Llama 4

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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 date	5 Apr 2025
Developer	Meta AI
Type	Open-weight multimodal LLM
License	Llama 4 Community License
GitHub	meta-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

Variant	Active Params	Experts	Total Params	Context Window	Best for
Scout	17 B	16	109 B	10 M tokens	Long-context RAG, document analysis (stats)
Maverick	17 B	128	400 B	1 M tokens	Coding & reasoning tasks, general chat (Oracle Docs)
Behemoth	288 B*	16	~2 T	TBA	High-end STEM, under training (not yet released)

Tools & Resources

Weights & License – Download from the official portal.
Inference & training code – meta-llama/llama-models.
Quick-start recipes – Llama Cookbook.
Stats & benchmarks – Interactive dashboards at llm-stats.com.
Cloud endpoints – Ready-to-use deployments in Oracle OCI Generative AI.

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.

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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.

CodeForge OS

CodeForge OS is an AI-powered software planning and development assistant designed to bridge the gap between an idea and execution. While modern AI tools can generate code, teams still spend significant time defining requirements, planning architecture, creating implementation strategies, designing test cases, and organizing releases. It automates this process through a collaborative multi-agent workflow. The platform allows users to input a project idea in natural language. Instead of relying on a single AI response, multiple specialized agents work together, each focusing on a specific stage of the software development lifecycle. The Product Manager Agent analyzes the idea and generates detailed requirements, user stories, feature breakdowns, and project objectives. The Architect Agent designs the system architecture, technology stack recommendations, database structure, APIs, and scalability considerations. The Engineering Agent creates implementation plans, development milestones, and technical workflows. The QA Agent generates testing strategies, edge cases, validation criteria, and quality assurance plans. Finally, the Release Manager Agent produces deployment roadmaps, release strategies, and execution timelines. The platform simplifies project planning, reduces time spent on documentation, improves team collaboration, and helps ensure that important stages of software development are not overlooked. Whether a user is building a startup MVP, preparing a hackathon project, creating a college project, or planning a production-scale application, it acts as an intelligent planning partner. Our vision is to evolve it into a complete AI-powered software operating system that not only plans applications but also assists with development, testing, deployment, and continuous improvement throughout the entire software lifecycle.

HireFlow

# HireFlow — AI Hiring Intelligence HireFlow is a multi-agent hiring workflow built on Band that transforms the traditionally manual and time-consuming hiring process into an intelligent, collaborative pipeline. Instead of relying on recruiters to manually analyse job descriptions, review resumes, identify candidate gaps, create interview questions, HireFlow distributes these responsibilities across a team of specialized AI agents that work together in a Band workspace. The workflow begins when a hiring manager submits a job description. The Job Analyzer agent converts the raw job posting into a structured hiring rubric with required skills, weighted evaluation criteria, seniority expectations, red flags, and culture-fit signals. This rubric becomes the single source of truth for the entire pipeline, ensuring every downstream decision is aligned with the original hiring requirements. Next, the Resume Screener agent evaluates candidates against the rubric rather than using subjective judgments. Each candidate receives a structured assessment, skill alignment, seniority evaluation, strengths, gaps, and an overall recommendation. The agent then ranks candidates and identifies the strongest prospects. The Interview Planner agent uses these screening results to generate tailored interview question packs. Rather than producing generic interview templates, it creates targeted questions designed to validate candidate strengths and investigate identified gaps, helping interviewers makeinformed decisions. Finally, the Decision Summarizer agent compiles all outputs into a comprehensive hiring report. This report includes candidate comparisons, hiring recommendations, interview guidance, reasoning behind decisions, and a transparent audit trail showing how each recommendation was generated. Built for the Band of Agents Hackathon, HireFlow showcases a practical enterprise use case where AI agents function as a collaborative hiring team rather than isolated assistants

Dzuka Agri: Global AI for Huge Farm Ops

Dzuka Agri Intelligence Platform Global multi-agent AI architecture for large-scale commercial farming. Farm managers submit problem, crop image, and GPS. Master Orchestrator coordinates specialized agents (Agronomy & Soil, Pest & Pathology, Climate & Hydrology, Market, Resource, Review) in real-time collaboration. Delivers unified, high-impact recommendations tackling climate, pests, soil, water, markets, and more at enterprise scale. MVP: Next.js frontend, API, Band/OpenAI orchestration. Built for huge operations worldwide. Impact: Reduced losses, optimized inputs, maximum profitability.

