Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Maksim Riabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, Colin Raffel
| Challenge: | Recent studies show that pretrained language models can solve practical tasks using more than 100 billion parameters. |
| Approach: | They propose a system for inference and fine-tuning of large models collaboratively by joining the resources of multiple parties. |
| Outcome: | The proposed system outperforms offloading for very large models running on consumer GPUs with 1 step per second, enough for many interactive LLM applications. |
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SkyLLM: Cross-LLM-APIs Federation for Cost-effective Query Processing (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated exceptional capabilities across a wide range of tasks, from text generation to complex problem-solving. |
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MELABenchv1: Benchmarking Large Language Models against Smaller Fine-Tuned Models for Low-Resource Maltese NLP (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance across various NLP tasks, largely due to their generalisability and ability to perform tasks without additional training. |
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