = await generateText({
  model: fireworks('accounts/fireworks/models/llama-v3p3-70b-instruct'),
  prompt: 'Build a TypeScript function that validates email addresses',
});

console.log(text);

Option 2: Via Together AI

npm install @ai-sdk/togetherai

import { togetherai } from '@ai-sdk/togetherai';
import { generateText } from 'ai';

const { text } = await generateText({
  model: togetherai('meta-llama/Meta-Llama-3.3-70B-Instruct-Turbo'),
  prompt: 'Explain the difference between useState and useReducer in React',
});

Option 3: Local with Ollama

# Install and run locally
ollama run llama3.3

# Then stream responses locally
const response = await fetch('http://localhost:11434/api/generate', {
  method: 'POST',
  body: JSON.stringify({
    model: 'llama3.3',
    prompt: 'How do I implement pagination in a SQL query?',
    stream: true,
  }),
});

Option 4: Self-Hosted with vLLM

# Deploy Llama 3.3 70B on your server
vllm serve meta-llama/Meta-Llama-3.3-70B-Instruct

# Query your local server
const response = await fetch('http://your-server:8000/v1/completions', {
  method: 'POST',
  body: JSON.stringify({
    model: 'llama3.3',
    prompt: 'Write a middleware function for authentication in Express',
  }),
});

Fine-Tuning Example

// On platforms like Lambda Labs or Modal, fine-tune Llama for your domain
import os
from modal import Image, Stub
from modal.functions import gpu_memory

stub = Stub("finetune-llama")

@stub.function(image=Image.from_registry("nvidia/cuda:12.1.1-devel-ubuntu22.04"))
def train():
    # Use Hugging Face trainer to fine-tune on your dataset
    from transformers import AutoModelForCausalLM, TrainingArguments, Trainer

    model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-70b")
    # ... training code

When to Use Llama vs. Alternatives

Use Llama when you need full control, want to self-host, or plan to fine-tune. It's excellent for local development and privacy-critical applications. Use Claude for the strongest proprietary reasoning. Use GPT for the fastest, most reliable general-purpose model.

Available Models

• Llama 3.3 70B — Latest, strongest open model
• Llama 3.1 405B — Largest, good for complex tasks
• Llama 3.1 8B — Lightweight, fast
• Code Llama — Specialized for code generation

Resources

• Meta AI Official
• Llama Models on HuggingFace
• Ollama (Local Deployment)
• vLLM (Self-Hosted)
• Fireworks AI Llama Models
• Together AI Llama Models
• See the full Llama profile on LLMReference →