Llama 3.1: Our most capable models to date, open source

Breaking Barriers in AI Development

Meta's Commitment to Open AI

• Mark Zuckerberg explains why open source AI is beneficial for developers, Meta, and the world.

Expanding Open Intelligence

• Our latest models now support context lengths up to 128K and eight languages.
• Introducing Llama 3.1 405B, the first frontier-level open source AI model with unmatched capabilities.

Llama 3.1 405B Highlights

• Llama 3.1 405B offers flexibility, control, and top-tier performance that rivals the best closed source models.
• It enables new workflows like synthetic data generation and model distillation.

Building Out the Llama System

• We’re adding more components to work with Llama, including a reference system for developers.
• New tools like Llama Guard 3 and Prompt Guard help ensure security and responsible use.
• We're releasing a draft for the Llama Stack API to make it easier for third-party projects to use Llama models.

Strong Partner Ecosystem

• Over 25 partners, including AWS, NVIDIA, Databricks, Groq, Dell, Azure, Google Cloud, and Snowflake, are offering services from day one.

Try Llama 3.1 405B

• You can test Llama 3.1 405B in the US on WhatsApp or at meta.ai by asking a challenging math or coding question.

Open source language models have often been behind closed ones in terms of capabilities and performance. But that’s changing now.

Meta  introducing Meta Llama 3.1 405B, the world’s largest and most powerful open-source model. With over 300 million downloads of all Llama versions, we’re just getting started.

Introducing Llama 3.1

• First open model to rival top AI models in general knowledge, control, math, tool use, and multilingual translation.
• Aims to boost innovation and offer new opportunities for growth and exploration.
• Expected to inspire new applications like: Creating synthetic data for training smaller models, Refining models on a large scale (model distillation).

Upgraded Versions of 8B and 70B Models

• Support multiple languages.
• Extended context length of 128K.
• Enhanced tool use and reasoning capabilities.
• Ideal for advanced tasks such as: Summarizing long texts, Creating multilingual chatbots, Developing coding assistants.

Developer-Friendly License

• Updated license allows developers to use outputs from Llama models, including the 405B, to enhance other models.

Open Source Commitment

• Models available for download on: llama.meta.com and Hugging Face
• Ready for immediate development on partner platforms.

Model Evaluations

• Llama tested Llama 3.1 on over 150 benchmark datasets in various languages.
• Conducted extensive human evaluations to compare Llama 3.1 with other models in real-world scenarios.
• Results show that Llama 3.1 competes well with top models like GPT-4, GPT-4o, and Claude 3.5 Sonnet.
• Our smaller models also perform well against similar-sized closed and open models.

Model Architecture

1: Training Llama 3.1 405B

• Massive Training Effort: We trained Llama 3.1 405B on over 15 trillion tokens using more than 16,000 H100 GPUs, making it our largest model ever.

2: Optimized Training Process:

• Meta significantly improved our training stack to handle the large scale.
• Meta used a standard decoder-only transformer model with minor tweaks to keep training stable.

3: Iterative Post-Training:

• Each round of training involved supervised fine-tuning and direct preference optimization.
• This helped us create high-quality synthetic data and improve model performance.

4: Enhanced Data Quality:

• Meta improved the quality and quantity of data for both pre- and post-training.
• This involved better pre-processing, curation, quality assurance, and filtering techniques.

5: Model Performance:

• As expected, Llama 3.1 405B outperforms smaller models trained in the same way.
• The 405B model also helps improve the post-training quality of our smaller models.

6: Efficient Inference:

• Meta reduced compute requirements by quantizing our models from 16-bit (BF16) to 8-bit (FP8).
• This allows the model to run on a single server node, supporting large-scale production.

Instruction and Chat Fine-Tuning

Goal: With Llama 3.1 405B, our aim was to make the model more helpful, detailed, and safe when responding to user instructions.

1: Challenges:

• Supporting more capabilities.
• Handling a 128K context window.
• Managing larger model sizes.

2: Post-Training Process:

• We improve the model through several rounds of alignment after initial training.
• Each round includes: Supervised Fine-Tuning (SFT), Rejection Sampling (RS), Direct Preference Optimization (DPO)

3: Synthetic Data Generation:

• Llama 3.1 generate most of our SFT examples using synthetic data.
• Multiple iterations help us produce higher quality synthetic data for all capabilities.
• We use various data processing techniques to filter and improve this data.

4: Balancing Quality:

• Meta ensure the model maintains high quality across all capabilities.
• Meta keep the quality high on short-context benchmarks, even with a 128K context.
• The model remains helpful while adding safety measures.

The Llama System

Vision: Llama models are part of a bigger system designed to work with various components, including external tools. Our goal is to provide developers with a flexible system to create custom solutions that fit their needs. This idea started last year when we began integrating components beyond the core language model.

1: New Components:

• Llama Guard 3: A multilingual safety model.
• Prompt Guard: A filter to prevent prompt injection.
• Sample Applications: These are open source and can be built upon by the community.

2: Collaboration and Standards:

• The current implementation of Llama System components is fragmented.
• Llama is working with industry, startups, and the community to better define the interfaces of these components.
• Llama is are releasing a request for comment on GitHub for "Llama Stack," a set of standardized interfaces for building components like fine-tuning and synthetic data generation.
• Our goal is for these standards to be adopted across the ecosystem, making it easier for different parts to work together.

3: Community Engagement:

• Llama welcome feedback on improving the Llama Stack proposal.
• Meta is  excited to grow the Llama ecosystem and make it easier for developers and platform providers to use.

Openness Drives Innovation

1: Why Open Models Matter:

• Llama model weights are available for download.
• Developers can customize, train on new datasets, and fine-tune the models to fit their needs.
• This approach empowers the broader developer community and maximizes the potential of generative AI.

2: Flexibility and Privacy:

• Developers can run Llama models anywhere: on-premises, in the cloud, or locally on a laptop.
• No need to share data with Meta, ensuring privacy.

3: Cost-Effective:

• Llama models offer some of the lowest costs per token in the industry, according to Artificial Analysis.
• Mark Zuckerberg emphasizes that open source makes AI benefits accessible to more people, distributing power and opportunities more evenly.

4: Community Success Stories:

• AI Study Buddy: Built with Llama and used in WhatsApp and Messenger.
• Medical LLM: Tailored to assist clinical decision-making.
• Healthcare Startup in Brazil: Helps organize and communicate patient information securely.

5: Encouraging Future Innovation:

• We look forward to seeing what developers will create with our latest models, leveraging the advantages of open source.

Building with Llama 3.1 405B

Using a model as large as Llama 3.1 405B can be challenging for the average developer. It’s very powerful but needs significant computing power and expertise. We’ve listened to the community and understand that generative AI development involves more than just using models. Here’s how we want to help you get the most out of the 405B:

• Real-time and batch inference
• Supervised fine-tuning
• Evaluating models for specific applications
• Continual pre-training
• Retrieval-Augmented Generation (RAG)
• Function calling
• Synthetic data generation

1: The Llama Ecosystem:

• Developers can start using all the advanced features of the 405B model right away.
• Explore advanced workflows like easy synthetic data generation, model distillation, and seamless RAG with partners like AWS, NVIDIA, and Databricks.
• Groq has optimized low-latency inference for cloud deployments, and Dell has done similar optimizations for on-premises systems.

2: Community Support:

• We’ve worked with projects like vLLM, TensorRT, and PyTorch to ensure support from day one, making it easier for the community to deploy in production.

3: Encouraging Innovation:

• We hope the release of the 405B model will inspire innovation, making it easier to use and fine-tune large models, and drive the next wave of research in model distillation.

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