Understanding the open source Llama 4 model in one article

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What do you think of the Llama 4 model?

      At the same time, Meta has integrated Llama 4 into Meta AI Assistant, covering applications in 40 countries such as WhatsApp, Messenger and Instagram, and plans to launch independent applications. This not only improves the user experience, but also provides low-cost AI solutions for small and medium-sized enterprises. In addition, Meta emphasizes that Llama 4 reduces the rejection rate for "controversial" questions, indicating that it is trying to find a breakthrough in the balance between openness and security.

How much do you know about the Llama 4 model family?

      1. Llama 4 Scout: Small, fast, and smart

     As the most efficient member of the Llama 4 family, Scout is designed to be a lightweight and fast-response model, especially suitable for developers and researchers who do not have access to large GPU clusters. It combines high performance with low resource requirements, making it an ideal choice for multimodal applications.

     Next, let's take a look at   the relevant features of the Scout product. For details, please refer to the following:

     In terms of architectural design, Scout adopts a Mixture of Experts (MoE) architecture, equipped with 16 expert modules, activating only 2 experts at a time, thereby calling 17 billion active parameters from a total of 109 billion parameters. It supports an amazing 10 million token context window, making it a pioneer in long text processing.

     At the same time, through Int4 quantization technology, Scout can run smoothly on a single Nvidia H100 GPU, significantly reducing hardware costs and providing a cost-effective option for users with limited budgets.

     In multiple benchmark tests, Scout surpassed similar models such as Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1, demonstrating strong language understanding and generation capabilities.

     In addition, during the model training process, the model was pre-trained on 200 languages, of which 100 languages ​​had more than 1 billion tokens. It also incorporated diverse image and video data and supported processing of up to 8 images in a single prompt.

     In terms of application scenarios, thanks to advanced image region grounding technology, Scout achieves precise visual reasoning, which is particularly suitable for long-context memory chatbots, code summarization tools, educational question-and-answer robots, and optimization assistants for mobile devices or embedded systems.

      2. Llama 4 Maverick: A powerful and reliable flagship choice

     As the flagship open source model of the Llama 4 family, Maverick is designed for advanced reasoning, encoding, and multimodal applications. Although its performance far exceeds that of Scout, Maverick still maintains high efficiency through the same MoE strategy, making it a powerful tool trusted by enterprises and developers.

     Compared with  the lightweight features of Scout products, the core features of Maverick are mainly reflected in the following aspects, for details, please refer to:

     In terms of architectural design, Maverick uses a hybrid expert architecture, including 128 routing experts and 1 shared expert, and only activates 17 billion parameters (402 billion parameters in total) during inference. It is trained through early fusion technology of text and images, and supports processing 8 image inputs at a time.

     In terms of execution efficiency, Maverick can run efficiently on a single H100 DGX host or be seamlessly expanded through a multi-GPU cluster, balancing performance and flexibility.

     In terms of comparative testing, on the LMSYS Chatbot Arena, Maverick achieved an ELO score of 1417, surpassing GPT-4o and Gemini 2.0 Flash, and being on par with DeepSeek v3.1 in reasoning, encoding, and multilingual capabilities.

     Unlike  Scout, Maverick uses cutting-edge technologies, including MetaP hyperparameter scaling, FP8 precision training, and a 30 trillion token dataset. Its powerful image understanding, multi-language reasoning, and cost-effectiveness are better than the Llama 3.3 70B model.

    In terms of application scenarios, Maverick's advantages make it an ideal choice for AI pair programming, enterprise-level document understanding, and educational tutoring systems, especially for complex tasks that require high precision and multi-language support.

     3. Llama 4 Behemoth: A Behemoth-level Teaching Model

     Behemoth is Meta's largest model to date, and while it has not yet been released to the public, it plays a vital "teacher" role in the training of Scout and Maverick, laying the foundation for the family members' excellence.

     Compared with the previous two products in the family, Behemoth is the best in terms of comprehensiveness, and its core features are as follows:

     In terms of architectural design, Behemoth uses a hybrid expert architecture with 16 expert modules, activating 288 billion parameters during reasoning (nearly 2 trillion parameters in total). As a native multimodal model, Behemoth performs well in reasoning, mathematics, and visual language tasks.

     In terms of performance, in STEM benchmarks such as MATH-500, GPQA Diamond, and BIG-bench, Behemoth continues to surpass GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro, demonstrating its strong strength in the scientific field.