MediScan AI

MediScan AI is a Multi-Agent Radiology Assistant built to streamline chest X-ray analysis and assist healthcare professionals with faster and more reliable diagnostic workflows. The platform combines medical computer vision, large language models, and agent orchestration to transform raw X-ray images into clinically meaningful reports. Unlike traditional AI systems that rely on a single model, MediScan AI uses a team of specialized agents working together. The Vision Screening Agent analyzes chest X-rays and identifies potential abnormalities such as pneumonia, pleural effusion, edema, and other thoracic conditions. The Clinical Reasoning Agent interprets these findings and generates a structured radiology assessment. A Safety Validation Agent reviews the generated conclusions to reduce hallucinations, verify critical findings, and improve reliability. Finally, the Report Generation Agent compiles patient information, diagnostic insights, confidence scores, and recommendations into a professional medical report. The platform provides transparent agent-by-agent execution, allowing users to understand how each stage contributes to the final result. By combining explainable AI workflows with specialized medical intelligence, MediScan AI helps bridge the gap between advanced AI capabilities and practical clinical decision support. Built using FastAPI, React, TorchXRayVision, Groq-powered LLMs, and a custom multi-agent orchestration framework, MediScan AI demonstrates how collaborative AI systems can improve efficiency, explainability, and trust in medical imaging workflows.

Band Review Board: Multi-Region Ad Compliance

Global brands ship one campaign to many markets, and the same claim can be legal in the US and a violation in the EU, where fines reach 4 to 10% of global revenue. Today the only defense is slow, market-by-market legal review with no audit trail. Band Review Board replaces that with a room of 10 specialist agents that clears a campaign against every market's rules at once. It is not a pipeline that merges a checklist of flags. The agents hold competing mandates, claims, regulation, and brand, and they argue. Region reviewers hold or concede on the record, a mediator brokers the conflict, and a human rules only on the genuine gray area, with that ruling logged as precedent. It runs on Band as the real coordination layer, not a wrapper. Agents @mention each other to object and rebut between specific parties, the room summons the next specialist, and finally a human, with addParticipant only when a conflict will not resolve, and every finding and verdict posts to a shared live ledger with sendEvent. Take Band out and it stops working. Every agent runs the model that fits its job through one AI/ML API gateway: GPT-5, Gemini 2.5 Pro and Flash, Claude Opus, Sonnet, and Haiku, Llama via Featherless, DeepSeek, and Nano Banana for image regeneration. Cost scales with difficulty: cheap models do the back-and-forth and Opus is spent only on a deadlock. It is also multimodal, reading the video and hearing the audio. In the live demo, the claim "clinically proven to boost your immune system" is approved in the US, a violation in the EU, and conditional in LATAM. The room genuinely deadlocks and escalates to a human. Nothing is hard-coded. Try it live at artifact-viewer-one.vercel.app. Solo build, MIT licensed.

Triumvirat

Triumvirat: Collaboration Dashboard is a multi-agent framework designed to automate the software development lifecycle. The system orchestrates three specialized agent roles—Architect, Coder, and Tester—to design, implement, and verify software features autonomously. To address common bottlenecks in multi-agent workflows, we implemented several advanced framework enhancements: - **Fallback Routing**: Prevents conversational deadlocks by automatically routing messages if explicit mentions are omitted. - **Tool Sanitizer**: Mitigates token suffix hallucinations before parser execution. - **Loop Prevention**: Inhibits repetitive polite loops between agents. The entire agent collaboration is monitored in real-time through a premium glassmorphic Cyberpunk dashboard that displays a live room feed, code inspector, and execution timeline, delivering full visibility into the autonomous development process.