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danieldk 
posted an update about 2 months ago
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205
We have recently added Torch Stable ABI support to kernels and kernel-builder. This allows kernel developers to target a particular Torch version and the kernel will be supported on that Torch version and later Torch versions (up to ~2 years).

This makes it much easier to write kernels with long-term support and not just the last two Torch releases.

We have also started rolling out Stable ABI support to kernels in kernels-community, starting with Flash Attention 3, supporting Torch 2.9 and later as well as CUDA versions starting at 12.6:

https://huggingface.co/kernels/kernels-community/flash-attn3/tree/v1/build
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danieldk 
posted an update 3 months ago
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Two large changes in kernel-builder this week:

kernel-builder now links libstdc++ dynamically. To support a wide range of systems, we build against libstdc++ from manylinux_2_28 (EL 8 and later).

Following our Torch support policy that the current and previous Torch versions are supported, Torch 2.10 support was removed. We will soon also support the Torch stable ABI, so that it is possible to write kernels that support a large number of Torch versions.
clefourrier 
authored a paper 5 months ago
mryab 
submitted a paper to Daily Papers 6 months ago
danieldk 
posted an update 7 months ago
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2845
kernels 0.12 is out! 🎉

Changes:

* Support for kernel version branches to gracefully roll out kernel API changes.
* Support for PyTorch 2.10.
* kernel-builder is now merged into the kernels repo.
* Initial support for standardized kernel benchmarks.

https://github.com/huggingface/kernels/releases/tag/v0.12.0
pcuenq 
posted an update 8 months ago
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👉 What happened in AI in 2025? 👈

We prepared the 2025 version of the HF AI Timeline Grid, highlighting open vs API-based model releases, and allowing you to browse and filter by access, modality, and release type!

Play with it here:
2025-ai-timeline/2025-ai-timeline

Here's my personal quarterly TL;DR:

1️⃣ Q1 — Learning to Reason
Deepseek not only releases a top-notch reasoning model, but shows how to train them and compete with closed frontier models. OpenAI debuts Deep Research.

Significant milestones: DeepSeek R1 & R1-Zero, Qwen 2.5 VL, OpenAI Deep Research, Gemini 2.5 Pro (experimental)

2️⃣ Q2 — Multimodality and Coding
More LLMs embrace multimodality by default, and there's a surge in coding agents. Strong vision, audio, and generative models emerge.

Significant milestones: Llama 4, Qwen 3, Imagen 4, OpenAI Codex, Google Jules, Claude 4

3️⃣ Q3 — "Gold" rush, OpenAI opens up, the community goes bananas
Flagship models get gold in Math olympiads and hard benchmarks. OpenAI releases strong open source models and Google releases the much anticipated nano-banana for image generation and editing. Agentic workflows become commonplace.

Significant milestones: Gemini and OpenAI IMO Gold, gpt-oss, Gemini 2.5 Flash Image, Grok 4, Claude Sonnet 4.5

4️⃣ Q4 — Mistral returns, leaderboard hill-climbing
Mistral is back with updated model families. All labs release impressive models to wrap up the year!

Significant milestones: Claude Opus 4.5, DeepSeek Math V2, FLUX 2, GPT 5.1, Kimi K2 Thinking, Nano Banana Pro, GLM 4.7, Gemini 3, Mistral 3, MiniMax M2.1 🤯

Credits
🙏 NHLOCAL for the source data https://github.com/NHLOCAL/AiTimeline

🫡 @reach-vb for the original idea, design and recipe

🙌 @ariG23498 and yours truly for compiling and verifying the 2025 edition

🥳 Here's to 2026, wishing it becomes the best year ever for open releases and on-device-first use-cases! 🥂
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danieldk 
posted an update 10 months ago
hfkwr 
authored a paper 11 months ago
lysandre 
posted an update 11 months ago
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We're kick-starting the process of Transformers v5, with @ArthurZ and @cyrilvallez !

v5 should be significant: we're using it as a milestone for performance optimizations, saner defaults, and a much cleaner code base worthy of 2025.

Fun fact: v4.0.0-rc-1 came out on Nov 19, 2020, nearly five years ago!
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Wauplin 
posted an update about 1 year ago
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Say hello to hf: a faster, friendlier Hugging Face CLI ✨

We are glad to announce a long-awaited quality-of-life improvement: the Hugging Face CLI has been officially renamed from huggingface-cli to hf!

So... why this change?

Typing huggingface-cli constantly gets old fast. More importantly, the CLI’s command structure became messy as new features were added over time (upload, download, cache management, repo management, etc.). Renaming the CLI is a chance to reorganize commands into a clearer, more consistent format.

We decided not to reinvent the wheel and instead follow a well-known CLI pattern: hf <resource> <action>. Isn't hf auth login easier to type and remember?

The full rationale, implementation details, and migration notes are in the blog post: https://huggingface.co/blog/hf-cli

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danieldk 
posted an update about 1 year ago
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2075
kernels 0.8.0 is out: https://github.com/huggingface/kernels/releases/tag/v0.8.0

This release refines kernel selection in the kernelize function:

• You can now register kernels for certain CUDA capability ranges.
• Rather than doing exact mating of modes, fall back to other compatible modes. If you are kernelizing for inference, but you only registered a training + torch.compile kernel, it will use that kernel since it is compatible with inference as well.
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danieldk 
posted an update about 1 year ago
danieldk 
posted an update about 1 year ago
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Kernels 0.7.0 is out: https://github.com/huggingface/kernels/releases/tag/v0.7.0 🚀

This release makes it possible to register multiple kernels for a layer. Do you have a super-fast kernel for inference and another kernel for training? Register them both and kernelize will pick the kernel depending on whether you are going to do training or inference.
dvilasuero 
posted an update about 1 year ago
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Super excited to launch Hugging Face Sheets: Spreadsheets meet AI and unstructured data.

