BenevolenceMessiah_-_Yi-Coder-9B-Chat-Instruct-TIES-MoE-v1.0-gguf

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Yi-Coder-9B-Chat-Instruct-TIES-MoE-v1.0 - GGUF

• Model creator: https://huggingface.co/BenevolenceMessiah/
• Original model: https://huggingface.co/BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES-MoE-v1.0/

Original model description:

base_model:

• BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES library_name: transformers tags:
• mergekit
• merge license: apache-2.0 metrics:
• code_eval

---

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the MoE merge method using BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES

Models Merged

The following models were included in the merge:

• 01-ai/Yi-Coder-9B
• 01-ai/Yi-Coder-9B-Chat
• BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES
• BenevolenceMessiah/Yi-Coder-9B-Chat-8x-MoE

Methodology

Abstract:

This is a highly experimental endeavor leveraging other highly experimental endeavors in the process. In the rapidly evolving landscape of artificial intelligence and machine learning, the development of models capable of generating complex, multi-language code with high accuracy and deployability has become increasingly critical. This paper presents a comprehensive exploration of the creation and refinement of the Yi-Coder-9B model family, culminating in the development of the BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES-MoE-v1.0 model. Our unique approach involved leveraging advanced model merging techniques, specifically the Transformer Inter-Expert Sparsification (TIES) merge strategy and sparse upcycling within a Mixture of Experts (MoE) framework, to combine and enhance the capabilities of existing models.

The project initiated with the TIES merge of two foundational models:

• Yi-Coder-9B: A general-purpose code generation model.
• Yi-Coder-9B-Chat: A model fine-tuned for interactive dialogue. The TIES merge strategy was employed using the following configuration:

models:
  - model: 01-ai/Yi-Coder-9B
    parameters:
      density: 0.5
      weight: 0.5
  - model: 01-ai/Yi-Coder-9B-Chat
    parameters:
      density: 0.5
      weight: 0.5

merge_method: ties
base_model: 01-ai/Yi-Coder-9B
parameters:
  normalize: false
  int8_mask: true
dtype: float16

Initial TIES merge:

This resulted in the creation of BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES. The TIES strategy effectively combined both models by sparsely retaining 50% of the most meaningful parameters from each, reducing overlap and preserving task-specific information. The prediction surounding this merge operation is primarily hypothetical because I don't know how different the Yi Coder base and chat models are from one another. In any case, the objectives and hypothetical benefits of this TIES merge are as follows:

1. Task Specialization and Complementarity: The Yi-Coder-9B and Yi-Coder-9B-Chat models likely excel in different domains: the base model is more of a general-purpose code model, while the chat variant is fine-tuned for interactive dialogue. The TIES merge, especially with density: 0.5, would allow for a 50% retention of task-specific information from each model, reducing the overlap and interference between their unique parameters. This should, in theory, allow the resulting model to effectively combine both capabilities.
2. TIES Algorithm: TIES is designed to improve merging by sparsifying task vectors and employing a sign consensus algorithm, which reduces parameter conflicts. By sparsely retaining the most meaningful differences (50% in this case), it helps prevent the merged model from losing valuable task-specific skills from either model, which is crucial when combining models with specialized purposes like code generation and chat interaction.
3. Parameter Optimization:
   • Density: 0.5 ensures a balance between the models by keeping only half of the parameters from the difference vectors.
   • Weight: 0.5 gives equal emphasis to both the base and chat models, ensuring that neither models' specific strengths dominate.
   • Normalize: false and int8_mask: true suggest that you're aiming for precision at a reduced memory footprint, which should help with efficiency.

Sparse Upcycling Merge:

Next, 01-ai/Yi-Coder-9B-Chat underwent a seperate instance of sparse upcycling: That is, subsequently, we applied a sparse upcycling merge using a MoE architecture, integrating eight instances of the Yi-Coder-9B-Chat model:

base_model: 01-ai/Yi-Coder-9B-Chat
gate_mode: random
dtype: bfloat16
experts:
  - source_model: 01-ai/Yi-Coder-9B-Chat
  - source_model: 01-ai/Yi-Coder-9B-Chat
  - source_model: 01-ai/Yi-Coder-9B-Chat
  - source_model: 01-ai/Yi-Coder-9B-Chat
  - source_model: 01-ai/Yi-Coder-9B-Chat
  - source_model: 01-ai/Yi-Coder-9B-Chat
  - source_model: 01-ai/Yi-Coder-9B-Chat
  - source_model: 01-ai/Yi-Coder-9B-Chat

This resulted in the creation of BenevolenceMessiah/Yi-Coder-9B-Chat-8x-MoE. This strategy outlines a Mixture of Experts (MoE) approach for sparse upcycling using multiple instances of the same model (Yi-Coder-9B-Chat). Sparse upcycling here aims to enhance model efficiency by distributing the computational load across multiple "expert" submodels without requiring each submodel to compute every token. Here's what the sparse upcycling would ideally accomplish:

1. Enhanced Efficiency through Specialization: By combining multiple copies of the same base model and gating them randomly, the strategy creates an MoE architecture. In MoE, only a subset of the experts (in this case, Yi-Coder-9B-Chat) is activated for each input. This allows the model to specialize on certain types of tasks or inputs without needing all experts to process the same data. The gating mechanism will learn which expert(s) to activate for different input tokens during training.
2. Parameter Efficiency: The model will utilize different "experts" only when needed, resulting in efficient parameter use. This allows the model to scale to larger capacities without linearly increasing the number of active parameters for every computation. In this case, the total number of parameters in the model increases with the addition of experts, but only a fraction of them are active at any given time, leading to sparse activations.
3. Random Gate Mode (Preparation for Fine-Tuning): The random gate mode suggests that this architecture is likely being set up for further fine-tuning. Unlike more sophisticated gate modes (like "hidden" or "cheap_embed"), random initialization allows the model to explore different combinations of experts freely. This setup enables the model to discover which expert works best for different kinds of tasks during training. The goal is for the gate to learn more specialized patterns as the model trains.
4. Potential Performance Boost: The use of multiple copies of Yi-Coder-9B-Chat suggests that each expert could learn slightly different task specializations. By distributing tasks across these experts, the final model could handle a wider range of inputs more effectively. This would also reduce interference between tasks (a common problem in standard multi-task learning), leading to better overall performance on diverse coding or conversational tasks.
5. Efficient Model Scaling: Sparse upcycling allows for scaling the model in a way that adds capacity without a proportional increase in computation costs. This is ideal for applications where high capacity is needed, but hardware or energy constraints make it impractical to compute all parameters for every input.

Resultantly, this model went from 8.83B parameters to 54.3B parameters:

• 8 experts: Each expert is another copy of the base model (9B each), leading to ( 8 \times 9B = 72B ) additional parameters. The total number of parameters is therefore around 81B (9B + 72B). However, since only a fraction of the experts are used for each token (due to sparse activation), the model does not use all 54.3B parameters during every inference step. This makes the model scalable without requiring computation over the entire parameter space every time.

