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Adam Molnar

lunarflu
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AI & ML interests

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reacted to julien-c's post with ๐Ÿ˜Ž about 2 hours ago
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.
reacted to julien-c's post with ๐Ÿ‘ about 2 hours ago
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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lunarflu's activity

reacted to julien-c's post with ๐Ÿ˜Ž๐Ÿ‘๐Ÿš€๐Ÿ”ฅ about 2 hours ago
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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 as-cle-bert's post with ๐Ÿค— about 5 hours ago
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Finding a job that matches with our resume shouldn't be difficult, especially now that we have AI... And still, we're drowning in unclear announcements, jobs whose skill requirements might not really fit us, and tons of material๐Ÿ˜ตโ€๐Ÿ’ซ
That's why I decided to build ๐‘๐ž๐ฌ๐ฎ๐ฆ๐ž ๐Œ๐š๐ญ๐œ๐ก๐ž๐ซ (https://github.com/AstraBert/resume-matcher), a fully open-source application that scans your resume and searches the web for jobs that match with it!๐ŸŽ‰
The workflow is very simple:
๐Ÿฆ™ A LlamaExtract agent parses the resume and extracts valuable data that represent your profile
๐Ÿ—„๏ธThe structured data are passed on to a Job Matching Agent (built with LlamaIndex๐Ÿ˜‰) that uses them to build a web search query based on your resume
๐ŸŒ The web search is handled by Linkup, which finds the top matches and returns them to the Agent
๐Ÿ”Ž The agent evaluates the match between your profile and the jobs, and then returns a final answer to you

So, are you ready to find a job suitable for you?๐Ÿ’ผ You can spin up the application completely locally and with Docker, starting from the GitHub repo โžก๏ธ https://github.com/AstraBert/resume-matcher
Feel free to leave your feedback and let me know in the comments if you want an online version of Resume Matcher as well!โœจ
reacted to AdinaY's post with ๐Ÿš€ about 5 hours ago
reacted to as-cle-bert's post with โค๏ธ about 5 hours ago
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396
Finding a job that matches with our resume shouldn't be difficult, especially now that we have AI... And still, we're drowning in unclear announcements, jobs whose skill requirements might not really fit us, and tons of material๐Ÿ˜ตโ€๐Ÿ’ซ
That's why I decided to build ๐‘๐ž๐ฌ๐ฎ๐ฆ๐ž ๐Œ๐š๐ญ๐œ๐ก๐ž๐ซ (https://github.com/AstraBert/resume-matcher), a fully open-source application that scans your resume and searches the web for jobs that match with it!๐ŸŽ‰
The workflow is very simple:
๐Ÿฆ™ A LlamaExtract agent parses the resume and extracts valuable data that represent your profile
๐Ÿ—„๏ธThe structured data are passed on to a Job Matching Agent (built with LlamaIndex๐Ÿ˜‰) that uses them to build a web search query based on your resume
๐ŸŒ The web search is handled by Linkup, which finds the top matches and returns them to the Agent
๐Ÿ”Ž The agent evaluates the match between your profile and the jobs, and then returns a final answer to you

So, are you ready to find a job suitable for you?๐Ÿ’ผ You can spin up the application completely locally and with Docker, starting from the GitHub repo โžก๏ธ https://github.com/AstraBert/resume-matcher
Feel free to leave your feedback and let me know in the comments if you want an online version of Resume Matcher as well!โœจ
reacted to yjernite's post with ๐Ÿ”ฅ 4 days ago
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3037
Today in Privacy & AI Tooling - introducing a nifty new tool to examine where data goes in open-source apps on ๐Ÿค—

HF Spaces have tons (100Ks!) of cool demos leveraging or examining AI systems - and because most of them are OSS we can see exactly how they handle user data ๐Ÿ“š๐Ÿ”

That requires actually reading the code though, which isn't always easy or quick! Good news: code LMs have gotten pretty good at automatic review, so we can offload some of the work - here I'm using Qwen/Qwen2.5-Coder-32B-Instruct to generate reports and it works pretty OK ๐Ÿ™Œ

The app works in three stages:
1. Download all code files
2. Use the Code LM to generate a detailed report pointing to code where data is transferred/(AI-)processed (screen 1)
3. Summarize the app's main functionality and data journeys (screen 2)
4. Build a Privacy TLDR with those inputs

