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Microsoft Interactive AI Agent Foundation Model steps towards AGI

Microsoft Interactive AI Agent Foundation Model

In addition to OpenAI announcing it’s new focus on developing AI Agents. Microsoft has introduced an innovative AI Agent Foundation Model, which is seen as a significant step toward Artificial General Intelligence (AGI). This model is designed to incorporate various human-like cognitive abilities and skills, such as decision-making, perception, memory, motor skills, language processing, and communication. The model’s versatility is demonstrated across different domains, including robotics, gaming AI, and healthcare, showcasing its ability to generate contextually relevant outputs.

The advanced Microsoft AI Foundation model could be a significant stride toward the creation of Artificial General Intelligence (AGI). This new AI, known as the AI Agent Foundation Model, is designed to replicate human cognitive functions such as decision-making, perception, memory, language processing, and communication. It’s a substantial development for Microsoft, aiming to create AI systems that can operate across a wide array of tasks and sectors, including robotics, gaming AI, and healthcare.

At the heart of this new model is a training approach that allows the AI to learn from different domains, datasets, and tasks. This flexibility means the AI isn’t limited to one specific area but is robust enough to handle various challenges. The model combines sophisticated pre-trained methods, including image recognition techniques, text comprehension and generation, and the ability to predict future events.

Microsoft AI Agent Foundation Model

In real-world scenarios, the AI Agent Foundation Model has undergone testing in several fields. In robotics, it has shown more human-like movements through its advanced motor skills and perception. In the realm of gaming AI, it has led to more realistic and engaging gameplay by enhancing decision-making and action prediction. In healthcare, the model’s advanced data processing and communication abilities could potentially assist in diagnoses and treatment planning.

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Microsoft explains a little more about its Interactive Agent Foundation Model research paper :

“The development of artificial intelligence systems is transitioning from creating static, task-specific models to dynamic, agent-based systems capable of performing well in a wide range of applications. We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents across a wide range of domains, datasets, and tasks. Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction, enabling a versatile and adaptable AI framework.

We demonstrate the performance of our framework across three separate domains — Robotics, Gaming AI, and Healthcare. Our model demonstrates its ability to generate meaningful and contextually relevant outputs in each area. The strength of our approach lies in its generality, leveraging a variety of data sources such as robotics sequences, gameplay data, large-scale video datasets, and textual information for effective multimodal and multi-task learning. Our approach provides a promising avenue for developing generalist, action-taking, multimodal systems.”

Multimodal AI Agents

What sets this model apart is its ability to learn from multiple modes and tasks. It uses data from different sources, such as robotic sequences, gameplay data, video databases, and textual content. This diverse learning environment improves the model’s understanding of the world and its interactions within it.

The scalability and adaptability of the AI Agent Foundation Model are also key features. Instead of relying on several specialized AI systems, this model can be fine-tuned to perform a variety of functions. This approach is more efficient than creating separate models for each specific task. Training the model involves the use of synthetic data, which can be generated by AI models like GPT-4. This approach is not only efficient but also addresses privacy concerns by reducing the reliance on sensitive or personal real-world data.

One of the most exciting prospects of the AI Agent Foundation Model is its ability to generalize learning across different domains. This generalization indicates that the model can apply its knowledge to new and unfamiliar tasks, suggesting a future where AI can seamlessly integrate into various industries, enhancing productivity and driving innovation.

Microsoft’s AI Agent Foundation Model research represents a significant advancement in the quest for AGI. Its innovative training methods, the integration of pre-trained strategies, and the focus on multitask and multimodal learning position it as a versatile and powerful tool for the future of AI in numerous fields.

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How to select the right AI model for your needs

how to choose the right AI foundation model

When embarking on a new generative AI project, one of the most crucial decisions you’ll face is selecting the appropriate AI foundation model. This choice is far from trivial; it’s a decision that will have a significant impact on the success of your project. The model you choose must not only be capable of meeting your specific needs but also fit within your budget and align with your organization’s risk management strategies.

To start, it’s essential to have a clear understanding of what you want to achieve with your AI project. Whether you’re looking to create lifelike images, generate text, or produce synthetic speech, the nature of your task will guide you towards the right type of model. It’s important to consider the complexity of the task and the level of quality you expect from the output. Having a clear goal in mind is the first step towards making an informed decision.