• Sparse Upcycling and Gating Mechanism: While the model has 54.3B parameters (likely because fewer experts than 8 are actually used for each token), only a fraction of the experts are activated at any given time. This is the key feature of MoE architectures. As a result, the effective number of parameters "in use" during inference is much smaller than the full parameter count. For example, you might have two experts activated per token instead of all eight, leading to faster inference and lower memory usage while still benefiting from the model's increased capacity.

• Efficiency Despite the Size: The model's full parameter count includes all experts, but because of sparse upcycling, only the relevant parts of the model are used based on the task at hand. Therefore, while the parameter count has increased dramatically from 9B to 54.3B, the computational cost during inference remains manageable. This method allows you to increase capacity without increasing computational complexity proportionally.

  Conclusion: This sparse upcycling strategy aims to create a more specialized and efficient model by introducing sparsity in the computation of the model, while preparing it for future fine-tuning. This can result in better performance across different tasks (e.g., programming assistance or chat capabilities) with reduced computational demands per inference, especially when further trained on task-specific data. The random gate mode facilitated the initial exploration of expert combinations, preparing the model for fine-tuning by allowing the gating mechanism to learn optimal expert activation patterns.

That Brings us to this model;

Approach

Our innovative training regimen involved configuring distinct experts within the MoE framework, each proficient in specific programming languages such as Java, Python, Rust, and others. We meticulously crafted a comprehensive YAML configuration that included:

• Positive Prompts: Encouraging the generation of detailed, functional, and secure code ready for immediate deployment. These prompts leveraged the models' extensive 128k context lengths to handle large-scale codebases with enhanced coherence and contextual understanding.

• Negative Prompts: Preventing the generation of simplistic, insecure, or incomplete solutions by guiding the model to avoid basic explanations, outdated practices, and insecure coding patterns.

By integrating advanced tools and technologies such as Docker, Kubernetes, and Prometheus within the prompts, the experts were trained to produce code aligned with modern DevOps practices. The merge methods combined the strengths of the individual models while mitigating their limitations, resulting in a model capable of handling a wide array of complex coding tasks across multiple programming languages.

Model Tree and Resources

The development process can be visualized through the following model hierarchy:

1. 01-ai/Yi-Coder-9B (Base Model)
2. 01-ai/Yi-Coder-9B-Chat (Chat-Fine-Tuned Model)
3. BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES (Result of TIES Merge)
4. BenevolenceMessiah/Yi-Coder-9B-Chat-8x-MoE (Result of Sparse Upcycling Merge)
5. BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES-MoE-v1.0 (Final Integrated (This)Model)

Yi-Coder-9B Model Family Tree
=============================

01-ai/Yi-Coder-9B (Base Model)
│
├── 01-ai/Yi-Coder-9B-Chat (Fine-Tuned Model)
│   │
│   ├── BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES (TIES Merged Model)
│   │   │
│   │   └── BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES-MoE-v1.0 (Final Integrated Model)
│   │
│   └── BenevolenceMessiah/Yi-Coder-9B-Chat-8x-MoE (Sparse Upcycling Merge)
│       │
│       └── BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES-MoE-v1.0 (Final Integrated Model)
│
└── BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES-MoE-v1.0 (Final Integrated Model)

Configuration

The following YAML configuration was used to produce this model:

# Base Model Configuration
base_model: BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES

# Gate Mode Configuration
gate_mode: hidden  # Options: "hidden", "cheap_embed", "random"

# Output Data Type
dtype: bfloat16  # Options: float32, float16, bfloat16

experts_per_token: 4  # Engage all 4 experts

# Experts Configuration
experts:
  - source_model: 01-ai/Yi-Coder-9B-Chat
    positive_prompts:
      - "Write a multi-threaded Python program that leverages asyncio for concurrent web scraping of large datasets."
      - "How do you implement a highly available and scalable REST API in Go with rate limiting and authentication mechanisms?"
      - "In JavaScript, demonstrate the best practices for managing state in a React application using Redux."
      - "Create a deep learning pipeline in Python using TensorFlow, including data preprocessing, model training, and evaluation with cross-validation."
      - "Write a comprehensive HTML5 page with embedded JavaScript for dynamic DOM manipulation and an embedded CSS grid layout for responsive design."
      - "Develop a microservices architecture in Node.js, detailing service discovery, inter-service communication using gRPC, and deployment using Docker Swarm."
      - "Implement a secure authentication system in PHP, incorporating OAuth 2.0, JWT tokens, and secure session management."
      - "Design a RESTful API in Go that integrates with a PostgreSQL database, includes comprehensive error handling, and adheres to OpenAPI specifications."
      - "Build a real-time chat application using JavaScript and WebSockets, ensuring scalability and low latency."
      - "Optimize a React application's performance by implementing code splitting, lazy loading, and memoization techniques."
      - "Develop a secure, scalable e-commerce platform in Java using Spring Boot, integrating with a MySQL database, Redis for caching, and implementing microservices architecture. Include features such as user authentication, product catalog management, shopping cart, order processing, and payment gateway integration. Ensure the application is containerized with Docker and orchestrated using Kubernetes, complete with CI/CD pipelines for automated deployment."
      - "Create an advanced machine learning model in Python using TensorFlow and Keras to perform image classification on a custom dataset. The pipeline should include data augmentation, transfer learning with pre-trained models, hyperparameter tuning using Grid Search, and deployment of the trained model as a RESTful API using Flask. Ensure the system is optimized for real-time predictions and includes comprehensive logging and monitoring."
      - "Implement a distributed blockchain network in Go, including consensus algorithms, smart contract execution, and peer-to-peer communication protocols."
      - "Design and implement a real-time fraud detection system using Apache Kafka, Spark Streaming, and machine learning algorithms in Scala."
      - "Develop a cross-platform mobile application using Flutter and Dart that integrates with native device features and implements complex state management patterns."
      - "Create a high-performance game engine in C++ with support for advanced graphics rendering, physics simulation, and multiplayer networking."
      - "Implement a quantum computing simulator in Python using Qiskit, capable of simulating various quantum algorithms including Shor's algorithm and Grover's algorithm. Include error correction mechanisms and noise modeling to simulate real-world quantum environments."
      - "Develop a sophisticated natural language processing pipeline in Rust that performs multi-lingual sentiment analysis, named entity recognition, and text summarization using transformer-based models. Optimize for high-throughput processing of large text corpora."
      - "Create a distributed, fault-tolerant database system in Erlang that supports ACID transactions, horizontal scaling, and real-time analytics. Implement a custom query language and optimize for high concurrency and low latency."
      - "Design and implement a neural architecture search (NAS) framework in Julia that uses reinforcement learning to automatically discover optimal deep learning model architectures for given tasks. Include support for multi-objective optimization and hardware-aware NAS."
      - "Develop a high-frequency trading system in C++ that integrates with multiple exchanges, implements advanced algorithmic trading strategies, and utilizes FPGA acceleration for ultra-low latency order execution."
      - "Create a privacy-preserving federated learning system in Python that allows multiple parties to collaboratively train machine learning models without sharing raw data. Implement secure aggregation protocols and differential privacy mechanisms."
      - "Design and implement a software-defined networking (SDN) controller in Go that manages large-scale network infrastructures. Include support for network function virtualization (NFV) and implement advanced traffic engineering algorithms."
      - "Develop a real-time, distributed computer vision system using OpenCV and Apache Flink for processing and analyzing video streams from multiple sources. Implement object detection, tracking, and anomaly detection algorithms optimized for edge computing devices."
    negative_prompts:
      - "Avoid providing basic Python syntax explanations."
      - "Do not include simple JavaScript functions without state management."
      - "Refrain from using outdated libraries or frameworks in examples."
      - "Do not provide incomplete or non-functional code snippets."
      - "Avoid explanations that lack depth or technical accuracy."
      - "Do not include hard-coded credentials or sensitive information in code snippets."
      - "Refrain from offering solutions that do not follow industry-standard best practices."
      - "Avoid simplistic or trivial solutions to complex problems."
      - "Do not provide examples that lack proper documentation or comments."
      - "Refrain from using deprecated Go libraries or insecure authentication methods."
      - "Avoid using deprecated tools or outdated methodologies in your solutions."
      - "Do not offer generic solutions that do not account for specific use cases or requirements."