     Based on the roles and training process, Behemoth, as a teacher model, uses co-distillation technology with Scout and Maverick to guide the optimization of the two models using an innovative loss function (balancing soft and hard supervision). Its training uses FP8 accuracy, optimized MoE parallel technology (10 times faster than Llama 3), and new reinforcement learning strategies, including hard prompt sampling, multi-capability batch processing construction, and diversified system instruction sampling.

     In terms of application scenarios, although it is currently limited to internal use, Behemoth, as the gold evaluation standard of Meta, drives the performance improvement of family models and lays the technical foundation for future open source.

Llama 4 model internal implementation analysis 

     Based on a structured and innovative training process, Meta AI divides the development of the Llama 4 series models into two key stages: pre-training and post-training. This process incorporates a number of advanced technologies, significantly improving the performance, scalability and efficiency of the model, setting a new benchmark for technological advancement in the field of AI.

     Below, we will analyze in depth the training details of the Llama 4 family - Scout, Maverick and Behemoth, combining professional technical descriptions with popular analogies to give you a comprehensive understanding of the science and engineering wisdom behind their training.

      1. Llama 4 model pre-training

     Pre-training is the foundation of Llama 4 model knowledge and capabilities. Meta introduced a number of breakthrough innovations in this phase to ensure that the model reaches industry-leading levels in multimodality and efficiency.

• Multimodal data fusion

    The Llama 4 series is pre-trained on a diverse dataset of more than 30 trillion tokens, covering multiple sources such as text, images, and videos. These models have native multimodal capabilities from the beginning, and can seamlessly process language and visual inputs, laying the foundation for cross-modal reasoning.

• Mixture of Experts (MoE)

     Pre-training uses the MoE architecture, which only activates a portion of the model parameters in each inference. For example, Maverick has 400 billion total parameters, but only 17 billion active parameters are activated each time; while Behemoth activates 288 billion of its approximately 2 trillion total parameters. This selective routing technology allows ultra-large-scale models to remain efficient during inference, significantly reducing computational costs.

• Early Fusion Architecture

     Text and visual inputs are jointly trained through early fusion techniques and integrated into a shared model backbone. This approach enhances semantic consistency between different modalities and provides solid support for multimodal tasks.

• MetaP Hyperparameter Tuning

      Meta developed MetaP technology, which allows to set personalized learning rate and initialization scale for each layer. This innovation ensures good transferability of hyperparameters between different model scales and training configurations, and optimizes training stability.

• FP8 precision training

     All models are trained using FP8 precision. This technology improves computing efficiency while maintaining the reliability of model quality and significantly reduces energy consumption and hardware requirements.

• iRoPE Architecture

     At the same time, a new iRoPE architecture with interleaved attention layers was introduced, which abandoned traditional positional embeddings and used temperature scaling technology during inference to help the Scout model achieve the generalization capability of ultra-long inputs (up to 10 million tokens).

     In addition to the above core mechanisms, Llama 4 also introduces a "readability hint" mechanism. You can imagine that pre-training is like "laying the foundation for AI", and Meta is like an "architect" who uses multimodal "building materials", MoE "structure" and iRoPE "design" to create a "smart building".

     2. Post-training of Llama 4 model

    After pre-training, Meta further improves the performance, security, and applicability of the model through a carefully designed post-training process. This phase includes multiple steps to ensure that the model performs well on complex tasks.

• Lightweight Supervised Fine-Tuning (SFT)

     Meta uses the Llama model as a "referee" to filter out simple prompts and only retain more difficult examples for fine-tuning. This strategy focuses on complex reasoning tasks and significantly enhances the model's performance in challenging scenarios.

• Online Reinforcement Learning (RL)

     Implement continuous online reinforcement learning, using hard prompts, adaptive filtering, and curriculum design to continuously optimize the model's reasoning, encoding, and conversation capabilities.

• Direct Preference Optimization (DPO)

     After reinforcement learning, a lightweight DPO technique is applied to fine-tune for specific edge cases and response quality. This approach balances the helpfulness and safety of the model, ensuring that the output is both practical and compliant.

• Behemoth Codistillation

     Behemoth acts as a "teacher" model to generate training outputs for Scout and Maverick. Meta introduces an innovative loss function that dynamically balances soft supervision and hard supervision objectives, and significantly improves the performance of the two models through knowledge distillation technology.