Once you’ve defined your use case, the next step is to explore the various AI foundation models available. These models come in different sizes and are designed to handle various tasks. Some are specialized for specific functions, while others are more versatile. It’s important to include models that have been successful in tasks similar to yours in your consideration set.

Selecting the right AI Foundation Model

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After identifying potential models, you need to examine their characteristics closely. Larger models may be able to handle more complex tasks, but they also come with higher costs and greater computational requirements. You’ll need to weigh their performance capabilities against your budget constraints. It’s also important to consider the risks associated with each model, such as potential biases or data privacy concerns.

The next step is to test the models you’ve shortlisted to see how they perform with your specific data and within your operational context. It’s crucial that the model you choose can be integrated smoothly into your existing systems and workflows. This practical testing phase is vital to ensure that the model you select will work harmoniously with your operations.

During the testing phase, you should focus on evaluating the accuracy, reliability, and processing speed of each model. Accuracy is critical for the credibility of the output, while reliability ensures consistent performance. Processing speed is especially important for applications where time is of the essence. These performance metrics will help you narrow down your choices.

Another important consideration is how you plan to deploy your chosen model. You’ll need to decide whether to use public cloud services, which offer scalability and accessibility, or opt for on-premise deployment, which provides more control and security. The decision will largely depend on the nature of your application, especially if it involves handling sensitive data.

How to choose the correct AI model for your business

Choosing the right AI foundation model is a multifaceted process that involves understanding your project’s specific requirements, evaluating the capabilities of various models, and considering the operational context in which the model will be deployed. This guide synthesizes the provided reference material and integrates additional insights to offer a structured approach to selecting an AI foundation model.

1. Define Your Project Goals and Use Case

The first step in selecting an AI foundation model is to have a clear understanding of what you aim to achieve with your project. Whether your objective is to generate text, create images, or produce synthetic speech, the nature of your task will significantly influence the type of model that best suits your needs. Consider the complexity of the task and the level of output quality you require. A well-defined goal will serve as a guiding light throughout the selection process.

2. Identify Model Options

Begin by exploring the various AI foundation models available, paying attention to models that have demonstrated success in tasks similar to yours. Foundation models vary greatly in size, specialization, and versatility. Some models are designed with a focus on specific functions, while others offer more general capabilities. This exploration phase should include a review of model documentation, such as model cards, which provide essential information on the model’s training data, architecture, and intended use cases.

3. Evaluate Model Characteristics

After identifying potential models, assess their characteristics in detail. This evaluation should consider the model’s size, as larger models often handle complex tasks more effectively but come with higher computational costs and requirements. Key factors to evaluate include:

  • Performance capabilities: How well does the model perform tasks similar to yours?
  • Costs: Both in terms of computational resources and financial expenses.
  • Risks: Including potential biases, data privacy concerns, and ethical considerations.
  • Deployment options: Whether the model supports deployment in cloud environments, on-premise, or both, depending on your needs for control and security.

4. Conduct Practical Testing

Testing the models with your specific data and in your operational context is crucial. This step ensures that the chosen model can be integrated into your existing systems and workflows seamlessly. During testing, focus on evaluating the model’s accuracy, reliability, and processing speed. These metrics are vital for determining the model’s practicality in your use case.

5. Deployment Considerations

Decide on the deployment method that best suits your project. Cloud services offer scalability and ease of access, while on-premise deployment provides more control over security and data privacy. The choice here will largely depend on the nature of your application, especially if it involves sensitive data. Also, consider the flexibility and scalability of the deployment option to accommodate future growth or changes in requirements.

6. Use a Multi-Model Strategy if Necessary

For organizations with a range of different use cases, a single model might not be sufficient. In such cases, a multi-model strategy can be beneficial. This approach allows you to leverage the strengths of various models for different tasks, providing a more flexible and robust solution.

Choosing the right AI foundation model is a complex process that requires a careful analysis of your project’s needs and a thorough examination of the potential models’ characteristics and performance. By following a structured approach, you can select a model that not only meets your current requirements but also positions you well for future developments in the fast-evolving field of generative AI. This decision is not just about solving a current problem; it’s about setting up your project for long-term success in an area that continues to grow and change at a rapid pace.

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Eagle-7B open-source AI model uses RWKV-v5 architecture

Eagle-7B open-source AI model uses RWKV-v5 architecture

A new open source AI model has emerged that could reshape the way we think about language processing. The Eagle-7B model, a brainchild of RWKV and supported by the Linux Foundation, is making waves with its unique approach to handling language. Unlike the Transformer models that currently dominate the field, Eagle-7B is built on a recurrent neural network (RNN) framework, specifically the RWKV-v5 architecture. This model is not just another iteration in AI technology; it’s a step forward that promises to make language processing faster and more cost-effective.

