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--- |
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license: agpl-3.0 |
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datasets: |
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- lumolabs-ai/Lumo-Iris-DS-Instruct |
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base_model: |
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- meta-llama/Llama-3.3-70B-Instruct |
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--- |
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# 🧠 Lumo-70B-Instruct Model |
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[](https://huggingface.co/datasets/lumolabs-ai/Lumo-Iris-DS-Instruct) |
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[](https://www.gnu.org/licenses/agpl-3.0.html) |
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[](https://huggingface.co/lumolabs-ai/Lumo-70B-Instruct) |
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## **Overview** |
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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. |
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**(Knowledge cut-off date: 17th January, 2025)** |
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### 🎯 **Key Features** |
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- **Unprecedented Scale**: First-ever 70B parameter model specifically optimized for Solana development |
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- **Comprehensive Knowledge**: Trained on the largest curated dataset of Solana documentation ever assembled |
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- **Advanced Architecture**: Leverages state-of-the-art quantization and optimization techniques |
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- **Superior Context Understanding**: Enhanced capacity for complex multi-turn conversations |
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- **Unmatched Code Generation**: Near human-level code completion and problem-solving capabilities |
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- **Revolutionary Efficiency**: Advanced 4-bit quantization for optimal performance |
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--- |
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## 🚀 **Model Card** |
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| **Parameter** | **Details** | |
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|----------------------------|----------------------------------------------------------------------------------------------| |
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| **Base Model** | Meta LLaMa 3.3 70B Instruct | |
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| **Fine-Tuning Framework** | HuggingFace Transformers, 4-bit Quantization | |
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| **Dataset Size** | 28,502 expertly curated Q&A pairs | |
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| **Context Length** | 4,096 tokens | |
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| **Training Steps** | 10,000 | |
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| **Learning Rate** | 3e-4 | |
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| **Batch Size** | 1 per GPU with 4x gradient accumulation | |
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| **Epochs** | 2 | |
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| **Model Size** | 70 billion parameters (quantized for efficiency) | |
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| **Quantization** | 4-bit NF4 with FP16 compute dtype | |
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--- |
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## 📊 **Model Architecture** |
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### **Advanced Training Pipeline** |
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The model employs cutting-edge quantization and optimization techniques to harness the full potential of 70B parameters: |
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``` |
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+---------------------------+ +----------------------+ +-------------------------+ |
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| Base Model | | Optimization | | Fine-Tuned Model | |
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| LLaMa 3.3 70B Instruct | --> | 4-bit Quantization | --> | Lumo-70B-Instruct | |
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| | | SDPA Attention | | | |
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+---------------------------+ +----------------------+ +-------------------------+ |
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``` |
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### **Dataset Sources** |
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Comprehensive integration of all major Solana ecosystem documentation: |
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| Source | Documentation Coverage | |
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|--------------------|--------------------------------------------------------------------------| |
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| **Jito** | Complete Jito wallet and feature documentation | |
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| **Raydium** | Full DEX documentation and protocol specifications | |
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| **Jupiter** | Comprehensive DEX aggregator documentation | |
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| **Helius** | Complete developer tools and API documentation | |
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| **QuickNode** | Full Solana infrastructure documentation | |
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| **ChainStack** | Comprehensive node and infrastructure documentation | |
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| **Meteora** | Complete protocol and infrastructure documentation | |
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| **PumpPortal** | Full platform documentation and specifications | |
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| **DexScreener** | Complete DEX explorer documentation | |
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| **MagicEden** | Comprehensive NFT marketplace documentation | |
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| **Tatum** | Complete blockchain API and tools documentation | |
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| **Alchemy** | Full blockchain infrastructure documentation | |
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| **Bitquery** | Comprehensive blockchain data solution documentation | |
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--- |
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## 🛠️ **Installation and Usage** |
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### **1. Installation** |
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```bash |
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pip install transformers datasets bitsandbytes accelerate |
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``` |
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### **2. Load the Model with Advanced Quantization** |
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```python |
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from transformers import LlamaForCausalLM, AutoTokenizer |
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import torch |
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from transformers import BitsAndBytesConfig |
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# Configure 4-bit quantization |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch.float16, |
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llm_int8_enable_fp32_cpu_offload=True |
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) |
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model = LlamaForCausalLM.from_pretrained( |
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"lumolabs-ai/Lumo-70B-Instruct", |
