What OpenELM language models say about Apples generative AI strategy

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Small Language Models: A Strategic Opportunity for the Masses

These can increase efficiency in broadly deployed server CPUs like AWS Graviton and NVIDIA Grace, as well as the recently announced Microsoft Cobalt and Google Axion as they come into production. In summary, though AI technologies are advancing rapidly and foundational tools are available today, organizations must proactively prepare for future developments. Balancing current opportunities with forward-looking strategies and addressing human and process-related challenges will be necessary to stay ahead in this fast-moving technological landscape.

SLMs have applications in various fields, such as chatbots, question-answering systems, and language translation. SLMs are also suitable for edge computing, which involves processing data on devices rather than in the cloud. This is because SLMs require less computational power and memory compared to LLMs, making them more suitable for deployment on mobile devices and other resource-constrained environments.

Apple Intelligence Foundation Language Models

The adapter parameters are initialized using the accuracy-recovery adapter introduced in the Optimization section. As LLMs entered the stage, the narrative was straightforward — bigger is better. Models with more parameters are expected to understand the context better, make fewer mistakes, and provide better answers. Training these behemoths became an expensive task, one that not everyone is willing (nor able) to pay for. Even though Phi 2 has significantly fewer parameters than, say, GPT 3.5, it still needs a dedicated training environment.

More often, the extracted information is automatically added to a system and only flagged for human review if potential issues arise. This website is using a security service to protect itself from online attacks. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data. According to Gartner, 80% of conversational offerings will embed generative AI by 2025, and 75% of customer-facing applications will have conversational AI with emotion. Digital humans will transform multiple industries and use cases beyond gaming, including customer service, healthcare, retail, telepresence and robotics. ACE NIM microservices run locally on RTX AI PCs and workstations, as well as in the cloud.

Small language models have fewer parameters but are great for domain-specific tasks

And while they’re truly powerful, some use cases call for a more domain-specific alternative. “Although LLM is more powerful in terms of achieving outcomes at a much wider spectrum, it hasn’t achieved full-scale deployment at the enterprise level due to complexity. Use of high-cost computational resource (GPU vs CPU) varies directly with the degree of inference that needs to be drawn from a dataset. Trained over a focused dataset with a defined outcome, SLM could be a better alternative in certain cases such as deploying applications with similar accuracy at the Edge level,” Brokerage firm, Prabhudas Lilladher wrote in a note. Another benefit of SLMs is their potential for enhanced privacy and security.

Interestingly, even smaller models like Mixtral 8x7B and Llama 2 – 70B are showing promising results in certain areas, such as reasoning and multi-choice questions, where they outperform some of their larger counterparts. This suggests that the size of the model may not be the sole determining factor in performance and that other aspects like architecture, training data, and fine-tuning techniques could play a significant role. The Cognite Atlas AI™ Benchmark Report for Industrial Agents will initially focus on natural language search as a key data retrieval tool for industrial AI agents. The test set includes a wide range of data models designed for sectors like Oil & Gas and Manufacturing, with real-life question-answer pairs to evaluate performance across different scenarios. These benchmark datasets enable systematic evaluation of the system’s performance in answering complex questions, like tracking open safety-critical work orders in a facility.

Due to the large data used in training, LLMs are better suited for solving different types of complex tasks that require advanced reasoning, while SLMs are better suited for simpler tasks. Unlike LLMs, SLMs use less training data, but the data used must be of higher quality to achieve many of the capabilities found in LLMs in a tiny package. In contrast, SLMs have a smaller model size, enabling LLM-type capabilities, including natural language processing, albeit with fewer parameters and required resources.

Chinchilla and the Optimal Point for LLMs Training

At the heart of the developer kit is the Jetson AGX Orin module, featuring an Nvidia Ampere architecture GPU with 2048 CUDA cores and 64 tensor cores, alongside a 12-core Arm Cortex-A78AE CPU. The kit comes with a reference carrier board that exposes numerous standard hardware interfaces, enabling rapid prototyping and development. OpenELM uses a series of tried and tested techniques to improve the performance and efficiency of the models. Compared to techniques like Retrieval-Augmented Generation (RAG) and fine-tuning of LLMs, SLMs demonstrate superior performance in specialized tasks.