One of the most striking aspects of Eagle-7B is its commitment to energy efficiency. In a world where the environmental impact of technology is under scrutiny, Eagle-7B stands out for its low energy consumption during training. This makes it one of the most eco-friendly options among large language models (LLMs), a critical consideration for sustainable development in AI.

But Eagle-7B’s prowess doesn’t stop at being green. It’s also a polyglot’s dream, trained on an extensive dataset that includes over 1.1 trillion tokens across more than 100 languages. This extensive training has equipped Eagle-7B to handle multilingual tasks with ease, often performing on par with or even better than much larger models like Falcon 1.5 trillion and Llama 2 trillion.

Eagle-7B – RWKV-v5

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The technical innovation of Eagle-7B doesn’t end with its linguistic abilities. The model’s hybrid architecture, which combines RNNs with temporal convolutional networks (TCNs), brings a host of benefits. Users can expect faster inference times, less memory usage, and the ability to process sequences of indefinite length. These features make Eagle-7B not just a theoretical marvel but a practical tool that can be applied to a wide range of real-world scenarios.

Accessibility is another cornerstone of the Eagle-7B model. Thanks to its open-source licensing under Apache 2, the model fosters collaboration within the AI community, encouraging researchers and developers to build upon its foundation. Eagle-7B is readily available on platforms like Hugging Face, which means integrating it into your projects is a straightforward process.

Features of the Eagle-7B  AI model include :

  • Built on the RWKV-v5 architecture
    (a linear transformer with 10-100x+ lower inference cost)
  • Ranks as the world’s greenest 7B model (per token)
  • Trained on 1.1 Trillion Tokens across 100+ languages
  • Outperforms all 7B class models in multi-lingual benchmarks
  • Approaches Falcon (1.5T), LLaMA2 (2T), Mistral (>2T?) level of performance in English evals
  • Trade blows with MPT-7B (1T) in English evals
  • All while being an “Attention-Free Transformer”
  • Is a foundation model, with a very small instruct tune – further fine-tuning is required for various use cases!
  • We are releasing RWKV-v5 Eagle 7B, licensed as Apache 2.0 license, under the Linux Foundation, and can be used personally or commercially without restrictions
  • Download from Huggingface, and use it anywhere (even locally)
  • Use our reference pip inference package, or any other community inference options (Desktop App, RWKV.cpp, etc)
  • Fine-tune using our Infctx trainer

d continuous performance improvements, ensuring that it remains adaptable and relevant for various applications. Its scalability is a testament to its potential, as it can be integrated into larger and more complex systems, opening up a world of possibilities for future advancements.

The launch of Eagle-7B marks a significant moment in the development of neural networks and AI. It challenges the prevailing Transformer-based models and breathes new life into the potential of RNNs. This model shows that with the right data and training, RNNs can achieve top-tier performance.

Eagle-7B is more than just a new tool in the AI arsenal; it represents the ongoing quest for innovation within the field of neural networks. With its unique combination of RNN and TCN technology, dedication to energy efficiency, multilingual capabilities, and open-source ethos, Eagle-7B is set to play a pivotal role in the AI landscape. As we continue to explore and expand the boundaries of AI technology, keep an eye on how Eagle-7B transforms the standards of language processing.

Image Credit : RWKV

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Locally run AI vision with Moondream tiny vision language model

Install a local AI vision language model using Tiny AI

If you would like the ability to run AI vision applications on your home computer you might be interested in a new language model called Moondream.  Capable of processing what you say, what you write, and even what you show it. Moondream, is a small size sophisticated artificial intelligence (AI) vision language mode that’s offers impressive performance from such a small AI model. With a staggering small 1.6 billion parameters, Moondream is poised to redefine how we interact with machines, making them more intuitive and responsive to our needs.

Moondream is not just another AI tool; it’s a leap forward in machine learning. It’s designed to comprehend a wide array of inputs, including spoken language, written text, and visual content. Moondream1 is a tiny (1.6B parameter) vision language model trained by @vikhyatk that performs on par with models twice its size. It is trained on the LLaVa training dataset, and initialized with SigLIP as the vision tower and Phi-1.5 as the text encoder.

