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Ultime pubblicazioni e brevetti sui modelli linguistici di piccole dimensioni (SLM)

Modelli linguistici di piccole dimensioni

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Modelli linguistici di piccole dimensioni
I modelli linguistici di piccole dimensioni consentono di elaborazione del linguaggio naturale su dispositivi consumer e embedded.

I modelli linguistici di piccole dimensioni designano i sistemi di elaborazione del linguaggio naturale basati su transformer che operano al di sotto di circa 7 miliardi di parametri: una soglia definita meno da un confine formale che dal vincolo pratico di implementabilità su hardware di consumo, dispositivi mobili e sistemi embedded senza infrastruttura di inferenza cloud.

Questo ambito di ricerca è emerso come risposta diretta ai costi computazionali ed economici dei modelli di frontiera: sebbene le architetture con oltre un miliardo di parametri dimostrino un'ampia capacità generale, il loro ingombro di memoria, la latenza di inferenza e il consumo energetico le rendono strutturalmente incompatibili con l'implementazione su dispositivo, le applicazioni sensibili alla privacy e i contesti operativi a bassa larghezza di banda o offline.

Il programma di ricerca centrale mira a colmare il divario di capacità tra modelli compatti e modelli di frontiera attraverso una combinazione di distillazione della conoscenza (addestramento di un modello studente più piccolo rispetto alle distribuzioni di output di un modello insegnante più grande), potatura strutturata e non strutturata, quantizzazione aggressiva dei pesi fino alle rappresentazioni INT4 e INT8 e metodi di fine-tuning efficienti in termini di parametri come LoRA e QLoRA, che adattano un modello base compresso a compiti specifici del dominio con un costo computazionale aggiuntivo minimo.

Le pubblicazioni e i brevetti elencati di seguito trattano tecniche di compressione dei modelli, algoritmi di quantizzazione, protocolli di distillazione, architetture di trasformatori efficienti, ottimizzazione dell'inferenza su dispositivo e pipeline di fine-tuning specifiche per dominio:

Questa è la nostra ultima selezione di pubblicazioni e brevetti in inglese su Small Language Models (SLM), tra numerose riviste scientifiche online, classificate e focalizzate su small language model, SLM, on-device language model, edge language model, compact transformer, sub-7B parameter model, language model compression, knowledge distillation NLP, structured pruning language model, modello linguistico di potatura non strutturato, modello linguistico di quantizzazione del peso, NLP di quantizzazione INT4, NLP di quantizzazione INT8, fine-tuning efficiente dei parametri, fine-tuning LoRA, fine-tuning QLoRA, modello linguistico di sintonizzazione dell'adattatore, inferenza su dispositivo, NLP di inferenza sul bordo, decodifica speculativa, trasformatore di distillazione del modello, formato di quantizzazione GGUF e modello compatto misto di esperti.

Deformable high-strength aluminum alloy compositions and methods of making the same

Patent published on the 2026-06-04 in US under Ref US20260152827 by PURDUE RES FOUNDATION [US] (Zhang Xinghang [us], Wang Haiyan [us], Stegman Benjamin Thomas [us], Shang Anyu [us])

Abstract: [0000] An alloy comprising 92 at % aluminum, 2 at % titanium, 2 at % iron, 2 at % cobalt, and 2 at % nickel. A method of making an alloy is disclosed. The method contains the steps of providing particles of desired composition, utilizing a selective leaser melting (SLM) apparatus producing a first layer of the particles on a substrate and melting and solidifying a first group selected areas of the layer of particles, wherein the melting and the solidification results in an alloy of desired compo[...]


