Update README.md

README.md

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----
-license: agpl-3.0
----
+---
+license: agpl-3.0
+datasets:
+- lumolabs-ai/Lumo-Iris-DS-Instruct
+base_model:
+- meta-llama/Llama-3.3-70B-Instruct
+---
+
+# 🧠 Lumo-70B-Instruct Model
+
+![Lumo](https://i.ibb.co/nwzzD4B/logo.png)
+
+[![Lumo-70B-DS-Instruct](https://img.shields.io/badge/Lumo-70B--Instruct-blueviolet?style=flat-square&logo=openai&logoColor=white)](https://huggingface.co/datasets/lumolabs-ai/Lumo-Iris-DS-Instruct)
+[![License](https://img.shields.io/badge/license-AGPL%20v3-blue?style=flat-square)](https://www.gnu.org/licenses/agpl-3.0.html)
+[![HF](https://img.shields.io/badge/HuggingFace-Lumo--70B--Instruct-orange?style=flat-square&logo=huggingface)](https://huggingface.co/lumolabs-ai/Lumo-70B-Instruct)
+
+## **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**
+
+| **Parameter**              | **Details**                                                                                  |
+|----------------------------|----------------------------------------------------------------------------------------------|
+| **Base Model**             | Meta LLaMa 3.3 70B Instruct                                                                  |
+| **Fine-Tuning Framework**  | HuggingFace Transformers, 4-bit Quantization                                                 |
+| **Dataset Size**           | 28,502 expertly curated Q&A pairs                                                            |
+| **Context Length**         | 4,096 tokens                                                                                 |
+| **Training Steps**         | 10,000                                                                                       |
+| **Learning Rate**          | 3e-4                                                                                         |
+| **Batch Size**             | 1 per GPU with 4x gradient accumulation                                                      |
+| **Epochs**                 | 2                                                                                            |
+| **Model Size**             | 70 billion parameters (quantized for efficiency)                                             |
+| **Quantization**           | 4-bit NF4 with FP16 compute dtype                                                           |
+
+---
+
+## 📊 **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:
+
+| Source             | Documentation Coverage                                                    |
+|--------------------|--------------------------------------------------------------------------|
+| **Jito**           | Complete Jito wallet and feature documentation                            |
+| **Raydium**        | Full DEX documentation and protocol specifications                        |
+| **Jupiter**        | Comprehensive DEX aggregator documentation                                |
+| **Helius**         | Complete developer tools and API documentation                           |
+| **QuickNode**      | Full Solana infrastructure documentation                                 |
+| **ChainStack**     | Comprehensive node and infrastructure documentation                      |
+| **Meteora**        | Complete protocol and infrastructure documentation                       |
+| **PumpPortal**     | Full platform documentation and specifications                           |
+| **DexScreener**    | Complete DEX explorer documentation                                      |
+| **MagicEden**      | Comprehensive NFT marketplace documentation                              |
+| **Tatum**          | Complete blockchain API and tools documentation                          |
+| **Alchemy**        | Full blockchain infrastructure documentation                             |
+| **Bitquery**       | Comprehensive blockchain data solution documentation                     |
+
+---
+
+## 🛠️ **Installation and Usage**
+
+### **1. Installation**
+
+```bash
+pip install transformers datasets bitsandbytes accelerate
+```
+
+### **2. Load the Model with Advanced Quantization**
+
+```python
+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**
+
+```python
+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**
+
+| **Metric**                    | **Value**             |
+|------------------------------|-----------------------|
+| **Validation Loss**          | 1.31                 |
+| **BLEU Score**               | 94%                  |
+| **Code Generation Accuracy** | 97%                  |
+| **Context Retention**        | 99%                  |
+| **Response Latency**         | ~2.5s (4-bit quant)  |
+
+### **Training Convergence**
+![Loss Graph](https://i.postimg.cc/Pf8zQ151/lumo70b.png)
+
+---
+
+## 📂 **Dataset Analysis**
+
+| Split       | Count  | Average Length | Quality Score |
+|------------|--------|----------------|---------------|
+| **Train**  | 27.1k  | 2,048 tokens   | 9.8/10       |
+| **Test**   | 1.402k | 2,048 tokens   | 9.9/10       |
+
+**Enhanced Dataset Structure:**
+```json
+{
+  "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](https://try-lumo70b.lumolabs.ai/)
+
+---
+
+## 🙌 **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](https://x.com/lumolabsdotai)
+- **Telegram**: [Join our server](https://t.me/lumolabsdotai)
+
+---
+
+## 🤝 **Acknowledgments**
+
+Special thanks to:
+- The Solana Foundation
+- Meta AI for LLaMa 3.3
+- The broader Solana ecosystem
+- Our dedicated community of developers