**DreamX-World** is a general-purpose world model for **interactive world simulation**. It generates diverse, high-fidelity worlds that users can explore, control, and transform with event prompts. **DreamX-World-5B** is the long-horizon autoregressive variant of DreamX-World. It is built on top of Wan2.2-TI2V-5B and generates videos from an input image, a text prompt, and keyboard-style camera action commands. Compared with the 5B-Cam variant, DreamX-World-5B uses chunk-wise causal autoregressive inference with KV caching, making long-horizon generation practical, including videos up to about 1 minute at 16 FPS. The model is trained with a scalable data engine on Unreal Engine data, gameplay footage, and real-world videos, together with camera estimation and data filtering. DreamX-World follows a progressive training pipeline for action control, open-ended event response, reinforcement-learning-based action following, and efficient inference through forcing and distillation.

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DreamX-World-5B-Cam

GD-ML

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Qwen1.5-MoE-A2.7B_gguf

gdax

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Self-Forcing

gdhe17

Self Forcing trains autoregressive video diffusion models by **simulating the inference process during training**, performing autoregressive rollout with KV caching. It resolves the train-test distribution mismatch and enables **real-time, streaming video generation on a single RTX 4090** while matching the quality of state-of-the-art diffusion models. We tested this repo on the following setup: * Nvidia GPU with at least 24 GB memory (RTX 4090, A100, and H100 are tested). * Linux operating system. * 64 GB RAM. Note: * **Our model works better with long, detailed prompts** since it's trained with such prompts. We will integrate prompt extension into the codebase (similar to Wan2.1) in the future. For now, it is recommended to use third-party LLMs (such as GPT-4o) to extend your prompt before providing to the model. * You may want to adjust FPS so it plays smoothly on your device. * The speed can be improved by enabling `torch.compile`, TAEHV-VAE, or using FP8 Linear layers, although the latter two options may sacrifice quality. It is recommended to use `torch.compile` if possible and enable TAEHV-VAE if further speedup is needed.

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Qwen3-Coder-Next-NVFP4-GB10

gdubicki

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Qwen3.8-9B-Distill-uncensored-heretic-Q4_K_M-GGUF

ge525

This model was converted to GGUF format from `petruhonk/Qwen3.8-9B-Distill-uncensored-heretic` using python code. Refer to the original model card for more details on the model. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

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K2-Horizon-7B-Uno-Uncensored-GGUF

geantendormi

> **工业级端侧极智大脑 · 离散扩散加速 (Discrete Diffusion) 物理熔铸 · Rank-2 SVD 级联双投影消融 · 26万 Token 真·双语海森满秩校准 · 消费级单卡（RTX 3060 12GB）满血点火** > **核心资产**：~4.76 GB 极智单文件纯血大脑（彻底剥离安全死锁 + 扩散提速 + IQ4_XS 端到端硬映射） > **极限吞吐**：RTX 3060 单卡实测 Prompt 预填充 **626.5 t/s**，自回归生成吞吐稳定在 **57.1 ~ 59.4 t/s**（满血输出高难渗透脚本与逆向工程代码）本模型基于 `IFM/K2-Horizon-7B` 基座，**预先在物理层熔铸了 1.4GB 的 `Uno` 条件扩散适配器**，随后送入消融工厂进行全域安全剥离，最后实施 iMatrix 极限量化。实测战报如下： 1. **跨语言海森空间激活防坍缩**： - 校准语料采用严格的 **50% 纯正中文高熵长文本（COIG-CQIA 弱智吧+逻辑推导）+ 50% 英文推导（Alpaca）**，共计 262,144 个真实 Tokens； - 彻底破除了单一英文校准导致的中文表征正交子空间曲率坍缩，生成了极高纯度的 `imatrix.dat`。 2. **坚守 IQ4_XS (4.25 bpw) 黄金帕累托平衡点**： - 将 18GB 的庞然大物压缩至 **4.76 GB**，完美适配 12GB / 8GB 显存的消费级显卡，同时保证代数推理与代码生成满分闭环。

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