Grokverse

Pioneering true decentralization of AI

Abstract

The AI development world is full of failed projects, highlighting the challenges and uncertainties in this field. From natural language processing to computer vision, there are many obstacles like limited data, biased algorithms, and scalability issues that can slow progress. For crypto projects getting into AI, the challenges are even tougher. Unlike traditional markets, they often lack funding, talent, and clear regulations. Building AI from scratch needs lots of resources and expertise that can be hard to find in the crypto world. That's why even big AI projects in crypto might struggle to keep up with models like ChatGPT or Claude. These models dominate because they're closed-source, making it hard for others to collaborate or innovate. But there's a better way: open-source models like Grok. They give everyone access to the model's inner workings, data, and code, so anyone can improve it to suit their needs.

In this paper, we argue for using open-source AI models to tackle the problems linked with closed ones. With open-source, developers can work together to make models better, encourage new ideas, and ensure transparency. Open-source also makes it easier to share knowledge, reproduce results, and compare different models. Grok is a great example of an open-source model. It's accessible, flexible, and easy to understand. Unlike closed models, anyone can join in, from collecting data to training and deploying the model. Grok's transparency and detailed guides make it perfect for both beginners and experts to learn and contribute to AI advancements.

Keywords

Artificial Intelligence, Decentralization, Integrated Development Environment. Programming

1. Introduction

In the dynamic landscape of AI development, NavyAI emerges with a bold initiative: to revolutionize AI training through decentralized GPU infrastructure. Our mission is clear - to democratize access to GPU resources and make AI training more affordable by harnessing the power of decentralized networks.

At the heart of our endeavor lies a pivotal decision - the selection of an AI model that not only embodies the principles of openness and accessibility but also possesses the technical prowess to drive innovation forward. After careful consideration, we have chosen Grok as our flagship AI model. Grok's open-source nature aligns perfectly with our vision of democratizing AI, while its endorsement by visionaries such as Elon Musk underscores its credibility and potential impact.

Grok holds immense promise as a cornerstone of our initiative. Leveraging its robust architecture and comprehensive documentation, we envision a future where AI training becomes more accessible and efficient. Beyond training, Grok's versatility extends to integration within Integrated Development Environments (IDEs), empowering developers to code more efficiently and effectively.

As we embark on this journey, we introduce the concept of the Grokverse - a decentralized ecosystem built upon the principles of AI democratization and collaboration. Through this research paper, we aim to elucidate the vision, capabilities, and potential applications of the Grokverse. From AI training to developer productivity tools, the Grokverse promises to redefine the boundaries of innovation and empowerment in the AI landscape.

2. Literature

2.1 Grok’s Detail

Grok-1.5 is xAI’s latest artificial intelligence model, representing an upgrade over the previously released Grok-1.

The improvements include enhanced reasoning abilities, superior mathematical computation, and more efficient code generation capabilities. Furthermore, Grok-1.5’s ability to process up to 128,000 tokens greatly extends its capacity for understanding and generating long-context information. Capabilities and Reasoning

Grok-1.5 has shown significant advancements in its capabilities, particularly in coding and math-related tasks. On the MATH benchmark, which assesses a wide range of math problems from grade school to high school competition level, Grok-1.5 scored 50.6%. In the GSM8K benchmark, which focuses on advanced math problem-solving, it achieved a score of 90%. Moreover, Grok-1.5 demonstrated its prowess in code generation and problem-solving by scoring 74.1% on the HumanEval benchmark. These improvements highlight the AI's growing proficiency in handling complex mathematical and programming tasks.

Source: https://x.ai/blog/grok-1.5

Long Context Understanding Grok-1.5 also introduces an innovative feature that significantly enhances its context processing capabilities. It can now handle long contexts of up to 128K tokens, a substantial increase compared to its predecessor. This improvement allows Grok to store up to 16 times more information in its context window. Consequently, Grok-1.5 is capable of utilizing information from much longer documents, effectively enhancing its performance in tasks that involve long-form content.