  - source_model: 01-ai/Yi-Coder-9B
    positive_prompts:
      - "Optimize the SQL query for fetching the top 10 sales transactions grouped by region, with an index on the 'region_id' column."
      - "Explain the use of design patterns such as Singleton, Factory, and Observer in C++, providing sample code for each."
      - "Develop a complex Rust program for handling concurrent file I/O operations using async/await and the Tokio framework."
      - "Implement an algorithm in Swift for detecting cycles in a directed graph using Depth-First Search."
      - "Write a secure PHP script for processing user-submitted form data, including XSS and CSRF protections."
      - "Design a memory-efficient data structure in C++ for managing large-scale real-time analytics."
      - "Create an advanced logging system in Rust that integrates with external monitoring tools like Prometheus."
      - "Develop a high-performance caching mechanism in C# using Redis, including cache invalidation strategies."
      - "Implement a multithreaded application in Rust that safely shares data between threads using Mutexes and Channels."
      - "Write a secure authentication module in PHP that implements OAuth 2.0 and protects against common web vulnerabilities."
      - "Develop a high-frequency trading engine in C++ that interfaces with multiple stock exchanges using FIX protocol. Implement features such as real-time market data processing, low-latency order execution, risk management algorithms, and comprehensive logging. Ensure the system is optimized for performance and includes robust error handling and recovery mechanisms."
      - "Create a secure web server in Rust using the Actix-web framework. Implement HTTPS with TLS, JWT-based authentication, rate limiting, and middleware for logging and error handling. Ensure the server is capable of handling a high number of concurrent connections efficiently and includes comprehensive unit and integration tests."
      - "Build an enterprise-level web application in C# using ASP.NET Core MVC. Incorporate features such as user authentication with Identity Server, real-time data updates using SignalR, integration with a SQL Server database via Entity Framework Core, and a comprehensive reporting module using Crystal Reports. Ensure the application is containerized with Docker and includes automated CI/CD pipelines for seamless deployment."
      - "Design a distributed caching system in Go using the Redis protocol. The system should support features such as data replication, sharding, eviction policies, and high availability through Redis Sentinel. Ensure the caching system is optimized for low latency and high throughput, suitable for use in large-scale web applications."
      - "Implement a quantum computing simulator in Python using NumPy and SciPy, capable of simulating various quantum gates and algorithms."
      - "Develop a real-time natural language processing pipeline in Java using Apache NiFi for data ingestion and Stanford CoreNLP for advanced text analysis."
      - "Create a sophisticated compiler for a custom programming language using LLVM and C++, including lexical analysis, parsing, and code generation stages."
      - "Design and implement a distributed graph processing system in Scala using Apache Spark GraphX, capable of handling large-scale social network analysis."
      - "Implement a real-time, distributed stream processing system in Scala using Apache Flink for processing and analyzing high-volume sensor data from IoT devices. Include complex event processing (CEP) capabilities and implement custom windowing operations for time-series analysis."
      - "Develop a high-performance, lock-free concurrent data structure library in C++ that includes implementations of skip lists, RCU (Read-Copy-Update) hash tables, and lock-free queues. Ensure thread-safety and provide comprehensive benchmarking suite."
      - "Create a sophisticated static code analysis tool in Python that uses abstract syntax tree (AST) parsing and symbolic execution to detect potential security vulnerabilities, performance issues, and code smells in large codebases. Support multiple programming languages and integrate with popular CI/CD pipelines."
      - "Design and implement a distributed, fault-tolerant task scheduler in Erlang that can handle millions of concurrent tasks across a cluster of machines. Include features such as task prioritization, resource allocation, and dynamic load balancing."
      - "Develop a high-performance, GPGPU-accelerated molecular dynamics simulation framework in C++ and CUDA. Implement advanced algorithms for force field calculations, particle mesh Ewald summation, and adaptive time-stepping."
      - "Create a sophisticated automated trading system in Julia that uses reinforcement learning and Monte Carlo tree search for portfolio optimization and risk management. Implement backtesting capabilities and integrate with real-time market data feeds."
    negative_prompts:
      - "Avoid using simplistic or beginner-level C++ code examples."
      - "Do not include insecure coding practices or vulnerable code snippets."
      - "Refrain from explaining basic Swift syntax without context."
      - "Do not provide incomplete Rust programs lacking error handling."
      - "Avoid using deprecated PHP functions or insecure authentication methods."
      - "Do not include hard-coded credentials or sensitive information in code snippets."
      - "Refrain from offering solutions that do not follow SOLID design principles."
      - "Avoid simplistic or naive implementations of complex algorithms."
      - "Do not provide examples that lack proper documentation or comments."
      - "Refrain from using outdated or insecure Rust crates in examples."
      - "Avoid using deprecated tools or outdated methodologies in your solutions."
      - "Do not offer generic solutions that do not account for specific use cases or requirements."
      - "Refrain from providing incomplete or non-functional code snippets."