Our summary: The content describes a high-strength aluminum alloy with specific composition percentages. It outlines a method for creating the alloy using selective laser melting to achieve desired thickness and shape. The process involves layering particles, melting, and solidifying selected areas to form intermetallic structures.

aluminum alloy, selective laser melting, intermetallic lamellae, high-strength

Patent

Quantization-aware lora fine-tuning for llm

Patent published on the 2026-06-04 in US under Ref US20260154540 by MEDIATEK SINGAPORE PTE LTD [SG] (Lim Jia Yao Christopher [sg], Huang Ya-lin [tw], Li Huai-ting [tw], Wong Wai Mun [sg], Liang Jen-wei [tw], Lee Timothy Jun Jie [sg])

Abstract: [0000] In an aspect of the disclosure, a method of using a LoRA for inference with a FC layer of a LLM is provided. The method includes: dequantizing an INT input to an FP output; processing the FP output from the DQ and a first FP input from first weights of a down projection module of the LoRA, to output a first FP output; processing the first FP output from the first BMM and a second FP input from second weights of an up projection module of the LoRA, to output a second FP output; quantizing [...]


Our summary: The method describes using LoRA for inference in a fully connected layer of a large language model. It involves dequantizing inputs, processing them through down and up projection modules, and quantizing outputs. The final output is an INT inference result derived from the LoRA adjustments.

Quantization, LoRA, fine-tuning, LLM

Patent

Systems and methods for assisting operation and maintenance of marine machine equipment

Patent published on the 2026-06-03 in EP under Ref EP4752805 by ALFA LAVAL CORP AB [SE] (Karlsson Jimmie [se], Boman Jesper [se])

Abstract: [0001] The present invention relates to a method of operating and maintaining a piece of marine machine equipment. The piece of marine machine equipment is connected to a local processor. The method comprising the steps of obtaining a set of training data specific to the piece of marine machine equipment and training a Small Language Model (SLM) with the set of training data specific to the piece of marine machine equipment. The method further comprising the step of executing the trained SLM on [...]


Our summary: The invention describes a method for operating and maintaining marine machine equipment using a local processor. It involves training a Small Language Model (SLM) with specific training data for the equipment. The trained SLM provides offline operational advice utilizing real-time data from the equipment.

marine machine equipment, operational advice, Small Language Model, real-time data

Patent

Parameter-free method for efficient and accurate llm inference acceleration via speculative decoding

Patent published on the 2026-05-07 in WO under Ref WO2026092843 by MARZOLLO MICHELE [DE] (Marzollo Michele [de], Mueller Lorenz [de], Zhuang Jiawei [de], Roemer Niklas [de], Cavigelli Lukas [de])

Abstract: In some examples, apparatus and methods are provided for selecting a draft token sequence for verification by using a large language model, LLM. Different sources of statistics on text data (prompt, generated output, large dataset of text data) can be utilized in order to choose candidates to use for speculative decoding via look-ups.[...]


Our summary: This method accelerates LLM inference without parameters by using speculative decoding. It selects draft token sequences for verification through statistical analysis of text data. The approach utilizes various sources of statistics to optimize candidate selection for decoding.

speculative decoding, LLM inference, token sequence selection, text data statistics

Patent

Automated synthesis of planar linkage mechanisms with diverse joint types via spring-connected link models and contrastive graph learning

Published on 2026-03-28 by @OXFORD

Abstract: AbstractThe automated synthesis of planar linkage mechanisms has long been a challenge in mechanism design, requiring both geometric feasibility and motion accuracy. Recent advances in data-driven and neural network–based methods have shown promise in automating linkage synthesis, improving efficiency and scalability compared to traditional analytical or optimization-based techniques. Nevertheless, existing data-driven approaches remain limited in handling diverse joint configurations and ofte[...]


Our summary: This study presents a framework for automating the synthesis of planar linkage mechanisms using deep learning and physics-based modeling. It employs a spring-connected link model for diverse joint configurations and utilizes contrastive graph learning for efficient linkage retrieval. The method demonstrates improved accuracy and optimization stability compared to traditional approaches.

mechanism synthesis, deep learning, contrastive graph learning, optimization stability

Publication

Enhancing Whisper Fine-Tuning with Discrete Wavelet Transform-Based LoRA Initialization

Published on 2026-01-29 by Liang Lan, Molin Fang, Yuxuan Chen, Daliang Wang, Wenyong Wang @MDPI

Abstract: In low-resource automatic speech recognition (ASR) scenarios, parameter-efficient fine-tuning (PEFT) has become a crucial approach for adapting large pre-trained speech models. Although low-rank adaptation (LoRA) offers clear advantages in efficiency, stability, and deployment friendliness, its performance remains constrained because random initialization fails to capture the time&amp;ndash;frequency structural characteristics of speech signals. To address this limitation, this work proposes[...]