Moreover, the model excels at processing lengthy and intricate prompts while preserving its ability to follow instructions as its context window grows. In the Needle In A Haystack (NIAH) assessment, Grok-1.5 showcased remarkable retrieval capabilities, accurately locating embedded text within contexts extending to 128K tokens.2.1.2. Grok-1.5 Infrastructure

Advancements in Large Language Models (LLMs) research, operating on colossal GPU clusters, necessitate a resilient and adaptable infrastructure. Grok-1.5 is founded upon a custom distributed training framework incorporating JAX, Rust, and Kubernetes. This infrastructure empowers our team to quickly experiment with ideas and effectively scale new architectures. A significant hurdle in training LLMs on vast computing clusters is ensuring the training job's reliability and uptime. Our custom training orchestrator guarantees the automatic detection and ejection of problematic nodes from the training job. Additionally, we have optimized checkpointing, data loading, and training job restarts to reduce downtime in the event of a failure.

2.2 Limitations

2.2.1 Training Limitations

Grok is still in its early beta stage, the result of just two months of training. Yet, within the broader realm of Language Model models (LLMs), this training duration of two months and 33 billion parameters may seem relatively limited, being 50 times smaller than GPT-4 and almost 4 times smaller than Claude 2

2.2.2 Hardware cost

To install Grok locally, you will need a system with sufficient GPU memory due to the model's large size (314B parameters). The exact requirements may vary based on the specifics of your setup and the tasks you plan to perform with Grok, but a machine with multiple GPUs is generally recommended. For example, one instance of Grok-1 is known to be hosted on a cluster with 8 NVIDIA Ampere Tesla A40 GPUs. The cost of a single NVIDIA Ampere Tesla A40 GPU is approximately $5,000. Therefore, 8 NVIDIA Ampere Tesla A40 GPUs would cost around $40,000 pre-tax, meaning not everyone can afford to train Grok by themselves. This does not even include expenses such as electricity, cooling, and maintenance for the hardware, as well as costs associated with training the model, acquiring data, and other operational expenses.

2.2.3 Data Privacy

Until now, xAI has yet to confirm how they will store or access users' data. Therefore, it's crucial to determine where to store this data so that users can utilize Grok for their research

That’s why we think we can solve this problem by encrypted data storage

2.3 Solution

By using de-GPU through io.net, users can train Grok with a cheaper cost than using centralized GPUs providers.

3. AI and Crypto

The fast growth in this area, driven by the mix of AI and Crypto, will shake up the economy, as AI brings new opportunities to every sector. This blend will create many new projects, some solving the discussed challenges, while others might combine AI and Crypto in basic ways without much real value. If you look closer, most projects don’t even know how they are going to use AI models in their projects. There’s no real use case except for wanting to include the buzzword “AI” in their name. That's why we want to ensure that we bring the most benefits from AI to the crypto space by introducing Grokverse.

What is IDE (Integrated Development Environment)

IDE stands for Integrated Development Environment. It's a software application that provides comprehensive facilities to programmers for software development. An IDE typically includes a code editor, a compiler or interpreter, build automation tools, and debugging capabilities, all integrated into a single user interface. This helps streamline the development process by providing developers with everything they need to write, test, and debug code in one place. Popular examples of IDEs include Visual Studio, IntelliJ IDEA, Eclipse, and PyCharm.

Aven IDE typically consists of:

• Source code editor: A text editor that can assist in writing software code with features such as syntax highlighting with visual cues, providing language specific auto-completion, and checking for bugs as code is being written.

• Local build automation: Utilities that automate simple, repeatable tasks as part of creating a local build of the software for use by the developer, like compiling computer source code into binary code, packaging binary code, and running automated tests.

• Debugger: A program for testing other programs that can graphically display the location of a bug in the original code.

By integrating Grok AI for blockchain coding, developers can now leverage their potential to build products much faster and more efficiently. Here are some benefits of integrating AI into coding:

• Automated Code Generation: AI can assist developers in generating code snippets or templates for smart contracts, improving development efficiency and reducing coding time.

• Error Detection and Correction: AI-powered tools can analyze blockchain code to detect common programming errors or vulnerabilities, helping developers identify and fix issues early in the development process.

• Code Optimization: AI algorithms can optimize blockchain code by identifying redundant or inefficient code segments and suggesting improvements to enhance performance and reduce resource consumption.

• Natural Language Processing (NLP): NLP-based AI tools can interpret and understand natural language requirements or specifications provided by developers, facilitating communication and collaboration in the development process.

