Search Results for "sha-256"
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Showing 9 open source projects for "sha-256"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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IronClaw
IronClaw is OpenClaw inspired but focused on privacy & security
IronClaw is a security-first, open-source personal AI assistant built in Rust and designed to keep your data fully under your control. It operates on the principle that your AI should work for you, not external vendors, ensuring all data is stored locally, encrypted, and never shared. The platform emphasizes transparency, offering auditable code with no hidden telemetry or data harvesting. IronClaw runs untrusted tools inside isolated WebAssembly (WASM) sandboxes with strict capability-based... -
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Stake Crash Predictor
Stake Crash Predictor is a toolkit for stake mines predictor & Plinko.
The Stake Crash Predictor is a focused toolkit that combines statistical analysis, optional server fairness seed hash decrypt helpers, and AI-assisted summaries to help you study rounds on Stake.us. This project centers on the stake mines predictor and stake predictor workflows Demo-focused stake crash predictor app — seed-inspection helpers (SHA-512 / SHA-256), AI-assisted summaries, and demo bot templates for stake mines predictor too, Start in demo mode to test safely. Disclaimer: For educational and testing purposes only. No predictive or gameplay guarantees. -
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DALL-E in Pytorch
Implementation / replication of DALL-E, OpenAI's Text to Image
...Currently only the VAE with a codebook size of 1024 is offered, with the hope that it may train a little faster than OpenAI's, which has a size of 8192. In contrast to OpenAI's VAE, it also has an extra layer of downsampling, so the image sequence length is 256 instead of 1024 (this will lead to a 16 reduction in training costs, when you do the math). -
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BytePS
A high performance and generic framework for distributed DNN training
...BytePS outperforms existing open-sourced distributed training frameworks by a large margin. For example, on BERT-large training, BytePS can achieve ~90% scaling efficiency with 256 GPUs (see below), which is much higher than Horovod+NCCL. In certain scenarios, BytePS can double the training speed compared with Horovod+NCCL. We show our experiment on BERT-large training, which is based on GluonNLP toolkit. The model uses mixed precision. We use Tesla V100 32GB GPUs and set batch size equal to 64 per GPU. Each machine has 8 V100 GPUs (32GB memory) with NVLink-enabled. ... -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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PyTorch pretrained BigGAN
PyTorch implementation of BigGAN with pretrained weights
An op-for-op PyTorch reimplementation of DeepMind's BigGAN model with the pre-trained weights from DeepMind. This repository contains an op-for-op PyTorch reimplementation of DeepMind's BigGAN that was released with the paper Large Scale GAN Training for High Fidelity Natural Image Synthesis. This PyTorch implementation of BigGAN is provided with the pretrained 128x128, 256x256 and 512x512 models by DeepMind. We also provide the scripts used to download and convert these models from the... -
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VedaViz
Text to graphics (*3d, animation) (semi)automatically
When students are creating themselves, learning is taking place. And teachers will be at the epicenter of this. Anyone who believes differently has never had a good teacher. I would trade all of my technology for an afternoon with Socrates. Steve Jobs http://www.thedailybeast.com/newsweek/2001/10/28/the-classroom-of-the-future.html Когда ученик творит, он обучается. Учитель же будет в эпицентре этого всего. Кто думает иначе, - у него никогда не было хорошего учителя. Я бы продал все... -
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Ministral 3 14B Base 2512
Powerful 14B-base multimodal model — flexible base for fine-tuning
...The model remains efficient enough for on-prem or local deployment — it fits in ~32 GB VRAM in BF16, and requires under ~24 GB when quantized. It supports dozens of languages, making it suitable for multilingual applications around the world. With a large 256 k-token context window, Ministral 3 14B Base 2512 can handle very long inputs, complex documents, or large contexts. -
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Ministral 3 3B Reasoning 2512
Compact 3B-param multimodal model for efficient on-device reasoning
Ministral 3 3B Reasoning 2512 is the smallest reasoning-capable model in the Ministal-3 family, yet delivers a surprisingly capable multimodal and multilingual base for lightweight AI applications. It pairs a 3.4B-parameter language model with a 0.4B-parameter vision encoder, enabling it to understand both text and image inputs. This reasoning-tuned variant is optimized for tasks like math, coding, and other STEM-related problem solving, making it suitable for applications that require... -
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Ministral 3 8B Base 2512
Versatile 8B-base multimodal LLM, flexible foundation for custom AI
Ministral 3 8B Base 2512 is a mid-sized, dense model in the Ministral 3 series, designed as a general-purpose foundation for text and image tasks. It pairs an 8.4B-parameter language model with a 0.4B-parameter vision encoder, enabling unified multimodal capabilities out of the box. As a “base” model (i.e., not fine-tuned for instruction or reasoning), it offers a flexible starting point for custom downstream tasks or fine-tuning. The model supports a large 256k token context window, making... -
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