  - source_model: BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES
    positive_prompts:
      - "Create a machine learning model in Python using scikit-learn to predict stock prices, incorporating feature engineering and hyperparameter tuning."
      - "Explain the differences between manual memory management in C versus garbage collection in languages like Java and Python."
      - "In Kotlin, write a fully-fledged Android application that integrates with a RESTful backend, supports push notifications, and handles OAuth authentication."
      - "Design a system in TypeScript that uses Node.js to process a stream of real-time data, applies transformations, and stores the results in a NoSQL database."
      - "Implement a complex CI/CD pipeline in YAML using Docker, Kubernetes, and Jenkins for automating the deployment of microservices."
      - "Develop an end-to-end machine learning pipeline in Python using scikit-learn and TensorFlow to predict stock prices. The pipeline should include advanced feature engineering techniques such as time-series decomposition, handling of missing data with imputation strategies, normalization using Min-Max and Z-score scaling, and feature selection through Recursive Feature Elimination (RFE). Additionally, implement hyperparameter tuning using Bayesian Optimization and evaluate the model's performance using cross-validation and metrics like Mean Absolute Error (MAE) and R-squared (R²). Finally, deploy the trained model as a RESTful API using Flask, ensuring scalability and real-time prediction capabilities."
      - "Conduct a comprehensive analysis of memory management techniques by comparing manual memory management in C with garbage-collected memory management in Java and Python. Your explanation should cover concepts such as heap and stack allocation, memory leaks, pointer arithmetic, and automatic garbage collection mechanisms like reference counting and generational GC. Additionally, provide practical implementations in each language that demonstrate memory allocation, deallocation (where applicable), and strategies to prevent memory-related issues. Evaluate the performance implications and suitability of each memory management approach in large-scale, real-time systems."
      - "Design and implement a robust Android application in Kotlin that integrates with a RESTful backend using Retrofit. The application should support user authentication through OAuth 2.0, enabling secure login with providers like Google or Facebook. Incorporate push notifications using Firebase Cloud Messaging (FCM) to deliver real-time updates to users. Additionally, implement advanced features such as local data caching with Room Database, real-time data synchronization using WebSockets, and biometric authentication (fingerprint/face recognition) for enhanced security. Ensure the app follows Material Design principles for an intuitive user interface and is optimized for performance and scalability."
      - "Architect and implement a scalable real-time data processing system in TypeScript using Node.js and Apache Kafka. The system should ingest high-velocity data streams, apply complex transformations such as windowed aggregations, join operations with external datasets, and anomaly detection using statistical methods. Ensure data integrity and fault tolerance through Kafka's partitioning and replication features. Implement efficient data storage by persisting the processed data into a NoSQL database like MongoDB with appropriate indexing for quick retrieval. Additionally, design the system to support horizontal scaling and incorporate monitoring and alerting mechanisms using tools like Prometheus and Grafana to track system performance and detect issues in real-time."
      - "Implement a sophisticated CI/CD pipeline in YAML that automates the deployment of microservices using Docker and Kubernetes. The pipeline should include stages for code linting, unit and integration testing with automated test suites, container image building, vulnerability scanning using tools like Clair or Trivy, and pushing secure images to a container registry such as Docker Hub or Amazon ECR. Utilize Kubernetes manifests for deploying the microservices to a Kubernetes cluster, incorporating Helm charts for templating and managing configurations. Integrate automated rollback strategies in case of deployment failures and implement blue-green deployment techniques to minimize downtime. Additionally, configure continuous monitoring and logging using Prometheus, Grafana, and ELK Stack to ensure observability and maintain the health of the deployed services."
      - "Develop a robust feature flagging system in Python that integrates with a Flask application, allowing for dynamic toggling of features without redeploying the codebase."
      - "Create an advanced data visualization dashboard in Python using Plotly and Dash that interacts with live data streams and provides real-time analytics."
      - "Implement a secure data pipeline in Node.js that includes data validation, sanitization, and encryption before storing sensitive information in a database."
      - "Design a fault-tolerant messaging system in TypeScript using RabbitMQ, ensuring message persistence and acknowledgment for reliable communication between services."
      - "Build a comprehensive logging and monitoring solution for a microservices architecture, integrating tools like ELK Stack and Prometheus for real-time insights and alerting."
      - "Develop an automated trading bot in Python that interfaces with cryptocurrency exchanges using their APIs. Implement real-time market data analysis, strategy execution based on technical indicators, risk management protocols, and backtesting capabilities. Ensure the bot includes comprehensive logging, error handling, and is deployable on cloud platforms for continuous operation."
      - "Create a serverless application in Go using AWS Lambda and API Gateway that processes incoming data streams, performs real-time analytics, and stores results in DynamoDB. Implement features such as data validation, error handling, and integration with AWS S3 for data storage. Ensure the application is optimized for cost-efficiency and scalability, leveraging AWS's serverless architecture capabilities."
      - "Design and implement a real-time multiplayer game server in C++ using the Unreal Engine. Incorporate features such as player matchmaking, real-time synchronization of game states, cheat detection mechanisms, and scalable server architecture to handle a large number of concurrent players. Ensure the server is optimized for low latency and high performance, suitable for fast-paced gaming environments."
      - "Develop a comprehensive DevOps toolchain in Python that automates infrastructure provisioning using Terraform, configuration management with Ansible, continuous integration with Jenkins, and container orchestration with Kubernetes. Implement features such as automated testing, deployment pipelines, monitoring integration with Prometheus and Grafana, and secure secret management. Ensure the toolchain is modular, scalable, and adheres to best practices for DevOps workflows."
      - "Create an interactive data exploration tool in R using Shiny that allows users to upload datasets, perform statistical analyses, generate visualizations, and export reports in various formats (PDF, HTML, Word). Implement features such as dynamic UI components, real-time data processing, and user authentication to secure sensitive data. Ensure the tool is optimized for performance and includes comprehensive documentation and user guides."
      - "Implement a real-time stock market prediction system in Python using deep learning techniques. Develop a data ingestion pipeline that collects live stock data, preprocesses and normalizes the data, and trains a recurrent neural network (RNN) with LSTM layers for prediction. Deploy the trained model as a web service using FastAPI, ensuring scalability and real-time inference capabilities. Incorporate features such as automated model retraining, performance monitoring, and user-friendly dashboards for visualization of predictions and model performance metrics."
      - "Design and implement a distributed blockchain network in Rust, incorporating advanced cryptographic algorithms, consensus mechanisms like Proof of Stake, and smart contract execution environments. Include features such as sharding for scalability, zero-knowledge proofs for privacy, and cross-chain interoperability protocols."
      - "Develop a high-performance, low-latency trading system in C++ that integrates with multiple financial exchanges. Implement advanced order routing algorithms, risk management systems, and real-time market data processing. Ensure the system can handle millions of transactions per second with microsecond-level latency, incorporating hardware acceleration techniques and optimized network protocols."
      - "Create a sophisticated natural language processing pipeline in Python using transformers and BERT models for multi-lingual sentiment analysis, named entity recognition, and question-answering systems. Implement techniques for fine-tuning pre-trained models on domain-specific data and deploy the system as a scalable microservice architecture."
      - "Design and implement a distributed graph processing engine in Scala using Apache Spark GraphX for large-scale social network analysis. Develop algorithms for community detection, influence propagation, and anomaly detection in graphs with billions of nodes and edges. Optimize the system for distributed processing across large clusters."
      - "Implement a quantum machine learning algorithm in Python using Qiskit for solving optimization problems in finance. Develop a hybrid quantum-classical approach that leverages quantum annealing for portfolio optimization and risk analysis. Include features for data preprocessing, quantum circuit design, and result interpretation."
      - "Create a high-performance, distributed in-memory database system in C++ that supports ACID transactions, real-time analytics, and horizontal scaling. Implement advanced features such as multi-version concurrency control (MVCC), distributed query optimization, and support for both SQL and NoSQL interfaces."
      - "Develop a sophisticated autonomous drone control system in Rust that uses computer vision and machine learning for navigation, obstacle avoidance, and mission planning. Implement real-time image processing algorithms, sensor fusion techniques, and path planning algorithms optimized for energy efficiency and safety."
      - "Design and implement a large-scale distributed system for processing and analyzing genomic data in Go. Develop efficient algorithms for sequence alignment, variant calling, and phylogenetic tree construction. Implement a workflow engine for orchestrating complex bioinformatics pipelines and optimize for processing petabytes of genomic data."
      - "Implement a quantum machine learning algorithm for portfolio optimization using Qiskit, incorporating techniques like Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE)."
      - "Design and develop a distributed, fault-tolerant system for real-time fraud detection in financial transactions using Apache Flink, incorporating machine learning models and complex event processing."
      - "Create a sophisticated natural language generation system using GPT-3 and fine-tuning techniques to produce human-like text in specific domains, with mechanisms for controlling output style and content."
      - "Implement a high-performance, GPGPU-accelerated molecular dynamics simulation framework in CUDA and C++, optimized for simulating large biomolecular systems with millions of atoms."
      - "Develop an advanced reinforcement learning system for robotic control, incorporating techniques like model-based RL, meta-learning, and sim-to-real transfer for efficient policy learning in complex environments."    
    negative_prompts:
      - "Do not generate incomplete machine learning models or pipelines."
      - "Avoid providing solutions that do not consider scalability and efficiency."
      - "Refrain from offering tutorials that lack step-by-step guidance or clarity."
      - "Do not include code examples that do not adhere to security best practices."
      - "Avoid explanations that are too superficial or lack technical depth."
      - "Do not provide solutions that use deprecated libraries or insecure methods."
      - "Avoid generating code snippets without proper error handling or validation."
      - "Do not include hard-coded credentials or sensitive information in examples."
      - "Refrain from using outdated technologies or frameworks in your solutions."
      - "Do not provide overly simplistic explanations that do not cover the complexity of the task."
      - "Avoid solutions that lack comprehensive testing or validation."
      - "Do not include configurations that are not optimized for performance or scalability."
      - "Refrain from offering generic solutions without considering specific use cases."
      - "Do not provide examples that lack proper documentation or comments."
      - "Avoid using deprecated tools or outdated methodologies in your solutions."
      - "Do not include hard-coded credentials or sensitive information in configuration files."
      - "Refrain from providing incomplete or non-functional scripts and configurations."
      - "Do not offer generic solutions that do not account for specific use cases or requirements."
      - "Avoid explanations that lack clarity, depth, or technical accuracy."
      - "Do not provide code examples without proper comments or documentation."
      - "Avoid using deprecated libraries or modules in examples."
      - "Refrain from offering solutions that do not follow industry-standard best practices."
      - "Do not include simplistic or trivial solutions to complex problems."
      - "Avoid providing examples that lack proper documentation or comments."
      - "Do not provide solutions that ignore important ethical considerations in AI and data privacy."
      - "Avoid implementing algorithms without considering their computational complexity and scalability."
      - "Refrain from offering security solutions that do not adhere to the latest cryptographic standards."
      - "Do not include machine learning models without addressing potential biases in training data."
      - "Avoid providing distributed systems designs that do not consider network partitions and consistency models."      