• Predictive Analysis: AI can analyze historical blockchain code repositories to identify patterns or trends in coding practices, helping developers make informed decisions and anticipate future coding challenges.

• Code Refactoring: AI-powered tools can automatically refactor blockchain code to improve readability, maintainability, and adherence to coding standards, saving developers time and effort in manual refactoring tasks.

• Debugging Assistance: AI can assist developers in debugging blockchain code by analyzing runtime data, identifying bugs or anomalies, and providing suggestions for troubleshooting and resolution.

Indeed, developers can leverage AI agents tailored for specific languages such as Rust. These AI agents can assist developers in various aspects of blockchain coding, including code generation, error detection, optimization, and more. By integrating AI into the development process, developers can create new blockchain applications much faster and with less effort. AI can automate repetitive tasks, provide intelligent suggestions and insights, and streamline the overall development workflow. This not only accelerates the development process but also enhances productivity and allows developers to focus on more innovative aspects of app creation. As the demand for consumer apps continues to grow rapidly, the integration of AI can significantly benefit developers by enabling them to meet market demands more efficiently and effectively.

TVL Breakdown by Smart Contract Languages - Defillama

Source: DefiLlama

cursor.sh

Cursor is a cutting-edge AI-powered code editor revolutionizing the software development process. By deeply understanding your project, Cursor saves valuable time by providing tailored answers within your codebase, eliminating the need for tedious searching. With its ability to reference files and documentation directly, it streamlines development tasks by swiftly pinpointing relevant information. Moreover, Cursor enhances productivity by seamlessly translating natural language instructions into code, whether it's editing entire methods or generating code from scratch. Its predictive capabilities anticipate your next move, ensuring a smooth workflow. Additionally, the advanced Copilot ++ feature augments productivity by suggesting mid-line completions and entire code differentials, trained to understand and expedite sequences of edits. By leveraging Cursor's intelligent functionalities, developers can focus on the creative aspects of coding, accelerating project completion and fostering innovation.

Source: Curshor.sh

4. Grokverse

On November 6, 2023, xAI announced PromptIDE : a new integrated development environment tailored for prompt engineering and interpretability research. With transparent access to Grok-1, PromptIDE accelerates prompt engineering through an SDK for implementing advanced techniques and rich analytics for visualizing network outputs. Featuring a Python code editor, users can explore capabilities and access helpful analytics like precise tokenization and sampling probabilities. Plus, automatic prompt saving, built-in versioning, and easy file uploads streamline the user experience. Revolutionize your approach to large language models with PromptIDE.

We will incorporate PromptIDE from Grok into our ecosystem since they have already developed one for Python. Additionally, we will expand the language support within our own IDE to create a significantly improved version tailored for coding blockchain applications, distinguishing it from other IDEs on the market. Leveraging their optimization for coding products will further enhance our offering.

Features

The PromptIDE offers the following features:

• Sample from Grok-1,

  • See sampling probabilities (after Nucleus-thresholding has been applied),

  • See aggregated attention masks for each sampled token,

  • Restrict sampling to a small set of tokens (e.g. to implement multiple choice tests),

  • Disallow the model from sampling a small set of tokens (e.g. to avoid some behaviour),

  • Control the sampling temperature,

  • Control the Nucleus (Top-P)-sampling parameter,

• See the precise tokenization of a prompt and the corresponding token IDs

• Implement complex multi-step and branching prompting techniques,

• Implement concurrent prompts,

• Upload and batch-process small CSV files (up to 5 MiB per file),

• Create and manage API keys,

• Export prompts and run them locally using Python SDK

• Simple built-in versioning of prompts,

• Share prompts publicly,

• Create custom chatbots based on Grok-1,

• See and understand your token limits and token usage.

PromptIDE

The PromptIDE is an integrated development environment (IDE) for prompt engineering and capabilities research. It offers effortless access to xAI's latest large language model (LLM) Grok-1. The IDE is available to members of Early Access program who are subscribed to X-Premium+ and are based in the USA.

The SDK utilizes Python coroutines, enabling the creation of concurrent execution contexts.

You have the ability to upload minor-sized files and incorporate them into your prompts using the read_file() function provided by the SDK. The tutorial demonstrates the process of conducting an MMLU evaluation within the IDE, using our more compact model, Grok-0.

