Llama 3 - AI Language Models Tool

Overview

Llama 3 is Meta’s April 2024 follow-up to the Llama family: an open-weights, instruction-capable large language model released in 8B and 70B parameter variants (each with base and instruct-tuned versions). The release focuses on higher-quality pretraining (Meta reports >15 trillion pretraining tokens), a larger tokenizer (≈128,256 vocabulary), an 8,192-token context window, and engineering changes such as Grouped-Query Attention (GQA) to improve inference efficiency and longer-context handling. Llama 3’s instruction-tuned models were trained with a combination of supervised fine-tuning and reinforcement learning techniques and include safety tooling (Llama Guard / Code Shield) and a community license that requires attribution for derivative works. ([huggingface.co](https://huggingface.co/blog/llama3?utm_source=openai)) The ecosystem emphasis with Llama 3 is broad deployability: Meta published model cards and partnered with providers to make the models available through Hugging Face (Transformers & Inference integration), Google Cloud (Vertex AI / Text Generation Inference), Amazon SageMaker, and other managed inference platforms. The 8B model is explicitly intended to be runnable on more modest hardware (and was shown to fine-tune on a single GPU in examples), while the 70B instruct model targets higher-quality assistant, coding, and reasoning use cases. Community response has been generally positive about improved helpfulness and coding capability, but reviewers and reporting outlets also highlight remaining risks (hallucinations, licensing restrictions for some high-scale commercial users). ([huggingface.co](https://huggingface.co/blog/llama3?utm_source=openai))

Key Features

• Open-weights release in 8B and 70B parameter variants, available for download under Meta's license.
• Instruction-tuned versions optimized for dialogue and assistant-style workflows.
• 8,192-token context window for longer inputs and multi-step tasks.
• Expanded tokenizer (≈128,256 vocabulary) for more efficient multilingual tokenization.
• Grouped-Query Attention (GQA) for inference efficiency on longer contexts.
• Safety toolset: Llama Guard (safety classifier) and Code Shield for risky code detection.
• Integrated with Hugging Face Transformers, Hugging Chat, Google Cloud Vertex, and AWS SageMaker.

Example Usage

Example (python):

import requests

# Example: call Llama 3 via Hugging Face Inference API (replace HF_API_TOKEN)
API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-70B-Instruct"
headers = {"Authorization": f"Bearer {"YOUR_HF_API_TOKEN"}"}

payload = {
    "inputs": "Write a short Python function that returns the Fibonacci sequence up to n.",
    "parameters": {"max_new_tokens": 200, "temperature": 0.2}
}

resp = requests.post(API_URL, headers=headers, json=payload)
print(resp.json())

# Notes:
# - Replace YOUR_HF_API_TOKEN with a valid Hugging Face token or use your cloud provider's endpoint.
# - For local deployment with transformers, use the model card and follow provider-specific instructions; large models require appropriate hardware and configuration.

Benchmarks

MMLU (Llama 3 70B, 5-shot): 82.0 (Source: https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)

HumanEval (Llama 3 70B, 0-shot): 81.7 (Source: https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)

GSM-8K (Llama 3 70B, 8-shot, CoT): 93.0 (Source: https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)

MMLU (Llama 3 8B, 5-shot): 68.4 (Source: https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)

Training corpus (approx.): >15 trillion tokens (pretraining) (Source: https://huggingface.co/blog/llama3)

Key Information

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