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Found 7 papers, 5 papers with code
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.
DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference
The deployment and scaling of large language models (LLMs) have become critical as they permeate various applications, demanding high-throughput and low-latency serving systems.
DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention
Most of the existing multi-modal models, hindered by their incapacity to adeptly manage interleaved image-and-text inputs in multi-image, multi-round dialogues, face substantial constraints in resource allocation for training and data accessibility, impacting their adaptability and scalability across varied interaction realms.
DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales
ChatGPT-like models have revolutionized various applications in artificial intelligence, from summarization and coding to translation, matching or even surpassing human performance.
ZeRO++: Extremely Efficient Collective Communication for Giant Model Training
Zero Redundancy Optimizer (ZeRO) has been used to train a wide range of large language models on massive GPUs clusters due to its ease of use, efficiency, and good scalability.
SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement
In this paper, we propose a fully automated and lightweight adaptive batching methodology to enable fine-grained batch size adaption (e. g., at a mini-batch level) that can achieve state-of-the-art performance with record breaking batch sizes.
The Age of Correlated Features in Supervised Learning based Forecasting
In these applications, a neural network is trained to predict a time-varying target (e. g., solar power), based on multiple correlated features (e. g., temperature, humidity, and cloud coverage).