This means that whether you’re a developer looking to integrate AI into your app, a student eager to learn about the latest in technology, or simply an AI enthusiast, Moondream is tailored for you. It’s a versatile model that can convert various types of information into text or speech outputs, enhancing the way we communicate with our devices. Moondream is a 1.6B parameter model built using SigLIP, Phi-1.5 and the LLaVA training dataset. Weights are licensed under CC-BY-SA due to using the LLaVA dataset.

Tiny AI Vision Language Model 1.6B

Getting started with Moondream is a breeze. The developers have made sure that anyone interested can easily set it up by providing detailed installation instructions on GitHub. Whether you’re incorporating it into a complex project or just tinkering with it for personal learning, these guidelines make the process straightforward. But Moondream’s commitment to education doesn’t stop there. In collaboration with Brilliant.org, it offers interactive courses that delve into AI, helping users to understand and harness the power of this cutting-edge technology.

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The performance of Moondream is as impressive as its versatility. It has been rigorously tested to ensure that it not only understands inputs accurately but also responds rapidly. These tests aren’t hidden away in some lab; they’re openly available for anyone to see on GitHub. This transparency allows users to set realistic expectations for how Moondream can be applied in real-world situations, from powering smart home devices to enhancing customer service interactions.

Moondream is more than just a tool; it’s a a fantastic example to the incredible strides being made in local AI technology. It’s a model that not only processes complex inputs with ease but also offers flexible outputs that can be tailored to a wide range of uses. The educational resources provided by Brilliant.org further highlight its value, not just as a technological innovation but also as a learning platform. By joining the community and engaging with others, you can help shape the future of this remarkable AI vision language model. For more information jump over to the official GitHub project page.

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DJI releases Modify its first intelligent 3D model editing software

DJI releases Modify its first intelligent 3D model editing software

In the world of drone aerial surveying, professionals are constantly seeking ways to enhance their workflow and produce more accurate 3D models. DJI Enterprise, a leading name in the industry, has introduced a new software tool that is set to make a significant impact on how these professionals work. DJI Modify is a sophisticated 3D model editing tool that simplifies the editing process, addressing common issues that have previously required a lot of manual intervention. DJI explains more about the new 3D model editing software. DJI  is also offering

“DJI Modify offers a seamless workflow for mapping and surveying professionals, effortlessly integrating with DJI Terra for aerial surveying and modeling. This all-in-one platform caters to the diverse operational needs of industries such as surveying and mapping, urban planning, and emergency response, ensuring efficient model sharing to meet the dynamic demands of various landscapes.”

DJI Modify 3D model editing software features

DJI Modify stands out with its one-click import feature, which seamlessly integrates 3D models from DJI Terra. This eliminates complicated import steps and saves valuable time. The software’s automated system is designed to identify and remove floating parts in large areas without human input, leading to cleaner models and reducing the need for manual cleanup.

One of the most challenging aspects of 3D modeling is dealing with imperfections caused by water reflections, which can affect the accuracy of the models. DJI Modify offers a solution by allowing users to quickly fix these issues. It provides instant previews so that edits can be tracked and adjusted in real-time, ensuring the final product is of the highest quality.

DJI Modify 3D model editing software

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Drone footage 3D modelling software

Editing complex cityscapes is also made more manageable with DJI Modify. The software uses advanced algorithms to identify and remove numerous vehicles per square kilometer, significantly enhancing the urban modeling process. For areas that require more detailed attention, the manual selection tool allows for precise flattening and texture repairs.

“DJI Modify is DJI’s first intelligent 3D model editing software. It features a streamlined and intuitive interface that makes it simple to complete model editing efficiently. Paired with a DJI Enterprise drone and DJI Terra, it forms a comprehensive solution from aerial surveying, modeling, and model editing to sharing these models easily to meet operational needs in surveying and mapping, firefighting, emergency response, and transportation.”

The inpainting technology in DJI Modify is noteworthy for its ability to blend repaired textures seamlessly with their surroundings, making the edits virtually undetectable. This is especially useful when working with complex textures and patterns. The batch repair function is another feature that boosts efficiency by fixing multiple issues at once, such as gaps in building reflections and inconsistencies in road sign textures, ensuring uniformity across the model.

Sharing edited models is also streamlined with DJI Modify’s cloud uploading feature. Users can easily upload their models and share them via a unique link, eliminating the need for additional software or complicated file transfers.

