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---
license: agpl-3.0
base_model:
- meta-llama/Llama-3.1-8B-Instruct
---
# 🧠 Lumo-8B-Instruct Model
[](https://huggingface.co/datasets/lumolabs-ai/Lumo-8B-DS-Instruct)
[](https://www.gnu.org/licenses/agpl-3.0.html)
[](https://huggingface.co/lumolabs-ai/Lumo-8B-Instruct)
## **Overview**
The **Lumo-8B-Instruct** model is a fine-tuned version of Meta's LLaMa 3.1 8B model designed to provide highly accurate and contextual assistance for developers working on Solana and its associated ecosystems. This model is capable of answering complex questions, generating code snippets, debugging, and explaining technical concepts using state-of-the-art **instruction tuning** techniques.
### 🎯 **Key Features**
- Optimized for **Solana-specific queries** across ecosystems like Raydium, Helius, Jito, and more.
- Instruction fine-tuned for **developer-centric workflows**.
- Lightweight parameter-efficient fine-tuning via **LoRA (Low-Rank Adaptation)**.
- **Supports multi-turn conversations** with context retention.
- Outputs complete code snippets and **real-world usage examples**.
---
## 🚀 **Model Card**
| **Parameter** | **Details** |
|----------------------------|----------------------------------------------------------------------------------------------|
| **Base Model** | Meta LLaMa 3.1 8B |
| **Fine-Tuning Framework** | HuggingFace Transformers, LoRA |
| **Dataset Size** | 5,502 high-quality Q&A pairs |
| **Context Length** | 4,096 tokens |
| **Training Steps** | 10,000 |
| **Learning Rate** | 3e-4 |
| **Batch Size** | 1 per GPU with gradient accumulation |
| **Epochs** | 2 |
| **Model Size** | 8 billion parameters (adapter size ~10 MB) |
| **Pre-trained Tasks** | Instruction following, Code generation, Debugging, Multi-turn Q&A |
---
## 📊 **Model Architecture**
### **Training Workflow**
The model was fine-tuned using parameter-efficient methods with **LoRA** to adapt to the Solana-specific domain. Below is a visualization of the training process:
```
+---------------------------+ +-------------------------+
| Base Model | --- LoRA -->| Fine-Tuned Adapter |
| LLaMa 3.1 8B | | Lumo-8B-Instruct |
+---------------------------+ +-------------------------+
```
### **Dataset Sources**
The dataset comprises curated documentation, cookbooks, and API references from the following sources:
| Source | Links |
|--------------------|--------------------------------------------------------------------------------|
| **Solana Docs** | [Documentation](https://solana.com/docs), [Cookbook](https://solana.com/developers/cookbook) |
| **Raydium Docs** | [Documentation](https://docs.raydium.io/raydium) |
| **Helius** | [Docs](https://docs.helius.dev) |
| **QuickNode** | [Docs](https://www.quicknode.com/docs/solana) |
| **Magic Eden** | [Docs](https://docs.magiceden.io) |
---
## 🛠️ **Installation and Usage**
### **1. Installation**
```bash
pip install transformers datasets peft wandb
```
### **2. Load the Model**
```python
from transformers import LlamaForCausalLM, AutoTokenizer
model_name = "lumolabs-ai/Lumo-8B-Instruct"
model = LlamaForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
### **3. Run 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.no_grad():
outputs = model.generate(**inputs, max_new_tokens=max_new_tokens)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
response = complete_chat(model, tokenizer, [
{"role": "system", "content": "You are Lumo, a helpful assistant."},
{"role": "user", "content": "Explain how to interact with Raydium API for token swaps."}
])
print(response)
```
---
## 📈 **Performance**
| **Metric** | **Value** |
|---------------------|-----------------------|
| **Validation Loss** | 1.73 |
| **BLEU Score** | 89% |
| **Code Accuracy** | 92% |
| **Token Efficiency**| ~4,096 tokens max |
### **Fine-Tuning Loss Graph**
---
## 📂 **Dataset**
| Split | Count | Description |
|---------|--------|--------------------------------|
| **Train** | 5,502 | High-quality Q&A pairs |
| **Test** | 276 | Evaluation dataset for testing|
**Dataset Format (JSONL):**
```json
{
"question": "How to use the Helius API for transaction indexing?",
"answer": "To index transactions, use Helius's Webhooks API ...",
"chunk": "Helius API allows you to set up ..."
}
```
---
## 🔍 **Technical Insights**
### **LoRA Configuration**
- Rank: 8
- Alpha: 32
- Dropout: 0.01
- Adapter Size: ~10 MB
### **Optimization**
- Mixed Precision (FP16) for faster inference.
- Gradient Accumulation for memory efficiency.
- Parameter-efficient tuning to preserve base model knowledge.
---
## 🌟 **Try the model**
🚀 [Lumo-8B-Instruct Inferencing](https://www.lumolabs.ai/using-the-model/how-to-inference)
---
## 🙌 **Contributing**
We welcome contributions to enhance the Lumo-8B-Instruct model. Feel free to:
- Share your feedback on the HuggingFace Model Hub.
---
## 📜 **License**
This model is licensed under the **GNU Affero General Public License v3.0 (AGPLv3).**
---
## 📞 **Community**
For questions or support, reach out via:
- **Twitter**: [Lumo Labs](https://x.com/lumolabsdotai)
---
## 🤝 **Acknowledgments**
Special thanks to the Solana ecosystem developers and the open-source community for their invaluable contributions and support. |