Fine-Tuned Meta's LLama3 8B Parameter Model
Fine-tuned the Llama 3 8B model for instruction-following, prioritizing efficiency and speed.
Discovery phase
It started when I was searching for a research topic on LLM and Generative AI. I used Perplexity and Grok for popular yet new research topics and one of them was Fine tuning for a specific use case without any GPU costs and even 40-6-% faster. It hit me instantly and Jumped right into docs and techniques for fine-tuning models.
Through Research papers , Vaious articles and Videos, I gathered information about the pain points , issues and why behind fine tuning. These Insights shaped the foundation
Tools & Techniques Used
Hugging Face | Google Colab | Python & Libraries | Q-LorA | Unsloth
Category
LLM Fine-Tuning | Generative AI
Live Project
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Ideation Development
Based on the insights gathered during the research phase, I began a possible solutions and techniques. Watched and read various articles to find out unsloth was a game changer. This allowed me to translate abstract idea into a fine-tune the Llama 3 8B model for instruction-following, prioritizing efficiency and speed.
Short Summary
1. Leveraged 4-bit Quantization (QLoRA) and Unsloth AI for accelerated, memory-efficient training. 2. Deployed the full training job on a standard Tesla T4 GPU, peaking at just 7.9GB VRAM. 3. Observed significant learning convergence, dropping the training loss from 1.58 to 0.88.
Applications & Use Cases
Key applications include personalized recommendations (retail), fraud detection (finance), contract analysis (legal), and creating specialized chatbots, reducing reliance on lengthy prompts for consistent, efficient results.
This technique can provide significant operational value by automating time tasks thus improving overall efficiency and memory usage for solopreneurs to small startups. With the ability to operate at a minimal cost and accelerated, memory-efficient training.