DeepSeek-Coder-V2 is an open source model built through the Mixture-of-Experts (MoE) machine learning technique. As we can find out from its ‘Read me’ documents on GitHub, it comes pre-trained with 6 trillion tokens, supports 338 languages, and has a context length of 128k tokens. Comparisons show that, when handling coding tasks, it can reach performance rates similar to GPT4-Turbo. If the company lives up to their promise, we can expect the phi-3 family to be among the best small language models on the market. The first to come from this Microsoft small language models’ family is Phi-3-mini, which boasts 3.8 billion parameters.

To simulate an imperfect SLM classifier, the researchers sample both hallucinated and non-hallucinated responses from the datasets, assuming the upstream label as a hallucination. While LLMs are powerful, they often generate responses that are too generalized and may be inaccurate. Again, the technology is fairly new, and there are still issues and areas that require refinement and improvement. SLMs still possess considerable capabilities and, in certain cases, can perform on par with their larger LLM counterparts. Thank you, #GITEXGlobal, for including us to speak on this moment in technology where we can truly make a difference.

According to Mistral, the new Ministral models outperform other SLMs of similar size on major benchmarks in different fields, including reasoning (MMLU and Arc-c), coding (HumanEval), and multilingual tasks. Descriptive, diagnostic, and prescriptive analytics will also leverage the capabilities of SLMs. This will result in highly personalized patient care, where healthcare providers can offer tailored treatment options.

Small language models vs. large language models

We are actively conducting both manual and automatic red-teaming with internal and external teams to continue evaluating our models’ safety. We use a set of diverse adversarial prompts to test the model performance on harmful content, sensitive topics, and factuality. We measure the violation rates of each model as evaluated by human graders on this evaluation set, with a lower number being desirable.

We have applied an extensive set of optimizations for both first token and extended token inference performance. We also filter profanity and other low-quality content to prevent its inclusion in the training corpus. In addition to filtering, we perform data extraction, deduplication, and the application of a model-based classifier to identify high quality documents. Our foundation models are trained on Apple’s AXLearn framework, an open-source project we released in 2023. It builds on top of JAX and XLA, and allows us to train the models with high efficiency and scalability on various training hardware and cloud platforms, including TPUs and both cloud and on-premise GPUs. We used a combination of data parallelism, tensor parallelism, sequence parallelism, and Fully Sharded Data Parallel (FSDP) to scale training along multiple dimensions such as data, model, and sequence length.

This new, optimized SLM is also purpose-built with instruction tuning, a technique for fine-tuning models on instructional prompts to better perform specific tasks. This can be seen in Mecha BREAK, a video game in which players can converse with a mechanic game character ChatGPT and instruct it to switch and customize mechs. Models released today will fast become deprecated, and the company will have to spend millions of dollars training the next generation of models, as shown in this graphic shared by Mistral with the release of the new models.

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For on-device inference, we use low-bit palletization, a critical optimization technique that achieves the necessary memory, power, and performance requirements. To maintain model quality, we developed a new framework using LoRA adapters that incorporates a mixed 2-bit and 4-bit configuration strategy — averaging 3.7 bits-per-weight — to achieve the same accuracy as the uncompressed models. More aggressively, the model can be compressed to 3.5 bits-per-weight without significant quality loss. We use shared input and output vocab embedding tables to reduce memory requirements and inference cost.

“Some customers may only need small models, some will need big models, and many are going to want to combine both in a variety of ways,” Luis Vargas, vice president of AI at Microsoft, said in an article posted on the company’s website. Mistral’s models and Falcon are commercially available under the Apache 2.0 license. In January, the consultancy Sourced Group, an Amdocs company, will help a few telecoms and financial services firms take advantage of GenAI using an open source SLM, lead AI consultant Farshad Ghodsian said. Initial projects include leveraging natural language to retrieve information from private internal documents.