A few months ago, we started imagining new ways to build and transform datasets with the latest open-source models.

Today, I'm thrilled to introduce our first step in this direction.


In a nutshell:

📁 Effortlessly run prompts and models over your data.
🌐 Agentic search for accuracy and real-time information.
🖼️ Familiar, minimalistic interface for interacting with data.
🎯 Human feedback 2.0: Your input directly improves generated data.
💯 Access hundreds of open models and leading inference providers.

Go to this space to try it out!

aisheets/sheets

Leave your questions below, we're just getting started!
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danieldk 
posted an update about 1 year ago
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We have been working on a project called kernels. kernels makes it possible to load compute kernels directly from the Hub! 🚀

We plan to give kernels a more proper introduction soon. But for those who have been following along, we are happy to announce a new release:

- New layer API with torch.compile support.
- Experimental support for loading Apple Silicon Metal 🤘 Kernels.
- Generate wheels from Hub kernels for legacy deployments.

Full release notes here: https://github.com/huggingface/kernels/releases/tag/v0.6.0
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joaogante 
posted an update over 1 year ago
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Let's go! Custom generation code has landed in transformers 🚀

Have you designed a new cool KV cache? Maybe you're comparing new test-time compute ideas you've been researching? Have you found a way to do diffusion with existing models? You can now easily share your findings with the community with custom generation code, sharing the well-known generate interface 🤓

In a nutshell, we have expanded the support of custom modeling code on the Hub with *model-agnostic* custom generation code. Write for one model, reuse with any model -- hopefully, this will democratize access to new generation ideas 🫡

As a creator, you gain the ability to get your ideas in transformers with minimal effort. You'll also have access to all Hub features: a landing page for your creation, discussions, usage metrics, ... 🤓

💎 Resources 💎
- docs: https://huggingface.co/docs/transformers/generation_strategies#custom-decoding-methods
- minimal example: transformers-community/custom_generate_example
- discussion: transformers-community/support#10
clefourrier 
posted an update over 1 year ago
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Always surprised that so few people actually read the FineTasks blog, on
✨how to select training evals with the highest signal✨

If you're serious about training models without wasting compute on shitty runs, you absolutely should read it!!

An high signal eval actually tells you precisely, during training, how wel & what your model is learning, allowing you to discard the bad runs/bad samplings/...!

The blog covers in depth prompt choice, metrics, dataset, across languages/capabilities, and my fave section is "which properties should evals have"👌
(to know on your use case how to select the best evals for you)

Blog: HuggingFaceFW/blogpost-fine-tasks
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philschmid 
posted an update over 1 year ago
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Gemini 2.5 Flash is here! We excited launch our first hybrid reasoning Gemini model. In Flash 2.5 developer can turn thinking off.

**TL;DR:**
- 🧠 Controllable "Thinking" with thinking budget with up to 24k token
- 🌌 1 Million multimodal input context for text, image, video, audio, and pdf
- 🛠️ Function calling, structured output, google search & code execution.
- 🏦 $0.15 1M input tokens; $0.6 or $3.5 (thinking on) per million output tokens (thinking tokens are billed as output tokens)
- 💡 Knowledge cut of January 2025
- 🚀 Rate limits - Free 10 RPM 500 req/day
- 🏅Outperforms 2.0 Flash on every benchmark

Try it ⬇️
https://aistudio.google.com/prompts/new_chat?model=gemini-2.5-flash-preview-04-17
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pcuenq 
authored a paper over 1 year ago
Wauplin 
posted an update over 1 year ago
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‼️ huggingface_hub's v0.30.0 is out with our biggest update of the past two years!

Full release notes: https://github.com/huggingface/huggingface_hub/releases/tag/v0.30.0.

🚀 Ready. Xet. Go!

Xet is a groundbreaking new protocol for storing large objects in Git repositories, designed to replace Git LFS. Unlike LFS, which deduplicates files, Xet operates at the chunk level—making it a game-changer for AI builders collaborating on massive models and datasets. Our Python integration is powered by [xet-core](https://github.com/huggingface/xet-core), a Rust-based package that handles all the low-level details.

You can start using Xet today by installing the optional dependency:

pip install -U huggingface_hub[hf_xet]


With that, you can seamlessly download files from Xet-enabled repositories! And don’t worry—everything remains fully backward-compatible if you’re not ready to upgrade yet.

Blog post: https://huggingface.co/blog/xet-on-the-hub
Docs: https://huggingface.co/docs/hub/en/storage-backends#xet


⚡ Inference Providers

- We’re thrilled to introduce Cerebras and Cohere as official inference providers! This expansion strengthens the Hub as the go-to entry point for running inference on open-weight models.

- Novita is now our 3rd provider to support text-to-video task after Fal.ai and Replicate.

- Centralized billing: manage your budget and set team-wide spending limits for Inference Providers! Available to all Enterprise Hub organizations.

from huggingface_hub import InferenceClient
client = InferenceClient(provider="fal-ai", bill_to="my-cool-company")
image = client.text_to_image(
    "A majestic lion in a fantasy forest",
    model="black-forest-labs/FLUX.1-schnell",
)
image.save("lion.png")


- No more timeouts when generating videos, thanks to async calls. Available right now for Fal.ai, expecting more providers to leverage the same structure very soon!
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