How Do Private LLMs Transform Your Data into Precious Safe Assets, Emerging as Saviors for Enterprises – Shifting from Generic Bots to Bespoke Brains?

In the wake of the proliferation of Language Models (LLMs) in the market, fueled by fine-tuning with proprietary company datasets and the emergence of re-trainable models, we've witnessed a fascinating shift in how startups and product-based companies have embraced this technology.?

While some smaller players have eagerly integrated LLMs into their products to gain a competitive edge, more giant corporations have taken a more cautious approach in 2023.?

Despite recognizing LLMs' tremendous value, top executives are now hesitant to share their data with third-party trained models. This hesitance has led to significant restrictions not only on using LLMs within their organizations but also in preventing vendors from incorporating LLMs into their product offerings, impacting product differentiation and core features.?

Whether you're on the side of caution or innovation, the answer remains a resounding "NO," highlighting the elephant in the room. The question now becomes: How can we navigate this dilemma effectively? Enter the concept of private, sizable LLMs – a promising solution to bridge this gap.

Why Private LLM’s?

Enterprises crave private LLMs not just for security but also for superpower customization. Public models lack the finesse of understanding a company's unique jargon, data, and goals. Imagine a chatbot trained on internal documents spitting out competitor secrets or a customer service AI programmed on generic queries stumbling through industry-specific terms. Private LLMs, meticulously fed on your proprietary data, become bespoke tools, generating accurate reports, crafting personalized emails, and automating tasks flawlessly. These tailored language models become integral parts of an enterprise's identity at each application level in the race for efficiency and innovation.

• Reduced Latency - By hosting open-source LLMs in your environment, you minimize API calls to external servers.
• Data Privacy - Your private and sensitive information can be kept in your own ecosystem (on-premise or external cloud provider).
• Customization and Control - You have more control over the model. Your machine configuration can be optimized, optimization techniques can be applied, the model can be fine-tuned, and it can be integrated further into your ecosystem.
• Offline Access - The model may be hosted in a secure environment without an internet connection, depending on the use case.

What are private LLM offerings, and how can we implement them in our enterprise?

Let me share a couple of private LLM offerings and one demo to run the LLM within my local computer. However, there are a lot of LLM models as a service in the model catalog (e.g., Microsoft Model Catalog).

1. Meta Llama 2

Llama 2 is a family of pre-trained and fine-tuned open-source large language models (LLMs), ranging in scale from 7B to 70B parameters, from the AI group at Meta, the parent company of Facebook. According to Meta AI, Llama 2 Chat LLMs are optimized for dialogue use cases and outperform open-source chat models on most benchmarks they tested. Based on Meta’s human evaluations for helpfulness and safety, the company says Llama 2 may be “a suitable substitute for closed source models (proprietary models).”

Meta Llama 2: Open-Source Language Model Powerhouse

Notable Llama 2 Architecture Design Patterns:

• Transformer-based: Like most state-of-the-art LLMs, Llama 2 uses the Transformer architecture, known for its efficient attention mechanism that allows it to process long data sequences.
• Pre-trained on massive dataset: Trained on 2 trillion publicly available tokens, it has a broad understanding of the world and diverse tasks.
• Three size variants: Choose from 7B, 13B, and 70B parameter models, matching your needs for speed or performance.
• Novel features: Rope embeddings enhance positional encoding, while RMSprop and Swigglu activate functions to improve training efficiency and performance.

Tasks Particularly Well-Suited for Llama 2:

• Creative Text Generation: Excels at generating text that is both creative and engaging, making it useful for tasks like writing poems, scripts, emails, or even code.
• Question Answering: Can accurately answer questions based on its knowledge of the world, although it might not be as precise as Mistral in factual accuracy and reasoning.
• Summarization: Can create concise and informative summaries of text or code.
• Chatbots and Virtual Assistants: Its ability to hold engaging conversations makes it suitable for powering chatbots and virtual assistants that need to interact with users naturally and creatively.
• Personalization: This can be used to personalize text content, such as emails or marketing materials, to better align with individual preferences or interests.

"Foundation models are pre-trained models provided for us by cloud providers - our job is to get them deployed to the cloud environments and get an endpoint so they can be invoked from our applications."

2. Mistral AI

Mixtral is a large language model (LLM) developed by Mistral AI. Mixtral 8X7 is the latest breakthrough model from Mistral AI, an emerging startup, and it's a great alternative to OpenAI and Llama 2 that's cheaper and better. Most recent large language models (LLMs) use very similar neural architectures. For instance, the Falcon, Mistral, and Llama 2 models use a similar combination of self-attention and MLP modules.

In contrast, Mistral AI, which also created Mistral 7B, just released a new LLM with a significantly different architecture: Mixtral-8x7B, a sparse mixture of 8 expert models. Despite its small size, the Mistral model with 7 billion parameters provided impressive performance.

Mistral AI: A Breezy Breeze of Large Language Models

In a nutshell, Mixtral 8X7 is an innovative "mixture of experts" architecture. The Mistral 7B model combines 8 distinct models, each with specialized strengths, such as mathematical reasoning or coding.

Notable Mistral AI Architecture Design Patterns:

• Efficient Transformer: Utilizing the Transformer architecture, Mistral prioritizes efficiency with optimized attention mechanisms and smaller model sizes (137B parameters).
• Focused on Reasoning: Trained on a dataset emphasizing logical reasoning and factual language understanding, Mistral excels at answering complex questions and performing tasks requiring structured thinking.
• Adaptive Embeddings: Adjusting its embedding based on context, Mistral captures subtle nuances in language, leading to more accurate and informative responses.

Tasks Particularly Well-Suited for Mistral AI:

• Question Answering: Excels at accurately answering complex questions based on factual knowledge and logical reasoning.
• Summarization: Can create concise and informative summaries of text or code.
• Reasoning Tasks: Capable of solving logical puzzles, understanding cause-and-effect relationships, and making inferences.
• Factual Search: Effectively retrieves accurate information from large text databases.
• Information Extraction: Accurately extracts key information, such as dates, names, and entities, from text or code.
• Content Moderation: Identifies and filters harmful or misleading content, promoting a safe and informative online environment.

"Cloud providers, such as Azure and AWS, fortunately created a mechanism to deploy and use the LLMs as a PaaS (and IaaS) service. We can surely take advantage of the platform support they provide."

Top high-level differences between Llama 2 vs. Mistral AI

Running Mixtral Private LLM on My Computer - Demo

Wrap up

In conclusion, I highly recommend opting for an open-source model, whether deployed in a cloud service provider or on-premises, not only for the purpose of pre-training your organization's specific jargon, acronyms, and customized datasets but also for safety and securing your precious organization assets.

This approach can be particularly beneficial for tasks such as text generation, translations, summarization, theme identification, classification, and the utilization of pre-defined question templates.

However, I would exercise caution when considering the direct use of open-source models for "Chatbot agent" applications unless you possess a strong level of confidence in content moderation and the safety of responses. In other words, prioritize safety in responses, ensuring truthfulness, non-toxicity, and freedom from biased content.

Imagine crafting a savvy private LLM service tailored for every application within the department of your organization. How cool would that be?