🧠 Lumo-DeepSeek-R1-8B Model

Overview

The Lumo-DeepSeek-R1-8B model is a fine-tuned version of DeepSeek-R1-Distill-Llama-8B, specifically optimized for Solana and its associated ecosystems. This model is designed to provide highly accurate and contextual assistance for developers, offering capabilities such as answering complex questions, generating code snippets, debugging, and explaining technical concepts. The fine-tuning process leverages the Lumo-Iris-DS-Instruct dataset, ensuring the model is well-suited for Solana-specific tasks.

(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

Parameter	Details
Base Model	DeepSeek-R1-Distill-Llama-8B
Fine-Tuning Framework	HuggingFace Transformers, LoRA
Dataset Size	28,518 high-quality Q&A pairs
Context Length	128,000 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:

Dataset Sources

The dataset comprises curated documentation, cookbooks, and API references from the following sources:

Source	Links
Lumo-Iris-DS-Instruct	About Lumo-Iris

🛠️ 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-DeepSeek-R1-8B"
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

Metric	Value
Validation Loss	1.73
BLEU Score	89%
Code Accuracy	92%
Token Efficiency	~128,000 tokens max

Fine-Tuning Loss Graph

📂 Dataset

Split	Count	Description
Train	27.1k	High-quality Q&A pairs
Test	1.43k	Evaluation dataset for testing

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-DeepSeek-R1-8B Inferencing

🙌 Contributing

We welcome contributions to enhance the Lumo-DeepSeek-R1-8B 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 - Telegram: Lumo Labs

🤝 Acknowledgments

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

Trained on Dataset: https://huggingface.co/datasets/lumolabs-ai/Lumo-Iris-DS-Instruct

Readme