This initial step allows for rapid screening of input, significantly reducing the computational load on the system. When the SLM flags a piece of text as potentially containing a hallucination, it triggers the second stage of the process. With a smaller model, creating, deploying and managing is more cost-effective.

Open source model providers have an opportunity next year as enterprises move from the learning stage to the actual deployment of GenAI. In June, supply chain security company Rezilion reported that 50 of the most popular open source GenAI projects on GitHub had an average security score of 4.6 out of 10. Weaknesses found in the technology could lead to attackers bypassing access controls and compromising sensitive information or intellectual property, Rezilion wrote in a blog post. For example, users can access the parameters, or weights, that reveal how the models forge their responses. The inaccessible weights used by proprietary models concern enterprises fearful of discriminatory biases. In conclusion, Small Language Models are becoming incredibly useful tools in the Artificial Intelligence community.

Small language models vs large language models

This makes the architecture more complicated but enables OpenELM to better use the available parameter budget for higher accuracy. SLMs offer a clear advantage in relevance and value creation compared to LLMs. Their specific domain focus ensures direct applicability to the business context. SLM usage correlates with improved operational efficiency, customer satisfaction, and decision-making processes, driving tangible business outcomes. Because SLMs don’t consume nearly as much energy as LLMs, they can also run locally on devices like smartphones and laptops (instead of in the cloud) to preserve data privacy and personalize them to each person. In March, Google rolled out Gemini Nano to the company’s Pixel line of smartphones.

In this article, I share some of the most promising examples of small language models on the market. I also explain what makes them unique, and what scenarios you could use them for. The scale and black-box nature of LLMs can also make them challenging to interpret and debug, which is crucial for building trust in the model’s outputs. Bias in the training data and algorithms can lead to unfair, inaccurate or even harmful outputs.

Enterprises running cloud-based models will have the option of using the provider’s tools. For example, Microsoft recently introduced GenAI developer tools in Azure AI Studio that detect erroneous model outputs and monitor user inputs and model responses. Ultimately, enterprises will choose from various types of models, including slm vs llm open source and proprietary LLMs and SLMs, Chandrasekaran said. However, choosing the model is only the first step when running AI in-house. “Model companies are trying to strike the right balance between the performance and size of the models relative to the cost of running them,” Gartner analyst Arun Chandrasekaran said.

Since they use computational resources efficiently, they can offer good performance and run on various devices, including smartphones and edge devices. Additionally, since you can train them on specialized data, they can be extremely helpful when handling niche tasks. Another significant issue with LLMs is their propensity for hallucinations – generating outputs that seem plausible but are not actually true or factual. This stems from the way LLMs are trained to predict the next most likely word based on patterns in the training data, rather than having a true understanding of the information. As a result, LLMs can confidently produce false statements, make up facts or combine unrelated concepts in nonsensical ways.

I implemented a proof of concept of this approach based on Microsoft Phi-3 running on Jetson Orin locally, a MongoDB database exposed as an API, and GPT-4o available from OpenAI. In the next part of this series, I will walk you through the code and the step-by-step guide to run this in your own environment. The progress in SLMs indicates a shift towards more accessible and versatile AI solutions, reflecting a broader trend of optimizing AI models for efficiency and practical deployment across various platforms. One solution to preventing hallucinations is to use Small Language Models (SLMs) which are “extractive”.

LLaMA-65B (I know, not that small anymore, but still…) is competitive with the current state-of-the-art models like PaLM-540B, which use proprietary datasets. This clearly indicates how good data not only improves a model’s performance but can also make it democratic. A machine learning engineer would not need enormous budgets to get good model training on a good dataset. Having a lightweight local SLM fine-tuned on custom data or used as part of a local RAG application, where the SLM provides the natural language interface to a search, is an intriguing prospect.

