Llama 3: Open-Source Multimodal LLM

Updated 2 July 2025

Llama 3 is a family of large language models built on dense Transformer architectures with multimodal extensions for advanced multilingual and reasoning tasks.
It scales efficiently using empirical laws, featuring variants from 8B to 405B parameters that achieve competitive performance across diverse benchmarks.
Its open release, modular adapters, and robust safety tools enable practical applications in vision, speech, video, and code generation.

Llama 3 is a family of LLMs introduced by Meta AI, notable for its substantial architectural scale, open release, and capabilities that encompass multilingual understanding, code generation, long-context reasoning, tool usage, and compositional extension to vision, video, and speech modalities. The Llama 3 suite establishes new empirical baselines across foundation model research, offering models that approach or match the performance of leading proprietary systems on a broad spectrum of benchmarks, while providing open access to pre-trained and post-trained checkpoints for research and application development.

1. Model Architecture and Scaling Laws

Llama 3 models are based on a dense Transformer architecture, following the conventions of previous Llama generations with optimizations for very large-scale learning (The Llama 3 Herd of Models, 31 Jul 2024). The flagship model, Llama 3 405B, is defined by:

405 billion parameters across 126 layers
Token embedding dimension: 16,384
Feed-forward network (FFN) dimension: 53,248
Attention heads: 128 (grouped into 8 key-value heads for efficiency)
Vocabulary size: 128,000 tokens, with explicit expansion for non-English capacity
Positional encoding: Rotary Position Embedding (RoPE) with base θ = 500,000 to accommodate up to 128K-token context windows
Activation: SwiGLU
Context window: up to 128,000 tokens

Smaller variants include 8B and 70B parameter models, sharing the fundamental design principles but scaled appropriately in width and depth.

A distinctive feature of Llama 3 is its adherence to empirical scaling laws to balance compute, data, and model size: the optimal number of pretraining tokens $N^*(C)$ for a given compute budget $C$ obeys $N^*(C) = A C^\alpha$ with observed $\alpha \approx 0.53$ and $A \approx 0.29$ , guiding efficient allocation of training resources.

2. Multilingual, Reasoning, and Tool Use Capabilities

Llama 3 is extensively multilingual, with pretraining including 15T tokens and at least 8% dedicated to 176 languages. The tokenizer incorporates an extra 28K tokens tailored for non-English corpora, further enhancing downstream linguistic efficiency.

The models natively support:

Coding: High accuracy on HumanEval, MBPP, MultiPL-E (Python/other languages), improved by domain-specific pretraining and execution-based feedback.
Reasoning: Advanced performance on logic and math datasets (e.g., GSM8K, MATH, ARC, MMLU-Pro), with explicit support for chain-of-thought reasoning and reward modeling during training.
Tool use: Direct integration with API calls, code interpreters, web search, and mathematical engines (e.g., Wolfram Alpha), trained via mixed human- and synthetic demonstration datasets.
Long context: Zero-shot retrieval, summarization, and reasoning with inputs well beyond 100K tokens, supported by continued pretraining stages and synthetic data designed for context scaling.

These capabilities position Llama 3 at or near state-of-the-art, with empirical results matching GPT-4 on major leaderboard tasks in multilingual understanding and code generation.

3. Context Extension Methods and Robustness

Llama 3’s design facilitates efficient extension to significantly longer context windows, as demonstrated in experimental work extending Llama-3-8B-Instruct from 8,192 to 80,000 tokens using QLoRA-based fine-tuning (Extending Llama-3's Context Ten-Fold Overnight, 30 Apr 2024). This process relies on:

Synthesis of 3,500 long-context training samples via GPT-4, comprising question answering and summarization tasks with contexts up to 80K tokens
Data mixing with general-domain and instruction-tuning samples to prevent catastrophic forgetting
Adjusting RoPE base from 500,000 to 200 million, enabling high-fidelity position encoding over the full window

This super-efficient recipe (8 hours on a single 8xA800 node) achieves perfect retrieval performance up to and beyond 80K tokens on "needle-in-a-haystack" and topic retrieval tasks and preserves short-context capabilities with only minor degradation (MMLU drop from 65.91 to 64.44). The extrapolation potential demonstrated implies that Llama 3’s context window can scale even further with computational investment.

