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pcuenq 
posted an update 26 days ago
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3076
👉 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! 🥂
  • 1 reply
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Wauplin 
posted an update 6 months ago
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3348
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
pcuenq 
authored a paper 10 months ago
Wauplin 
posted an update 10 months ago
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2345
‼️ 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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Wauplin 
posted an update over 1 year ago
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3222
What a great milestone to celebrate! The huggingface_hub library is slowly becoming a cornerstone of the Python ML ecosystem when it comes to interacting with the @huggingface Hub. It wouldn't be there without the hundreds of community contributions and feedback! No matter if you are loading a model, sharing a dataset, running remote inference or starting jobs on our infra, you are for sure using it! And this is only the beginning so give a star if you wanna follow the project 👉 https://github.com/huggingface/huggingface_hub
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Wauplin 
posted an update over 1 year ago
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4716
🚀 Exciting News! 🚀

We've just released 𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎_𝚑𝚞𝚋 v0.25.0 and it's packed with powerful new features and improvements!

✨ 𝗧𝗼𝗽 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:

• 📁 𝗨𝗽𝗹𝗼𝗮𝗱 𝗹𝗮𝗿𝗴𝗲 𝗳𝗼𝗹𝗱𝗲𝗿𝘀 with ease using huggingface-cli upload-large-folder. Designed for your massive models and datasets. Much recommended if you struggle to upload your Llama 70B fine-tuned model 🤡
• 🔎 𝗦𝗲𝗮𝗿𝗰𝗵 𝗔𝗣𝗜: new search filters (gated status, inference status) and fetch trending score.
• ⚡𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝗖𝗹𝗶𝗲𝗻𝘁: major improvements simplifying chat completions and handling async tasks better.

We’ve also introduced tons of bug fixes and quality-of-life improvements - thanks to the awesome contributions from our community! 💪

💡 Check out the release notes: Wauplin/huggingface_hub#8

Want to try it out? Install the release with:

pip install huggingface_hub==0.25.0

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Wauplin 
posted an update over 1 year ago
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2049
🚀 Just released version 0.24.0 of the 𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎_𝚑𝚞𝚋 Python library!

Exciting updates include:
⚡ InferenceClient is now a drop-in replacement for OpenAI's chat completion!

✨ Support for response_format, adapter_id , truncate, and more in InferenceClient

💾 Serialization module with a save_torch_model helper that handles shared layers, sharding, naming convention, and safe serialization. Basically a condensed version of logic scattered across safetensors, transformers , accelerate

📁 Optimized HfFileSystem to avoid getting rate limited when browsing HuggingFaceFW/fineweb

🔨 HfApi & CLI improvements: prevent empty commits, create repo inside resource group, webhooks API, more options in the Search API, etc.

Check out the full release notes for more details:
Wauplin/huggingface_hub#7
👀
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Wauplin 
posted an update over 1 year ago
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3406
🚀 I'm excited to announce that huggingface_hub's InferenceClient now supports OpenAI's Python client syntax! For developers integrating AI into their codebases, this means you can switch to open-source models with just three lines of code. Here's a quick example of how easy it is.

Why use the InferenceClient?
🔄 Seamless transition: keep your existing code structure while leveraging LLMs hosted on the Hugging Face Hub.
🤗 Direct integration: easily launch a model to run inference using our Inference Endpoint service.
🚀 Stay Updated: always be in sync with the latest Text-Generation-Inference (TGI) updates.

More details in https://huggingface.co/docs/huggingface_hub/main/en/guides/inference#openai-compatibility
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Wauplin 
posted an update over 1 year ago
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1867
🚀 Just released version 0.23.0 of the huggingface_hub Python library!

Exciting updates include:
📁 Seamless download to local dir!
💡 Grammar and Tools in InferenceClient!
🌐 Documentation full translated to Korean!
👥 User API: get likes, upvotes, nb of repos, etc.!
🧩 Better model cards and encoding for ModelHubMixin!

Check out the full release notes for more details:
Wauplin/huggingface_hub#6
👀
pcuenq 
posted an update almost 2 years ago
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10281
OpenELM in Core ML

Apple recently released a set of efficient LLMs in sizes varying between 270M and 3B parameters. Their quality, according to benchmarks, is similar to OLMo models of comparable size, but they required half the pre-training tokens because they use layer-wise scaling, where the number of attention heads increases in deeper layers.

I converted these models to Core ML, for use on Apple Silicon, using this script: https://gist.github.com/pcuenca/23cd08443460bc90854e2a6f0f575084. The converted models were uploaded to this community in the Hub for anyone that wants to integrate inside their apps: corenet-community/openelm-core-ml-6630c6b19268a5d878cfd194

The conversion was done with the following parameters:
- Precision: float32.
- Sequence length: fixed to 128.

With swift-transformers (https://github.com/huggingface/swift-transformers), I'm getting about 56 tok/s with the 270M on my M1 Max, and 6.5 with the largest 3B model. These speeds could be improved by converting to float16. However, there's some precision loss somewhere and generation doesn't work in float16 mode yet. I'm looking into this and will keep you posted! Or take a look at this issue if you'd like to help: https://github.com/huggingface/swift-transformers/issues/95

I'm also looking at optimizing inference using an experimental kv cache in swift-transformers. It's a bit tricky because the layers have varying number of attention heads, but I'm curious to see how much this feature can accelerate performance in this model family :)

Regarding the instruct fine-tuned models, I don't know the chat template that was used. The models use the Llama 2 tokenizer, but the Llama 2 chat template, or the default Alignment Handbook one that was used to train, are not recognized. Any ideas on this welcome!
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Wauplin 
posted an update almost 2 years ago
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2355
🚀 Just released version 0.22.0 of the huggingface_hub Python library!

Exciting updates include:
✨ Chat-completion API in the InferenceClient!
🤖 Official inference types in InferenceClient!
🧩 Better config and tags in ModelHubMixin!
🏆 Generate model cards for your ModelHubMixin integrations!
🏎️ x3 download speed in HfFileSystem!!

Check out the full release notes for more details: Wauplin/huggingface_hub#5 👀
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Wauplin 
posted an update almost 2 years ago
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🚀 Just released version 0.21.0 of the huggingface_hub Python library!

Exciting updates include:
🖇️ Dataclasses everywhere for improved developer experience!
💾 HfFileSystem optimizations!
🧩 PyTorchHubMixin now supports configs and safetensors!
✨ audio-to-audio supported in the InferenceClient!
📚 Translated docs in Simplified Chinese and French!
💔 Breaking changes: simplified API for listing models and datasets!

Check out the full release notes for more details: Wauplin/huggingface_hub#4 🤖💻
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sbrandeis 
authored a paper over 2 years ago
sbrandeis 
authored a paper almost 3 years ago