Mistral AI's Open Source Initiative | Arthur Mensch, Mistral AI | #aiPULSE 2023
TLDRThe CEO of M delivers an insightful presentation on the company's mission to develop foundational AI models and make them accessible to real-world application creators. M focuses on training efficient generative AI models using a fraction of the compute resources of larger companies, with a commitment to open-source to accelerate technology adoption. The company's 7B model, designed for low latency and local deployment, outperforms larger models and is being adopted across various applications. M's future plans include refining inference efficiency, developing hosted solutions, and creating specialized models, all while actively recruiting talent to drive innovation in AI and business development.
Takeaways
- 🚀 The CEO of M aims to develop foundational AI models for real-world applications and make them accessible to developers.
- 🌐 M's approach is to release open-weight models and open-source software to accelerate technology adoption.
- 🤖 The company's first release, M 7B, is a 7 billion parameter model that can run on smartphones and MacBook Pros.
- 📈 M 7B outperforms larger models like Llama 2 on benchmarks, showcasing efficiency in training and inference.
- 🔍 M's team has recreated the entire stack needed to train large language models from scratch, leveraging existing clusters.
- 💡 The company focuses on making models efficient at inference, reducing memory pressure, and enabling low-latency applications.
- 📚 M's architecture, inspired by long-former, reduces memory usage by limiting attention to the previous 4K tokens.
- 🌟 M 7B's open-source nature has led to its adoption in various projects and as a replacement for closed-source APIs.
- 📈 The company is growing rapidly, with a focus on recruiting AI scientists, engineers, and business professionals.
- 🔮 Future plans include new open-source models, a hosted solution, and optimized verticalized models for various industries.
- 🌍 M is committed to empowering open-source projects and providing independence from proprietary solutions, especially for government and regulated industries.
Q & A
What is the primary goal of the CEO's company, Mr?
-The primary goal of Mr is to develop frontier models that are foundational to the AI revolution and put them in the hands of real-world application makers.
How does the CEO plan to achieve the company's ambition?
-The CEO plans to achieve the ambition by enabling developers to create differentiated applications by providing deep access to AI models through open-weight models and open software.
What is the significance of open-source models in the company's strategy?
-Open-source models are significant because they allow for deep access to technology, enabling developers to make proprietary choices, add editorial content, and integrate reward modeling into the models, thus creating differentiated applications.
How did the company manage to train models efficiently with limited resources?
-The company managed to train models efficiently by using a tiny fraction of the compute of their previous employees, showing that generative AI can be trained more efficiently than observed in large companies.
What is the size of the MRAL 7B model, and why is it significant?
-The MRAL 7B model has 7 billion parameters, which is small enough to run on a smartphone. This size is significant because it allows for low-latency applications and local deployment, making it useful for developers.
How does the MRAL 7B model compare to other models in terms of performance?
-The MRAL 7B model outperforms the Llama 2, which was the best open-source model at the time, on all benchmarks, despite being almost two times smaller.
What is the company's approach to reducing memory pressure in large language models?
-The company proposed a new architecture inspired by Longformer, which reduces memory pressure by having each token attend only to the previous 4K tokens instead of all 16K tokens, thus reducing the memory required for key-value storage.
How does the company plan to address the challenges of deploying models on edge devices like smartphones?
-The company plans to address these challenges by making models smaller than 7B and improving software to optimize memory usage. They also expect advancements in dynamic memory technology to help with this issue.
What are the company's future plans for open-source models and business development?
-The company plans to release new open-source models, work on a hosted solution, develop a self-deployed platform, and create optimized, verticalized models. They are also actively recruiting AI scientists, engineers, and business professionals.
How does the company view the role of the community in contributing to AI development?
-The company encourages community contributions, derivative work, and creative ideas. They believe that the community can contribute new data sets, ideas, and paradigms to improve models and build new capabilities.
Outlines
📢 CEO's Introduction and Company Overview
The CEO introduces himself and provides an overview of the company's activities over the past six months. He emphasizes that the company's focus is on developing foundational AI models and making them accessible to real-world application creators. The CEO highlights the importance of open-source models and software for deep technology adoption and announces the company's first release at the end of September.