DJI Modify is set to change the way professionals approach 3D model editing from drone aerial surveys. Its smart features are designed to streamline the editing process, making it less labor-intensive and more cost-effective. With this powerful software, professionals can focus on capturing the highest quality aerial data, while DJI Modify takes care of the complex editing tasks.

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NeuralBeagle14-7B new Powerful 7B open source AI model

NeuralBeagle14-7B new Powerful 7B open source AI model

The artificial intelligence field has just welcomed a significant new artificial intelligence (AI) large language model in the form of NeuralBeagle14-7B. This advanced AI model is making waves with its 7 billion parameters, and it’s quickly climbed the ranks to become a top contender among large language models.

NeuralBeagle is not just any model; it’s a hybrid, created by combining the best features of two existing models, Beagle and Mar Coro. This fusion has been further enhanced by a unique technique called the Lazy Merge Kit. NeuralBeagle14-7B is a DPO fine-tune of mlabonne/Beagle14-7B using the argilla/distilabel-intel-orca-dpo-pairs preference dataset

Mergekit is a toolkit for merging pre-trained language models. Mergekit uses an out-of-core approach to perform unreasonably elaborate merges in resource-constrained situations. Merges can be run entirely on CPU or accelerated with as little as 8 GB of VRAM. Many merging algorithms are supported, with more on their way.

NeuralBeagle’s success is rooted in the strong performance of the Beagle model, which had already shown its capabilities by scoring high on a well-known AI leaderboard. By integrating Beagle with Mar Coro, the developers have created a powerhouse model that draws on the strengths of both. However, the team didn’t stop there. They also applied a fine-tuning process known as Domain Preferred Option (DPO). While this fine-tuning didn’t drastically improve the model’s performance, it did provide important insights into the fine-tuning process and its effects on AI models.

NeuralBeagle14-7B

What sets NeuralBeagle apart is its versatility. It has been rigorously tested on various platforms, including AGI Evol and GPT-4-All, demonstrating its ability to perform a wide array of tasks. This adaptability is a testament to the model’s sophisticated design and its potential uses in different applications. NeuralBeagle14-7B uses a context window of 8k. It is compatible with different templates, like chatml and Llama’s chat template. NeuralBeagle14-7B ranks first on the Open LLM Leaderboard in the ~7B category.

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For those eager to see NeuralBeagle in action, the model is available for trial on Hugging Face Spaces. This interactive platform allows users to directly engage with NeuralBeagle and see how it performs. And for those who want to integrate NeuralBeagle into their own projects, there are detailed installation instructions for LM Studio, making it easy to get started.

NeuralBeagle represents a significant step forward in the world of open-source AI models. Its innovative combination of two models and the exploration of DPO fine-tuning offer a glimpse into the ongoing evolution of AI. The model is now available for researchers, developers, and AI enthusiasts to test and incorporate into their work. With options for online testing and local installation, NeuralBeagle is poised to become a valuable tool in the AI community.

Image Credit mlabonne

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TinyLlama 1.1B powerful small AI model trained on 3 trillion tokens

TinyLlama powerful AI model trained on 3 trillion tokens

If you are interested in using and installing TinyLlama 1.1B, a new language model that packs a punch despite its small size. This quick guide will take you through the process. TinyLlama is an innovative compact AI model making waves by offering high-level language processing capabilities that can be used on a variety of devices, from desktops to smartphones. It’s a big deal for developers and researchers who need advanced language understanding but don’t have the luxury of unlimited computing power.

TinyLlama 1.1B is built on the efficient Llama 2 architecture, which means it’s not only powerful but also designed to work smoothly with many different open-source projects. This is great news for users who want to add TinyLlama’s advanced features to their existing systems without any hassle. The model comes with a specialized tokenizer that ensures it can communicate effectively with other software, which is a key requirement for anyone looking to upgrade their tech with the latest AI capabilities.

The development of TinyLlama was no small feat. It underwent a rigorous 90-day training period that started on September 1st, 2023, using 16 high-performance GPUs. The goal was to make the model as efficient as possible, teaching it to understand complex language and concepts, including logic and common sense. The training process was closely watched to avoid overfitting, which can reduce a model’s effectiveness. The result is a language model that performs exceptionally well, even when compared to other models that have many more parameters.

How to install TinyLlama 1.1B

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What sets TinyLlama 1.1B apart is its ability to handle complex tasks using far fewer resources than you might expect. This efficiency is a testament to the developers’ focus on optimizing training and making sure the model learns as much as possible without wasting energy or computing power.

