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84,999 款模型档案13202 家厂商 / 发布者45 个能力分类465 款有可比报价 · 30 款有公开评测

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MiniMax-H3

ibuildproducts

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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LTX-2.5 — Video, Audio & World Simulation Full control and customization — self-host on your infrastructure. LTX-2.5 — Video, Audio & World Simulation Full control and customization — self-host on your infrastructure. Under $10M annual revenue Commercial and production use at no cost under the LTX-2.x Community License. Transfer of fine-tunes may require a paid license, in accordance with the LTX-2.x Community License. Read the Documentation

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Lightricks-LTX-2-DISTILLED-10-Eros

ibyteohdear

An uncensored video generation model based on LTX 2.3 supporting both t2v and i2v natively, as well as all of the other ltx 2.3 formats. Support the next version of the project, even just a few dollars would go a long way: **Kofi** **Get Started:** To get started with the model, I recommend downloading either of the dev versions, (fp8mixed or bf16) and downloading the distill lora provided. By the way, I'm aware the workflows contain sulphur_final right now, just use the lora or use the full models, don't use both at the same time.

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视频生成

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ClinLinker-KB-GP

ICB-UMA

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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DeepSeek-V4.1-Flash-1017-dsx-2.6bit

icecubetr

DeepSeek-V4.1-Flash-1017 (552B MoE + 196B Engram) quantized to **187.6 GB**, intended for two RTX 3090s and 192 GB of RAM with exact routing. Runs on **dsx**, a Rust + CUDA engine made for this model. This is a V4.1 GSQ GGUF for dsx, not a llama.cpp / Ollama / LM Studio GGUF. Measured with `dsx eval` on Lucebox's 1017 eval set: 8 × 1024 held-out tokens, KL and top-1 against the FP8/FP4 teacher's top-256 log-probs. All experts were resident (`0 left in the file`). Measured on this machine with the new `dsx` engine: 2× RTX 3090, 192 GB RAM, NVMe SSD, `--ctx 262144`, all experts resident.

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视觉问答

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Kumru-2B-GGUF

icecubetr

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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FLux_Klein_9B_NSFW

IcedSundae

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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IceWhiskeyRP-7b-8bpw-exl2

icefog72

If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.

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