Understanding LLMs: A Comprehensive Overview from Training to Inference

The introduction of ChatGPT has led to a significant increase in the utilization of Large Language Models (LLMs) for addressing downstream tasks. There's an increasing focus on cost-efficient training and deployment within this context. Low-cost training and deployment of LLMs represent the future development trend. This paper reviews the evolution of large language model training techniques and inference deployment technologies aligned with this emerging trend. The discussion on training includes various aspects, including data preprocessing, training architecture, pre-training tasks, parallel training, and relevant content related to model fine-tuning. On the inference side, the paper covers topics such as model compression, parallel computation, memory scheduling, and structural optimization. It also explores LLMs' utilization and provides insights into their future development.Comment: 30 pages,6 figure

Application of heat pump combined two-stage desiccant wheel fresh air system of residential buildings in mixed climate zone

The building requires dehumidification for a long period of time in mixed climate zone of China. As a conventional method for dehumidification, vapor compression systems remove the water vapor by cooling the process air below dew point. This system consumes a lot of energy for reheating the air to meet the requirement of supply air temperature. A heat pump combined with two-stage desiccant wheel (TSDW&HP) is proposed as an air conditioning and dehumidification system in this study. The operation performance of proposed system applied in a hypothetical residence with 3 residents was investigated and simulated by using TRNSYS software. The operation modes of the system are discussed for different scenarios of season and outdoor air humidity ratio. In dehumidification season, fresh air deals with all of the latent load. In air conditioning season, fresh air deals with all of the moisture load with part of the cooling load. When evaporation temperature of HP is reduced and more moisture load is processed by evaporator in air conditioning season, there is a balance point between the performance of DWs and heat pump. The energy consumption of TSDW&HP fresh air system was compared with a conventional fresh air conditioner during dehumidification season and air conditioning season. It was found that the energy-saving potential of this system is 27.3% compared with conventional air conditioner

Application of heat pump combined two-stage desiccant wheel fresh air system of residential buildings in mixed climate zone

The building requires dehumidification for a long period of time in mixed climate zone of China. As a conventional method for dehumidification, vapor compression systems remove the water vapor by cooling the process air below dew point. This system consumes a lot of energy for reheating the air to meet the requirement of supply air temperature. A heat pump combined with two-stage desiccant wheel (TSDW&amp;HP) is proposed as an air conditioning and dehumidification system in this study. The operation performance of proposed system applied in a hypothetical residence with 3 residents was investigated and simulated by using TRNSYS software. The operation modes of the system are discussed for different scenarios of season and outdoor air humidity ratio. In dehumidification season, fresh air deals with all of the latent load. In air conditioning season, fresh air deals with all of the moisture load with part of the cooling load. When evaporation temperature of HP is reduced and more moisture load is processed by evaporator in air conditioning season, there is a balance point between the performance of DWs and heat pump. The energy consumption of TSDW&amp;HP fresh air system was compared with a conventional fresh air conditioner during dehumidification season and air conditioning season. It was found that the energy-saving potential of this system is 27.3% compared with conventional air conditioner.</jats:p

The climate adaptability evaluation of biogas fermentation assisted by solar greenhouse

The production efficiency of biogas digesters is largely restricted by the low environment temperature in winter, for most regions of China. As a feasible means of warming and heat preservation, solar greenhouse has the ability to expand the application scope and service time of biogas in rural China. The evaluation of climate adaptability of solar greenhouse is of great importance and imminent, due to the fact that both solar energy and biomass resources are affected by climate. In this paper, a complete evaluation index system for climate adaptability of biogas fermentation assisted by solar greenhouse was established. The indicators of the evaluation index system were selected by means of frequency analysis and theoretical analysis. The weights of the indicators, including solar radiation, outdoor air temperature, crop yields and human and animal manure, were determined by analytic hierarchy process combined with literature research, and the scoring rules were based on the objective significance of each indicator. The climate adaptability of typical cities in the Yangtze River Delta was evaluated with the evaluation index system. Among the cities, Hefei shows the best comprehensive adaptability, and then is Xuzhou, Shanghai, Nanjing, and the lowest adaptability, Hangzhou. The comprehensive adaptability results of these cities depend not only on the gas production capacity, but also on the biomass resource and solar radiation