For those eager to try out TinyLlama, the model is readily available for download on Hugging Face, a popular platform for sharing machine learning models. This move makes cutting-edge AI technology accessible to a wide audience, from experienced developers to those just starting to dip their toes into the world of artificial intelligence.

TinyLlama 1.1B is a noteworthy development in the field of language modeling and more information is available over on the Huggingface website. It manages to balance a compact size with strong computational abilities, making it an excellent choice for anyone interested in exploring AI. Its compatibility with standard devices and ease of integration make it a valuable resource for those who want to push the boundaries of what’s possible with AI, without needing a supercomputer to do so.

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Apple releases Ferret 7B multimodal large language model (MLLM)

Apple releases Ferret 7B multimodal large language model

Apple has recently introduced the Ferret 7B, a sophisticated large language model (LLM) that represents a significant step forward in the realm of artificial intelligence. This new technology is a testament to Apple’s commitment to advancing AI and positions the company as a formidable player in the tech industry. The Ferret 7B is engineered to integrate smoothly with both iOS and macOS, taking full advantage of Apple’s powerful silicon to ensure users enjoy a fluid experience.

The standout feature of the Ferret 7B is its multimodal capabilities, which allow it to interpret and create content that combines images and text. This breakthrough goes beyond what traditional text-based AI models can do. The Ferret 7B’s capabilities are showcased in systems like the Google 5.2 coding model and MixL 8X 7B, which are built on Apple’s MLX platform and utilize its unique tools.

  • Ferret Model – Hybrid Region Representation + Spatial-aware Visual Sampler enable fine-grained and open-vocabulary referring and grounding in MLLM.
  • GRIT Dataset (~1.1M) – A Large-scale, Hierarchical, Robust ground-and-refer instruction tuning dataset.
  • Ferret-Bench – A multimodal evaluation benchmark that jointly requires Referring/Grounding, Semantics, Knowledge, and Reasoning.

There’s buzz around the upcoming iOS 18, which is expected to incorporate AI more comprehensively, potentially transforming how users interact with Apple devices. The collaboration between AI advancements and Apple’s silicon architecture is likely to result in a more cohesive and powerful ecosystem for both iOS and macOS users.

Apple Ferret 7B MLLM

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For those interested in the technical performance of the Ferret 7B, Apple has developed the Ferret Bench, a benchmarking tool specifically for this model. This tool will help developers and researchers evaluate the model’s efficiency and flexibility in various situations.

Apple’s approach to AI is centered on creating practical applications that provide tangible benefits to users of its devices. The company’s dedication to this strategy is clear from its decision to make the Ferret 7B open-source, offering the code and checkpoints for research purposes. This move encourages further innovation and collaboration within the AI community.

Training complex models like the Ferret 7B requires considerable resources, and Apple has invested in this by using NVIDIA A100 GPUs. This reflects the company’s deep investment in AI research and development.

Apple multimodal large language model (MLLM)

It’s important to note the differences between the 7B and the larger 13B versions of the model. The 7B is likely tailored for iOS devices, carefully balancing performance with the constraints of mobile hardware. This strategic decision is in line with Apple’s focus on the user experience, ensuring that AI improvements directly benefit the user.

# 7B
python3 -m ferret.model.apply_delta \
    --base ./model/vicuna-7b-v1-3 \
    --target ./model/ferret-7b-v1-3 \
    --delta path/to/ferret-7b-delta
# 13B
python3 -m ferret.model.apply_delta \
    --base ./model/vicuna-13b-v1-3 \
    --target ./model/ferret-13b-v1-3 \
    --delta path/to/ferret-13b-delta

Usage and License Notices: The data, and code is intended and licensed for research use only. They are also restricted to uses that follow the license agreement of LLaMA, Vicuna and GPT-4. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.

With the release of the Ferret 7B LLM, Apple has made a bold move in the AI space. The launch showcases the company’s technical prowess and its commitment to creating powerful, user-friendly AI. This development is set to enhance device functionality and enrich user interactions. As Apple continues to invest in AI, we can expect to see more innovations that will significantly impact how we interact with technology.

