Using the famous cnn model in Pytorch, we run benchmarks on various gpu.

Remove Backgroud from a video 🧤🎥

A kubernetes operator for managing nvidia MIG instances.

Z-AI V1.4 发行版本

Dataset and code for "Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning"

ClusterOps is an enterprise-grade Python library developed and maintained by the Swarms Team to help you manage and execute agents on specific CPUs and GPUs across clusters. This tool enables advanced CPU and GPU selection, dynamic task allocation, and resource monitoring, making it ideal for high-performance distributed computing environments.

Implement named entity recognition (NER) using regex and fine-tuned LLM, with a total of 15 categories. The ultimate goal is to apply the model to detect personally identifiable information (PII) in student writing.

Verified Indiana University Big Red 200 and Quartz HPC reference for AI coding agents — SLURM, modules, workflow recipes.

Implementing science-related multiple-choice question answering based on LLMs and RAG.

This competition challenges you to predict which responses users will prefer in a head-to-head battle between chatbots powered by large language models (LLMs).

Found out that using A100 and V100 on Vicuna and Llama2 have a different result, while other model such as Falcon doesn't has such question.

Text to Image Gen [ Demo ]

CLI tool to check Oracle Cloud compute shape availability - find capacity across regions

Dashboard for AI Studio, Open Source Continuous Inference | Deepseek-R1, Qwen2.5, Llama3.1 | 4xRTX-5090 inside PRU2500, 2xH100 inside PRU2500, 8xMI210 in SuperMicro

Analyze a dataset of conversations from the Chatbot Arena, where various LLMs provide responses to user prompts. The goal is to develop a model that enhances chatbot interactions, ensuring they align more closely with human preferences.

Transformer implementation in pytorch trained on NVIDIA A100 in fp16

Performance of CUDA example benchmark code on NVIDIA A100.

An implementation of Speculative RAG exploring latency-quality trade-offs in multi-draft retrieval. Features batched parallel drafting via vLLM and log-probability verifier selection for fast, high-quality QA on a single A100 GPU.

Faithful from-scratch reimplementation of DeepSeek-V3 (MLA + MoE + MTP), scaled for Chinchilla-optimal 422M training on a single A100 80GB

Reference Flash Attention implementation in PyTorch with V1/V2, GQA/MQA, Triton kernels, benchmark and docs.