Further reading
- Hewage, Machine Learning Operations: A Survey on MLOps Tool Support, 2022, https://arxiv.org/abs/2202.10169
- Park, LlamaDuo: LLMOps Pipeline for Seamless Migration from Service LLMs to Small-Scale Local LLMs, 2024, https://arxiv.org/abs/2408.13467
- Zhao, A Survey of Large Language Models, 2023, https://arxiv.org/abs/2303.18223
- Chang, A Survey on Evaluation of Large Language Models, 2023, https://arxiv.org/abs/2307.03109
- IBM, LLM evaluation: Why Testing AI Models Matters, https://www.ibm.com/think/insights/llm-evaluation
- Guo, Evaluating Large Language Models: A Comprehensive Survey, 2023, https://arxiv.org/abs/2310.19736
- Shi, Keep the Cost Down: A Review on Methods to Optimize LLM’s KV-Cache Consumption, 2024, https://arxiv.org/abs/2407.18003
- Li, A Survey on Large Language Model Acceleration based on KV Cache Management, 2024, https://arxiv.org/abs/2412.19442
- Zhou, A Survey on Efficient Inference for Large Language Models, 2024...
