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All HF Hub posts

merveΒ 
posted an update 1 day ago
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1885
Small yet mighty! πŸ’«

We are releasing SmolVLM: a new 2B small vision language made for on-device use, fine-tunable on consumer GPU, immensely memory efficient 🀠

We release three checkpoints under Apache 2.0: SmolVLM-Instruct, SmolVLM-Synthetic and SmolVLM-Base HuggingFaceTB/smolvlm-6740bd584b2dcbf51ecb1f39

Learn more from our blog here: huggingface.co/blog/smolvlm
This release comes with a demo, fine-tuning code, MLX integration and TRL integration for DPO πŸ’
Try the demo: HuggingFaceTB/SmolVLM
Fine-tuning Recipe: https://github.com/huggingface/smollm/blob/main/finetuning/Smol_VLM_FT.ipynb
Also TRL integration for DPO πŸ’—
davanstrienΒ 
posted an update 1 day ago
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1211
First dataset for the new Hugging Face Bluesky community organisation: bluesky-community/one-million-bluesky-posts πŸ¦‹

πŸ“Š 1M public posts from Bluesky's firehose API
πŸ” Includes text, metadata, and language predictions
πŸ”¬ Perfect to experiment with using ML for Bluesky πŸ€—

Excited to see people build more open tools for a more open social media platform!
maxiwΒ 
posted an update 1 day ago
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1479
You can now try out computer use models from the hub to automate your local machine with https://github.com/askui/vision-agent. πŸ’»

import time
from askui import VisionAgent

with VisionAgent() as agent:
    agent.tools.webbrowser.open_new("http://www.google.com")
    time.sleep(0.5)
    agent.click("search field in the center of the screen", model_name="Qwen/Qwen2-VL-7B-Instruct")
    agent.type("cats")
    agent.keyboard("enter")
    time.sleep(0.5)
    agent.click("text 'Images'", model_name="AskUI/PTA-1")
    time.sleep(0.5)
    agent.click("second cat image", model_name="OS-Copilot/OS-Atlas-Base-7B")


Currently these models are integrated with Gradio Spaces API. Also planning to add local inference soon!

Currently supported:
- Qwen/Qwen2-VL-7B-Instruct
- Qwen/Qwen2-VL-2B-Instruct
- AskUI/PTA-1
- OS-Copilot/OS-Atlas-Base-7B
  • 2 replies
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prithivMLmodsΒ 
posted an update 2 days ago
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2392
HF Posts Receipts πŸ†πŸš€

[ HF POSTS RECEIPT ] : prithivMLmods/HF-POSTS-RECEIPT

πŸ₯ The one thing that needs to be remembered is the 'username'.

πŸ₯ And yeah, thank you, @maxiw , for creating the awesome dataset and sharing them here! πŸ™Œ

πŸ₯ [ Dataset ] : maxiw/hf-posts

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@prithivMLmods
davidberenstein1957Β 
posted an update 1 day ago
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1069
Let’s make a generation of amazing image-generation models

The best image generation models are trained on human preference datasets, where annotators have selected the best image from a choice of two. Unfortunately, many of these datasets are closed source so the community cannot train open models on them. Let’s change that!

The community can contribute image preferences for an open-source dataset that could be used for building AI models that convert text to image, like the flux or stable diffusion families. The dataset will be open source so everyone can use it to train models that we can all use.

Blog: https://huggingface.co/blog/burtenshaw/image-preferences
clemΒ 
posted an update 2 days ago
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1716
I've been in Brazil for 10 days now πŸ‡§πŸ‡·πŸ‡§πŸ‡·πŸ‡§πŸ‡·

I've been surprised by the gap between the massive number of people interested in AI (chatgpt adoption is crazy here) and the relatively low number of real AI builders - aka people and companies building their own AI models, datasets and apps.

Lots of efforts needed across the world for everyone to participate, control and benefit this foundational technology, starting with open-source & multi-lingual AI, more access to GPUs & AI builder training for all!
luigi12345Β 
posted an update 3 days ago
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3495
MinimalScrap
Only Free Dependencies. Save it.It is quite useful uh.


!pip install googlesearch-python requests
from googlesearch import search
import requests
query = "Glaucoma"
for url in search(f"{query} site:nih.gov filetype:pdf", 20):
    if url.endswith(".pdf"):
        with open(url.split("/")[-1], "wb") as f: f.write(requests.get(url).content)
        print("βœ…" + url.split("/")[-1])
print("Done!")

anditoΒ 
posted an update about 7 hours ago
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259
Let's go! We are releasing SmolVLM, a smol 2B VLM built for on-device inference that outperforms all models at similar GPU RAM usage and tokens throughputs.

- SmolVLM generates tokens 7.5 to 16 times faster than Qwen2-VL! 🀯
- Other models at this size crash a laptop, but SmolVLM comfortably generates 17 tokens/sec on a macbook! πŸš€
- SmolVLM can be fine-tuned on a Google collab! Or process millions of documents with a consumer GPU!
- SmolVLM even outperforms larger models in video benchmarks, despite not even being trained on videos!

Check out more!
Demo: HuggingFaceTB/SmolVLM
Blog: https://huggingface.co/blog/smolvlm
Model: HuggingFaceTB/SmolVLM-Instruct
Fine-tuning script: https://github.com/huggingface/smollm/blob/main/finetuning/Smol_VLM_FT.ipynb
xiaozaaΒ 
posted an update about 24 hours ago
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634
Hey everyone! πŸ‘‹ Just launched a cool virtual try-on demo on Hugging Face Spaces! πŸš€
Try on any upper body garment with just 3 simple steps:
πŸ“Έ Upload your photo
✏️ Draw a quick mask
πŸ‘• Add the garment image
Super accurate results and really easy to use! Give it a spin and let me know what you think πŸ€—

Find here:
xiaozaa/catvton-flux-try-on

jsulzΒ 
posted an update 1 day ago
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781
Something I love about working at Hugging Face is the opportunity to design and work in public. Right now, we’re redesigning the architecture that supports uploads and downloads on the Hub.

Datasets and models are growing fast, and so are the challenges of storing and transferring them efficiently. To keep up, we're introducing a new protocol for uploads and downloads, supported by a content-addressed store (CAS).

Here’s what’s coming:

πŸ“¦ Smarter uploads: Chunk-level management enables advanced deduplication, compression, and reduces redundant transfers, speeding up uploads.
⚑ Efficient downloads: High throughput and low latency ensure fast access, even during high-demand model releases.
πŸ”’ Enhanced security: Validate uploads before storage to block malicious or invalid data.

We analyzed 24 hours of global upload activity in October (88 countries, 130TB of data!) to design a system that scales with your needs.

The result? A proposed infrastructure with CAS nodes in us-east-1, eu-west-3, and ap-southeast-1.

πŸ”— Read the blog post for the full details: https://huggingface.co/blog/rearchitecting-uploads-and-downloads

🌟 Check out our interactive demo to explore the data yourself!
xet-team/cas-analysis

We’d love to hear your feedback - let us know if you have questions or want to see more.
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