Vyxa Model Architecture & Training

This page provides a deep dive into Vyxa's model architecture, training methodology, optimization techniques, and benchmark results.

1. Introduction to Vyxa Model Architecture

Vyxa is a high-capacity large language model (LLM) designed for scalability, efficiency, and developer accessibility. It is based on LLaMA architecture and optimized for Web3, blockchain, and general AI applications. Vyxa's design balances performance, memory efficiency, and inference speed while maintaining strong language understanding capabilities.

2. Base Model & Architecture

Vyxa is built upon LLaMA-based architecture with several modifications for efficiency.

Key Technical Specifications

• Model Type: LLaMA-based LLM

• Parameters: 70.6B

• Quantization: Q4_K_M (43GB)

• Tokenizer: SentencePiece-based subword tokenizer

• Embedding Dimension: 8192

• Attention Heads: 64

• Layers: 80

Compute Requirements

Vyxa's training was conducted on a high-performance cluster, requiring:

• Hardware: A100 GPUs (40GB VRAM) & TPUv4 pods

• Total Training Time: Multi-week training with distributed parallelism

• Training Frameworks: PyTorch, DeepSpeed, TensorParallel

3. Training Data & Fine-Tuning

Vyxa was trained on a diverse dataset containing trillions of tokens, ensuring strong generalization across multiple domains.

Training Dataset Sources

Vyxa's dataset is a mix of:

• General NLP: The Pile, C4, OpenWebText, Wikipedia, ArXiv, PubMed

• Code Datasets: GitHub Code Dataset, CodeParrot, StarCoder

• Web3 & Blockchain-Specific:

  1️⃣ Blockchain Foundations & Whitepapers

  These documents provide core blockchain principles, cryptographic concepts, and decentralized consensus mechanisms.

  🔗 General Blockchain Concepts

  • Bitcoin Whitepaper (https://bitcoin.org/bitcoin.pdf)

  • Ethereum Whitepaper (https://ethereum.org/en/whitepaper/)

  • Ethereum Yellow Paper (https://ethereum.github.io/yellowpaper/paper.pdf)

  • Ethereum Improvement Proposals (EIPs) (https://eips.ethereum.org/)

  • Ethereum Consensus Layer Docs (https://ethereum.org/en/developers/docs/consensus-mechanisms/)

  • Web3 Foundation Documentation (https://web3.foundation/research/)

  • Decentralized Identity Whitepaper (W3C) (https://www.w3.org/TR/did-core/)

  ---

  2️⃣ Smart Contract Development

  Vyxa is optimized for smart contract development, using data from major frameworks and libraries.

  🔗 Ethereum Development

  • Solidity Documentation (https://soliditylang.org/docs/)

  • Vyper Documentation (https://vyper.readthedocs.io/en/stable/)

  • Hardhat Docs (https://hardhat.org/docs/)

  • Foundry Documentation (https://book.getfoundry.sh/)

  • Truffle Suite Docs (https://trufflesuite.com/docs/)

  • OpenZeppelin Contracts (https://docs.openzeppelin.com/contracts/)

  • Remix IDE Documentation (https://remix-ide.readthedocs.io/en/latest/)

  🔗 Cross-Chain & Smart Contract Interoperability

  • Chainlink Docs (Oracles) (https://docs.chain.link/)

  • LayerZero Protocol Docs (https://layerzero.network/)

  • Wormhole Protocol Docs (https://docs.wormhole.com/wormhole/)

  • Cosmos SDK Docs (https://docs.cosmos.network/)

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  3️⃣ DeFi (Decentralized Finance) Protocols

  Vyxa understands DeFi mechanics through documentation and technical research papers from leading protocols.

  🔗 DEXs (Decentralized Exchanges)

  • Uniswap Docs (https://docs.uniswap.org/)

  • SushiSwap Docs (https://dev.sushi.com/)

  • Curve Finance Docs (https://resources.curve.fi/)

  • Balancer Protocol Docs (https://docs.balancer.fi/)

  🔗 Lending & Borrowing Protocols

  • Aave Docs (https://docs.aave.com/)

  • MakerDAO Whitepaper (https://makerdao.com/en/whitepaper/)

  • Compound Finance Docs (https://compound.finance/docs)

  • Venus Protocol Docs (https://docs.venus.io/)

  🔗 Stablecoins & Asset Management

  • Dai (MakerDAO) Docs (https://docs.makerdao.com/)

  • Terra Classic Whitepaper (https://classic-docs.terra.money/)

  • FRAX Protocol Docs (https://docs.frax.finance/)

  • Tether (USDT) Whitepaper (https://tether.to/en/whitepaper/)

  🔗 Yield Farming & Staking

  • Yearn Finance Docs (https://docs.yearn.finance/)

  • Lido Staking Docs (https://docs.lido.fi/)

  • Rocket Pool Docs (https://docs.rocketpool.net/)

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  4️⃣ Layer-1 & Layer-2 Blockchains

  Vyxa’s training includes Layer-1 & Layer-2 blockchain documentation, improving its knowledge of scalability solutions.