The Phi-3 models are designed for efficiency and accessibility, making them suitable for deployment on resource-constrained edge devices and smartphones. They feature a transformer decoder architecture with a default context length of 4K tokens, with a long context version (Phi-3-mini-128K) extending to 128K tokens. In this tutorial, I will walk you through the steps involved in configuring Ollama, a lightweight model server, on the Jetson Orin Developer Kit, which takes advantage of GPU acceleration to speed up the inference of Phi-3. This is one of the key steps in configuring federated language models spanning the cloud and the edge. The journey towards leveraging SLMs begins with understanding their potential and taking actionable steps to integrate them into your organization’s AI strategy. The time to act is now – embrace the power of small language models and unlock the full potential of your data assets.

You can foun additiona information about ai customer service and artificial intelligence and NLP. To further evaluate our models, we use the Instruction-Following Eval (IFEval) benchmark to compare their instruction-following capabilities with models of comparable size. The results suggest that both our on-device and server model follow detailed instructions better than the open-source and commercial models of comparable size. Whether the model is in the cloud or data center, enterprises must establish a framework for evaluating the return on investment, experts said.

• The largeness consists of having a large internal data structure that encompasses the modeled patterns, typically using what is called an artificial neural network or ANN, see my in-depth explanation at the link here.
• This targeted approach makes them well-suited for real-time applications where speed and accuracy are crucial.
• They enable users to fine-tune the models to unique requirements while keeping the number of trainable parameters relatively low.
• Because of their lightweight design, SLMs provide a flexible solution for a range of applications by balancing performance and resource usage.
• Yet, they still rank in the top 6 in the Stanford Holistic Evaluation of Language Models (HELM), a benchmark used to evaluate language models’ accuracy in specific scenarios.

What’s more interesting, Microsoft’s Phi-3-small, with 7 billion parameters, fared remarkably better than GPT-3.5 in many of these benchmarks. In the case of telcos, for example, some of the common use cases are AI assistants in contact centers, personalized offers in service delivery and AI-powered chatbots for enhanced customer experience. RAG techniques, which combine LLMs ChatGPT App with external knowledge bases to optimize outputs, “will become crucial for [organizations] that want to use LLMs without sending them to cloud-based LLM providers,” Penchikala and co-authors explain. Its content is written by and for software engineers and developers, but much of it—like the Trends report—is accessible by, and of interest to, general technology watchers.

There’s less room for error, and it is easier to secure from hackers, a major concern for LLMs in 2024. The number of SLMs grows as data scientists and developers build and expand generative AI use cases. Okay, with those noted caveats, I will give you a kind of example showcasing what the difference between an SLM and an LLM might be, right now.

When an enterprise uses an LLM, it will transmit data via an API, and this poses the risk of sensitive information being exposed. The Arm CPU architecture is enabling quicker AI experiences with enhanced security, unlocking new possibilities for AI workloads at the edge. We’ll close with a discussion of the and some examples of firms we see investing to advance this vision. Note this is not an encompassing list of firms, rather a sample of companies within the harmonization layer and the agent control framework.

This is important given the heavy expenses for infrastructure like GPUs (graphics processing units). In fact, an SLM can be run on inexpensive commodity hardware—say, a CPU—or it can be hosted on a cloud platform. Consequently, most businesses are currently experimenting with these models in pilot phases. Depending on the application—whether it’s chatting, style transfer, summarization, or content creation—the balance between prompt size, token generation, and the need for speed or quality shifts accordingly.

For example, fine-tuning involves adjusting the weights and biases of a model. This is an advanced technique that enhances the functionality of the SLM by incorporating external documents, usually from vector databases. This method optimizes the output of LLMs, making them more relevant, accurate and useful in various contexts. The lack of customization can lead to a gap in how effectively these models understand and respond to industry-specific jargon, processes and data nuances.

This feature is particularly valuable for telehealth products that monitor and serve patients remotely. However, this chatbot would be limited to answering questions within its defined parameters. It wouldn’t be able to compare products with those of a competitor or handle subjects unrelated to John’s company, for example. Moving on, SLMs are currently perceived as the way to get narrowly focused generative AI working on an even wider scale than it is today.