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Build a custom AI large language model GPU server (LLM) to sell

Setup a custom AI large language model (LLM) GPU server to sell

Deploying a custom language model (LLM) can be a complex task that requires careful planning and execution. For those looking to serve a broad user base, the infrastructure you choose is critical. This guide will walk you through the process of setting up a GPU server, selecting the right API software for text generation, and ensuring that communication is managed effectively. We aim to provide a clear and concise overview that balances simplicity with the necessary technical details.

When embarking on this journey, the first thing you need to do is select a suitable GPU server. This choice is crucial as it will determine the performance and efficiency of your language model. You can either purchase or lease a server from platforms like RunPod or Vast AI, which offer a range of options. It’s important to consider factors such as GPU memory size, computational speed, and memory bandwidth. These elements will have a direct impact on how well your model performs. You must weigh the cost against the specific requirements of your LLM to find a solution that is both effective and economical.

After securing your server, the next step is to deploy API software that will operate your model and handle requests. Hugging Face and VM are two popular platforms that support text generation inference. These platforms are designed to help you manage API calls and organize the flow of messages, which is essential for maintaining a smooth operation.

How to set up a GPU servers for AI models

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Efficient communication management is another critical aspect of deploying your LLM. You should choose software that can handle function calls effectively and offers the flexibility of creating custom endpoints to meet unique customer needs. This approach will ensure that your operations run without a hitch and that your users enjoy a seamless experience.

As you delve into the options for GPU servers and API software, it’s important to consider both the initial setup costs and the potential for long-term performance benefits. Depending on your situation, you may need to employ advanced inference techniques and quantization methods. These are particularly useful when working with larger models or when your GPU resources are limited.

Quantization techniques can help you fit larger models onto smaller GPUs. Methods like on-the-fly quantization or using pre-quantized models allow you to reduce the size of your model without significantly impacting its performance. This underscores the importance of understanding the capabilities of your GPU and how to make the most of them.

For those seeking a simpler deployment process, consider using Docker images and one-click templates. These tools can greatly simplify the process of getting your custom LLM up and running.

Another key metric to keep an eye on is your server’s ability to handle multiple API calls concurrently. A well-configured server should be able to process several requests at the same time without any delay. Custom endpoints can also help you fine-tune your system’s handling of function calls, allowing you to cater to specific tasks or customer requirements.

Things to consider when setting up a GPU server for AI models

  • Choice of Hardware (GPU Server):
    • Specialized hardware like GPUs or TPUs is often used for faster performance.
    • Consider factors like GPU memory size, computational speed, and memory bandwidth.
    • Cloud providers offer scalable GPU options for running LLMs.
    • Cost-effective cloud servers include Lambda, CoreWeave, and Runpod.
    • Larger models may need to be split across multiple multi-GPU servers​​.
  • Performance Optimization:
    • The LLM processing should fit into the GPU VRAM.
    • NVIDIA GPUs offer scalable options in terms of Tensor cores and GPU VRAM​​.
  • Server Configuration:
    • GPU servers can be configured for various applications including LLMs and Natural Language Recognition​​.
  • Challenges with Large Models:
    • GPU memory capacity can be a limitation for large models.
    • Large models often require multiple GPUs or multi-GPU servers​​.
  • Cost Considerations:
    • Costs include GPU servers and management head nodes (CPU servers to coordinate all the GPU servers).
    • Using lower precision in models can reduce the space they take up in GPU memory​​.
  • Deployment Strategy:
    • Decide between cloud-based or local server deployment.
    • Consider scalability, cost efficiency, ease of use, and data privacy.
    • Cloud platforms offer scalability, cost efficiency, and ease of use but may have limitations in terms of control and privacy​​​​.
  • Pros and Cons of Cloud vs. Local Deployment:
    • Cloud Deployment:
      • Offers scalability, cost efficiency, ease of use, managed services, and access to pre-trained models.
      • May have issues with control, privacy, and vendor lock-in​​.
    • Local Deployment:
      • Offers more control, potentially lower costs, reduced latency, and greater privacy.
      • Challenges include higher upfront costs, complexity, limited scalability, availability, and access to pre-trained models​​.
  • Additional Factors to Consider:
    • Scalability needs: Number of users and models to run.
    • Data privacy and security requirements.
    • Budget constraints.
    • Technical skill level and team size.
    • Need for latest models and predictability of costs.
    • Vendor lock-in issues and network latency tolerance​​.

Setting up a custom LLM involves a series of strategic decisions regarding GPU servers, API management, and communication software. By focusing on these choices and considering advanced techniques and quantization options, you can create a setup that is optimized for both cost efficiency and high performance. With the right tools and a solid understanding of the technical aspects, you’ll be well-prepared to deliver your custom LLM to a diverse range of users.

