Why is Llama 3.1 Such a Big deal?
Source: tai_blog

Note: this post was written by 3 ML & AI engineers behind the High Learning Rate newsletter.   Good morning everyone!   As you probably already know  earlier this week  Meta released Llama 3.1  marking a significant milestone in AI  notably for its open-source nature and impressive capabilities (it is the first-ever SOTA open-source flagship LLM).   In this iteration  we wanted to cover this news a bit differently than all content weve seen online  focusing specifically on the types of questions managers and others in leadership roles may want or need to know.   So here it is the 10 (+1) questions you need to know the answers:   1  Why is Llama 3.1 such a big deal?   Llama 3.1 is a game-changing 405 billion parameter open-source AI model that supports multilingualism (fun fact  this was an emerging ability from large datasets and works with surprisingly little other language data!)  coding  reasoning  and tool usage  matching or surpassing closed-source models like GPT-4 (0125) in various benchmarks. Its open-source nature democratizes access to cutting-edge AI technology (following the steps of GPT-2  GPT-Neo  GPT-J)  enabling businesses and developers to leverage state-of-the-art language models without vendor lock-in  while its competitive performance and extensive functionality make it highly attractive for researchers and businesses looking to fine-tune and deploy advanced AI at lower costs.   2  How does the open-source nature of Llama 3.1 benefit compared to closed-source models  and what are the long-term strategic benefits of adopting an open-source AI model like Llama 3.1?   The open-source nature of Llama 3.1 allows for greater customization  transparency  and community-driven improvements  providing organizations the flexibility to fine-tune models to their specific needs without vendor lock-in. Long-term strategic benefits include reduced dependency on single vendors (you dont want to be dependent on OpenAI)  potential cost savings (eg. by hosting a smaller fine-tuned version of it yourself vs. cost per token)  better explainability (vs. an API)  control over server and inference speed  and fostering innovation through community contributions  ultimately leading to broader economic and societal benefits.   3  What partnerships and integrations with public cloud providers (e.g.  Together AI  Groq  Fireworks  AWS  Azure) are available to support our deployment of Llama 3.1  and how can my team leverage Metas partnerships with cloud providers to experiment with and implement Llama 3?   Meta has partnered with major cloud providers like AWS  Azure  Google Cloud  and Oracle to make Llama 3.1 easily accessible  offering full suites of services for developers to fine-tune and deploy Llama models. Additionally  up-and-coming LLM providers such as Together AI  FireworksAI  and Groq offer low prices and fast token processing speeds  providing teams with options to experiment and implement Llama 3.1 without significant infrastructure investment while considering cost-effectiveness. Fun fact again: Meta gave Groq access to a weight-randomized version of the Llama 405B model before releasing it to allow them to prepare and optimize the distribution of the model.   4  What kind of infrastructure and resources are necessary to deploy and run Llama 3.1 models  especially the 405 billion parameter version (also the 70B  8B)?   For the 405B parameter version  substantial GPU resources are required  up to 16K H100 GPUs for training  with 80GB HBM3 memory each  connected via NVLink within servers equipped with eight GPUs and two CPUs. Smaller versions (70B  8B) have lower resource requirements  using Nvidia Quantum2 InfiniBand fabric with 400 Gbps interconnects between GPUs  making them more accessible for many organizations  while storage requirements include a distributed file system offering up to 240 PB of storage with a peak throughput of 7 TB/s. Recently  Elie Bakouch (known for training LLMs on Hugging Face) shared that one can fine-tune Llama 3 405B using 8 H100 GPUs.   5  What specific advantages does Llama 3.1 offer in terms of performance  cost  and potential cost savings compared to closed models like GPT-4o?   Llama 3.1 offers significant advantages in performance  matching or surpassing GPT-4 in many benchmarks  while being more economical to run  with inference operations costing roughly 50% less than comparable closed models like GPT-4o according to an interview with Mark Zuckerberg. The open-source nature allows for more efficient customization and fine-tuning  potentially leading to better performance on specific tasks at a lower cost compared to closed models  while the ability to run the model on-premises or on preferred cloud providers gives organizations more control over their infrastructure costs.   6  What kind of skills/team does it take to work with Llama models effectively for our specific use cases?   a  For Fine-tuning  training  distilling   A team needs expertise in machine learning  particularly in natural language processing and transformer architectures. Skills in data preprocessing  model optimization  and distributed computing are crucial. Knowledge of PyTorch and experience with large-scale model training is essential. The team should include ML engineers  ML ops specialists  and developers.   