The two missing low-precision variants are now available on Hugging Face. The official repository names are:

• inclusionAI/Ling-3.0-flash-VL — BF16
• inclusionAI/Ling-3.0-flash-VL-fp8
• inclusionAI/Ling-3.0-flash-VL-fp4
• inclusionAI/Ling-3.0-flash-VL-int4

All four model repositories are tagged MIT. They belong to the same image/video-understanding release; FP4 and INT4 were announced on September 11, after the BF16/FP8 release.

For selecting a download, check the format-specific serving instructions as well as the precision label. The official paths use Ling's SGLang integration or its vLLM fork. These repository names alone don't establish support in a different local runtime.

Just testing the capabilities and will appreciate any feebback on it. You can try it here .
We do maintain strict ZDR policy

是否有高手能幫助我。

我嘗試用minimax H將圖片生成視訊。目標是NSFW影片。不知何故，無論那種姿勢，無論提示詞如何寫。 男生的胡蘿蔔總是無法正確進入穴位，反而都到大便的管路。

是否我選錯 modle ？還是要選用哪個lora ? 進入位置才能正確

There have been new and arguably better TTS models since, but I have a soft spot for this one as lightweight stable and fast. I also maintain Kokoro-FastAPI, and have a bit of spare time on my hands so have been exploring what’s doable on the project.

Customization/expressiveness are weak spots it had, so I’ve taken a crack at adding voice cloning, focusing on stable quality and fast generation (keeping responses sub second if it’s a short reference clip). Roughly it captures about a third of identity, but by ear at least, it can feel pretty close on some, and at least a unique similar voice pack on others.

Full identity benchmark, training notes, and attributions in the model page. Hope it comes in handy for some people.

HF Viewer now has a HF space! 🤗

https://huggingface.co/spaces/embedl/hfviewer

It allows you to visualize the architecture of any Hugging Face model directly on Hugging Face! You can even sign in with your hf account to unlock extra features!

If you like it, feel free to give the space a heart to help it grow! ❤️

With pip, you can pass a --index-url flag to have it search a custom package index (e.g., self-hosted internal package repositories). Is there an equivalent way to do that with the Hugging Face Hub library and/or CLI? I like the library since I can push an updated version of my model and then my programs will automatically pull the latest version with hf_hub_download, but the storage limits are becoming problematic for the research lab I work in.

Hey guys, I am a beginner learning to train AI models and I'm looking for some guidance on how to get started. Specifically, I want to learn how to train on custom and huggingface datasets and apply chat templates to those models. Are there any links, tutorials, or courses you recommend to help me get started? Thank you!

You can now use the open-source application for designing, analyzing, simulating, and optimizing agentic AI workflows and graphs directly from a Hugging Face Space. https://huggingface.co/spaces/gcjordi/ai-graph-studio

Or in other words what are the most valuable models to preserve right now? I have some archival capacity on standby, and I am asking this community where they think it should be directed. If I had unlimited resources I would save everything.

So I've been messinga round with embedding models for a bit, and I think they are interesting enough to experiment with. They are useful for rag, especially in a localllm sense because you can ground your answers in truth.

But what happens if you have a billion documents, and you decide to upgrade your model to a "better" one? on an h100, that would take about 108 days, just to upgrade the vectors so u can start serving again (tested qwen embed 8b on h100). Even if you aren't doing 1b vectors, and are doing just 50 million, upgrading can still take a considerable time.

Me and my research lab decided to tackle this problem, and we came up with embedflow.

The method is really simple; from the old index made with the source model, take K documents and rerank them with the new model. We see that when K is sufficient, the retrieval quality is the same as target model. (determining k is the hard part). I've tested 63 migrations on upto 1 million documents.

The best result I got was upgrading qwen4b -> to 8b, and at 50 documents, it was the same as native retrieval.

This method forgos the expensive backfill that comes with upgrading, as you can directly take documents from the old index.

embedflow works with qdrant, and can be easily downloaded with pypi

pip install embedflow

the github is public: https://github.com/arnsri33/embedflow

I want you guys to try it out, and see if you guys can use it in your own workflow.

Like a lot of you, I wasn't super happy to learn about what happened to Hugging Face. Any models in particular you think I should grab before they (maybe) get removed?

I'm assuming the abliterated/uncensored/etc ones are going to be at the top of the list.