Abubakar Abid's picture

Abubakar Abid

abidlabs

AI & ML interests

self-supervised learning, applications to medicine & biology, interpretation, reproducibility

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abidlabs's activity

reacted to RiverZ's post with ๐Ÿ”ฅ๐Ÿค— 10 days ago
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2663
๐Ÿš€ Excited to Share Our Latest Work: In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer๏ฝž

๐ŸŽจ Daily Paper:
In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer (2504.20690)


๐Ÿ”“ Code is now open source!
๐Ÿ”ฅ Huggingface DEMO:
RiverZ/ICEdit

๐ŸŒ Project Website: https://river-zhang.github.io/ICEdit-gh-pages/
๐Ÿ  GitHub Repository: https://github.com/River-Zhang/ICEdit/blob/main/scripts/gradio_demo.py
๐Ÿค— Huggingface:
sanaka87/ICEdit-MoE-LoRA

๐Ÿ“„ arxiv Paper:
In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer (2504.20690)


๐Ÿ”ฅ Why itโ€™s cool:
- Achieves high-quality, multi-task image editing.
- Uses only 1% of the training parameters and 0.1% of the training data compared to existing methods โ€” extremely efficient
- Beats several commercial models on background preservation, ID control, and consistency
- Open-source, low-cost, faster, and stronger โ€” think of it as the โ€œDeepSeek of image editingโ€ ๐Ÿ‘€

We also implemented a Gradio demo app, available directly in our GitHub repo! And we made a flashy demo video โ€” happy to send it your way!
replied to their post 11 days ago
posted an update 12 days ago
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3914
HOW TO ADD MCP SUPPORT TO ANY ๐Ÿค— SPACE

Gradio now supports MCP! If you want to convert an existing Space, like this one hexgrad/Kokoro-TTS, so that you can use it with Claude Desktop / Cursor / Cline / TinyAgents / or any LLM that supports MCP, here's all you need to do:

1. Duplicate the Space (in the Settings Tab)
2. Upgrade the Gradio sdk_version to 5.28 (in the README.md)
3. Set mcp_server=True in launch()
4. (Optionally) add docstrings to the function so that the LLM knows how to use it, like this:

def generate(text, speed=1):
    """
    Convert text to speech audio.

    Parameters:
        text (str): The input text to be converted to speech.
        speed (float, optional): Playback speed of the generated speech.


That's it! Now your LLM will be able to talk to you ๐Ÿคฏ
posted an update 13 days ago
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2560
Hi folks! Excited to share a new feature from the Gradio team along with a tutorial.

If you don't already know, Gradio is an open-source Python library used to build interfaces for machine learning models. Beyond just creating UIs, Gradio also exposes API capabilities and now, Gradio apps can be launched Model Context Protocol (MCP) servers for LLMs.

If you already know how to use Gradio, there are only two additional things you need to do:
* Add standard docstrings to your function (these will be used to generate the descriptions for your tools for the LLM)
* Set mcp_server=True in launch()


Here's a complete example (make sure you already have the latest version of Gradio installed):


import gradio as gr

def letter_counter(word, letter):
    """Count the occurrences of a specific letter in a word.
    
    Args:
        word: The word or phrase to analyze
        letter: The letter to count occurrences of
        
    Returns:
        The number of times the letter appears in the word
    """
    return word.lower().count(letter.lower())

demo = gr.Interface(
    fn=letter_counter,
    inputs=["text", "text"],
    outputs="number",
    title="Letter Counter",
    description="Count how many times a letter appears in a word"
)

demo.launch(mcp_server=True)



This is a very simple example, but you can add the ability to generate Ghibli images or speak emotions to any LLM that supports MCP. Once you have an MCP running locally, you can copy-paste the same app to host it on [Hugging Face Spaces](https://huggingface.co/spaces/) as well.

All free and open-source of course! Full tutorial: https://www.gradio.app/guides/building-mcp-server-with-gradio
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reacted to Jaward's post with ๐Ÿค— 22 days ago
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2676
nice clean GRPO implementation:
- no transformers
- no vllm
- has improved grpo (DAPO)
- under 300 lines
- runs on 24GB (RTX 4090 GPU)
Code: https://github.com/policy-gradient/GRPO-Zero
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reacted to clem's post with ๐Ÿค— about 1 month ago
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1357
Now in Enterprise Hub organizations, you can centralize your billing not only for HF usage but also inference through our inference partners.

Will prevent some headaches for your finance & accounting teams haha (so feel free to share that with them).
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posted an update about 1 month ago
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3798
JOURNEY TO 1 MILLION DEVELOPERS

5 years ago, we launched Gradio as a simple Python library to let researchers at Stanford easily demo computer vision models with a web interface.

Today, Gradio is used by >1 million developers each month to build and share AI web apps. This includes some of the most popular open-source projects of all time, like Automatic1111, Fooocus, Oobaboogaโ€™s Text WebUI, Dall-E Mini, and LLaMA-Factory.