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AI 3D model and image creator Stable Zero123 – Stability AI

AI 3D model and image creator Stable Zero123 unveiled by Stability AI

Stability AI has unveiled a new AI 3D model and image creator that is set to transform how we generate 3D content from simple 2D images. Named Stable Zero123, this new 3D image AI model creator is currently in a research preview phase and is making waves among creators and developers, particularly those involved in video and gaming industries.

The model’s ability to interpret and reconstruct the depth and dimensions of objects from a single photograph is a significant leap forward, potentially enhancing virtual reality experiences and simplifying design processes across various fields, including engineering and architecture.

Stable Zero123 utilizes a unique method called Score Distillation Sampling (SDS), which is at the heart of its capability to convert flat images into three-dimensional wonders. This breakthrough could be a boon for virtual reality, where immersive environments are paramount, and in industries like architecture, where visualizing designs in 3D is crucial.

Stable Zero123 new AI 3D image creator

The AI 3D model maker is made available through the Hugging Face platform, which is known for facilitating the sharing of machine learning models. Stability AI also recommends pairing Stable Zero123 with Three Studio software to manage 3D content effectively.

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In addition to Stable Zero123, Stability AI has been working on other tools designed to augment the model’s functionality. These include a sky replacer and a tool for creating 3D models, both of which are currently in private preview. These tools are intended to provide specialized functions that work in tandem with Stable Zero123, further expanding its utility for users.

Despite its impressive capabilities, Stable Zero123 does come with some requirements that may pose challenges for certain users. The AI model demands significant computational power, which means that high-end graphics cards or professional training GPUs are necessary to harness its full potential. This hardware requirement could limit the model’s accessibility, particularly for hobbyists or small-scale creators who may not have access to such resources.

  • Stable Zero123:
    • Generates novel views of an object, showing 3D understanding from various angles.
    • Notable improvement in quality over previous models like Zero1-to-3 and Zero123-XL.
    • Enhancements due to improved training datasets and elevation conditioning.
  • Technical Details:
    • Based on Stable Diffusion 1.5.
    • Consumes the same amount of VRAM as SD1.5 for generating one novel view.
    • Requires more time and memory (24GB VRAM recommended) for generating 3D objects.
  • Model Usage and Accessibility:
    • Released for non-commercial and research use.
    • Downloadable weights available.
  • Innovations and Improvements:
    • Improved training dataset from Objaverse, focusing on high-quality 3D objects.
    • Elevation conditioning provided during training and inference for higher quality predictions.
    • A pre-computed dataset and improved dataloader, leading to a 40X speed-up in training efficiency.
  • Availability and Application:
    • Released on Hugging Face for researchers and non-commercial users.
    • Improved open-source code of threestudio for supporting Zero123 and Stable Zero123.
    • Uses Score Distillation Sampling (SDS) for optimizing a NeRF with Stable Zero123.
    • Can be adapted for text-to-3D generation.
  • Restrictions and Contact Information:
    • Model intended exclusively for research, not commercial use.
    • Contact details provided for inquiries about commercial applications.
    • Updates and further information available through newsletter, social media, and Discord community.

Current limitations of Stable Zero123

One of the current drawbacks of Stable Zero123 is its inability to produce images with transparent backgrounds, a feature that is crucial for integrating visuals seamlessly into videos. Nevertheless, the model’s promise in the video and gaming sectors is undeniable, given the growing demand for high-quality 3D content in these areas.

Stability AI is not resting on its laurels; the company is actively working to improve Stable Zero123’s applications and overcome its current limitations. To help users make the most of AI models like Stable Zero123, Stability AI is also offering a comprehensive course on machine learning and stable diffusion. This educational initiative is part of the company’s commitment to empowering creators with the knowledge and tools they need to excel in their creative projects.

The introduction of Stable Zero123 from Stability AI marks a significant milestone in the field of AI-driven 3D imagery. Although still in the early stages of development, the model’s potential to impact content creation is immense. As Stability AI continues to refine and enhance this technology, the future looks promising for the development of more sophisticated and accessible tools for creators and developers around the world. The anticipation for what Stable Zero123 will bring to the table is high, and the creative community is watching closely as Stability AI paves the way for new possibilities in digital content creation.

Image Credit:  Stability AI

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