Our summary: This work introduces a structured initialization mechanism combining LoRA with discrete wavelet transform for fine-tuning in low-resource ASR. The proposed DWTLoRA method enhances convergence speed, stability, and accuracy by aligning with speech signal characteristics. Experimental results show DWTLoRA outperforms standard LoRA and other PEFT methods in character error rate and training efficiency.

Fine-Tuning, Discrete Wavelet Transform, Low-Rank Adaptation, Automatic Speech Recognition

Publication

Influence and Optimization of Process Parameters on Surface Roughness of Selective Laser Melting of 316L Stainless Steel

Published on 2026-01-20 by Pin Dong, Kamonpong Jamkamon, Suppawat Chuvaree @MDPI

Abstract: To achieve better surface quality in selective laser melting (SLM), this study used 316L stainless steel powder and conducted a systematic design experiment to investigate the influence mechanism of process parameters on the surface roughness of the top and vertical surfaces. Response surface methodology (RSM) was then used for parameter optimization. The results showed that scanning speed has the greatest impact on surface roughness, followed by laser power, while scanning spacing has the least[...]


Our summary: This study investigates the impact of process parameters on the surface roughness of 316L stainless steel in selective laser melting. Scanning speed significantly affects surface quality, with optimal conditions identified for minimal roughness. The findings validate the effectiveness of the response surface methodology used for parameter optimization.

Selective Laser Melting, Surface Roughness, Process Parameters, Response Surface Methodology

Publication

A Lightweight LLM-Based Semantic&ndash;Spatial Inference Framework for Fine-Grained Urban POI Analysis

Published on 2026-01-16 by Zhuo Huang, Yixing Guo, Shuo Huang, Miaoxi Zhao @MDPI

Abstract: Unstructured POI name texts are widely used in fine-grained urban analysis, yet missing labels and semantic ambiguity often limit their value for spatial inference. This study proposes a large language model-based semantic&amp;ndash;spatial inference framework (LLM-SSIF), a lightweight semantic&amp;ndash;spatial pipeline that translates POI texts into interpretable, fine-grained spatial evidence through an end-to-end workflow that couples scalable label expansion with scale-controlled sp[...]


Our summary: This study introduces LLM-SSIF, a lightweight framework for translating unstructured POI texts into spatial evidence. It employs LoRA-based fine-tuning for efficient adaptation and enhances label coverage. The model demonstrates strong performance in urban analysis, revealing cultural differences between cities.

LLM, semantic inference, spatial analysis, fine-grained POI

Publication

Argomenti trattati: Modelli linguistici di piccole dimensioni, elaborazione del linguaggio naturale, sistemi basati su trasformatori, efficienza dei parametri, distillazione della conoscenza, compressione dei modelli, potatura strutturata, potatura non strutturata, quantizzazione dei pesi, INT4, INT8, metodi di regolazione fine, distribuzione sul dispositivo, latenza dell'inferenza, consumo energetico, applicazioni sensibili alla privacy, operazioni a bassa larghezza di banda, contesti operativi non in linea, IEEE 80211, ISO/IEC 30170, ISO/IEC 27001, ISO/IEC 25010 e NIST SP 800-53.

Glossario dei termini utilizzati

Natural Language Processing (NLP): Un campo dell'intelligenza artificiale incentrato sull'interazione tra computer e linguaggio umano, che consente alle macchine di comprendere, interpretare e generare testo o discorso in linguaggio naturale. Comprende attività come la traduzione linguistica, l'analisi del sentiment e il riconoscimento vocale.

Small Language Models (SLM): Reti neurali compatte progettate per attività di elaborazione del linguaggio naturale, tipicamente caratterizzate da un numero inferiore di parametri e requisiti computazionali ridotti rispetto a modelli più grandi, pur essendo in grado di generare testo coerente e comprendere il contesto entro ambiti limitati.

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