| Summary | AL1. I developed a serverless training pipeline for fine-tuning LLMs. |
| Problem | Inefficient LLM Fine-Tuning: The existing process for fine-tuning Large Language Models (LLMs) was inefficient and lacked a structured pipeline. |
| Mission | Develop an Efficient LLM Training Pipeline: My mission was to create a streamlined and cost-effective training pipeline for fine-tuning LLMs. |
| Action | - Developed a Training Pipeline:
- Data Retrieval from S3: I implemented efficient data download functionality from Amazon S3.
- Model Storage on Hugging Face: I integrated with Hugging Face to store and share trained models.
- Experiment Tracking with WandB: I utilized Weights & Biases (WandB) for comprehensive experiment and parameter tracking.
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| Challenge | Cost Optimization for LLM Fine-Tuning: A primary challenge was managing and optimizing the significant computational costs associated with fine-tuning LLMs. |
| Overcome | Leveraged Serverless Architecture: I designed and implemented the pipeline using a serverless architecture, which significantly reduced idle compute costs and optimized resource utilization. |
| Result | Efficient and Cost-Effective LLM Fine-Tuning: The new pipeline enabled efficient fine-tuning of LLMs while significantly optimizing compute resource utilization and reducing overall training costs. |
| Skill | AI/LLM |