Model Card: DeepSolanaCoder

By 8BitLabs
First-of-its-Kind Solana-Centric Language Model
Release Date: 2025-01-24

---

Model Overview

DeepSolanaCoder is a specialized large language model (LLM) trained to excel in Solana blockchain development, leveraging ZK-compressed datasets, recursive Solana program library (SPL) data, and NFT metadata for vision analysis. Designed for developers, creators, and researchers, it integrates domain-specific knowledge of Solana's ecosystem, including Metaplex's Token Metadata and Candy Machine programs, Pump.fun contracts, and SPL governance frameworks. The model's training corpus includes:

• 1,000+ Solana Q&A prompts covering blockchain mechanics, Rust programming, and SPL standards.
• 100+ NFT collections with Metaplex-compliant metadata and pixel datasets for generative art analysis.
• ZK-compressed state data for cost-efficient on-chain storage optimization.
• Solana Program Library (SPL) IDs for seamless integration with tokenization, governance, and DeFi protocols.

---

Model Details

Developed By

8BitLabs (Solana Ecosystem Partner).

Model Type

• Architecture: Hybrid causal language model (decoder-only), optimized for Rust/Solana code generation.
• Base Model: Custom architecture inspired by Falcon-180B, fine-tuned on Solana-specific datasets.

Languages

• Primary: Rust (Solana smart contracts), TypeScript (frontend integration).
• Secondary: English (documentation and Q&A).

License

Proprietary (commercial use permitted under 8BitLabs Agreement).

Unique Features

• Code Autocompletion: Generates boilerplate code for SPL tokens, NFT minting, and Candy Machine deployments.
• ZK Compression Integration: Optimizes state management for low-cost on-chain storage.
• Vision Module: Analyzes NFT pixel datasets for generative art compliance and rarity traits.

---

Intended Uses

Direct Use

1. Smart Contract Development:
   • Generate Rust code for Solana programs (e.g., token minting, governance voting).
   • Debug common Anchor framework errors.
2. NFT Tooling:
   • Automate Metaplex metadata creation and Candy Machine configurations.
   • Analyze pixel datasets for generative art rarity (e.g., trait distributions).
3. Educational Support:
   • Answer Solana-specific questions (e.g., "How to handle PDAs in Rust?").

Downstream Use

• AI-Powered Dev Tools: Integrate into IDEs for real-time code suggestions.
• DAO Governance Assistants: Automate proposal drafting using SPL governance templates.

Out-of-Scope Use

• Financial advice or market predictions.
• Non-Solana blockchain development (e.g., Ethereum, Bitcoin).

---

Training Data

Core Datasets

1. Solana Q&A Prompts:
   • Curated from Solana Stack Exchange, developer forums, and official docs.
   • Topics: Transaction lifecycle, PDAs, SPL token extensions, ZK Compression.
2. NFT Metadata:
   • 100+ collections compliant with Metaplex's Token Metadata standard (e.g., name, URI, attributes).
3. Program Library IDs:
   • SPL token, governance, and compression program IDs for on-chain interoperability.
4. ZK-Compressed Data:
   • State roots and validity proofs for efficient ledger storage.

Preprocessing

• Tokenization: Custom Solana-Rust tokenizer with SPL-specific keywords.
• Compression: ZK-SNARK proofs applied to reduce dataset size by 160x.

---

Technical Specifications

Model Architecture

• Layers: 80 transformer layers with rotary positional embeddings.
• Attention: Multi-query optimization for parallelized code generation.
• Training Hardware: 512 A100 80GB GPUs (AWS SageMaker).

Software

• Frameworks: PyTorch 2.0, Solana CLI, Anchor Framework.
• Libraries: Metaplex's mpl-token-metadata, Light Protocol's ZK circuits.

---

Evaluation

Benchmarks

Task	Accuracy	Dataset
Rust Code Generation	92%	500 Solana Program Examples
NFT Metadata Compliance	88%	Metaplex Token Metadata
ZK Proof Generation	85%	Light Protocol Test Suite

---

Ethical Considerations

Bias and Risks

• Overfitting to Solana: Limited utility for non-Solana blockchains.
• Data Privacy: NFT metadata sourced from public collections only.

Recommendations

• Fine-tune for specific use cases (e.g., gaming NFTs, DAO governance).
• Pair with human review for critical financial applications.

---

How to Get Started

Code Example

from transformers import AutoModelForCausalLM, AutoTokenizer  

model = AutoModelForCausalLM.from_pretrained("8BitLabs/DeepSolanaCoder")  
tokenizer = AutoTokenizer.from_pretrained("8BitLabs/DeepSolanaCoder")  

prompt = "Write a Solana program to mint an NFT with Metaplex metadata."  
inputs = tokenizer(prompt, return_tensors="pt")  
outputs = model.generate(**inputs, max_length=512)  
print(tokenizer.decode(outputs[0]))  

Deployment Scripts

• Candy Machine Setup: Use sugar launch for automated NFT collection deployment.
• ZK Compression: Integrate Light Protocol's SDK for state optimization.

---

Environmental Impact

• Carbon Emissions: ~120 tCO2eq (estimated via ML Impact Calculator).
• Hardware: AWS P4d instances, 3D parallelism with ZeRO optimization.

---

Citation

@article{deepsolanacoder,  
  title={DeepSolanaCoder: A ZK-Compressed Language Model for Solana Blockchain Development},  
  author={8BitLabs},  
  year={2025},  
  url={https://8bitlabs.ai}  
}  

---

Model Card Contact: [email protected]
License Agreement: 8BitLabs DeepSolanaCoder License

---

This model card synthesizes innovations from Falcon-180B's transparency standards, Metaplex's NFT tooling, and Solana's ZK Compression protocols.