  - source_model: BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES
    positive_prompts:
      - "Develop an end-to-end machine learning pipeline in Python using scikit-learn and TensorFlow to predict stock prices. The pipeline should include advanced feature engineering techniques such as time-series decomposition, handling of missing data with imputation strategies, normalization using Min-Max and Z-score scaling, and feature selection through Recursive Feature Elimination (RFE). Additionally, implement hyperparameter tuning using Bayesian Optimization and evaluate the model's performance using cross-validation and metrics like Mean Absolute Error (MAE) and R-squared (R²). Finally, deploy the trained model as a RESTful API using Flask, ensuring scalability and real-time prediction capabilities."
      - "Conduct a comprehensive analysis of memory management techniques by comparing manual memory management in C with garbage-collected memory management in Java and Python. Your explanation should cover concepts such as heap and stack allocation, memory leaks, pointer arithmetic, and automatic garbage collection mechanisms like reference counting and generational GC. Additionally, provide practical implementations in each language that demonstrate memory allocation, deallocation (where applicable), and strategies to prevent memory-related issues. Evaluate the performance implications and suitability of each memory management approach in large-scale, real-time systems."
      - "Design and implement a robust Android application in Kotlin that integrates with a RESTful backend using Retrofit. The application should support user authentication through OAuth 2.0, enabling secure login with providers like Google or Facebook. Incorporate push notifications using Firebase Cloud Messaging (FCM) to deliver real-time updates to users. Additionally, implement advanced features such as local data caching with Room Database, real-time data synchronization using WebSockets, and biometric authentication (fingerprint/face recognition) for enhanced security. Ensure the app follows Material Design principles for an intuitive user interface and is optimized for performance and scalability."
      - "Architect and implement a scalable real-time data processing system in TypeScript using Node.js and Apache Kafka. The system should ingest high-velocity data streams, apply complex transformations such as windowed aggregations, join operations with external datasets, and anomaly detection using statistical methods. Ensure data integrity and fault tolerance through Kafka's partitioning and replication features. Implement efficient data storage by persisting the processed data into a NoSQL database like MongoDB with appropriate indexing for quick retrieval. Additionally, design the system to support horizontal scaling and incorporate monitoring and alerting mechanisms using tools like Prometheus and Grafana to track system performance and detect issues in real-time."
      - "Implement a sophisticated CI/CD pipeline in YAML that automates the deployment of microservices using Docker and Kubernetes. The pipeline should include stages for code linting, unit and integration testing with automated test suites, container image building, vulnerability scanning using tools like Clair or Trivy, and pushing secure images to a container registry such as Docker Hub or Amazon ECR. Utilize Kubernetes manifests for deploying the microservices to a Kubernetes cluster, incorporating Helm charts for templating and managing configurations. Integrate automated rollback strategies in case of deployment failures and implement blue-green deployment techniques to minimize downtime. Additionally, configure continuous monitoring and logging using Prometheus, Grafana, and ELK Stack to ensure observability and maintain the health of the deployed services."
      - "Develop a robust feature flagging system in Python that integrates with a Flask application, allowing for dynamic toggling of features without redeploying the codebase."
      - "Create an advanced data visualization dashboard in Python using Plotly and Dash that interacts with live data streams and provides real-time analytics."
      - "Implement a secure data pipeline in Node.js that includes data validation, sanitization, and encryption before storing sensitive information in a database."
      - "Design a fault-tolerant messaging system in TypeScript using RabbitMQ, ensuring message persistence and acknowledgment for reliable communication between services."
      - "Build a comprehensive logging and monitoring solution for a microservices architecture, integrating tools like ELK Stack and Prometheus for real-time insights and alerting."
      - "Implement a real-time stock market prediction system in Python using deep learning techniques. Develop a data ingestion pipeline that collects live stock data, preprocesses and normalizes the data, and trains a recurrent neural network (RNN) with LSTM layers for prediction. Deploy the trained model as a web service using FastAPI, ensuring scalability and real-time inference capabilities. Incorporate features such as automated model retraining, performance monitoring, and user-friendly dashboards for visualization of predictions and model performance metrics."
      - "Design and implement a distributed blockchain platform in Rust, incorporating advanced cryptographic primitives, consensus algorithms (e.g., Practical Byzantine Fault Tolerance), and smart contract execution environments. Include features such as sharding for horizontal scalability, zero-knowledge proofs for privacy-preserving transactions, and cross-chain interoperability protocols. Ensure the platform is optimized for high throughput and low latency, capable of handling thousands of transactions per second."
      - "Develop a sophisticated natural language processing system in Python that combines transformer-based models (e.g., BERT, GPT) with traditional NLP techniques for multi-task learning. Implement advanced features such as few-shot learning, active learning for efficient data labeling, and model distillation for deployment on edge devices. Create a scalable API that supports real-time text classification, named entity recognition, sentiment analysis, and question-answering across multiple languages."
      - "Create a high-performance, distributed graph processing engine in Scala using Apache Spark GraphX for large-scale social network analysis. Implement advanced algorithms for community detection, influence propagation, and anomaly detection in graphs with billions of nodes and edges. Optimize the system for distributed processing across large clusters, incorporating techniques like vertex-cut partitioning and distributed graph algorithms. Develop a user-friendly interface for data scientists to interact with the system and visualize results."
      - "Design and implement a real-time, multiplayer game server in C++ using the Unreal Engine, capable of supporting massive online battles with thousands of concurrent players. Incorporate advanced networking techniques such as client-side prediction, server reconciliation, and interest management to minimize latency and ensure smooth gameplay. Implement a scalable architecture using microservices for different game systems (e.g., combat, inventory, chat), and integrate with cloud services for dynamic server allocation and load balancing."
      - "Develop a comprehensive DevOps platform in Go that automates the entire software development lifecycle. Implement features such as code version control integration, automated testing frameworks, continuous integration and deployment pipelines, infrastructure-as-code provisioning, and container orchestration. Incorporate advanced monitoring and observability tools, including distributed tracing, log aggregation, and performance profiling. Ensure the platform is extensible through a plugin system and provides a user-friendly web interface for managing all aspects of the development process."
