Accuracy and Consumption analysis from a compressed model by CompactifAI from Multiverse Computing
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913938815320064 |
|---|---|
| author | Fovet, Damien Chamoli, Shashank Oury, Sarah Singhal, Srishti |
| author_facet | Fovet, Damien Chamoli, Shashank Oury, Sarah Singhal, Srishti |
| contents | This study evaluates the performance of a compression method, called CompactifAI, developed by Multiverse Computing, applied to the large language model Llama 3.1 8B\cite{llama}. The evaluation focused on model efficiency (in terms of energy consumption) and accuracy using respectively the frameworks Codecarbon\cite{codecarbon} and Ragas\cite{ragas}. A comparison was performed between the model compressed with CompactifAI\cite{compactifai}\cite{compactifai2} and its full-size version. Our findings reveal that the compressed model using CompactifAI not only significantly reduced the computational resources but also maintained the model accuracy, making the model more efficient, scalable and cost-effective. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08836 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accuracy and Consumption analysis from a compressed model by CompactifAI from Multiverse Computing Fovet, Damien Chamoli, Shashank Oury, Sarah Singhal, Srishti Machine Learning Performance This study evaluates the performance of a compression method, called CompactifAI, developed by Multiverse Computing, applied to the large language model Llama 3.1 8B\cite{llama}. The evaluation focused on model efficiency (in terms of energy consumption) and accuracy using respectively the frameworks Codecarbon\cite{codecarbon} and Ragas\cite{ragas}. A comparison was performed between the model compressed with CompactifAI\cite{compactifai}\cite{compactifai2} and its full-size version. Our findings reveal that the compressed model using CompactifAI not only significantly reduced the computational resources but also maintained the model accuracy, making the model more efficient, scalable and cost-effective. |
| title | Accuracy and Consumption analysis from a compressed model by CompactifAI from Multiverse Computing |
| topic | Machine Learning Performance |
| url | https://arxiv.org/abs/2507.08836 |