Llama 3 - AI Language Models Tool

Overview

Llama 3 is Meta’s April 2024 open‑access LLM family that shipped initially in two main sizes (8B and 70B parameters), with both base pretrained and instruction‑tuned variants. The family was trained on a very large mixture of publicly available data (Meta reports ~15 trillion pretraining tokens) and includes architectural optimizations such as Grouped‑Query Attention (GQA) to improve inference scalability. Hugging Face and major cloud vendors integrated Llama 3 quickly, providing Transformers support, quantized inference paths (4‑bit / 8‑bit), and deployment artifacts for Hugging Chat, Vertex AI, SageMaker and Text Generation Inference. ([huggingface.co](https://huggingface.co/blog/llama3?utm_source=openai)) Since the initial release, Meta expanded the family with the Llama 3.1 line (released July 2024), adding a 405B parameter frontier model and dramatically longer context windows (128k tokens) across updated sizes — aiming at long‑context tasks, multilingual reasoning, and enterprise deployments. Llama 3’s tuned variants include safety‑focused fine‑tunes (e.g., Llama Guard) and Meta distributes the models under a community/commercial license with usage restrictions; many cloud providers offer hosted inference or import paths for production use. Users and practitioners report significant gains over Llama 2 in benchmarks and code tasks, while community threads note practical issues like quantization sensitivity and UX/endpoint stability depending on provider. ([huggingface.co](https://huggingface.co/meta-llama/Llama-3.1-405B-FP8?utm_source=openai))

Key Features

• Available as 8B and 70B parameter models, with base and instruction‑tuned variants.
• Pretrained on ~15 trillion tokens for broader knowledge coverage and improved generalization.
• Grouped‑Query Attention (GQA) for inference scalability across GPU configurations.
• Supports 4‑bit and 8‑bit quantized inference; single‑GPU fine‑tuning examples via TRL / QLoRA.
• Integrations and deployment: Hugging Face Transformers, Hugging Chat, Google Vertex AI, SageMaker, Bedrock/OCI.

Example Usage

Example (python):

from transformers import pipeline
import torch

# Example: load Llama 3 instruct model with recommended dtype and optional 4-bit quant
# (see Hugging Face Llama 3 blog / docs for full context and access gating). ([huggingface.co](https://huggingface.co/blog/llama3?utm_source=openai))
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"

pipe = pipeline(
    "text-generation",
    model=model_id,
    device_map="auto",
    model_kwargs={
        "torch_dtype": torch.bfloat16,
        # Example quantization config for lower VRAM (requires bitsandbytes & Transformers support)
        "quantization_config": {"load_in_4bit": True},
        "low_cpu_mem_usage": True,
    },
)

print(pipe("Write a short product description for a reusable water bottle.", max_new_tokens=120)[0]["generated_text"])

Benchmarks

MMLU (5‑shot) — Llama 3 (70B): 79.5 (Source: https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)

MMLU (5‑shot) — Llama 3 (8B): 66.6 (Source: https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)

HumanEval / code (pass@1) — Llama 3.1 (70B Instruct): ≈80.5 (pass@1, zero-shot reported for 3.1 variants) (Source: https://huggingface.co/meta-llama/Llama-3.1-405B-FP8)

Pretraining tokens reported: 15+ trillion tokens (Source: https://huggingface.co/blog/llama3)

Key Information

• Category: Language Models
• Type: AI Language Models Tool