b  For Deploying/using out-of-the-box   For deploying and using Llama models out-of-the-box  the necessary skills shift towards software development and cloud services expertise. Familiarity with cloud computing platforms such as AWS  GCP  or Azure  and knowledge of containerization tools like Docker  are important for setting up and maintaining the model infrastructure. Understanding model inference APIs and optimization techniques for efficient deployment is also essential. Having domain expertise to align the models output with specific business needs will ensure that the deployments are both effective and relevant to your organizations goals. DevOps professionals or AI engineers with an interest in practical AI applications will be well-suited for this task.   High Learning Rate U+007C Louis-Franois Bouchard U+007C SubstackReal-world solutions for real-world problems. Leverage AI's potential with insider tips from specialists in the field highlearningrate.substack.com   7  What kind of support and tools are available for fine-tuning  distilling  and post-training Llama 3.1 models to fit our specific needs?   Meta and its partners are working on comprehensive support for fine-tuning  distilling  and post-training Llama 3.1 models  including services from Amazon  Databricks  and NVIDIA for model customization. Companies like Scale.AI  Dell  Deloitte  and others are ready to help enterprises adopt Llama and train custom models with their own data. Techniques like supervised fine-tuning (SFT)  rejection sampling (RS)  direct preference optimization (DPO)  and QLORA + FSDP (available in the TRL Hugging Face library) are used for model alignment  with tools for efficient deployment such as low-latency  low-cost inference servers provided by innovators like Groq. For the 405B model  a minimum node of 8xH100 GPUs is recommended for fine-tuning.   8  What are the key benefits of synthetic data generation  and how can our organization leverage this for better AI models? What are the potential benefits and risks?   Synthetic data generation offers significant benefits  including lower costs  scalability  and the ability to generate large quantities of high-quality data for AI model training without constraints related to annotator expertise. Organizations can leverage synthetic data to improve model performance through methods like backtranslation for documentation and multilingual capabilities  enhancing both the breadth and quality of training datasets. However  risks include the potential propagation of incorrect data or biases  necessitating robust quality control and verification processes to ensure data fidelity and model reliability.   9  How should we approach evaluating and benchmarking with Llama 3.1 to ensure they meet our specific business needs?   To evaluate Llama 3.1  you would do the same as with other models. You should conduct a comparative analysis against other models of similar size across diverse tasks  using well-established academic benchmarks and extensive human evaluations. Additionally  developing custom benchmarks and human evaluations relevant to specific business use cases allows for assessing performance on company-specific tasks and data. Ensuring data decontamination and aligning evaluation methods with specific business needs will help guarantee that Llama 3.1 meets performance and functional requirements.   20  What are the practical applications of the 405 billion parameter model with a 128K token context window  and how can this benefit our business process?   The 405 billion parameter model  with a 128K token context window  allows for the execution of tasks such as complex reasoning  long document summarization  and extensive context-dependent applications. One other key benefit is the ability to distill this large model into smaller models (8B or 70B)  as the new license explicitly permits this compared to OpenAI models. We expect this will be the main usage of the larger model as it is hard for individuals and small companies to host it themselves.   11  What future developments and features can we expect from Llama models  particularly in multimodal capabilities  and how should we prepare for these advancements?   Future Llama models are expected to incorporate advanced multimodal capabilities  including image  video  and speech understanding. We believe organizations should prepare by investing in infrastructure that supports multimodal data integration; staff should brainstorm how to leverage these advanced features and consider how these capabilities could enhance their existing AI applications. Additionally  the open-source community will likely optimize this generation of models  making them faster during inference and reducing compute requirements  leading to smarter and more efficient AI systems.   And thats it! We hope youve enjoyed this short piece on the most relevant questions for managers. We share more insights in our weekly newsletters if you like them.   Thank you for reading    Louis-Franois  Franois  and Omar