  🔗 Layer-1 Blockchains

  • Bitcoin Developer Docs (https://developer.bitcoin.org/)

  • Solana Documentation (https://docs.solana.com/)

  • Binance Smart Chain Docs (https://docs.bnbchain.org/)

  • Avalanche Documentation (https://docs.avax.network/)

  • Tezos Docs (https://tezos.gitlab.io/tezos/)

  • Cardano Developer Docs (https://docs.cardano.org/)

  • Polkadot Documentation (https://wiki.polkadot.network/)

  🔗 Layer-2 Scaling Solutions

  • Polygon Documentation (https://wiki.polygon.technology/)

  • Optimism Docs (https://docs.optimism.io/)

  • Arbitrum Documentation (https://developer.arbitrum.io/)

  • zkSync Docs (https://docs.zksync.io/)

  • StarkNet Documentation (https://docs.starknet.io/)

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  5️⃣ DAOs & Governance Frameworks

  🔗 DAO Governance & Tokenomics

  • DAOstack Docs (https://daostack.notion.site/)

  • Aragon Documentation (https://aragon.org/docs/)

  • Snapshot Voting System (https://docs.snapshot.org/)

  • Gnosis Safe Multisig Docs (https://docs.safe.global/)

  • ENS (Ethereum Name Service) Docs (https://docs.ens.domains/)

  ---

  6️⃣ Web3 Infrastructure & Storage Solutions

  Vyxa has been trained on decentralized storage and infrastructure documentation.

  🔗 Decentralized Storage

  • IPFS (InterPlanetary File System) Docs (https://docs.ipfs.tech/)

  • Filecoin Docs (https://docs.filecoin.io/)

  • Arweave Documentation (https://docs.arweave.org/)

  🔗 Identity & Privacy Solutions

  • Zero-Knowledge Proofs (ZKP) Research (https://zkproof.org/)

  • Tornado Cash Docs (https://tornado-cash.gitbook.io/tornado-cash/)

  • zkSync Privacy Tech (https://zksync.io/)

  🔗 Decentralized Compute

  • Ethereum Virtual Machine (EVM) Docs (https://ethereum.org/en/developers/docs/evm/)

  • Golem Network Docs (https://docs.golem.network/)

• Multilingual Content: Diverse sources for extended language coverage

Pretraining Process

• Token Count: Trillions of tokens

• Training Techniques:

  • Dynamic data sampling

  • Mixed precision training (FP16/BF16)

  • Distributed training with FSDP & ZeRO-3

• Filtering & Curation:

  • Deduplication & redundancy removal

  • Bias mitigation and ethical filtering

Fine-Tuning & Adaptation

Vyxa undergoes fine-tuning for specific domains, including:

• Web3 & Smart Contract AI – Specialized on Ethereum, Solidity, and decentralized protocols

• Code Generation & Debugging – Optimized for software engineers

• Conversational AI & Chatbot – RLHF-based fine-tuning for natural conversations

4. Optimization Techniques

Vyxa is optimized for efficient inference and deployment using state-of-the-art compression and memory optimization techniques.

Quantization & Memory Optimization

• Q4_K_M Quantization: Reduces model size while maintaining accuracy

• LoRA & QLoRA: Fine-tuning with minimal compute requirements

• FlashAttention: Optimized memory usage in attention layers

• FSDP & ZeRO-3: Distributed training for reduced VRAM usage

Mixture of Experts (MoE) & Sparse Computation

• Selective activation of model layers for computational efficiency

• Sparse attention mechanisms for faster inference

KV Cache & Efficient Inference Optimization

• Efficient caching of key-value pairs in transformer blocks

• Reduced latency for real-time applications

5. Benchmarks & Comparisons

Vyxa has been benchmarked against other LLMs in various domains, showing competitive performance.

Benchmark	Vyxa (Q4_K_M)	LLaMA-2 70B	GPT-4
Code Generation	✅ Optimized	✅ Strong	🔥 Industry Leader
NLP Tasks	✅ High Accuracy	✅ Good	🔥 Industry Leader
Web3 Knowledge	🔥 Specialized	❌ Limited	❌ Not Focused
Quantization Efficiency	✅ 43GB (Q4_K_M)	❌ 140GB (FP16)	❌ Large

Benchmark Scores

• MMLU (Multi-task Language Understanding): Competitive with LLaMA-2 70B

• HumanEval (Code Generation Accuracy): Exceeds GPT-3.5 on certain tasks

• TruthfulQA & Bias Evaluation: Improved response alignment with factual correctness

6. Datasets & Documentation References

Vyxa's training leveraged diverse datasets and research papers for optimal model generalization.

Datasets Used

• General NLP: The Pile, C4, OpenWebText

• Code & Programming: StarCoder, GitHub Code Dataset

• Web3 & Blockchain: Ethereum Whitepapers, EIP Proposals

Official Documentation & Links

• GitHub Repository: Vyxa GitHub

• Hugging Face Model Page: Vyxa Model

• Research Papers & AI Reports:

  • LLaMA-2: Open Foundation and Fine-Tuned Chat Models

  • Efficient Training of LLMs with LoRA

  • Sparse Attention Mechanisms for LLMs

7. Future Improvements & Roadmap

Vyxa Foundation is continuously improving model architecture, training methods, and optimizations. Future updates include:

✅ Vyxa-Next Model Enhancements

• Improved efficiency for on-device & edge AI deployments

• Task-specific fine-tuned variants (Web3, Coding, Finance)

• Decentralized inference on blockchain compute networks

✅ Advanced Optimizations

• Full 8-bit & 4-bit quantization for reduced memory footprint

• MoE models for adaptive compute efficiency

✅ Community Contributions

Vyxa is an open-source model, and we encourage contributions! You can help by:

• Submitting training data improvements

• Developing fine-tuned models for new tasks

• Optimizing inference for better real-time performance