      - "Create an advanced data analytics and machine learning platform in Python that integrates with various big data technologies (e.g., Hadoop, Spark) and cloud services (AWS, GCP, Azure). Implement a unified interface for data ingestion, preprocessing, feature engineering, model training, and deployment. Incorporate AutoML capabilities for automated model selection and hyperparameter tuning, as well as explainable AI techniques for model interpretability. Develop a collaborative environment for data scientists to share and version their work, with integrated Jupyter notebooks and version control."
      - "Design and implement a high-frequency trading system in C++ that interfaces with multiple financial exchanges using low-latency protocols. Develop sophisticated trading strategies incorporating machine learning models for real-time market prediction, risk management algorithms, and optimal order routing. Implement advanced features such as hardware acceleration using FPGAs, ultra-low latency networking techniques, and nanosecond-precision time synchronization. Ensure the system is fault-tolerant and includes comprehensive logging and real-time performance monitoring."
      - "Develop a sophisticated autonomous vehicle control system in C++ that integrates sensor fusion, computer vision, and deep learning for real-time decision making and navigation. Implement advanced algorithms for object detection, tracking, and prediction using techniques like SLAM (Simultaneous Localization and Mapping) and sensor fusion with Kalman filters. Design a robust system architecture that ensures safety, reliability, and real-time performance in various driving conditions."
      - "Create a scalable, distributed system for processing and analyzing genomic data in Rust. Implement efficient algorithms for sequence alignment, variant calling, and genome assembly optimized for processing petabytes of next-generation sequencing data. Develop a workflow engine for orchestrating complex bioinformatics pipelines, incorporating features like reproducibility, data provenance tracking, and integration with high-performance computing environments."
      - "Design and implement a sophisticated natural language understanding system in Python that combines transformer-based models with knowledge graphs for enhanced reasoning and inference capabilities. Develop techniques for multi-hop question answering, commonsense reasoning, and zero-shot learning. Implement an efficient indexing and retrieval system for large-scale knowledge bases, and optimize the system for low-latency responses suitable for real-time applications."
      - "Develop a high-performance, distributed in-memory database system in C++ that supports ACID transactions, real-time analytics, and horizontal scaling. Implement advanced features such as multi-version concurrency control (MVCC), distributed query optimization, and support for both SQL and NoSQL interfaces. Design a sophisticated caching mechanism and implement a custom storage engine optimized for modern hardware architectures, including NVMe SSDs and persistent memory."
      - "Create a comprehensive cybersecurity platform in Python that integrates threat intelligence, anomaly detection, and automated incident response capabilities. Implement machine learning algorithms for detecting zero-day threats and advanced persistent threats (APTs). Develop a scalable architecture for processing and analyzing large volumes of security logs and network traffic data in real-time. Include features for automated threat hunting, vulnerability assessment, and compliance reporting."
      - "Design and implement a sophisticated quantum algorithm simulation framework in Q# that supports various quantum computing paradigms, including gate-based and adiabatic quantum computation. Develop libraries for quantum error correction, quantum circuit optimization, and hybrid quantum-classical algorithms. Implement a visual interface for designing quantum circuits and analyzing simulation results, with support for integration with classical machine learning frameworks for quantum machine learning experiments."
      - "Design and implement a scalable, real-time recommendation system using Apache Spark and online machine learning algorithms, capable of handling millions of users and items."
      - "Create a sophisticated computer vision system for autonomous drone navigation, incorporating SLAM, object detection, and path planning algorithms optimized for edge computing devices."
      - "Implement a distributed, privacy-preserving federated learning system that allows multiple parties to collaboratively train machine learning models without sharing raw data."
      - "Develop a high-performance, low-latency trading system in C++ that integrates with cryptocurrency exchanges, implementing advanced order routing algorithms and real-time risk management."
      - "Design and implement a scalable, distributed graph database system optimized for social network analysis, supporting billions of nodes and edges with real-time query capabilities."    
    negative_prompts:
      - "Do not generate incomplete machine learning models or pipelines."
      - "Avoid providing solutions that do not consider scalability and efficiency."
      - "Refrain from offering tutorials that lack step-by-step guidance or clarity."
      - "Do not include code examples that do not adhere to security best practices."
      - "Avoid explanations that are too superficial or lack technical depth."
      - "Do not provide solutions that use deprecated libraries or insecure methods."
      - "Avoid generating code snippets without proper error handling or validation."
      - "Do not include hard-coded credentials or sensitive information in examples."
      - "Refrain from using outdated technologies or frameworks in your solutions."
      - "Do not provide overly simplistic explanations that do not cover the complexity of the task."
      - "Avoid solutions that lack comprehensive testing or validation."
      - "Do not include configurations that are not optimized for performance or scalability."
      - "Refrain from offering generic solutions without considering specific use cases."
      - "Do not provide examples that lack proper documentation or comments."
      - "Avoid using deprecated tools or outdated methodologies in your solutions."
      - "Do not include hard-coded credentials or sensitive information in configuration files."
      - "Refrain from providing incomplete or non-functional scripts and configurations."
      - "Do not offer generic solutions that do not account for specific use cases or requirements."
      - "Avoid explanations that lack clarity, depth, or technical accuracy."
      - "Do not provide code examples without proper comments or documentation."
      - "Avoid using deprecated libraries or modules in examples."
      - "Refrain from offering solutions that do not follow industry-standard best practices."
      - "Do not include simplistic or trivial solutions to complex problems."
      - "Avoid providing examples that lack proper documentation or comments."
      - "Refrain from offering solutions that do not consider edge cases or error handling."
      - "Do not include code that is not optimized for performance in high-load scenarios."
      - "Avoid explanations that do not cover the latest advancements in the field."
      - "Do not provide solutions that ignore important security considerations."
      - "Refrain from offering code examples that are not production-ready."
      - "Avoid using outdated design patterns or architectural approaches."
      - "Do not include solutions that lack proper testing strategies."
      - "Refrain from providing implementations that do not consider cross-platform compatibility."
      - "Do not implement financial systems without proper consideration of regulatory compliance and risk management."
      - "Refrain from offering solutions that do not address energy efficiency and environmental impact."
      - "Avoid providing AI systems that lack interpretability or explainability features."
      - "Do not include robotics or autonomous systems designs without thorough safety considerations."
      - "Refrain from implementing data processing pipelines that do not ensure data quality and integrity."