🚀 MRAL 7B Model: Small, Efficient, and Open Source
The CEO discusses the MRAL 7B model, which has 7 billion parameters, making it small enough to run on a smartphone. He mentions the community's efforts to run it on an iPhone 15 and the model's competitive performance compared to larger models. The CEO explains how the company's understanding of scaling laws allowed them to create a smaller, yet powerful model, and the benefits of open-sourcing it, including its use in various applications and projects.
🔍 Addressing Memory Pressure in Large Language Models
The CEO addresses the challenge of memory pressure in large language models, particularly the high memory usage during the attention mapping process. He introduces a new architecture inspired by an older model, Longformer, which reduces memory pressure by limiting the number of tokens each token attends to. This innovation makes the MRAL 7B model not only small but also memory-efficient, which is crucial for memory-bound workloads.
🌐 MRAL 7B's Availability and Community Impact
The CEO talks about the widespread availability of the MRAL 7B model across various platforms and its adoption by companies and open-source projects. He emphasizes the model's permissiveness under the Apache 2.0 license, which allows for full independence from proprietary solutions. The CEO also mentions the potential for private, on-device deployment, which is beneficial for privacy and low latency.
🔥 Community Contributions and Future Plans
The CEO invites community contributions and derivative work, highlighting the importance of creativity and new ideas. He mentions the company's progress in multilingual capabilities and the potential for domain adaptation. The CEO also discusses future plans, including new open-source models, a hosted solution, and optimized, verticalized models. He emphasizes the company's growth and recruitment efforts, particularly in the AI and business sectors.
🤖 Domain Adaptation and On-Device Deployment Challenges
The CEO addresses questions about domain adaptation, explaining that pre-training covers a broad domain, allowing for easy modification with a few examples. He also discusses the challenges of deploying models on devices, particularly the memory capacity limitations of smartphones. The CEO suggests that future improvements may come from software optimization and dynamic memory technologies.
📈 Research and Development Focus
The CEO discusses the company's research and development focus, emphasizing the importance of practical application over academic publishing. He mentions the company's interest in working with hardware providers to improve compute efficiency and data utilization. The CEO also addresses the potential for smaller models with similar performance to the 7B model and the possibilities of larger models that could offer significant advancements.
Mindmap
Keywords
💡AI Revolution
💡Frontier Models
💡Low Latency Applications
💡Open Source
💡Generative AI
💡Model Scaling
💡Mral 7B
💡Inference Efficiency
💡KV Cache
💡Community Contribution
💡On-Device Deployment
Highlights
The CEO of M provides an overview of the company's activities over the last six months.
M's ambition is to develop foundational models for AI applications and make them accessible to real-world application makers.
M focuses on enabling developers to create differentiated applications by providing deep access to AI models through open-source models and software.
The company has shown that smaller models can be trained efficiently and are competitive for various tasks.
M released an open-source model, M 7B, which is small enough to run on smartphones and has competitive performance.
M 7B has been adopted by the community and is being used on iPhones and MacBook Pros.
The company has a team of 18-20 people and is growing rapidly.
M has recreated the entire stack needed to train large language models from scratch.
The company uses a new architecture, inspired by long-former, to reduce memory pressure and improve efficiency.
M 7B is available on major cloud platforms and is used by companies as a replacement for more expensive APIs.
The company is working on a hosted solution and a self-deployed platform for businesses.
M is actively recruiting AI scientists, engineers, and business professionals.
The CEO emphasizes the importance of community contributions and derivative work for the company's progress.
M is exploring new paradigms to improve reasoning, memory capacity, and training efficiency.
The company is working on optimizing verticalized models for specific industries.
M 7B's success is attributed to its permissive license, small size, and low latency, making it suitable for edge devices.
The CEO discusses the challenges of deploying large language models on devices due to memory capacity limitations.
M is considering the release of research papers on neural network architecture improvements.
The company is looking into dynamic memory solutions to improve model deployment on edge devices.
The CEO addresses the potential for smaller models with the same performance as M 7B and the possibility of creating larger models for more advanced capabilities.
