Qsur's space implementation rate: How the Novere Training Training is designed to improve the efficiency of large languages ​​of Language (LLMS)

Post-Training Nightingization (PTQ) It focuses on reducing the size and improvement of the speed of large languages ​​of Languages ​​(LLMS) to make them more effective use of the world. Such types require large data volumes, but the duration of a strong and most powerful data distribution during the measurement of measurements reflecting the great difficulty. This can increase inappropriately a range of energy reductions, which is making, many numbers, more speeches and ordinary performance in model. While PTQ methods aim to address these issues, challenges will always disseminate data throughout the value, reducing the strength of improvement and preventing broadcasting.

Current methods of post-training training (PTQ) of large languages ​​(lls) focus on weight use-only and weight loss. Only weights weigh, such as GPTQ, Wobilitybeside OwqTry to minimize the use of memory by reducing minor mistakes or dealing with activation vendors but failed to do the accuracy of all prices. The strategies are like Germ including Quip # Use random matrics and vector's quantity but remain limited to managing extreme data submission. The size of weight performance aims to accelerate to both metals and activation. However, the methods such as Smooth, Earlybeside Citizen Fighting to manage the first performance of working, causing errors in many numbers. Overall, these methods depend on the Heuristic ears and fail to increase data distribution across the amount, limiting performance and efficiency.

Dealing with the Heuristic Limits training for the training training (PTQ) and metric shortages of evaluation of inflation, researchers from the HOMO AI, Nanjing University, including The SOUTHLETE University proposed Average Use of the Value Space (QSur). QSur measures the weight of the pill and disseminating the Activation performance using the amount of value, providing a limited amount of testing and improving PTQ methods. The metric of texting mathematical statistics such as Eigenvalue Dekomition and self-esteem ellipsoid to calculate weight hypertumulation and activation of activation. QSur analysis shows how straight transformation and exchange affect the efficiency of prices, some strategies reducing channels and reductions to reduce performance.

The investigators proposed Irriererstone The outline, including orthogonal changes and measuring alteration to improve weight loss and transactions. This approach includes equal pets for diagonal ratings and artogalon's pairs, ensures computer efficiency while maintaining equity. It decreases overwhelming without compromising the original network out of step. Irriererstone uses learning blocked to spread global transformation across Llm blocks, using the strategies like Launch of weight loss of active startup. The way is up to the top OsurIt reduces over the top of the overhead, and improves the implementation of the price in lls.

For testing purposes, researchers applied Irriererstone in Llam Family (Llama-1, Llama-2, including Llama 3) and performance testing using confusion in Wikitext2 and nine shots of shooting. Compared to ways such as Smooth, GPTQ, Citizenbeside Hills, Irriererstone They are always separated, gain at least 99.5% The accuracy of a floating point under the 4-16-16 to set up and highly reduced apps. 3-8b entered only a 0.29-The tapes down in Forms of Shootingcompared to the excess loss 1.55 points to others. In difficult situations, Asquant was better than sponquant and found as much as 6.53 Points by LLAMA-2B in the setting of 4-4-16. KL-Top Losing Working Wops provides better qualification of Semantics and reduce the audio, thus developing performance and reducing spaces in the W4a4kv4 by 32% of the. These results show that Irriererstone It is effective in managing and manifesting distribution.

Finally, the proposed method promoted the distribution of data distribution based on the Qsur metric and loss work, kl-top, improves the functioning of major languages. With low measurement information, reducing the sound and stored in the simess of semantic compared to existing light strategies, achieving higher performance at multiple benches. This framework may serve as a future basis, begins the process that will contribute to the performance of the use of the prices and perform effective models through requests that require efficiency of resources.

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Divyesh is a contact in MarkteachPost. Pursuing BTech for agricultural and food engineers in the Indian Institute of Technology, Kharagpur. He is a scientific and typical scientific lover who wants to combine this leading technology in the agricultural background and resolve challenges.