Llama 4

Llama 4 is Meta AI’s newest open-weight model series.
It introduces Mixture-of-Experts (MoE) routing for efficient inference, accepts both text and images natively, and stretches context windows to record-breaking lengths—10 M tokens in the Scout variant. Meta positions Llama 4 as a research-friendly, production-ready alternative to proprietary frontier models, while keeping the code and weights downloadable from its GitHub repos and the official llama.com portal.

General
Release date5 Apr 2025
DeveloperMeta AI
TypeOpen-weight multimodal LLM
LicenseLlama 4 Community License
GitHubmeta-llama/llama-models

Core Features

  • Mixture-of-Experts architecture – Each query activates a subset of specialised “experts,” yielding higher throughput per FLOP while scaling to trillions of total parameters (TechCrunch).
  • Native multimodality – Models ingest both text and images without external adapters (The Verge).
  • Extended context windows – Scout handles up to 10 M tokens; Maverick supports 1 M tokens (llm-stats).
  • Multilingual training – Optimised across 200+ languages for global deployments (Data Scientist Guide).
  • Fine-tunable & agent-ready – Models ship with recipes for supervised fine-tuning, LoRA, and RAG inside the Llama Cookbook.

Model Variants

VariantActive ParamsExpertsTotal ParamsContext WindowBest for
Scout17 B16109 B10 M tokensLong-context RAG, document analysis (stats)
Maverick17 B128400 B1 M tokensCoding & reasoning tasks, general chat (Oracle Docs)
Behemoth288 B*16~2 TTBAHigh-end STEM, under training (not yet released)

Tools & Resources


Ecosystem & Integrations

  • Meta AI assistant now runs Llama 4 across WhatsApp, Messenger, Instagram, and web chat (The Verge).
  • OCI Generative AI offers managed Scout & Maverick endpoints for enterprise workloads (Oracle Docs).
  • Community hosting – Providers such as DeepInfra, Groq, and Together price Llama 4 as low as $0.08 / 1 M input tokens (llm-stats).
  • Research & open-source – Thousands of fine-tuned checkpoints already live on Hugging Face; Meta’s annual LlamaCon (29 Apr 2025) spotlights academic collaborations (TechCrunch).

Llama 4 pushes open-weight LLMs into frontier-model territory—combining trillion-scale capacity with permissive licensing. Start experimenting by cloning the GitHub repo, reading the cookbook, or provisioning a managed endpoint on Oracle OCI.

Meta Llama 4 AI technology Hackathon projects

Discover innovative solutions crafted with Meta Llama 4 AI technology, developed by our community members during our engaging hackathons.

Crew-7

Crew-7

Crew-7 is a next-generation multi-agent AI platform built to replicate the structure, behavior, and efficiency of real-world teams across every domain not just software development. Each Crew consists of seven AI agents: 1 Orchestrator and 6 specialists who collaborate using a hybrid communication model inspired by modern engineering, research, business, and creative teams. The Orchestrator plans missions, delegates tasks, and resolves ambiguities, while specialists communicate directly to solve dependencies. This creates a high-performance autonomous workforce capable of tackling complex, multi-step projects with speed and accuracy. Users can choose from prebuilt Crews such as Backend Builder, Frontend Builder, SaaS Architect, Product, Marketing, Research, or Business Analyst or create their own custom team. When a mission is launched, Crew-7 executes inside a secure sandbox, generating complete deliverables: system architectures, APIs, websites, documents, analyses, strategies, and production-grade assets depending on the domain. A real-time Agent Graph visualizes how agents think, plan, and communicate, giving users full transparency into orchestration, reasoning steps, and tool usage. Every event agent start, agent message, tool call, and artifact creation is captured for traceability. Crew-7 provide a built-in Marketplace allows users to install ready-made crews instantly, while the Web3 upgrade layer introduces digital ownership: crews can be minted, verified, traded, rented, or monetized as evolving AI assets backed by on-chain reputation. By combining orchestrated intelligence, domain specialization, real-time visualization, secure execution, and digital ownership, Crew-7 transforms AI from a single assistant into a coordinated, multi-skilled workforce capable of delivering full projects from concept to production across any industry.

AMAGI - Vultr Track

AMAGI - Vultr Track

Amagi is a proactive AI assistant that sees your screen, listens, remembers, and helps you stay focused—designed to run across devices with real-time context awareness." Long Description : " Amagi is not just another chatbot—it’s a context-aware, proactive AI assistant built to live with you across devices. Inspired by the chaos of modern digital life, Amagi observes your screen activity, listens to your voice, and keeps track of your context to act before you even ask. The vision was ambitious: A floating UI widget that lives on your screen Groq-powered LLaMA 4 model for both text and image reasoning Whisper-based STT for natural voice interaction Memory storage using vector databases for context recall Cross-device session management with Google login Real-time screen summary uploads for proactive suggestions Every part of the stack was handcrafted—from using FastAPI, Qdrant, and Authlib on the backend, to building a minimal floating widget with Tkinter on the client. The idea was to keep core interactions natural and human: "Hey Amagi, remind me about this video." clicks on widget — gets reminded about the anime suggestion from earlier. But execution wasn’t easy. Technical blockers hit hard: torch refused to install due to system limitations Qdrant’s vector filters bugged out OAuth2 verification required multiple rewrites Docker wasn’t even an option on my machine Time ran out And still, I kept building. Even as things broke, I pushed through the stack again and again—rewriting modules, replacing dependencies, switching APIs, debugging threads—only to run into new problems at every turn. Amagi didn’t ship. But it’s real. And it’s coming. This hackathon submission is just the beginning. The architecture is mapped, core components are wired, and the story is being told—because sometimes the biggest breakthrough isn’t in the code, but in not giving up. Amagi may have missed the deadline. But the journey has only started.