Summary of Ninjallm: Fast, Scalable and Cost-effective Rag Using Amazon Sagemaker and Aws Trainium and Inferentia2, by Tengfei Xue et al.
NinjaLLM: Fast, Scalable and Cost-effective RAG using Amazon SageMaker and AWS Trainium and Inferentia2
by Tengfei Xue, Xuefeng Li, Roman Smirnov, Tahir Azim, Arash Sadrieh, Babak Pahlavan
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes enhancements to retrieval-augmented generation (RAG) techniques, focusing on large language models fine-tuned on AWS Trainium and Inferentia2 AI chips via SageMaker. The goal is to improve tool usage, add citation capabilities, and mitigate risks of hallucinations and unsafe responses due to context bias. The RAG system achieves an accuracy of 62% on the Natural Questions dataset and 59% on HotPotQA, outperforming models like DBRX and Mixtral Instruct. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve how computers understand and share information in a natural way. It makes big language models better by fine-tuning them to work well with special computer chips that are affordable and efficient. The goal is to make the model more reliable and less likely to provide wrong or confusing answers. To test this, the researchers used two popular datasets and found that their system worked better than others on these tasks. |
Keywords
* Artificial intelligence * Fine tuning * Rag * Retrieval augmented generation
