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AI 模型 84760

• d_m15↗

  Best000

• zeta-GGUF↗

  brittlewis12

• flux1-dev-checkpoints↗

  civitai2026

• Spark-X2.5-4B-Therapy-InterleavedThink-Qwen38FlashNext-MiniMaxM3-GLM5.3-ZCode-SFT8K-MultiDPO-v3h↗

  denjhang

• Qwen3-VL-8B-Heretic-1.3.0↗

  DreamFast

  An abliterated version of Qwen's Qwen3-VL-8B-Instruct created using Heretic v1.3.0. This model has reduced refusals while maintaining model quality. You can see the docker, scripts and configurations used to make these files on Heretic Docker Github. ComfyUI converted text encoders are provided in various formats, along with the original bf16 safetensors. This is a vision-language model (supports both text and image inputs). It does **not** support thinking/reasoning tokens.

• flan-t5-xxl-gguf↗

  dumb-dev

• Qwen2.5-1.5B-Instruct-imatrix-GGUF↗

  duyntnet

• StreamRender-H3↗

  Efficient-Large-Model

  StreamRender-H3 splits an interactive world into a **Code Game Engine** and a **Streaming Diffusion Render**. The engine keeps the world state in code (maps, physics, rules) and streams a semantic map for every frame. A two-step streaming video model built on MiniMax-H3 Ref2VA renders those maps into photorealistic video as the player drives, with the look set by a reference image and a prompt. - Two denoising steps per chunk in the distilled student. - Diffusion render of 217.96 ms per streaming step on 8 GB200 GPUs, a 3.67× speedup over the PyTorch baseline. - Fast Causal H3 VAE decodes 2 latent frames at 720p in 24 ms on one GB200, built by a self-improving agent loop. There are five distinct model components. Qwen's two storage formats are the same model. The causal decoder is constructed directly from its architecture configuration and student checkpoint; no native H3 video VAE checkpoint is needed. Streaming reference encoding uses TAE, and video decoding uses the trained causal decoder.

• polyglot-ko-12.8b↗

  EleutherAI

• Qwen3.8-27B-W3Rot-INT3-Paiton-RDNA4↗

  EliovpAI