The effect of biogas fermentation assisted by simple solar greenhouse

Biogas fermentation rate is largely affected by environment temperature, causing a much more difficult production of biogas digesters in cold winter, for most regions in China. Combining the abundant solar energy resources and biomass dry anaerobic fermentation together, solar thermal energy can guarantee the production of biogas in winter. With this method, the prediction of the appropriate fermentation temperature is required. In this study, the effect of temperature on biogas fermentation was studied. To predict the fermentation temperature, a heat transfer model of biogas fermentation based on a project in city Xuzhou, which assisted with a simple solar greenhouse, was established according to the heat transfer theory. The maximum difference between the measured and calculated fermentation temperature was 2%. The effect of biogas fermentation assisted by simple solar greenhouse in typical city of different climate zones, including severe cold region, cold region and hot summer and cold winter region, was studied with the combination of heat transfer model and temperature-based biogas production rates prediction model. The results showed that, the gas production rate of biogas fermentation increases with the increase of temperature in a certain range. Assisted by simple solar greenhouse, the biogas digester temperature is increased by 6~8°C compared with the previous one, ensuring a better daily gas production rate of 0.5~0.7m3/m3 in winter.</jats:p

The climate adaptability evaluation of biogas fermentation assisted by solar greenhouse

The production efficiency of biogas digesters is largely restricted by the low environment temperature in winter, for most regions of China. As a feasible means of warming and heat preservation, solar greenhouse has the ability to expand the application scope and service time of biogas in rural China. The evaluation of climate adaptability of solar greenhouse is of great importance and imminent, due to the fact that both solar energy and biomass resources are affected by climate. In this paper, a complete evaluation index system for climate adaptability of biogas fermentation assisted by solar greenhouse was established. The indicators of the evaluation index system were selected by means of frequency analysis and theoretical analysis. The weights of the indicators, including solar radiation, outdoor air temperature, crop yields and human and animal manure, were determined by analytic hierarchy process combined with literature research, and the scoring rules were based on the objective significance of each indicator. The climate adaptability of typical cities in the Yangtze River Delta was evaluated with the evaluation index system. Among the cities, Hefei shows the best comprehensive adaptability, and then is Xuzhou, Shanghai, Nanjing, and the lowest adaptability, Hangzhou. The comprehensive adaptability results of these cities depend not only on the gas production capacity, but also on the biomass resource and solar radiation.</jats:p

The effect of biogas fermentation assisted by simple solar greenhouse

Biogas fermentation rate is largely affected by environment temperature, causing a much more difficult production of biogas digesters in cold winter, for most regions in China. Combining the abundant solar energy resources and biomass dry anaerobic fermentation together, solar thermal energy can guarantee the production of biogas in winter. With this method, the prediction of the appropriate fermentation temperature is required. In this study, the effect of temperature on biogas fermentation was studied. To predict the fermentation temperature, a heat transfer model of biogas fermentation based on a project in city Xuzhou, which assisted with a simple solar greenhouse, was established according to the heat transfer theory. The maximum difference between the measured and calculated fermentation temperature was 2%. The effect of biogas fermentation assisted by simple solar greenhouse in typical city of different climate zones, including severe cold region, cold region and hot summer and cold winter region, was studied with the combination of heat transfer model and temperature-based biogas production rates prediction model. The results showed that, the gas production rate of biogas fermentation increases with the increase of temperature in a certain range. Assisted by simple solar greenhouse, the biogas digester temperature is increased by 6~8°C compared with the previous one, ensuring a better daily gas production rate of 0.5~0.7m3/m3 in winter