This technical report evaluates the effectiveness of Low Rank Adaptation (LoRA) for fine-tuning Large Language Models (LLMs) across a wide range of tasks and base models. The study aims to assess the viability of training and serving LoRA-fine-tuned LLMs in real-world applications. Key findings include: 1. **Model Quality**: LoRA fine-tuning significantly enhances LLM performance, outperforming both non-fine-tuned base models and GPT-4. On average, fine-tuned models improve by 34 points compared to base models and 10 points compared to GPT-4. 2. **Base Model Selection**: Mistral-7B and Zephyr-7b-beta stand out as top performers, with Mistral-7B achieving high adaptability and Zephyr-7b-beta showing the highest overall average performance. 3. **Task Complexity**: Task complexity heuristics, such as input and output lengths, compressibility, and Rouge-L similarity, correlate with fine-tuning quality and performance lift. Narrower, easier tasks are more likely to benefit from fine-tuning. 4. **LoRAX Performance**: LoRAX, an open-source Multi-LoRA inference server, enables efficient deployment of multiple LoRA-fine-tuned models on a single GPU. Benchmarks show that LoRAX handles up to 100 concurrent users with minimal latency and throughput degradation. 5. **LoRA Land**: A web application powered by LoRAX serves 25 fine-tuned Mistral-7B LLMs, demonstrating the practical efficiency and cost-effectiveness of using multiple specialized LLMs over a single general-purpose model. The report concludes that LoRA fine-tuning is a powerful method for enhancing LLM performance, and the practical deployment of these models using LoRAX highlights the benefits of specialized, task-specific LLMs.This technical report evaluates the effectiveness of Low Rank Adaptation (LoRA) for fine-tuning Large Language Models (LLMs) across a wide range of tasks and base models. The study aims to assess the viability of training and serving LoRA-fine-tuned LLMs in real-world applications. Key findings include: 1. **Model Quality**: LoRA fine-tuning significantly enhances LLM performance, outperforming both non-fine-tuned base models and GPT-4. On average, fine-tuned models improve by 34 points compared to base models and 10 points compared to GPT-4. 2. **Base Model Selection**: Mistral-7B and Zephyr-7b-beta stand out as top performers, with Mistral-7B achieving high adaptability and Zephyr-7b-beta showing the highest overall average performance. 3. **Task Complexity**: Task complexity heuristics, such as input and output lengths, compressibility, and Rouge-L similarity, correlate with fine-tuning quality and performance lift. Narrower, easier tasks are more likely to benefit from fine-tuning. 4. **LoRAX Performance**: LoRAX, an open-source Multi-LoRA inference server, enables efficient deployment of multiple LoRA-fine-tuned models on a single GPU. Benchmarks show that LoRAX handles up to 100 concurrent users with minimal latency and throughput degradation. 5. **LoRA Land**: A web application powered by LoRAX serves 25 fine-tuned Mistral-7B LLMs, demonstrating the practical efficiency and cost-effectiveness of using multiple specialized LLMs over a single general-purpose model. The report concludes that LoRA fine-tuning is a powerful method for enhancing LLM performance, and the practical deployment of these models using LoRAX highlights the benefits of specialized, task-specific LLMs.