Papers with 4-bit quantisation
Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance (2025.emnlp-main)
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| Challenge: | specialised small models outperform general large models with few labelled samples (on average 100) performance variance is taken into account when comparing the number of labelled sample required to tune a specialised model with a larger number of samples. |
| Approach: | They find that specialised small models need only few labelled samples to outperform general large models with limited labelled data. |
| Outcome: | The proposed models outperform general large models with few labelled samples and take performance variance into account. |