kasterkeqi/Lumo-8B-Fork-Sol-Copilot

🧠 Lumo-8B-Instruct Model

  

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.

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

🎯 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

📊 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:

🛠️ Installation and Usage

1. Installation

pip install transformers datasets peft wandb

2. Load the Model

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

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

Fine-Tuning Loss Graph

📂 Dataset

Dataset Format (JSONL):

{
  "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

🙌 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

🤝 Acknowledgments

Special thanks to the Solana ecosystem developers and the open-source community for their invaluable contributions and support.