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RAG mainly, Feature extraction, tagging, Document and e-mail classification. You don't need a 24B parameter to know whether the e-mail should go to accounting o
by Beretta_Vexee 2y ago
RAG mainly, Feature extraction, tagging, Document and e-mail classification.
You don't need a 24B parameter to know whether the e-mail should go to accounting or customer support.
- pheeney 2y agoWhat models would you recommend for basic classification if you don't need a 24B parameter one?
- josh-sematic 2y agoYou might find this comparison chart helpful: https://www.airtrain.ai/blog/how-15-top-llms-perform-on-classification-accuracy-vs-cost-breakdown https://www.airtrain.ai/blog/how-15-top-llms-perform-on-clas... Note: from October; also I work at Airtrain
- Beretta_Vexee 2y agoThere's no alternative to testing with your own data. The majority of our data is in French, and our benchmarks differ greatly from public benchmarks generally based on English documents.
- elorant 2y agoI’m using Llama-3 8B to classify html files. It’s surprisingly good, and I run it on an RTX 4060 Ti at 8-bit quantization. No complains so far.
- Panoramix 2y agoWould this work for non-text data? Like finding outliers in a time series or classifying trends, that kind of thing
- Beretta_Vexee 2y agoDon't use LLM to do something that some python + panda lines could do better. Buy a data scientist a coffee and have a chat.