Deploying Your Own Private AI

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Large Language Models (LLMs) have emerged as powerful tools for businesses across various sectors out of a mechanism known as “Self Attention”. Understanding the basics of LLMs and their deployment is crucial for making informed decisions about AI implementation in your company.

Understanding LLMs: More Than Just Complex Software

At their core, Large Language Models are sophisticated software packages, not unlike the complex applications you already use, such as enterprise-level databases or advanced design software. However, instead of manipulating data or images, LLMs mostly process and generate human-like text.

Think of an LLM as a highly advanced, text-based application. It consists of large files (binary files with the file extension .bin), often called “weights” or “checkpoints,” which are loaded into specialized software frameworks like PyTorch. Once loaded, these models can be queried using natural language, similar to how you might query a database, but with far more flexibility and nuance.

Different LLMs are designed for various tasks:

  1. Text Generation: Creating content, similar to advanced autocomplete
  2. Question Answering: Responding to queries based on learned information
  3. Text Classification: Categorizing content, useful for sentiment analysis
  4. Language Translation: Converting text between languages

The Anatomy of an LLM: Weights, Checkpoints, and Internal Processes

To truly grasp how LLMs function, it’s crucial to understand their core components: weights and checkpoints. These elements form the backbone of the model’s knowledge and capabilities.

Weights: The Brain of the AI

Think of weights as the synapses in a human brain. In an LLM, weights are numerical values that determine how the model processes information. These weights are adjusted during the training process as the model learns from vast amounts of text data. The term “large” in Large Language Model refers primarily to the number of these weights – modern LLMs can have billions or even trillions of them.

Checkpoints: Snapshots of Knowledge

Checkpoints, on the other hand, are like snapshots of the model’s brain at a specific point in its training. They contain all the weight values and other necessary information to reconstruct the model’s state. When you “load” an LLM, you’re essentially loading one of these checkpoints, bringing the model’s brain to life in its most recently trained state.

The Internal Journey of Text in an LLM

When you send a piece of text to an LLM, a fascinating process unfolds behind the scenes:

  1. Tokenization: First, the input text is broken down into tokens. These are often subwords or individual characters, allowing the model to handle a wide variety of words, including ones it hasn’t seen before.
  2. Embedding: Each token is then converted into a numerical representation called an embedding. This translation allows the model to process the text mathematically.
  3. Processing: The embeddings are then fed through the model’s neural network. This is where the weights come into play. The model uses its vast network of weights to process the input, drawing upon its trained knowledge to understand context and generate relevant outputs.
  4. Generation: For tasks that require text generation, the model predicts the most probable next token based on the input and its training. This process repeats, with each generated token becoming part of the input for the next prediction, until the response is complete.
  5. Output: Finally, the generated tokens are converted back into human-readable text, which is then returned as the model’s response.

This entire process happens in milliseconds, allowing for near-instantaneous responses despite the complex calculations involved.

Key points:

  • LLMs are essentially large files (often called “weights” or “checkpoints”)
  • These files are loaded into specialized software frameworks, like PyTorch
  • Once loaded, LLMs can be queried much like a database, but with natural language

The Practical Side of LLM Deployment

Deploying an LLM involves several key steps that your IT team will need to undertake. First, they’ll need to select an appropriate model based on your specific needs. This could be an open-source option like GPT-J or BERT, or a proprietary model if you have the resources and requirements.

Next comes the hardware setup. LLMs are computationally intensive, requiring powerful GPUs, substantial RAM, and fast SSD storage. Cloud platforms like AWS or Google Cloud can provide these resources on-demand, offering flexibility and scalability.

The software installation phase involves setting up frameworks like PyTorch or TensorFlow, along with necessary dependencies. Your team will then load the model files, which can be several gigabytes in size, into this environment.

Finally, to make the model usable, your IT team will develop an interface for interacting with it, typically in the form of a REST API. This allows other systems and applications within your organization to send queries to the LLM and receive responses.

For your dev team, deploying an LLM involves several steps:

  1. Model Selection: Choose an LLM that fits your needs (e.g., GPT-J, BERT, Llama 2)
  2. Hardware Setup: Provision servers with sufficient GPU power and memory
  3. Software Installation: Install necessary frameworks (PyTorch, TensorFlow) and dependencies
  4. Model Deployment: Load the model files (typically several gigabytes in size)
  5. API Creation: Develop an interface for interacting with the model, usually a REST API

LLMs require substantial computing power:

  • High-performance GPUs (e.g., NVIDIA Tesla series)
  • Significant RAM (64GB to 256GB or more)
  • Fast SSD storage for quick data access

Your dev team will need to work with:

  1. PyTorch or TensorFlow: Frameworks for running the LLM
  2. Python: The primary programming language for AI development
  3. Web frameworks (e.g., Flask, FastAPI): For creating APIs to interact with the model
  4. CUDA: NVIDIA’s toolkit for GPU acceleration

Deployment Process

  1. Download the LLM files (often several GB in size)
  2. Set up a Python environment with necessary libraries
  3. Load the model into memory using PyTorch or TensorFlow
  4. Create an API endpoint that:
    • Accepts input text
    • Passes the input to the model
    • Returns the model’s output

This setup is conceptually similar to deploying a complex database with a custom query interface.

