Llama4 - AI Language Models Tool

Overview

Llama 4 is Meta’s fourth-generation autoregressive family that introduces a Mixture-of-Experts (MoE) architecture and native multimodality. Released in April 2025, the initial public variants are Llama 4 Scout and Llama 4 Maverick: both expose ~17B active parameters but differ in total capacity and expert count (Scout: ~109B total / 16 experts; Maverick: ~400B total / 128 experts). The MoE design activates only a small subset of experts per token, improving compute efficiency while enabling extremely large total parameter counts. Hugging Face adopted the models at launch and added direct transformers + TGI support, giving developers immediate access to multimodal pipelines, quantization recipes, and long-context tooling. (Source: Hugging Face release post.) Practically, Llama 4 emphasizes multimodal early-fusion (text+image), long context capabilities (instruction-tuned Scout supports up to 10 million tokens; Maverick up to 1 million in some variants), and deployment-focused features like on-the-fly INT4 quantization for Scout and BF16/FP8 formats for Maverick. The family is distributed under a Llama 4 Community License with access gating on model repos; the public launch generated strong interest but also mixed community reactions around gating and benchmark claims. For hands-on use, Hugging Face provides examples for flex_attention, quantization backends, device-mapping, and Text Generation Inference (TGI) integration. (Sources: Hugging Face model docs; reporting on community reactions.)

Key Features

• Mixture-of-Experts (MoE) backbone: large total params with a small set of active parameters per token.
• Two public variants: Scout (17B active / ~109B total / 16 experts) and Maverick (17B active / ~400B total / 128 experts).
• Native multimodality via early fusion — joint text + image input processing and visual question-answering.
• Long-context support: instruction-tuned Scout up to 10 million tokens; Maverick variants up to 1 million tokens.
• Deployment tools: on-the-fly INT4/8 quantization for Scout; BF16/FP8 formats and TGI + transformers support.

Example Usage

Example (python):

from transformers import pipeline
import torch

# Example: run an instruction-tuned Scout model (Hugging Face model id)
model_id = "meta-llama/Llama-4-Scout-17B-16E-Instruct"

pipe = pipeline(
    "text-generation",
    model=model_id,
    device_map="auto",           # automatic device placement
    dtype=torch.bfloat16,         # use bfloat16 where available
)

messages = [{"role": "user", "content": "Summarize the key findings of this report in three bullets."}]
output = pipe(messages, do_sample=False, max_new_tokens=200)
print(output[0]["generated_text"] if isinstance(output[0], dict) else output)

# Notes:
# - For multimodal usage, use the Llama4Processor and the corresponding AutoModel classes from transformers.
# - For very long context runs, enable the 'flex_attention' implementation and follow Hugging Face guidance.
# See the Llama4 docs on Hugging Face for detailed deployment and quantization examples.

Benchmarks

MMLU-Pro (instruction-tuned): Maverick: 80.5%; Scout: 74.3% (Source: https://huggingface.co/blog/llama4-release)

GPQA Diamond (instruction-tuned): Maverick: 69.8%; Scout: 57.2% (Source: https://huggingface.co/blog/llama4-release)

MATH (reasoning): Maverick: 61.2%; Scout: 50.3% (Source: https://huggingface.co/blog/llama4-release)

MBPP (code generation, pass@1): Maverick: 77.6%; Scout: 67.8% (Source: https://huggingface.co/blog/llama4-release)

ChartQA / DocVQA (multimodal visual QA): ChartQA — Maverick: 85.3%, Scout: 83.4%; DocVQA — Maverick: 91.6% (Source: https://huggingface.co/blog/llama4-release)

Key Information

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