Llama
free
Model Name: Llama 2/3
Docs: Meta AI
Keywords: Open-weights, Foundation-model, Self-hostable
Installation: HuggingFace
Introduction
Llama is Meta's series of open-weight large language models offering:
- State-of-the-art performance in its class
- Available in multiple sizes (7B to 70B parameters)
- Commercial-friendly licensing (Llama 2/3)
- Optimized for both research and production use
Unlike closed models, Llama provides transparency in weights and architecture while maintaining competitive performance with proprietary alternatives.
Instructions
1. Deployment Options
- Self-hosted: Run locally via HuggingFace Transformers
- Cloud API: Use providers like Replicate or Anyscale
- Quantized: GGUF models for consumer hardware
# Using HuggingFace Transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model = "meta-llama/Llama-2-7b-chat-hf"
tokenizer = AutoTokenizer.from_pretrained(model)
llm = AutoModelForCausalLM.from_pretrained(model)
inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = llm.generate(**inputs)
2. Prompt Engineering
- Use Llama 2/3's special chat format for best results
- Include system messages for context/behavior
- Example structure:
[INST] <<SYS>>
You are a helpful assistant
<</SYS>>
User's message here [/INST]
3. Key Parameters
- temperature: 0.1 (precise) to 1.0 (creative)
- max_new_tokens: Control response length
- top_p: 0.9 recommended for most cases
- repetition_penalty: Reduce word repetition
4-6. Optimization
- Fine-tune with LoRA for domain-specific tasks
- Use quantization (4-bit/8-bit) for efficiency
- Implement moderation layers for production
- Monitor performance with eval benchmarks
Capabilities
Language Tasks
- • Conversational AI (chat models)
- • Text summarization
- • Translation
- • Question answering
Code Generation
- • Code completion
- • Debugging assistance
- • Documentation generation
- • Multiple language support
Creative Applications
- • Story writing
- • Content creation
- • Poetry generation
Reasoning
- • Logical problem solving
- • Mathematical reasoning
- • Decision support
Examples
# Basic Text Generation
from transformers import pipeline
llm = pipeline("text-generation", model="meta-llama/Llama-2-7b-chat-hf")
response = llm("[INST] Explain quantum computing [/INST]")
# Chat Completion Example
messages = [
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "What's the capital of France?"}
]
response = llm(messages, temperature=0.7)
Table of Contents
Key Features
- • Open Weights: Full model access
- • Multiple Sizes: 7B to 70B parameters
- • Optimized Inference: Runs on consumer hardware
- • Fine-tuning Support: Adapt to specific domains
- • Commercial Use: Llama 2/3 license