How did we get here? How did Gradio keep growing in the very crowded field of open-source Python libraries? I get this question a lot from folks who are building their own open-source libraries. This post distills some of the lessons that I have learned over the past few years:

1. Invest in good primitives, not high-level abstractions
2. Embed virality directly into your library
3. Focus on a (growing) niche
4. Your only roadmap should be rapid iteration
5. Maximize ways users can consume your library's outputs

1. Invest in good primitives, not high-level abstractions

When we first launched Gradio, we offered only one high-level class (gr.Interface), which created a complete web app from a single Python function. We quickly realized that developers wanted to create other kinds of apps (e.g. multi-step workflows, chatbots, streaming applications), but as we started listing out the apps users wanted to build, we realized what we needed to do:

Read the rest here: https://x.com/abidlabs/status/1907886
reacted to julien-c's post with ๐Ÿš€๐Ÿ”ฅ 2 months ago
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3775
Important notice ๐Ÿšจ

For Inference Providers who have built support for our Billing API (currently: Fal, Novita, HF-Inference โ€“ 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.
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reacted to onekq's post with ๐Ÿš€ 2 months ago
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2525
I was puzzled by the scope of ๐Ÿ‹DeepSeek๐Ÿ‹ projects, i.e. why they built (then open sourced) so many pieces which are all over their technology stack. Good engineers are minimalists. They build only when they have to.

Then I realized that FP8 should be the main driving force here. So your raw inter-GPU bandwidth is cut in half (H800). But if you compress your data presentation from 16 bits to 8 bits, then the effective throughput of your workload stays unchanged!

The idea is simple but lots of work had to be done. Their v3 technical report will give you a wholistic view (better than reading the code). To summarize, data structure is the foundation to any software. Since FP8 was new and untried, the ecosystem wasn't there. So DeepSeek became the trailblazer. Before cooking your meals, you need to till the land, grow crops, and grind the flour ๐Ÿ˜…
reacted to clem's post with ๐Ÿ‘๐Ÿ”ฅ 2 months ago
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5939
Super happy to welcome Nvidia as our latest enterprise hub customer. They have almost 2,000 team members using Hugging Face, and close to 20,000 followers of their org. Can't wait to see what they'll open-source for all of us in the coming months!

Nvidia's org: nvidia
Enterprise hub: https://huggingface.co/enterprise
reacted to singhsidhukuldeep's post with ๐Ÿ‘๐Ÿš€ 4 months ago
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2595
While everyone is buzzing about DeepSeek AI R1's groundbreaking open-source release, ByteDance has quietly launched something remarkable - Trae, an adaptive AI IDE that's redefining the development experience and unlike competitors like Cursor, it' completely FREE!

Trae is a sophisticated development environment built on Microsoft's VSCode foundation(with a nice skin on top), offering unlimited free access to both OpenAI's GPT-4o and Anthropic's Claude-3.5-Sonnet models.

Technical Highlights:
- Real-time AI pair programming with comprehensive codebase understanding
- Natural language commands for code generation and project-level development
- Intelligent task decomposition for automated planning and execution
- Seamless VS Code and Cursor configuration compatibility
- Multi-language support with specialized optimization for English and Chinese interfaces

Currently available for macOS (Windows version in development), Trae is distributed through ByteDance's Singapore subsidiary, Spring (SG) Pte. What sets it apart is its ability to handle mixed-language workflows and enhanced localization features that address common pain points in existing IDEs.

The AI assistant can generate code snippets, optimize logic, and even create entire projects from scratch through natural language prompts. It also features an innovative AI Chat system accessible via keyboard shortcuts for real-time coding assistance.

For developers looking to enhance their productivity without breaking the bank, Trae offers enterprise-grade AI capabilities completely free during its initial release. This move by ByteDance signals a significant shift in the AI IDE landscape, challenging established players with a robust, accessible alternative.

Try it at trae.ai
reacted to nataliaElv's post with ๐Ÿ‘โค๏ธ 5 months ago
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1305
How do your annotations for FineWeb2 compare to your teammates'?

I started contributing some annotations to the FineWeb2 collaborative annotation sprint and I wanted to know if my labelling trends were similar to those of my teammates.

I did some analysis and I wasn't surprised to see that I'm being a bit harsher on my evaluations than my mates ๐Ÿ˜‚


Do you want to see how your annotations compare to others?
๐Ÿ‘‰ Go to this Gradio space: nataliaElv/fineweb2_compare_my_annotations
โœ๏ธ Enter the dataset that you've contributed to and your Hugging Face username.

How were your results?
- Contribute some annotations: data-is-better-together/fineweb-c
- Join your language channel in Rocket chat: HuggingFaceFW/discussion
reacted to julien-c's post with ๐Ÿ‘๐Ÿค—โค๏ธ 5 months ago
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10704
After some heated discussion ๐Ÿ”ฅ, 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 ๐Ÿ”ฅ

cc: @reach-vb @pierric @victor and the HF team
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