
FloTorch
FloTorch is an open-source tool for optimizing Generative AI workloads on AWS. It automates RAG proof-of-concept development with features like hyperparameter tuning, vector database optimization, and LLM integration. FloTorch streamlines experimentation, ensures security, and accelerates production with cost-efficient, validated workflows.
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README:
FloTorch is an innovative product poised to transform the field of Generative AI by simplifying and optimizing the decision-making process for leveraging Large Language Models (LLMs) in Retrieval Augmented Generation (RAG) systems. In today’s fast-paced digital landscape, selecting the right LLM setup is critical for achieving efficiency, accuracy, and cost-effectiveness. However, this process often involves extensive trial-and-error, significant resource expenditure, and complex comparisons of performance metrics. Our solution addresses these challenges with a streamlined, user-friendly approach.
- Well-Architected framework: Focuses on five pillars of service architecture: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization.
- Maximizes Efficiency: Ensures users achieve the best performance from their chosen LLMs in less time as multiple experiments can run parallelly.
- Eliminates Complexity: No more manual evaluations or tedious trial-and-error processes.
- Accelerates Selection: Streamlines the evaluation and decision-making process.
- Focus on Innovation: Allows users to dedicate resources to innovation and deployment rather than experimentation.
- Simple & Automatic: Simple UI, 1,000+ combinations, no human errors, no ‘It Depends’
- Saves time: Reduces experiments from months to hours
- Encourages Experiments: Test new LLMs / capabilities in hours with automation
- Secure: Your data, your AWS account, your ground truth Q&A
- Deterministic: Provides accuracy, performance, costs, and safety
Please refer to our Installation guide for the installation steps in detail.
Use our usage guide for more details on using FloTorch. Click here for frequently asked questions.
For those who'd like to contribute code, see our Contribution Guide.
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