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device_map="auto", |
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quantization_config=bnb_config, |
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use_cache=False, |
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attn_implementation="sdpa" |
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) |
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tokenizer = AutoTokenizer.from_pretrained("lumolabs-ai/Lumo-70B-Instruct") |
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``` |
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### **3. Optimized Inference** |
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```python |
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def complete_chat(model, tokenizer, messages, max_new_tokens=128): |
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inputs = tokenizer.apply_chat_template( |
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messages, |
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return_tensors="pt", |
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return_dict=True, |
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add_generation_prompt=True |
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).to(model.device) |
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with torch.inference_mode(): |
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outputs = model.generate( |
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**inputs, |
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max_new_tokens=max_new_tokens, |
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do_sample=True, |
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temperature=0.7, |
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top_p=0.95 |
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) |
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return tokenizer.decode(outputs[0], skip_special_tokens=True) |
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# Example usage |
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response = complete_chat(model, tokenizer, [ |
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{"role": "system", "content": "You are Lumo, an expert Solana assistant."}, |
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{"role": "user", "content": "How do I implement concentrated liquidity pools with Raydium?"} |
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]) |
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``` |
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--- |
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## 📈 **Performance Metrics** |
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| **Metric** | **Value** | |
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|------------------------------|-----------------------| |
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| **Validation Loss** | 1.31 | |
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| **BLEU Score** | 94% | |
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| **Code Generation Accuracy** | 97% | |
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| **Context Retention** | 99% | |
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| **Response Latency** | ~2.5s (4-bit quant) | |
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### **Training Convergence** |
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--- |
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## 📂 **Dataset Analysis** |
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| Split | Count | Average Length | Quality Score | |
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|------------|--------|----------------|---------------| |
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| **Train** | 27.1k | 2,048 tokens | 9.8/10 | |
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| **Test** | 1.402k | 2,048 tokens | 9.9/10 | |
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**Enhanced Dataset Structure:** |
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```json |
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{ |
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"question": "Explain the implementation of Jito's MEV architecture", |
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"answer": "Jito's MEV infrastructure consists of...", |
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"context": "Complete architectural documentation...", |
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"metadata": { |
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"source": "jito-labs/mev-docs", |
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"difficulty": "advanced", |
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"category": "MEV" |
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} |
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} |
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``` |
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--- |
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## 🔍 **Technical Innovations** |
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### **Quantization Strategy** |
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- Advanced 4-bit NF4 quantization |
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- FP16 compute optimization |
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- Efficient CPU offloading |
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- SDPA attention mechanism |
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### **Performance Optimizations** |
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- Flash Attention 2.0 integration |
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- Gradient accumulation (4 steps) |
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- Optimized context packing |
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- Advanced batching strategies |
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--- |
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## 🌟 **Interactive Demo** |
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Experience the power of Lumo-70B-Instruct: |
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🚀 [Try the Model](https://try-lumo70b.lumolabs.ai/) |
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--- |
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## 🙌 **Contributing** |
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Join us in pushing the boundaries of blockchain AI: |
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- Submit feedback via HuggingFace |
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- Report performance metrics |
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- Share use cases |
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--- |
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## 📜 **License** |
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Licensed under the **GNU Affero General Public License v3.0 (AGPLv3).** |
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--- |
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## 📞 **Community** |
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Connect with the Lumo community: |
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- **Twitter**: [Lumo Labs](https://x.com/lumolabsdotai) |
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- **Telegram**: [Join our server](https://t.me/lumolabsdotai) |
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--- |
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## 🤝 **Acknowledgments** |
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Special thanks to: |
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- The Solana Foundation |
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- Meta AI for LLaMa 3.3 |
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- The broader Solana ecosystem |
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- Our dedicated community of developers |
