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Integer Scale: A Free Lunch for Faster Fine-grained Quantization of LLMs
We introduce Integer Scale, a novel post-training quantization scheme for large language models that effectively resolves the inference bottleneck in current fine-grained quantization approaches while maintaining similar accuracies.
A Speed Odyssey for Deployable Quantization of LLMs
The large language model era urges faster and less costly inference.
ESimCSE Unsupervised Contrastive Learning Jointly with UDA Semi-Supervised Learning for Large Label System Text Classification Mode
The challenges faced by text classification with large tag systems in natural language processing tasks include multiple tag systems, uneven data distribution, and high noise.
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