Alignment: Ensuring AI Behaves as Intended

An critical aspect of LLM deployment that’s often overlooked is “alignment”. In the context of AI, alignment refers to ensuring that the model’s outputs and behaviors align with human values and intentions. It’s about making sure the AI does what we want it to do, in the way we want it done.

Alignment is not a separate component that’s added to a model, but rather a characteristic that’s built into the model during its training and fine-tuning processes. It encompasses several key areas:

  1. Safety: Ensuring the model doesn’t produce harmful or dangerous content.
  2. Ethics: Aligning the model’s responses with ethical principles and societal norms.
  3. Truthfulness: Encouraging the model to provide accurate information and admit uncertainty.
  4. Usefulness: Making sure the model’s outputs are relevant and helpful to users.

This is a controversial topic, as it highly subjective and political. Many claim is it the process of censoring topics, or “lobotomising” the model for political reasons.

When selecting or fine-tuning an LLM, it’s crucial to consider its alignment characteristics. Some models, like those from Anthropic or OpenAI, place a strong emphasis on alignment. Others may require additional fine-tuning to better align with your organization’s specific values and use cases.

List of Open Source AI LLMs

Pre-2023 LLMs:

  1. T5 (2019/10) – A versatile text-to-text transformer for various NLP tasks, available in multiple sizes.
  2. RWKV 4 (2021/08) – A language model using an RNN-based architecture with infinite context length.
  3. GPT-NeoX-20B (2022/04) – A 20B parameter autoregressive model, comparable to GPT-3.
  4. YaLM-100B (2022/06) – A large 100B parameter model from Yandex, optimized for various language tasks.
  5. UL2 (2022/10) – An open-source model focusing on unified language learning across multiple domains.
  6. Bloom (2022/11) – A 176B parameter multilingual model with broad language support.

2023 LLMs:

  1. ChatGLM (2023/03) – A 6B parameter model optimized for chat, with custom usage restrictions.
  2. Cerebras-GPT (2023/03) – A compute-efficient GPT model family, scalable up to 13B parameters.
  3. Open Assistant (Pythia family, 2023/03) – A 12B parameter model aimed at democratizing LLM alignment.
  4. Pythia (2023/04) – A suite of models (70M to 12B) designed for analyzing training and scaling in LLMs.
  5. Dolly (2023/04) – Instruction-tuned models (3B, 7B, 12B) offering open-source alternatives to proprietary LLMs.
  6. StableLM-Alpha (2023/04) – A suite of models ranging from 3B to 65B parameters, designed by Stability AI.
  7. FastChat-T5 (2023/04) – A compact, commercially friendly chatbot model at 3B parameters.
  8. DLite (2023/05) – Lightweight LLMs (0.124B to 1.5B) suitable for running on minimal hardware.
  9. h2oGPT (2023/05) – A model family (12B to 20B) designed by H2O.ai for open-source LLMs.
  10. MPT-7B (2023/05) – A 7B parameter model with an 84k context length, suited for various tasks.
  11. RedPajama-INCITE (2023/05) – Models ranging from 3B to 7B parameters with instruction-tuned capabilities.
  12. OpenLLaMA (2023/05) – Open reproduction of Meta’s LLaMA, available in sizes up to 13B parameters.
  13. Falcon (2023/05) – Powerful models (7B to 180B) trained on web data with high performance.
  14. GPT-J-6B (2023/06) – A 6B parameter model similar to GPT-3, optimized for efficiency.
  15. MPT-30B (2023/06) – A 30B parameter model with an 8k context length, designed for high performance.
  16. LLaMA 2 (2023/06) – Meta’s open-source model series (7B to 70B), available under custom licenses.
  17. ChatGLM2 (2023/06) – An improved version of ChatGLM, with up to 128k context length.
  18. XGen-7B (2023/06) – A 7B parameter model optimized for long sequence modeling, with 8k context length.
  19. Jais-13b (2023/08) – A 13B parameter Arabic-centric model with instruction-tuned capabilities.
  20. OpenHermes (2023/09) – An open-source model family (7B to 13B) designed by Nous Research.
  21. Mistral 7B (2023/09) – A 7B parameter model with sliding window capabilities up to 16k context length.
  22. ChatGLM3 (2023/10) – The latest version of ChatGLM, with multiple context length options up to 128k.
  23. Skywork (2023/10) – A 13B parameter model designed for high-performance NLP tasks.
  24. Jais-30b (2023/11) – An extended version of the Jais model, with 30B parameters and 8k context length.
  25. Zephyr (2023/11) – A 7B parameter model designed for various NLP applications.
  26. DeepSeek (2023/11) – A model family (7B to 67B) designed with custom licenses and usage restrictions.
  27. Mistral 7B v0.2 (2023/12) – An updated version of Mistral with a 32k context length.
  28. Mixtral 8x7B v0.1 (2023/12) – A mixture of experts model, totaling 46.7B parameters with a 32k context length.
  29. LLM360 Amber (2023/12) – A transparent open-source model family, with a 6.7B parameter model.
  30. SOLAR (2023/12) – A 10.7B parameter model focused on efficient language processing.
  31. phi-2 (2023/12) – A model with 2.7B parameters designed by Microsoft, focusing on efficient training.

