Llama by Meta - AI Model Hubs Tool

Overview

Llama is Meta’s family of open-weight large language models (LLMs) distributed for research and commercial use under Meta’s licensing terms. The most widely released member, Llama 2 (available in 7B, 13B and 70B parameter sizes), was published as open weights and chat-optimized variants, enabling organizations and researchers to run, fine-tune, and deploy foundation models locally or in cloud environments. Llama models emphasize a balance of performance and efficiency for instruction following, chat, and downstream fine-tuning. The Llama ecosystem is widely hosted on model hubs (notably Hugging Face), and the community has produced instruction- tuned, RLHF-style chat checkpoints, LoRA adapters, and evaluation suites for common NLP tasks. Because weights are available, teams can adapt Llama models for retrieval-augmented generation, custom knowledge grounding, or parameter-efficient fine-tuning. Note: my knowledge is current through mid-2024; later Llama 3.x or Llama 4 variants may exist but details and official releases after that date should be confirmed from Meta or the model hub.

Key Features

• Open-weight releases enabling local deployment and auditability.
• Multiple model sizes (e.g., 7B, 13B, 70B) to balance cost and performance.
• Chat-optimized checkpoints tuned for instruction-following and dialogs.
• Supports fine-tuning and parameter-efficient adapters (LoRA) from the community.
• Widely distributed via Hugging Face model hub with community tooling and evaluation.

Example Usage

Example (python):

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

# Example: load Llama 2 7B chat (Hugging Face model ID)
model_id = "meta-llama/Llama-2-7b-chat-hf"

# Requires transformers that supports the model and a machine with sufficient VRAM
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto")

gen = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a concise summary explaining retrieval-augmented generation for developers."
outputs = gen(prompt, max_new_tokens=200, do_sample=False)
print(outputs[0]["generated_text"])

Key Information

• Category: Model Hubs
• Type: AI Model Hubs Tool