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.
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:
Architecture
Dataset Sources
The dataset comprises curated documentation, cookbooks, and API references from the following sources:
1defcomplete_chat(model, tokenizer, messages, max_new_tokens=128):2 inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True, add_generation_prompt=True).to(model.device)3with torch.no_grad():4 outputs = model.generate(**inputs, max_new_tokens=max_new_tokens)5return tokenizer.decode(outputs[0], skip_special_tokens=True)6response = complete_chat(model, tokenizer,[7{"role":"system","content":"You are Lumo, a helpful assistant."},8{"role":"user","content":"Explain how to interact with Raydium API for token swaps."}9])10print(response)
📈 Performance
Metric
Value
Validation Loss
1.73
BLEU Score
89%
Code Accuracy
92%
Token Efficiency
~128,000 tokens max
Fine-Tuning Loss Graph
Loss Graph
📂 Dataset
Split
Count
Description
Train
27.1k
High-quality Q&A pairs
Test
1.43k
Evaluation dataset for testing
Dataset Format (JSONL):
json
1{2"question":"How to use the Helius API for transaction indexing?",3"answer":"To index transactions, use Helius's Webhooks API ...",4"chunk":"Helius API allows you to set up ..."5}
🔍 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.