It comes with a bunch of pre-reviewed apps/Spaces, great to see how many process data locally or through (private) HF endpoints ๐Ÿค—

Note that this is a POC, lots of exciting work to do to make it more robust, so:
- try it: yjernite/space-privacy
- reach out to collab: yjernite/space-privacy
reacted to jsulz's post with ๐Ÿš€๐Ÿ”ฅ๐Ÿ‘€ 10 days ago
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As xet-team infrastructure begins backing hundreds of repositories on the Hugging Face Hub, weโ€™re getting to put on our researcher hats and peer into the bytes. ๐Ÿ‘€ ๐Ÿค“

IMO, one of the most interesting ideas Xet storage introduces is a globally shared store of data.

When you upload a file through Xet, the contents are split into ~64KB chunks and deduplicated, but what if those same chunks already exist in another repo on the Hub?

If we can detect and reuse them, we skip them as well saving time and bandwidth for AI builders. More on how that works here:
๐Ÿ”— https://huggingface.co/blog/from-chunks-to-blocks#scaling-deduplication-with-aggregation

Because of this, different repositories can share bytes we store. That opens up something cool - we can draw a graph of which repos actually share data at the chunk level, where:

- Nodes = repositories
- Edges = shared chunks
- Edge thickness = how much they overlap

xet-team/repo-graph

Come find the many BERT islands. Or see how datasets relate in practice, not just in theory. See how libraries or tasks can tie repositories together. You can play around with node size using storage/likes/downloads too.

The result is a super fun visualization from @saba9 and @znation that Iโ€™ve already lost way too much time to. I'm excited to see how the networks grow as we add more repositories!
reacted to merterbak's post with โค๏ธ๐Ÿ‘€๐Ÿ”ฅ 12 days ago
reacted to fdaudens's post with ๐Ÿ‘€ 22 days ago
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Want to vibecode with DeepSeek? Just spent 10 minutes with this space and created a full world indicators dashboard - literally just by describing what I wanted!

Anyone can now prototype and deploy projects instantly.

Try out the app: enzostvs/deepsite

My dashboard: fdaudens/world-indicators
reacted to AdinaY's post with ๐Ÿค—๐Ÿš€๐Ÿ”ฅ 22 days ago
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Let's check out the latest releases from the Chinese community in March!

๐Ÿ‘‰ https://huggingface.co/collections/zh-ai-community/march-2025-releases-from-the-chinese-community-67c6b479ebb87abbdf8e2e76


โœจMLLM
> R1 Omni by Alibaba Tongyi - 0.5B
> Qwen2.5 Omni by Alibaba Qwen - 7B with apache2.0

๐Ÿ–ผ๏ธVideo
> CogView-4 by ZhipuAI - Apacha2.0
> HunyuanVideo-I2V by TencentHunyuan
> Open Sora2.0 - 11B with Apache2.0
> Stepvideo TI2V by StepFun AI - 30B with MIT license

๐ŸŽตAudio
> DiffDiffRhythm - Apache2.0
> Spark TTS by SparkAudio - 0.5B

โšก๏ธImage/3D
> Hunyuan3D 2mv/2mini (0.6B) by @TencentHunyuan
> FlexWorld by ByteDance - MIT license
> Qwen2.5-VL-32B-Instruct by Alibaba Qwen - Apache2.0
> Tripo SG (1.5B)/SF by VastAIResearch - MIT license
> InfiniteYou by ByteDance

> LHM by Alibaba AIGC team - Apache2.0
> Spatial LM by ManyCore

๐Ÿง Reasoning
> QwQ-32B by Alibaba Qwen - Apache2.0
> Skywork R1V - 38B with MIT license
> RWKV G1 by RWKV AI - 0.1B pure RNN reasoning model with Apache2.0
> Fin R1 by SUFE AIFLM Lab - financial reasoning

๐Ÿ” LLM
> DeepSeek v3 0324 by DeepSeek -MIT license
> Babel by Alibaba DAMO - 9B/83B/25 languages
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reacted to chansung's post with โค๏ธ 26 days ago
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3467
simple guide on the recipe for GRPO on Open-R1 which is built on top of TRL

I think FastAPI wrapper of vLLM with WeightSyncWorker is pretty cool feature. Also, we have many predefined reward functions out of the box!
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replied to prithivMLmods's post about 2 months ago
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curious how they "coax" the particles into existence?