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AI 모델 84760

• NVIDIA-Nemotron-3-Nano-4B-FP8↗

  unsloth

• Qwen2.5-Math-1.5B↗

  unsloth

• Qwen3-4B-Base↗

  unsloth

  Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Building upon extensive advancements in training data, model architecture, and optimization techniques, Qwen3 delivers the following key improvements over the previously released Qwen2.5: **Qwen3-4B-Base** has the following features: - Type: Causal Language Models - Training Stage: Pretraining - Number of Parameters: 4.0B - Number of Paramaters (Non-Embedding): 3.6B - Number of Layers: 36 - Number of Attention Heads (GQA): 32 for Q and 8 for KV - Context Length: 32,768 For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.

• Minimax-H3-R2VA-Veda-Preview↗

  Veda-Sparse

  **Veda** is a learned sparse-attention method for video diffusion models. This checkpoint brings Veda to MiniMax-H3 reference-to-audio-video generation. It selects visual reference and generated video tiles independently; text, VLM conditioning, and audio attention remain dense. The predictor is not a LoRA and leaves the backbone weights unchanged. This preview was fine-tuned for 600 updates from the T2VA predictor on 5.17-second R2VA clips. It uses the MiniMax-H3 Turbo LoRA for eight-step generation, with up to 32 visual-reference tiles and 32 current-video tiles per query tile and head. The predictor is stored in FP8 and dequantized to BF16 when loaded. - **Learned sparse attention.** Visual reference and generated-video attention are selected independently; text, VLM conditioning, and audio stay dense. - **Fixed visual budgets.** The released preview uses 32 reference tiles and 32 current-video tiles per query tile and head. - **Plug-and-play predictor.** The FP8 predictor is separate from the H3 backbone and Turbo LoRA weights.

• Wan2.2-Animate-14B↗

  Wan-AI

  💜 Wan &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ GitHub &nbsp&nbsp | &nbsp&nbsp🤗 Hugging Face &nbsp&nbsp | &nbsp&nbsp🤖 ModelScope &nbsp&nbsp | &nbsp&nbsp 📑 Paper &nbsp&nbsp | &nbsp&nbsp 📑 Blog &nbsp&nbsp | &nbsp&nbsp 💬 Discord &nbsp&nbsp 📕 使用指南(中文) &nbsp&nbsp | &nbsp&nbsp 📘 User Guide(English) &nbsp&nbsp | &nbsp&nbsp💬 WeChat(微信) &nbsp&nbsp We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations:

• qwen2.5-vl-7b-instruct↗

  wangkanai

• vit-small-patch16-224↗

  WinKawaks

  Google didn't publish vit-tiny and vit-small model checkpoints in Hugging Face. I converted the weights from the timm repository. This model is used in the same way as ViT-base.

• Qwen3.8-27B-ABLITERATED-GGUF↗

  Xeldia

• sarashina2-7b↗

  0x3

• DeepSeek-V4.1-Flash-MLX-MAC↗

  0xSojalSec