Python SDK

Getting Started

SDK is the recommended way of interacting with the xAI API. To get started using our SDK, install it via pip :

pip install xai-sdk

API Key

Before you can start using the SDK, you have to generate an API key in the PromptIDE. Please follow these instruction to create API Key :

Step 1

After signing in, click on your username in the top right hand corner of the screen and then select API Keys.

Step 2

Now click on the Create API Key button, which open a dialog window that allows you to customize the API key ACLs. By default, no ACLs are selected, which means the API key cannot be used to access any endpoint. As a best practice, select only the ACLs that are necessary for accomplishing the task you have in mind. If you want to use the API key to access Grok, add the chat:write ACL. If you would only like to sample from the raw Grok-1 model, add only the sampler:write ACL. The respective ACLs use the :write suffix because sampling from either Grok or Grok-1 will count towards your token limit and is this a data-mutating operation.

Step 3

After clicking Save, the list of API keys refreshes, and you see your newly created API key. From here, you can click the pencil icon to adjust the ACLs and the bin icon to delete an API key. To start using the API key with SDK, click the Copy button.

Step 4

Now you're ready to start playing with API using Python SDK from xAI

Once you have created your key, copy it to your clipboard and store it in an environment variable named XAI_API_KEY:

export XAI_API_KEY=[Your API key goes here]

Remember that API keys are associated with a set of ACLs. If you get a permission denied error, double check to make sure your API key has the right ACLs for the task you're trying to accomplish. You can validate that API access works, by running the following command:

python -c "import xai_sdk; xai_sdk.does_it_work()”

If everything works correctly, it will output the string Does it work? Yes, it does.. Note that your API key must have the sampler:write ACL in order for this sanity check to work.

Example

Now that you have installed SDK and configured your API key, you should be able to run the following example program. Note that your API key needs the sampler:write ACL in order for this program to work.

**simple_completion.py**
"""A simple example demonstrating text completion.""”

import asyncio

import xai_sdk

async def main():
    """Runs the example.""”
    client = xai_sdk.Client()

    prompt = "The answer to live and the universe is”
    print(prompt, end="")
    async for token in client.sampler.sample(prompt, max_len=3):
        print(token.token_str, end="")
 print("")

asyncio.run(main())

Trouble shooting

Missing XAI_API_KEY

If you get the following error, you have not configured your XAI_API_KEY environment variable correctly.

ValueError: Trying to read the xAI API key from the XAI_API_KEY environment variable but it doesn't exist.

Missing ACLs

If you get the following error, you have not configured the correct ACLs on your API key.

API key is missing ACLs required to perform this request.

Return to the PromptIDE and edit your API key.

API Reference

xai_sdk.Client

Client for connecting to the xAI API.

The client uses an API key, which is either read from the environment variable XAI_API_KEY or provided by the api_key constructor argument. API keys can be created and managed in our IDE, which is available under ide.x.ai (click on your username in the top right hand corner).

The API is hosted on api.x.ai, and we connect via port 443.

Source code in xai_sdk/client.py

class Client:
"""Client for connecting to the xAI API.

The client uses an API key, which is either read from the environment variable `XAI_API_KEY` or
provided by the `api_key` constructor argument. API keys can be created and managed in our IDE,
which is available under ide.x.ai (click on your username in the top right hand corner).

The API is hosted on api.x.ai, and we connect via port 443.
"""

chat: chat.AsyncChat
files: files.AsyncFiles
grok: grok.AsyncGrok
sampler: sampler.AsyncSampler

def __init__(
    self,
    api_key: Optional[str] = None,
    *,
    initial_rng_seed: Optional[int] = None,
    api_host: str = "api.x.ai",
) -> None:
    """Initializes a new instance of the `Client` class.

    Args:
        api_key: API key to use. If unspecified, the API key is read from the `XAI_API_KEY`
            environment variable.
        initial_rng_seed: Used to make calls to the API deterministic given the initial seed and
            the order of API calls. If unspecified, a random seed will be sampled for every new
            instance of the `Client` class.
        api_host: Hostname of the API server.

