Search Results for "scientific"
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Showing 10 open source projects for "scientific"
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Surya
Implementation of the Surya Foundation Model for Heliophysics
Surya is an open‑source, AI‑based foundation model for heliophysics developed collaboratively by NASA (via the IMPACT AI team) and IBM. Named after the Sanskrit word for “sun,” Surya is trained on nine years of high‑resolution solar imagery from NASA’s Solar Dynamics Observatory (SDO). It is designed to forecast solar phenomena—such as flares, solar wind, irradiance, and active region behavior—by predicting future solar images with a sophisticated long–short vision transformer architecture,... -
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AlphaGenome
Programmatic access to the AlphaGenome model
The AlphaGenome API provides access to AlphaGenome, Google DeepMind’s unifying model for deciphering the regulatory code within DNA sequences. This repository contains client-side code, examples, and documentation to help you use the AlphaGenome API. AlphaGenome offers multimodal predictions, encompassing diverse functional outputs such as gene expression, splicing patterns, chromatin features, and contact maps. The model analyzes DNA sequences of up to 1 million base pairs in length and can... -
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LingBot-World
Advancing Open-source World Models
LingBot-World is an open-source, high-fidelity world simulator designed to advance the state of world models through video generation. Built on top of Wan2.2, it enables realistic, dynamic environment simulation across diverse styles, including real-world, scientific, and stylized domains. LingBot-World supports long-term temporal consistency, maintaining coherent scenes and interactions over minute-level horizons. With real-time interactivity and sub-second latency at 16 FPS, it is well-suited for interactive applications and rapid experimentation. The project is fully open-access, releasing both code and models to help bridge the gap between closed and open world-model systems. ... -
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NVIDIA Isaac GR00T
NVIDIA Isaac GR00T N1.5 is the world's first open foundation model
NVIDIA Isaac‑GR00T N1.5 is an open-source foundation model engineered for generalized humanoid robot reasoning and manipulation skills. It accepts multimodal inputs—such as language and images—and uses a diffusion transformer architecture built upon vision-language encoders, enabling adaptive robot behaviors across diverse environments. It is designed to be customizable via post-training with real or synthetic data. The vision-language model remains frozen during both pretraining and... -
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FLUX.2-klein-4B
Flux 2 image generation model pure C inference
FLUX.2-klein-4B is a compact, high-performance C library implementation of the Flux optimization algorithm — an iterative approach for solving large-scale optimization problems common in scientific computing, machine learning, and numerical simulation. Written with a strong emphasis on simplicity, correctness, and performance, it abstracts the core logic of flux-based optimization into a minimal C API that can be embedded in broader applications without pulling in heavy dependencies. Because the implementation is in plain C and focuses on data locality and vectorized operations, flux2.c can be integrated into performance-critical code paths where control over memory layout and execution behavior matters, such as GPU kernels, embedded systems, or custom ML runtime engines. -
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FLUX.1-Krea-dev
Text-to-image model optimized for artistic quality and safe generation
...The model is distributed under a non-commercial license, with conditions to prevent misuse and support ethical AI development. FLUX.1-Krea-dev is available via Diffusers and ComfyUI, and integrates with the FluxPipeline for streamlined usage. Developers can use it for personal or scientific projects, but must comply with safety filters and content restrictions. Extensive pre- and post-training mitigations were applied to minimize risks like NSFW or abusive content generation. -
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Hy4 preview
770B MoE model for coding, research, reasoning, and long-context work
...Hy4-preview supports a native 1M-token context window, allowing it to process large codebases, numerous files, and complex extended workflows. Tencent specifically optimized the model for software engineering, office and financial analysis, game development, and scientific research. -
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Ling 3.0 Tiny
Lightweight MoE model for local reasoning, coding, and AI agents
...It contains 7.9B total parameters while activating only 1.3B per token, using a hybrid architecture that alternates Kimi Delta Attention and Multi-Head Latent Attention with a sparse 128-expert MoE. The model supports both fast responses and configurable multi-step thinking, covering general agents, coding, mathematics, scientific reasoning, and instruction following. It is specifically optimized for local and resource-constrained deployment and has been validated on NVIDIA DGX Spark, Apple Silicon MacBooks, and Mac mini systems. FP8 testing reached around 100–105 tokens/s on DGX Spark and 86–90 tokens/s on an M4 Pro MacBook. BF16, FP8, and INT4 weights are available, while deployment options include SGLang, vLLM, and experimental Ollama support on Apple Silicon. -
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Mistral Large 3 675B Base 2512
Frontier-scale 675B multimodal base model for custom AI training
...As the base version, it is not fine-tuned for instruction following or reasoning, making it ideal for teams planning their own domain-specific finetuning or custom training pipelines. The model is engineered for reliability, long-context comprehension, and stable performance across many enterprise, scientific, and knowledge-intensive workloads. Its architecture includes a powerful language MoE and a 2.5B-parameter vision encoder, enabling multimodal understanding out of the box. Mistral Large 3 Base supports deployment on-premises using FP8 or NVFP4 formats, enabling high-performance workflows on B200, H200, H100, or A100 hardware. -
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Mistral Large 3 675B Instruct 2512 NVFP4
Quantized 675B multimodal instruct model optimized for NVFP4
...This NVFP4 checkpoint is a post-training-activation quantized version of the original instruct model, created through a collaboration between Mistral AI, vLLM, and Red Hat using llm-compressor. It retains the same instruction-tuned behavior as the FP8 model, making it ideal for production assistants, agentic workflows, scientific tasks, and long-context enterprise systems. The model integrates a 673B-parameter MoE language backbone with a 2.5B-parameter vision encoder, enabling rich multimodal analysis across text and images. Designed for efficient deployment, it runs on a single H100 or A100 node in NVFP4 while delivering performance similar to FP8 for short- and mid-context workloads.
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