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AI Models 84759

• chrono-r17↗

  aljosadro

• Huihui-Qwen3.6-35B-A3B-abliterated-KO-i1-GGUF↗

  augustine223

• cerebras_MiniMax-M2-REAP-162B-A10B-GGUF↗

  bartowski

• DarkIdol-Llama-3.1-8B-Instruct-1.0-Uncensored-GGUF↗

  bartowski

• Llama-3.1-70B-ArliAI-RPMax-v1.1-GGUF↗

  bartowski

• nvidia_OpenReasoning-Nemotron-14B-GGUF↗

  bartowski

• stella_en_400M_v5↗

  billatsectorflow

  Hi, everyone, thanks for using stella models. After six months of work, I trained the jasper model on top of the stella model, which is a multimodal model, and it can be ranked 2 in mteb (submitted the results on 2024-12-11, which may need official review https://github.com/embeddings-benchmark/results/pull/68). I'll focus on the technical report, training data and related code, hopefully the tricks I've used will be of some help to you guys! The core training code will be integrated into the rag-retrieval library(https://github.com/NLPJCL/RAG-Retrieval) in the near future. (Welcome to star)

• Qwen3006B-transcriber-beta↗

  bingbangboom

• UI_Llama_3_2_1B↗

  BioTorch

• nopo4d↗

  bralani01

  This work presents **NoPo4D**, the first feed-forward system that jointly addresses dynamic content, multi-view input, and unknown camera poses in a single pass. In pursuit of pose-free 4D reconstruction, NoPo4D yields two key insights: 🏆 NoPo4D consistently outperforms prior feed-forward baselines across four multi-view dynamic benchmarks (ExoRecon, Immersive Light Field, Kubric, and N3DV). With an optional post-optimization stage, it surpasses per-scene optimization methods while running orders of magnitude faster. For full installation instructions (including the custom Depth Anything 3 backbone and optional `torch-scatter`), please refer to the NoPo4D GitHub repository.