    Raises:
        ValueError: If the `XAI_API_KEY` environment variable is not set.
        ValueError: If the API key is empty.
    """
    if api_key is None:
        api_key = _get_api_from_env()

    if not api_key:
        raise ValueError("Empty xAI API key provided.")

    # Create a channel to connect to the API host. Use the API key for authentication.
    call_credentials = grpc.metadata_call_credentials(_APIAuthPlugin(api_key))
    channel_credentials = grpc.ssl_channel_credentials()
    credentials = grpc.composite_channel_credentials(channel_credentials, call_credentials)
    async_channel = grpc.aio.secure_channel(api_host, credentials)

    # Create the stubs used by the SDK. Note that they don't create any connections until being
    # used.
    self.sampler = sampler.AsyncSampler(
        sampler_public_pb2_grpc.SamplerStub(channel=async_channel), initial_rng_seed
    )
    self.chat = chat.AsyncChat(stateless_chat_pb2_grpc.StatelessChatStub(channel=async_channel))
    self.grok = grok.AsyncGrok(chat_pb2_grpc.ChatStub(channel=async_channel))
    self.files = files.AsyncFiles(files_pb2_grpc.FileStub(channel=async_channel))

xai_sdk.Client.__init__(api_key=None, *, initial_rng_seed=None, api_host='api.x.ai')

Initializes a new instance of the Client class.

Parameters:

Name	Type	Description	Default
api_key	Optional[str]	API key to use. If unspecified, the API key is read from the XAI_API_KEY environment variable.	None
initial_rng_seed	Optional[int]	Used to make calls to the API deterministic given the initial seed and the order of API calls. If unspecified, a random seed will be sampled for every new instance of the Client class.	None
api_host	str	Hostname of the API server.	'api.x.ai'

Raises:

Type	Description
ValueError	If the XAI_API_KEY environment variable is not set.
ValueError	If the API key is empty.

Source code in xai_sdk/client.py

def **init**(
self,
api_key: Optional[str] = None,
*,
initial_rng_seed: Optional[int] = None,
api_host: str = "[api.x.ai](http://api.x.ai/)",
) -> None:
"""Initializes a new instance of the Client class.
Args:
    api_key: API key to use. If unspecified, the API key is read from the `XAI_API_KEY`
        environment variable.
    initial_rng_seed: Used to make calls to the API deterministic given the initial seed and
        the order of API calls. If unspecified, a random seed will be sampled for every new
        instance of the `Client` class.
    api_host: Hostname of the API server.

Raises:
    ValueError: If the `XAI_API_KEY` environment variable is not set.
    ValueError: If the API key is empty.
"""
if api_key is None:
    api_key = _get_api_from_env()

if not api_key:
    raise ValueError("Empty xAI API key provided.")

# Create a channel to connect to the API host. Use the API key for authentication.
call_credentials = grpc.metadata_call_credentials(_APIAuthPlugin(api_key))
channel_credentials = grpc.ssl_channel_credentials()
credentials = grpc.composite_channel_credentials(channel_credentials, call_credentials)
async_channel = grpc.aio.secure_channel(api_host, credentials)

# Create the stubs used by the SDK. Note that they don't create any connections until being
# used.
self.sampler = sampler.AsyncSampler(
    sampler_public_pb2_grpc.SamplerStub(channel=async_channel), initial_rng_seed
)
self.chat = chat.AsyncChat(stateless_chat_pb2_grpc.StatelessChatStub(channel=async_channel))
self.grok = grok.AsyncGrok(chat_pb2_grpc.ChatStub(channel=async_channel))
self.files = files.AsyncFiles(files_pb2_grpc.FileStub(channel=async_channel))

Source :

xAI Prompt IDE

xAI Python SDK

4.1 Introducing GDE

Introduction

GDE (Grok Integrated Development Environment) represents a significant leap forward in IDE technology, specifically tailored for Rust programming language with a specialized focus on efficient blockchain development. This document provides an in-depth overview of GDE's features, capabilities, and integration of AI technology, including a chatbot assistant

Overview:

GDE is meticulously designed to offer Rust developers working on blockchain projects a streamlined and efficient coding experience. Building upon the foundation of PromptIDE, GDE integrates advanced AI capabilities to provide mid-line completions, entire diffs suggestions, bug fixes, and more. These features empower developers to write, edit, and debug code with unprecedented speed and accuracy.

