🧠 Lumo-70B-Instruct Model

Overview

Introducing Lumo-70B-Instruct - the largest and most advanced AI model ever created for the Solana ecosystem. Built on Meta’s groundbreaking LLaMa 3.3 70B Instruct foundation, this revolutionary model represents a quantum leap in blockchain-specific artificial intelligence. With an unprecedented 70 billion parameters and trained on the most comprehensive Solana documentation dataset ever assembled, Lumo-70B-Instruct sets a new standard for developer assistance in the blockchain space.

(Knowledge cut-off date: 17th January, 2025)

🎯 Key Features

• Unprecedented Scale: First-ever 70B parameter model specifically optimized for Solana development
• Comprehensive Knowledge: Trained on the largest curated dataset of Solana documentation ever assembled
• Advanced Architecture: Leverages state-of-the-art quantization and optimization techniques
• Superior Context Understanding: Enhanced capacity for complex multi-turn conversations
• Unmatched Code Generation: Near human-level code completion and problem-solving capabilities
• Revolutionary Efficiency: Advanced 4-bit quantization for optimal performance

🚀 Model Card

📊 Model Architecture

Advanced Training Pipeline

The model employs cutting-edge quantization and optimization techniques to harness the full potential of 70B parameters:

+---------------------------+     +----------------------+     +-------------------------+
|    Base Model            |     |   Optimization       |     |    Fine-Tuned Model    |
|  LLaMa 3.3 70B Instruct  | --> | 4-bit Quantization  | --> |   Lumo-70B-Instruct    |
|                         |     |   SDPA Attention     |     |                         |
+---------------------------+     +----------------------+     +-------------------------+

Dataset Sources

Comprehensive integration of all major Solana ecosystem documentation:

🛠️ Installation and Usage

1. Installation

pip install transformers datasets bitsandbytes accelerate

2. Load the Model with Advanced Quantization

from transformers import LlamaForCausalLM, AutoTokenizer
import torch
from transformers import BitsAndBytesConfig

# Configure 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    llm_int8_enable_fp32_cpu_offload=True
)

model = LlamaForCausalLM.from_pretrained(
    "lumolabs-ai/Lumo-70B-Instruct",
    device_map="auto",
    quantization_config=bnb_config,
    use_cache=False,
    attn_implementation="sdpa"
)
tokenizer = AutoTokenizer.from_pretrained("lumolabs-ai/Lumo-70B-Instruct")

3. Optimized Inference

def complete_chat(model, tokenizer, messages, max_new_tokens=128):
    inputs = tokenizer.apply_chat_template(
        messages,
        return_tensors="pt",
        return_dict=True,
        add_generation_prompt=True
    ).to(model.device)
    
    with torch.inference_mode():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            do_sample=True,
            temperature=0.7,
            top_p=0.95
        )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example usage
response = complete_chat(model, tokenizer, [
    {"role": "system", "content": "You are Lumo, an expert Solana assistant."},
    {"role": "user", "content": "How do I implement concentrated liquidity pools with Raydium?"}
])

📈 Performance Metrics

Training Convergence

📂 Dataset Analysis

Enhanced Dataset Structure:

{
  "question": "Explain the implementation of Jito's MEV architecture",
  "answer": "Jito's MEV infrastructure consists of...",
  "context": "Complete architectural documentation...",
  "metadata": {
    "source": "jito-labs/mev-docs",
    "difficulty": "advanced",
    "category": "MEV"
  }
}

🔍 Technical Innovations

Quantization Strategy

• Advanced 4-bit NF4 quantization
• FP16 compute optimization
• Efficient CPU offloading
• SDPA attention mechanism

Performance Optimizations

• Flash Attention 2.0 integration
• Gradient accumulation (4 steps)
• Optimized context packing
• Advanced batching strategies

🌟 Interactive Demo

Experience the power of Lumo-70B-Instruct: 🚀 Try the Model

🙌 Contributing

Join us in pushing the boundaries of blockchain AI: - Submit feedback via HuggingFace - Report performance metrics - Share use cases

📜 License

Licensed under the GNU Affero General Public License v3.0 (AGPLv3).

📞 Community

Connect with the Lumo community: - Twitter: Lumo Labs - Telegram: Join our server

🤝 Acknowledgments

Special thanks to: - The Solana Foundation - Meta AI for LLaMa 3.3 - The broader Solana ecosystem - Our dedicated community of developers