2024 LLMs:

  1. RWKV 5 v2 (2024/01) – An updated RWKV model series with up to 7B parameters and infinite context length.
  2. OLMo (2024/02) – A model series by AI2, with 1B to 7B parameters.
  3. Qwen1.5 (2024/02) – A family of models (7B to 72B) with long context lengths up to 32k.
  4. LWM (2024/02) – A large world model series with context lengths up to 1M, available under the LLaMA 2 license.
  5. Jais-30b v3 (2024/03) – An updated 30B parameter model with 8k context length.
  6. Gemma (2024/02) – A model family (2B to 7B) with context lengths up to 8192, under restrictive licenses.
  7. Grok-1 (2024/03) – A 314B parameter model under the Apache 2.0 license.
  8. Qwen1.5 MoE (2024/03) – A mixture of experts model with 14.3B parameters, offering high efficiency.
  9. Jamba 0.1 (2024/03) – A 52B parameter model using an SSM-transformer architecture.
  10. Qwen1.5 32B (2024/04) – A 32B parameter model, the capstone of the Qwen1.5 series.
  11. Mamba-7B (2024/04) – A 7B parameter model using RNN architecture, designed by Toyota Research Institute.
  12. Mixtral8x22B v0.1 (2024/04) – A mixture of experts model totaling 141B parameters with a 64k context length.
  13. Llama 3 (2024/04) – Meta’s third iteration of LLaMA, with models ranging from 8B to 70B parameters.
  14. Phi-3 Mini (2024/04) – A small to medium model (3.8B to 14B parameters) with context lengths up to 128k.
  15. OpenELM (2024/04) – An efficient language model family, with open training and inference frameworks.
  16. Snowflake Arctic (2024/04) – A high-parameter model (480B) optimized for enterprise AI applications.
  17. Qwen1.5 110B (2024/04) – A 110B parameter model, the first 100B+ model in the Qwen1.5 series.
  18. RWKV 6 v2.1 (2024/05) – The latest RWKV model series with up to 7B parameters and infinite context length.
  19. DeepSeek-V2 (2024/05) – An advanced mixture of experts model with 236B parameters and up to 128k context length.
  20. Fugaku-LLM (2024/05) – A 13B parameter model trained on the Fugaku supercomputer.
  21. Falcon 2 (2024/05) – TII’s updated Falcon model series, with an 11B parameter model and 8192 context length.
  22. Yi-1.5 (2024/05) – A model family (6B to 34B) with context lengths up to 4096.
  23. DeepSeek-V2-Lite (2024/05) – A lighter version of DeepSeek-V2, with a 16B parameter model and 32k context length.
  24. Phi-3 small/medium (2024/05) – New additions to the Phi-3 family, with models ranging from 7B to 14B parameters.

Code-specific LLMs:

  1. SantaCoder (2023/01) – A 1.1B parameter model optimized for code generation.
  2. CodeGen2 (2023/04) – A family of models (1B to 16B) designed for programming and natural languages.
  3. StarCoder (2023/05) – A model family (1.1B to 15B) specialized for code, with 8192 context length.
  4. StarChat Alpha (2023/05) – A 16B parameter model optimized for code-related conversations.
  5. Replit Code (2023/05) – A 2.7B parameter model optimized for code generation, with infinite context length.
  6. CodeT5+ (2023/05) – An updated CodeT5 model, ranging from 0.22B to 16B parameters, focused on code understanding.
  7. XGen-7B (2023/06) – A 7B parameter model trained for long sequence modeling, including code.
  8. CodeGen2.5 (2023/07) – A 7B parameter model optimized for multilingual code generation.
  9. DeciCoder-1B (2023/08) – A 1.1B parameter model designed for efficient and accurate code generation.
  10. Code Llama (2023/08) – A model series (7B to 34B) designed by Meta, optimized for code-related tasks.

