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celinah
updated
a
dataset
about 8 hours ago
[bot] Conversion to Parquet
1
#2 opened 10 days ago
by
parquet-converter
Wauplin
updated
a
dataset
9 days ago
Post
2214
✨ Today we’re releasing Tiny Agents in Python — an MCP-powered Agent in ~70 lines of code 🐍
Inspired by Tiny Agents in JS from @julien-c , we ported the idea to Python and integrated it directly into
TL;DR: With MCP (Model Context Protocol), you can expose tools like web search or image generation and connect them directly to LLMs. It’s simple — and surprisingly powerful.
We wrote a blog post where we show how to run Tiny Agents, and dive deeper into how they work and how to build your own.
👉 https://huggingface.co/blog/python-tiny-agents
Inspired by Tiny Agents in JS from @julien-c , we ported the idea to Python and integrated it directly into
huggingface_hub — with a built-in MCP Client and a Tiny Agents CLI.TL;DR: With MCP (Model Context Protocol), you can expose tools like web search or image generation and connect them directly to LLMs. It’s simple — and surprisingly powerful.
pip install "huggingface_hub[mcp]>=0.32.0"We wrote a blog post where we show how to run Tiny Agents, and dive deeper into how they work and how to build your own.
👉 https://huggingface.co/blog/python-tiny-agents
- 1 reply
Update evalstate/hf.js-assistant/agent.json
🔥
1
1
#1 opened 15 days ago
by
evalstate
Post
4754
BOOOOM: Today I'm dropping TINY AGENTS
the 50 lines of code Agent in Javascript 🔥
I spent the last few weeks working on this, so I hope you will like it.
I've been diving into MCP (Model Context Protocol) to understand what the hype was all about.
It is fairly simple, but still quite powerful: MCP is a standard API to expose sets of Tools that can be hooked to LLMs.
But while doing that, came my second realization:
Once you have a MCP Client, an Agent is literally just a while loop on top of it. 🤯
➡️ read it exclusively on the official HF blog: https://huggingface.co/blog/tiny-agents
the 50 lines of code Agent in Javascript 🔥
I spent the last few weeks working on this, so I hope you will like it.
I've been diving into MCP (Model Context Protocol) to understand what the hype was all about.
It is fairly simple, but still quite powerful: MCP is a standard API to expose sets of Tools that can be hooked to LLMs.
But while doing that, came my second realization:
Once you have a MCP Client, an Agent is literally just a while loop on top of it. 🤯
➡️ read it exclusively on the official HF blog: https://huggingface.co/blog/tiny-agents
- 1 reply
Post
4682
Post
2189
‼️ 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:
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.
- 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!
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!
-
- 6 replies
Post
3891
Important notice 🚨
For Inference Providers who have built support for our Billing API (currently: Fal, Novita, HF-Inference – with more coming soon), we've started enabling Pay as you go (=PAYG)
What this means is that you can use those Inference Providers beyond the free included credits, and they're charged to your HF account.
You can see it on this view: any provider that does not have a "Billing disabled" badge, is PAYG-compatible.
For Inference Providers who have built support for our Billing API (currently: Fal, Novita, HF-Inference – with more coming soon), we've started enabling Pay as you go (=PAYG)
What this means is that you can use those Inference Providers beyond the free included credits, and they're charged to your HF account.
You can see it on this view: any provider that does not have a "Billing disabled" badge, is PAYG-compatible.
- 9 replies
Post
6135
Hey everyone, we've given https://hf.co/spaces page a fresh update!
Smart Search: Now just type what you want to do—like "make a viral meme" or "generate music"—and our search gets it.
New Categories: Check out the cool new filter bar with icons to help you pick a category fast.
Redesigned Space Cards: Reworked a bit to really show off the app descriptions, so you know what each Space does at a glance.
Random Prompt: Need ideas? Hit the dice button for a burst of inspiration.
We’d love to hear what you think—drop us some feedback plz!
Smart Search: Now just type what you want to do—like "make a viral meme" or "generate music"—and our search gets it.
New Categories: Check out the cool new filter bar with icons to help you pick a category fast.
Redesigned Space Cards: Reworked a bit to really show off the app descriptions, so you know what each Space does at a glance.
Random Prompt: Need ideas? Hit the dice button for a burst of inspiration.
We’d love to hear what you think—drop us some feedback plz!
- 6 replies
Post
3223
Finally, an open-source AI that turns your lyrics into full songs is here—meet YuE! Unlike other tools that only create short clips, YuE can make entire songs (up to 5 minutes) with vocals, melody, and instruments all working together. Letsss go!
m-a-p/YuE-s1-7B-anneal-en-cot
m-a-p/YuE-s1-7B-anneal-en-cot
Post
791
🚀 We've just dropped a new release v0.27.0 of the 𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎_𝚑𝚞𝚋 Python library!
This release includes:
- 💾 New torch model loading utilities in the serialization module — providing a standardized way to save and load torch models with built-in support for sharding and safe serialization.
- 📦 Tooling for something exciting — if you like single-file formats for models like GGUF, you'll love what we're cooking up 👀 More coming soon!
- 🛠️ Loads of quality-of-life improvements and bug fixes!
release notes and full details here 👇
Wauplin/huggingface_hub#10
$ pip install -U huggingface_hub
This release includes:
- 💾 New torch model loading utilities in the serialization module — providing a standardized way to save and load torch models with built-in support for sharding and safe serialization.
- 📦 Tooling for something exciting — if you like single-file formats for models like GGUF, you'll love what we're cooking up 👀 More coming soon!