Key Features:

1. Mid-line Completions: GDE's AI algorithms suggest completions within lines of code, intelligently predicting the next tokens based on context and learned patterns. This feature accelerates coding by reducing manual typing and offering contextually relevant suggestions as developers write code.

2. Entire Diffs Suggestions: GDE analyzes sequences of edits and offers suggestions for entire code differentials, aiding developers in identifying and resolving code inconsistencies or errors. By providing holistic suggestions, GDE streamlines the debugging process and enhances code quality.

3. Bug Fixes and Error Corrections: GDE's AI-powered capabilities extend to identifying and proposing fixes for common bugs and errors in Rust code. Developers can rely on GDE to provide insightful suggestions for resolving issues quickly, thereby minimizing debugging time and improving code reliability.

4. Trained Autocompletion: GDE's AI model is trained on extensive datasets of Rust code, enabling it to accurately predict and autocomplete code snippets based on developer input. This training ensures that GDE adapts to various coding styles and project requirements, offering tailored suggestions for each user.

Benefits:

• Increased Efficiency: GDE's AI-powered features streamline coding tasks, reducing manual effort and accelerating development cycles.

• Enhanced Productivity: Developers benefit from proactive suggestions, bug fixes, and instant access to assistance via the chatbot assistant, leading to improved productivity and faster code iteration.

• Improved Code Quality: GDE's AI capabilities aid in maintaining code consistency, identifying errors, and suggesting fixes, ultimately resulting in higher-quality software products.

• Specialized Support for Blockchain Development: GDE's optimization for Rust-based blockchain development ensures that developers have access to the tools and features necessary for building secure and reliable blockchain applications efficiently.

Conclusion:

GDE revolutionizes the Rust development experience by offering advanced AI-powered features tailored to blockchain development. From mid-line completions and entire diffs suggestions to bug fixes and a built-in chatbot assistant , GDE empowers developers to write, debug, and maintain code with unparalleled efficiency and precision.

4.2 Enerzion

In the realm of AI training and safeguarding developers' code confidentiality, ensuring data security presents a critical challenge. To address this concern, we are developing an encrypted solution that allows users to securely upload their original data to our app.

Introducing Enerzion - combines "energy" and "encryption," symbolizing the powerful protection and security provided to users' data. It conveys the idea of safeguarding valuable information with vigor and strength, reflecting the core mission of the app.

All uploaded data will undergo encryption before storage on a network device randomly assigned with disk access. User authentication will be required to decrypt the data, with specific authentication protocols to be detailed later. Currently, supported data types include images, sounds, and text, while video encryption is still in development. The current encryption speed stands at approximately 100 MB/s. For further information, please await our forthcoming announcement.

App Features:

4.2.1 Data Encryption Process

Our app automatically encrypts uploaded data using robust encryption algorithms, ensuring data security throughout storage and transmission processes.

4.2.2 Decrypting Data

Authorized users can seamlessly decrypt encrypted data using their authentication credentials, enabling smooth access to confidential information.

4.2.3 Managing Encrypted Data

Users can efficiently manage their encrypted data through our app's user-friendly interface, facilitating tasks such as organization, secure sharing, and deletion of files as required.

4.2.4 Proof of Ownership :

Upon submission of data for encryption, users receive a unique Non-Fungible Token (NFT) as proof of ownership. This NFT serves as a cryptographic key to unlock the encrypted data later. To enhance data security, we store data on several devices, each with limited storage capacity.

Data Distribution and Risk Management:

• Each network device can only store a portion of the data, minimizing the impact of device compromise on data security.

• Removal of data entails a risk of data loss, currently estimated at 20%. We are actively exploring solutions to mitigate this risk.

Our app provides a comprehensive solution for safeguarding sensitive data, addressing the unique challenges of AI training and code confidentiality protection. Stay tuned for updates on our progress and additional features.

5. References: https://arxiv.org/pdf/2403.08299.pdf

https://arxiv.org/pdf/2403.14592.pdf

https://arxiv.org/pdf/2401.10739.pdf

https://alexcheema.github.io/AIxCryptoPrimer.pdf https://cursor.sh/ https://x.ai/blog/grok