List Of Open-Source Generative AI Models

Image Generation Models:

  1. Stable Diffusion – A popular text-to-image model that generates high-quality images from text prompts (CreativeML Open RAIL-M).
  2. DALL·E Mini (Craiyon) – A smaller, open-source version of DALL·E for generating images from text (Apache 2.0).
  3. VQ-VAE-2 – A vector quantization-based generative model for producing high-fidelity images (Apache 2.0).
  4. BigGAN – A scalable GAN model known for generating high-resolution and diverse images (Apache 2.0).
  5. StyleGAN2-ADA – An advanced GAN model with adaptive data augmentation for high-resolution image generation (Nvidia Source Code License).
  6. DeepDream – A CNN-based model that creates dream-like, surreal images by enhancing patterns (Apache 2.0).
  7. CLIP-Guided Diffusion – Combines CLIP and diffusion models to generate text-guided images (MIT License).
  8. PixelCNN – A generative model that creates images pixel by pixel, modeling image distributions (Apache 2.0).
  9. Latent Diffusion Models (LDM) – A diffusion model that iteratively denoises latent representations to generate high-quality images (MIT License).

Audio Generation Models:

  1. WaveNet – A deep generative model that produces high-fidelity audio waveforms, mainly used for text-to-speech (Apache 2.0).
  2. Jukebox – A neural network that generates music in various genres and styles, available as open-source (MIT License).
  3. DDSP (Differentiable Digital Signal Processing) – Combines deep learning and DSP to generate audio and music (Apache 2.0).
  4. MelGAN – A GAN-based model designed to generate high-quality audio from mel-spectrograms (MIT License).
  5. Tacotron 2 – A sequence-to-sequence model for natural-sounding text-to-speech synthesis (Apache 2.0).
  6. WaveGlow – A flow-based generative model for generating audio waveforms, especially in TTS applications (Nvidia Source Code License).
  7. Spleeter – A music source separation model that isolates vocals, drums, bass, and other elements (MIT License).
  8. DiffWave – A diffusion model for high-quality audio generation, particularly speech synthesis (MIT License).
  9. NSynth – A neural network synthesizer that creates new musical sounds based on a large dataset of musical notes (Apache 2.0).

Video Generation Models:

  1. MoCoGAN – A GAN model that generates video by decoupling motion and content generation (MIT License).
  2. Video Diffusion Models – Generates video sequences from noise using an iterative denoising process (MIT License).
  3. VQ-VAE-2 for Video – An extension of VQ-VAE-2 applied to video, generating high-quality video sequences (Apache 2.0).
  4. TGAN (Temporal GAN) – A GAN-based model designed to generate video sequences by modeling temporal dependencies (MIT License).
  5. First Order Motion Model – Animates a still image by driving it with the motion from a reference video (Apache 2.0).
  6. Vid2Vid – A model for generating high-resolution videos from input video sequences, often for video-to-video translation (Nvidia Source Code License).
  7. 3DGAN – A generative model that creates 3D objects, useful for generating 3D video sequences (MIT License).
  8. StyleGAN-V – An extension of StyleGAN for generating consistent and high-quality video sequences (Nvidia Source Code License).

Customizing LLMs for Your Business Needs

One of the most powerful aspects of LLMs is their ability to be fine-tuned for specific industries or use cases. This process involves exposing the model to domain-specific data, allowing it to learn the nuances and terminology of your particular field.

To fine-tune an LLM, your team will need to collect relevant company data – documents, reports, customer interactions, and any other text-based information that represents your business’s knowledge. This data is then processed and used to further train the pre-existing model, adapting its knowledge to your specific domain.

This fine-tuning process is analogous to customizing off-the-shelf software for your specific business needs. It allows you to create an AI tool that not only understands general language but also speaks the unique language of your industry and organization.

Integration and Practical Considerations

Once deployed and fine-tuned, an LLM can be integrated with your existing systems to provide enhanced capabilities. It could be connected to internal databases for real-time information retrieval, integrated with customer-facing applications for improved interactions, or used in conjunction with analytics tools for advanced text analysis.

However, deploying an LLM also comes with important considerations. Data privacy is paramount – you’ll need to ensure that the model and its training data are securely managed. Scalability is another key factor, as you’ll want to plan for increased usage as applications grow. Regular maintenance, including updates and continued fine-tuning, will be necessary to keep the model performing optimally. Lastly, implementing robust monitoring systems to track performance and usage will be crucial for ongoing management and improvement.

By understanding these concepts and considerations, you can better guide your IT team in implementing AI solutions that align with your business objectives. While deploying an LLM is a significant undertaking, with proper planning and execution, it can provide substantial value to your organization, opening up new possibilities for innovation and efficiency in your operations.