- 🛠️ Loads of quality-of-life improvements and bug fixes!
release notes and full details here 👇
Wauplin/huggingface_hub#10
$ pip install -U huggingface_hub
Post
10775
After some heated discussion 🔥, we clarify our intent re. storage limits on the Hub
TL;DR:
- public storage is free, and (unless blatant abuse) unlimited. We do ask that you consider upgrading to PRO and/or Enterprise Hub if possible
- private storage is paid above a significant free tier (1TB if you have a paid account, 100GB otherwise)
docs: https://huggingface.co/docs/hub/storage-limits
We optimize our infrastructure continuously to scale our storage for the coming years of growth in Machine learning, to the benefit of the community 🔥
cc: @reach-vb @pierric @victor and the HF team
TL;DR:
- public storage is free, and (unless blatant abuse) unlimited. We do ask that you consider upgrading to PRO and/or Enterprise Hub if possible
- private storage is paid above a significant free tier (1TB if you have a paid account, 100GB otherwise)
docs: https://huggingface.co/docs/hub/storage-limits
We optimize our infrastructure continuously to scale our storage for the coming years of growth in Machine learning, to the benefit of the community 🔥
cc: @reach-vb @pierric @victor and the HF team
- 28 replies
Post
3230
wow 😮
INTELLECT-1 is the first collaboratively trained 10 billion parameter language model trained from scratch on 1 trillion tokens of English text and code.
PrimeIntellect/INTELLECT-1-Instruct
INTELLECT-1 is the first collaboratively trained 10 billion parameter language model trained from scratch on 1 trillion tokens of English text and code.
PrimeIntellect/INTELLECT-1-Instruct
Post
2229
Qwen/QwQ-32B-Preview shows us the future (and it's going to be exciting)...
I tested it against some really challenging reasoning prompts and the results are amazing 🤯.
Check this dataset for the results: victor/qwq-misguided-attention
I tested it against some really challenging reasoning prompts and the results are amazing 🤯.
Check this dataset for the results: victor/qwq-misguided-attention
- 2 replies
Post
2615
Perfect example of why
Qwen/Qwen2.5-Coder-32B-Instruct is insane?
Introducing: AI Video Composer 🔥
huggingface-projects/ai-video-composer
Drag and drop your assets (images/videos/audios) to create any video you want using natural language!
It works by asking the model to output a valid FFMPEG and this can be quite complex but most of the time Qwen2.5-Coder-32B gets it right (that thing is a beast). It's an update of an old project made with GPT4 and it was almost impossible to make it work with open models back then (~1.5 years ago), but not anymore, let's go open weights 🚀.
Introducing: AI Video Composer 🔥
huggingface-projects/ai-video-composer
Drag and drop your assets (images/videos/audios) to create any video you want using natural language!
It works by asking the model to output a valid FFMPEG and this can be quite complex but most of the time Qwen2.5-Coder-32B gets it right (that thing is a beast). It's an update of an old project made with GPT4 and it was almost impossible to make it work with open models back then (~1.5 years ago), but not anymore, let's go open weights 🚀.
Post
1863
Qwen2.5-72B is now the default HuggingChat model.
This model is so good that you must try it! I often get better results on rephrasing with it than Sonnet or GPT-4!!
This model is so good that you must try it! I often get better results on rephrasing with it than Sonnet or GPT-4!!
Post
1260
📣 𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎_𝚑𝚞𝚋 v0.26.0 is out with some new features and improvements!
✨ 𝗧𝗼𝗽 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:
- 🔐 Multiple access tokens support: Easily manage multiple access tokens with new CLI commands. Perfect for handling multiple tokens with specific permissions in production or when collaborating with external teams.
- 🖼️ Conversational VLMs inference is now supported with InferenceClient's chat completion!
- 📄 Daily Papers API: Seamlessly search and retrieve detailed paper information from the Hub!
We’ve also introduced multiple bug fixes and quality-of-life improvements - thanks to the awesome contributions from our community! 🤗
Check out the release notes here: Wauplin/huggingface_hub#9
and you can try it out now 👇
✨ 𝗧𝗼𝗽 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:
- 🔐 Multiple access tokens support: Easily manage multiple access tokens with new CLI commands. Perfect for handling multiple tokens with specific permissions in production or when collaborating with external teams.
- 🖼️ Conversational VLMs inference is now supported with InferenceClient's chat completion!
- 📄 Daily Papers API: Seamlessly search and retrieve detailed paper information from the Hub!
We’ve also introduced multiple bug fixes and quality-of-life improvements - thanks to the awesome contributions from our community! 🤗
Check out the release notes here: Wauplin/huggingface_hub#9
and you can try it out now 👇
pip install huggingface_hub==0.26.0Post
1683
nvidia/Llama-3.1-Nemotron-70B-Instruct-HF is now available in HuggingChat if you want to try it!
https://huggingface.co/chat/models/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
https://huggingface.co/chat/models/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
Post
2694
NEW - Inference Playground
Maybe like me you have always wanted a super easy way to compare llama3.2-1B vs. llama3.2-3B? or the same model with different temperatures?
Trying and comparing warm Inference API models has never been easier!
Just go to https://hf.co/playground, set your token and you're ready to go.
We'll keep improving, feedback welcome 😊
Maybe like me you have always wanted a super easy way to compare llama3.2-1B vs. llama3.2-3B? or the same model with different temperatures?
Trying and comparing warm Inference API models has never been easier!
Just go to https://hf.co/playground, set your token and you're ready to go.
We'll keep improving, feedback welcome 😊
- 2 replies