サクサク読めて、アプリ限定の機能も多数！
トップへ戻る
GWの過ごし方
github.com/facebookresearch
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
github.com/meta-llama
Code Llama is a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks. We provide multiple flavors to cover a wide range of applications: foundation models (Code Llama), Python specializations (Code Llama - Python)
Seamless is a family of AI models that enable more natural and authentic communication across languages. SeamlessM4T is a massive multilingual multimodal machine translation model supporting around 100 languages. SeamlessM4T serves as foundation for SeamlessExpressive, a model that preserves elements of prosody and voice style across languages and SeamlessStreaming, a model supporting simultaneous
This is the code for the EnCodec neural codec presented in the High Fidelity Neural Audio Compression [abs]. paper. We provide our two multi-bandwidth models: A causal model operating at 24 kHz on monophonic audio trained on a variety of audio data. A non-causal model operating at 48 kHz on stereophonic audio trained on music-only data. The 24 kHz model can compress to 1.5, 3, 6, 12 or 24 kbps, wh
Below we share, in reverse chronological order, the updates and new releases in VISSL. All VISSL releases are available here. [Feb 2022]: Releasing SEER 10B parameters model implementation and model weights. [Feb 2022]: Releasing implementation of Fairness Benchmarks for computer vision models proposed in the paper. [Jan 2022]: Implementation for Geolocalization test (gps prediction for an image)
PyTorch3D provides efficient, reusable components for 3D Computer Vision research with PyTorch. Key features include: Data structure for storing and manipulating triangle meshes Efficient operations on triangle meshes (projective transformations, graph convolution, sampling, loss functions) A differentiable mesh renderer Implicitron, see its README, a framework for new-view synthesis via implicit
次のページ
このページを最初にブックマークしてみませんか？
『Meta Research』の新着エントリーを見る
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