4. Modularity: Vision, Speech, Video, and Tool Use

Llama 3 supports compositional extension to multimodal tasks via modular adapters rather than joint retraining (The Llama 3 Herd of Models, 31 Jul 2024). The implemented strategy includes:

Vision: Integration of a ViT-H/14 encoder with cross-attention adapters (fused into every fourth LLM layer). Fine-tuning proceeds on 6B+ image-text pairs, with adapters trained via contrastive/fusion losses.
Video: Video adapters aggregate temporal feature representations from sampled frames, extending image tokens for cross-frame reasoning.
Speech: A large Conformer encoder trained on 15M hours in >30 languages, mapped into the text space via a convolution-rotary transformer stack, enabling automatic speech recognition (ASR), speech translation (AST), and multi-turn conversational tasks.
Evaluation: On benchmarks like MMMU, VQAv2, PerceptionTest, MLS, and LibriSpeech, Llama 3’s compositional models match or outperform previous state-of-the-art vision-language and speech-LLMs.

These adapters allow for multimodal capability while preserving the core LLM parameters, ensuring both stability and efficiency.

5. Safety Mechanisms and Public Release

Llama 3 is released under the Llama 3 Community License, with pre-trained and post-trained model weights made publicly available for the 8B, 70B, and 405B variants. Accompanying system-level safety tools include:

Llama Guard 3: A classifier for input/output moderation, targeting 13 harm categories and aware of code/tool misuse scenarios. Quantized variants and tuning tools are provided.
PromptGuard/CodeShield: Auxiliary classifiers to detect prompt injections, insecure code, and other adversarial manipulations.
Red/blue teaming: Systematic adversarial evaluation procedures, with iterative improvements to data and alignment strategies.

Public release of all core models, safety classifiers, and evaluation pipelines democratizes access and accelerates open research while providing flexible paths for applied model adaptation, auditing, and deployment.

6. Applications and Community Impact

Llama 3 has been leveraged in diverse domains:

Vision-Language Data Curation: Used as the linguistic engine for recaptioning 1.3B web images (What If We Recaption Billions of Web Images with LLaMA-3?, 12 Jun 2024), Llama 3 (via LLaVA-1.5) improved caption richness (avg. length up from 10 to 49 words), leading to novel datasets that measurably advanced the zero-shot retrieval and generation performance of CLIP and Diffusion Transformers.
Fine-Tuning for Privacy-Preserving Medical Text Generation: Locally fine-tuned for institution-specific letter generation in radiation oncology, Llama-3-8B with QLoRA outperformed Llama-2-13B and matched manual style conventions, demonstrating clinical acceptability and data privacy compliance (Fine-Tuning a Local LLaMA-3 Large Language Model for Automated Privacy-Preserving Physician Letter Generation in Radiation Oncology, 20 Aug 2024).
Model Editing and Custom Knowledge Injection: Studies on in-place fact editing techniques (ROME, MEMIT, EMMET) reveal that careful, sequential modifications (preferably at layer 1) preserve accuracy and mitigate negative side effects, establishing best practices for mass knowledge updates (Is Bigger Edit Batch Size Always Better? -- An Empirical Study on Model Editing with Llama-3, 1 May 2024).
Security Considerations: Llama 3’s open weights, while critical for transparency, permit rapid removal of safety alignment using parameter-efficient fine-tuning or activation editing, highlighting an unresolved challenge for open-weight LLM safety (Badllama 3: removing safety finetuning from Llama 3 in minutes, 1 Jul 2024).

A broad spectrum of evaluation data and open-source resources, including code, model checkpoints, training pipelines, and interpretability tools, underpin Llama 3’s position as a foundation for ongoing research, applied system building, and theoretical investigation.

7. Future Directions and Challenges

The open, modular, and extensible architecture of Llama 3 sets the stage for future research into efficient scaling laws, instruction-tuning, compositional multimodality, safety and red-teaming methods, and robust, high-context adaptation. The need for more deeply embedded safety mechanisms, intelligent batch editing protocols, scalable and language-specific adapters, and further reduction of computational barriers remains (The Llama 3 Herd of Models, 31 Jul 2024, Extending Llama-3's Context Ten-Fold Overnight, 30 Apr 2024, Badllama 3: removing safety finetuning from Llama 3 in minutes, 1 Jul 2024). The demonstrated capacity to adapt, extend, and specialize Llama 3 for domain-specific, privacy-sensitive, and multimodal applications suggests wide relevance for both academic and industrial settings, with ongoing community contributions expected to drive further advances.

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References (6)

The Llama 3 Herd of Models (2024)

Extending Llama-3's Context Ten-Fold Overnight (2024)

What If We Recaption Billions of Web Images with LLaMA-3? (2024)

Fine-Tuning a Local LLaMA-3 Large Language Model for Automated Privacy-Preserving Physician Letter Generation in Radiation Oncology (2024)

Is Bigger Edit Batch Size Always Better? -- An Empirical Study on Model Editing with Llama-3 (2024)

Badllama 3: removing safety finetuning from Llama 3 in minutes (2024)