Proposal for Adopting Mixtral 8x7B Model for Devolved AI

Summary We propose to adopt the MIxtral 8x7B open-source model as the foundational large language model (LLM) for Devolved AI. This model will be fine-tuned to meet the specific needs and goals of our community, ensuring optimal performance and integration with our existing systems.

Rationale The Mixtral 8x7B model stands out due to its innovative Sparse Mixture of Experts (SMoE) architecture, which allows it to efficiently utilize its extensive 45 billion parameters. Despite its large parameter size, the model's design ensures computational efficiency by activating only a subset of parameters per token, thereby reducing the overall computational load while maintaining high performance.

Key advantages of the Mixtral 8x7B model include:

1. Superior Performance: Outperforms models such as Llama 2 70B and GPT-3.5 on most benchmarks.
2. Multilingual Support: Capable of understanding and generating text in English, French, Italian, German, and Spanish.
3. Efficient Computation: Utilizes 12.9 billion parameters per token, providing a balance between performance and computational cost.
4. Large Context Handling: Can manage up to 32,000 tokens in a single context, ideal for complex and extensive text generation tasks.
5. Open Source: Licensed under Apache 2.0, promoting accessibility and adaptability for our specific use cases.

Integration with EvoHyve and Athena AI EvoHyve, our hybrid federated learning and distributed training system, is designed to build and fine-tune our decentralized AI LLM, Athena AI. The adoption of the Mixtral 8x7B model aligns perfectly with the goals of EvoHyve for several reasons:

1. Cost and Time Savings: By starting with a base LLM like Mixtral 8x7B, we save hundreds of thousands of dollars and several months of development time compared to starting from scratch.
2. Scalability and Efficiency: The SMoE architecture of Mixtral 8x7B allows for efficient scaling, essential for distributed training across our global GPU network. This ensures that we can manage large-scale training tasks without incurring prohibitive computational costs.
3. Flexibility in Fine-Tuning: Mixtral 8x7B's architecture is highly adaptable, making it suitable for fine-tuning on specific datasets approved by our community. This flexibility ensures that Athena AI can be continuously improved based on real-world data and evolving requirements.
4. Decentralized Training: EvoHyve's hybrid federated learning system benefits from Mixtral 8x7B's ability to handle large contexts and diverse data inputs. This capability is crucial for decentralized training, where data fragments are processed across multiple nodes.
5. Enhanced AI Capabilities: The model's superior performance on various benchmarks translates into a more robust and capable Athena AI. This will enable Devolved AI to deliver advanced AI services that are competitive with the best in the industry.

EvoHyve System Workflow The EvoHyve system will operate as follows:

1. Community Approval: Datasets are proposed and approved by the Devolved AI community for use in fine-tuning.
2. Data Fragmentation: Approved datasets are fragmented and distributed across our network.
3. Local Training: Clients in the EvoHyve network use their local GPUs to train on the fragmented data, updating their models.
4. Parameter Aggregation: The locally trained parameters are returned to Athena AI, our central AI model.
5. Model Update: Athena AI aggregates the parameters and updates all clients, ensuring the entire network benefits from the decentralized training process.

Starting with Mixtral 8x7B provides a robust foundation for Athena 2, allowing us to save resources and time while building a powerful and efficient LLM. For future iterations, such as Athena 3, we will be in a better position to consider starting from scratch.

Proposal Details

• Model Adoption: Start with the Mixtral 8x7B open-source model.
• Fine-Tuning: Customize the model to align with Devolved AI’s requirements, leveraging our unique datasets and use cases.
• Integration: Implement the fine-tuned model into Devolved AI’s infrastructure to enhance our AI capabilities.
• Performance Monitoring: Continuously monitor and evaluate the model’s performance, making adjustments as needed to ensure optimal functionality.

Voting We seek the approval of the Devolved AI community to proceed with this plan. Your vote will determine whether we adopt and fine-tune the Mixtral 8x7B model for our platform and fine tune it to make it our own.

We believe that adopting the Mixtral 8x7B model will significantly enhance our AI capabilities and align with our mission to advance the integration of AI and blockchain technologies.

Thank you for your participation and support.