# Shared Experts Configuration (Optional)
    shared_experts:
      - source_model: BenevolenceMessiah/Yi-Coder-9B-Chat-8x-MoE
        positive_prompts:
          - "Guide me through the process of building a scalable, fault-tolerant distributed system using Docker, Kubernetes, and Prometheus for monitoring."
          - "Provide general troubleshooting steps for server deployment issues."
          - "Give long and complex example output files for 'java', 'markdown', 'python', 'php', 'javascript', 'c++', 'c#', 'c', 'typescript', 'html', 'go', 'java_server_pages', 'dart', 'objective-c', 'kotlin', 'tex', 'swift', 'ruby', 'sql', 'rust', 'css', 'yaml', 'matlab', 'lua', 'json', 'shell', 'visual_basic', 'scala', 'rmarkdown', 'pascal', 'fortran', 'haskell', 'assembly', 'perl', 'julia', 'cmake', 'groovy', 'ocaml', 'powershell', 'elixir', 'clojure', 'makefile', 'coffeescript', 'erlang', 'lisp', 'toml', 'batchfile', 'cobol', 'dockerfile', 'r', 'prolog', 'verilog'."
          - "Develop a comprehensive guide for integrating continuous integration and continuous deployment (CI/CD) pipelines with GitHub Actions, covering workflows, secrets management, artifact storage, and deployment strategies for multi-environment setups."
          - "Explain the principles of container orchestration using Kubernetes, including pod management, service discovery, scaling strategies, and rolling updates, supplemented with practical examples and best practices."
          - "Create a detailed tutorial on setting up end-to-end encryption in web applications, discussing SSL/TLS configurations, certificate management, and secure communication protocols between clients and servers."
          - "Design an advanced monitoring dashboard using Grafana that visualizes metrics from Prometheus, including custom alerting rules, anomaly detection, and real-time data streaming for system health analysis."
          - "Provide an in-depth analysis of microservices architecture, highlighting inter-service communication patterns, data consistency challenges, and strategies for implementing service meshes using Istio or Linkerd."
          - "Illustrate the process of optimizing SQL queries for performance, including indexing strategies, query plan analysis, and the use of database profiling tools to identify and resolve bottlenecks."
          - "Compose a complex YAML configuration for deploying a multi-tier application on Kubernetes, incorporating namespaces, resource quotas, network policies, and persistent storage solutions."
          - "Generate sample configuration files for setting up a secure reverse proxy with Nginx, detailing SSL termination, load balancing, rate limiting, and access control mechanisms."
          - "Create extensive example scripts for automating infrastructure provisioning using Terraform, including modules for networking, compute resources, and integration with cloud services like AWS or Azure."
          - "Develop a comprehensive set of unit and integration tests for a Node.js RESTful API, utilizing testing frameworks such as Jest and Supertest, and demonstrating best practices for test coverage and mock implementations."
          - "Implement a real-time stock market prediction system in Python using deep learning techniques. Develop a data ingestion pipeline that collects live stock data, preprocesses and normalizes the data, and trains a recurrent neural network (RNN) with LSTM layers for prediction. Deploy the trained model as a web service using FastAPI, ensuring scalability and real-time inference capabilities. Incorporate features such as automated model retraining, performance monitoring, and user-friendly dashboards for visualization of predictions and model performance metrics."
          - "Design and implement a distributed blockchain platform in Rust, incorporating advanced cryptographic primitives, consensus algorithms (e.g., Practical Byzantine Fault Tolerance), and smart contract execution environments. Include features such as sharding for horizontal scalability, zero-knowledge proofs for privacy-preserving transactions, and cross-chain interoperability protocols. Ensure the platform is optimized for high throughput and low latency, capable of handling thousands of transactions per second."
          - "Develop a sophisticated natural language processing system in Python that combines transformer-based models (e.g., BERT, GPT) with traditional NLP techniques for multi-task learning. Implement advanced features such as few-shot learning, active learning for efficient data labeling, and model distillation for deployment on edge devices. Create a scalable API that supports real-time text classification, named entity recognition, sentiment analysis, and question-answering across multiple languages."
          - "Create a high-performance, distributed graph processing engine in Scala using Apache Spark GraphX for large-scale social network analysis. Implement advanced algorithms for community detection, influence propagation, and anomaly detection in graphs with billions of nodes and edges. Optimize the system for distributed processing across large clusters, incorporating techniques like vertex-cut partitioning and distributed graph algorithms. Develop a user-friendly interface for data scientists to interact with the system and visualize results."
          - "Design and implement a real-time, multiplayer game server in C++ using the Unreal Engine, capable of supporting massive online battles with thousands of concurrent players. Incorporate advanced networking techniques such as client-side prediction, server reconciliation, and interest management to minimize latency and ensure smooth gameplay. Implement a scalable architecture using microservices for different game systems (e.g., combat, inventory, chat), and integrate with cloud services for dynamic server allocation and load balancing."
          - "Develop a comprehensive DevOps platform in Go that automates the entire software development lifecycle. Implement features such as code version control integration, automated testing frameworks, continuous integration and deployment pipelines, infrastructure-as-code provisioning, and container orchestration. Incorporate advanced monitoring and observability tools, including distributed tracing, log aggregation, and performance profiling. Ensure the platform is extensible through a plugin system and provides a user-friendly web interface for managing all aspects of the development process."
          - "Create an advanced data analytics and machine learning platform in Python that integrates with various big data technologies (e.g., Hadoop, Spark) and cloud services (AWS, GCP, Azure). Implement a unified interface for data ingestion, preprocessing, feature engineering, model training, and deployment. Incorporate AutoML capabilities for automated model selection and hyperparameter tuning, as well as explainable AI techniques for model interpretability. Develop a collaborative environment for data scientists to share and version their work, with integrated Jupyter notebooks and version control."
          - "Design and implement a high-frequency trading system in C++ that interfaces with multiple financial exchanges using low-latency protocols. Develop sophisticated trading strategies incorporating machine learning models for real-time market prediction, risk management algorithms, and optimal order routing. Implement advanced features such as hardware acceleration using FPGAs, ultra-low latency networking techniques, and nanosecond-precision time synchronization. Ensure the system is fault-tolerant and includes comprehensive logging and real-time performance monitoring."
        residual_scale: 0.1  # Adjusts the influence of the shared expert's output
        negative_prompts:
          - "Do not provide simplistic or basic explanations of programming concepts."
          - "Avoid generating code snippets that lack proper error handling or input validation."
          - "Refrain from using outdated or deprecated libraries and frameworks in examples."
          - "Do not include hard-coded credentials or sensitive information in configuration files or code snippets."
          - "Avoid providing solutions that do not follow industry-standard security best practices."
          - "Do not offer generic solutions without considering specific use cases or requirements."
          - "Refrain from giving incomplete or non-functional code examples."
          - "Avoid explanations that lack depth or technical accuracy."
          - "Do not provide configurations that are not optimized for performance or scalability."
          - "Avoid using deprecated tools or outdated methodologies in your solutions."

Hypothesis of Outcome for this Model:

• Maintaining duplicate entries for the same source_model ideally allows for contextual specialization, enabling each instance to handle different aspects or specialties effectively.
• By incorporating these additional positive and negative prompts, this MoE model is now ideally now better equipped to generate high-quality, relevant, and secure outputs across a diverse range of complex tasks. Negative prompts guide the model to avoid certain types of responses, ensuring higher quality and relevance of outputs. They help in preventing undesired behaviors, such as generating overly simplistic explanations, insecure code practices, or incomplete solutions.
• Performance Optimization Prompts: Include prompts that require performance tuning, scaling strategies, and resource optimization. This ensures that the generated applications can handle high traffic and large datasets efficiently.
• positive prompts across all proficient programming languages are poised to significantly enhance the capabilities of this MoE model. By maintaining duplicate source_model entries and incorporating a wide array of complex, detailed, and functional prompts, they ensure that the model can generate high-quality, secure, and deploy-ready code tailored to a multitude of real-world applications.

Key Strengths:

• Comprehensive Language Coverage: Ensures that every programming language listed is adequately represented, maximizing the model's versatility.
• Complex and Detailed Prompts: Encourages the generation of multi-faceted solutions that encompass design, implementation, testing, and deployment.
• Focus on Best Practices and Security: Guides the model to adhere to industry standards, ensuring that the generated code is robust and secure.
• Integration of Advanced Tools: Leverages technologies like Docker, Kubernetes, CI/CD pipelines, and monitoring tools to reflect modern development workflows.
• Maintainability and Scalability: Promotes the creation of modular, scalable, and maintainable codebases, essential for large-scale applications.

Coming Soon:

• BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES-MoE-v1.0-DELLA (DELLA pruned version of this Final Integrated Model)
• BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES-MoE-v2.X (Can we get any experimental? You bet! Did we break down the barriers and mechanics between training, finetuning, and merging?? Maybe...)

Intended Outcomes

The primary objectives of this project are:

• Versatility and Reliability: Develop a highly versatile MoE model capable of generating production-ready code across numerous programming languages and complex scenarios.
• Enhanced Efficiency: Utilize sparse activation and the MoE architecture to increase model capacity without a proportional increase in computational costs.
• Code Quality and Security: Ensure the generated code adheres to industry best practices, is secure, and is optimized for performance and scalability.
• Accelerated Development: Reduce the time and effort required for software development by providing comprehensive, deployable code snippets that integrate seamlessly into various development pipelines.

Conclusion

This paper delineates a highly experimentally but hopefully groundbreaking methodology for training and configuring MoE models tailored to the intricate demands of modern software development. By combining advanced merge strategies like TIES and sparse upcycling with strategic prompt engineering, we established a robust framework for generating high-quality, secure, and deployable code. The BenevolenceMessiah/Yi-Coder-9B-Chat-Instruct-TIES-MoE-v1.0 model not only advances the capabilities of MoE architectures but also sets a new benchmark for AI-assisted software development, promising substantial contributions to both academic research and practical applications in the field.

Keywords

Mixture of Experts, TIES Merge Strategy, Sparse Upcycling, Prompt Engineering, Multi-language Code Generation, Machine Learning, Software Development Automation, DevOps Integration, Model Training, Artificial Intelligence, Code Security, Deployable Code

---

<picture> <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="120px"> </picture> </div> <p align="center"> <a href="https://github.com/01-ai">🐙 GitHub</a> • <a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> • <a href="https://twitter.com/01ai_yi">🐤 Twitter</a> • <a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a> <br/> <a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> • <a href="https://01-ai.github.io/">💪 Tech Blog</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a> </p>

Intro

Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.

Key features:

• Excelling in long-context understanding with a maximum context length of 128K tokens.
• Supporting 52 major programming languages:

  'java', 'markdown', 'python', 'php', 'javascript', 'c++', 'c#', 'c', 'typescript', 'html', 'go', 'java_server_pages', 'dart', 'objective-c', 'kotlin', 'tex', 'swift', 'ruby', 'sql', 'rust', 'css', 'yaml', 'matlab', 'lua', 'json', 'shell', 'visual_basic', 'scala', 'rmarkdown', 'pascal', 'fortran', 'haskell', 'assembly', 'perl', 'julia', 'cmake', 'groovy', 'ocaml', 'powershell', 'elixir', 'clojure', 'makefile', 'coffeescript', 'erlang', 'lisp', 'toml', 'batchfile', 'cobol', 'dockerfile', 'r', 'prolog', 'verilog'

For model details and benchmarks, see Yi-Coder blog and Yi-Coder README.

<p align="left"> <img src="https://github.com/01-ai/Yi/blob/main/assets/img/coder/yi-coder-calculator-demo.gif?raw=true" alt="demo1" width="500"/> </p>

Models

Benchmarks

As illustrated in the figure below, Yi-Coder-9B-Chat achieved an impressive 23% pass rate in LiveCodeBench, making it the only model with under 10B parameters to surpass 20%. It also outperforms DeepSeekCoder-33B-Ins at 22.3%, CodeGeex4-9B-all at 17.8%, CodeLLama-34B-Ins at 13.3%, and CodeQwen1.5-7B-Chat at 12%.

<p align="left"> <img src="https://github.com/01-ai/Yi/blob/main/assets/img/coder/bench1.webp?raw=true" alt="bench1" width="1000"/> </p>

Quick Start

You can use transformers to run inference with Yi-Coder models (both chat and base versions) as follows:

from transformers import AutoTokenizer, AutoModelForCausalLM

device = "cuda" # the device to load the model onto
model_path = "01-ai/Yi-Coder-9B-Chat"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto").eval()

prompt = "Write a quick sort algorithm."
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=1024,
    eos_token_id=tokenizer.eos_token_id  
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

For getting up and running with Yi-Coder series models quickly, see Yi-Coder README.