s3nh (s3nh)

reacted to andywu-kby's post with 👍 3 days ago

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Hello everyone,
I hope you’re doing well.

We’re currently developing a chatbot that can analyze and forecast sales directly from Excel files. Do you think this would be useful?

Miragic-AI/Miragic-Sales-Pilot

Please share your feedback by 👍 or 👎 this post.

Best regards,

reacted to Xenova's post with 👍🔥 3 days ago

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The next generation of AI-powered websites is going to be WILD! 🤯

In-browser tool calling & MCP is finally here, allowing LLMs to interact with websites programmatically.

To show what's possible, I built a demo using Liquid AI's new LFM2 model, powered by 🤗 Transformers.js: LiquidAI/LFM2-WebGPU

As always, the demo is open source (which you can find under the "Files" tab), so I'm excited to see how the community builds upon this! 🚀

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reacted to AdinaY's post with 🔥 13 days ago

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Qwen3Guard 🛡️ a series of safety moderation models built upon Qwen3

Qwen/qwen3guard-68d2729abbfae4716f3343a1

✨ 0.6B/4B/8B - Apache2.0
✨ Two variants: Gen & Steam
✨ Trained on a dataset of 1.19 million prompts
✨ Classifies content into Safe / Unsafe / Controversial
✨ Supports 119 languages & dialects

reacted to vikhyatk's post with 🔥 18 days ago

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Just released a preview of Moondream 3! moondream/moondream3-preview

This is a 9B parameter, 2B active MoE VLM with state of the art visual reasoning capabilities.

More details in the release blog post: https://moondream.ai/blog/moondream-3-preview

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reacted to MikeDoes's post with 🚀 2 months ago

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2015

🛡️ At Ai4Privacy, our goal is to empower researchers to build a safer AI ecosystem. Today, we're highlighting crucial research that does just that by exposing a new vulnerability.

The paper "Forget to Flourish" details a new model poisoning technique. It's a reminder that as we fine-tune LLMs, our anonymization and privacy strategies must evolve to counter increasingly sophisticated threats.

We're proud that the Ai4Privacy dataset was instrumental in this study. It served two key purposes:

Provided a Realistic Testbed: It gave the researchers access to a diverse set of synthetic and realistic PII samples in a safe, controlled environment.

Enabled Impactful Benchmarking: It allowed them to measure the actual effectiveness of their data extraction attack, proving it could compromise specific, high-value information.

This work reinforces our belief that progress in AI security is a community effort. By providing robust tools for benchmarking, we can collectively identify weaknesses and build stronger, more resilient systems. A huge congratulations to the authors on this important contribution.

🔗 Read the full paper: https://arxiv.org/html/2408.17354v1

#OpenSource #DataPrivacy #LLM #Anonymization #AIsecurity #HuggingFace #Ai4Privacy #World's largest open privacy masking dataset

reacted to AtAndDev's post with 🚀 2 months ago

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Qwen 3 Coder is a personal attack to k2, and I love it.
It achieves near SOTA on LCB while not having reasoning.
Finally people are understanding that reasoning isnt necessary for high benches...

Qwen ftw!

DECENTRALIZE DECENTRALIZE DECENTRALIZE

reacted to KnutJaegersberg's post with ❤️ 5 months ago

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The Intelligence Curse

The document warns of the "intelligence curse," a potential consequence of advanced AI (AGI) where powerful entities lose their incentive to invest in people as AI automates work[cite: 13, 297]. This could lead to job displacement, reduced social mobility, and a concentration of power and wealth based on AI ownership, similar to the "resource curse" in resource-rich states[cite: 17, 18, 31, 329, 353]. To counter this, the authors propose averting AI catastrophes to prevent centralization, diffusing AI widely to keep humans economically relevant, and democratizing institutions to remain anchored to human needs[cite: 22, 23, 25, 35, 36, 37, 566].

https://intelligence-curse.ai/intelligence-curse.pdf

reacted to loubnabnl's post with ❤️ 5 months ago

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SmolVLM is now available on PocketPal — you can run it offline on your smartphone to interpret the world around you. 🌍📱

And check out this real-time camera demo by @ngxson , powered by llama.cpp:
https://github.com/ngxson/smolvlm-realtime-webcam
https://x.com/pocketpal_ai

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reacted to merve's post with 👍🚀 5 months ago

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A real-time object detector much faster and accurate than YOLO with Apache 2.0 license just landed to Hugging Face transformers 🔥

D-FINE is the sota real-time object detector that runs on T4 (free Colab) 🤩

> Collection with all checkpoints and demo ustc-community/d-fine-68109b427cbe6ee36b4e7352

Notebooks:
> Tracking https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DFine_tracking.ipynb
> Inference https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DFine_inference.ipynb
> Fine-tuning https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DFine_finetune_on_a_custom_dataset.ipynb
h/t @vladislavbro @qubvel-hf @ariG23498 and the authors of the paper 🎩

Regular object detectors attempt to predict bounding boxes in (x, y, w, h) pixel perfect coordinates, which is very rigid and hard to solve 🥲☹️

D-FINE formulates object detection as a distribution for bounding box coordinates, refines them iteratively, and it's more accurate 🤩

Another core idea behind this model is Global Optimal Localization Self-Distillation ⤵️

this model uses final layer's distribution output (sort of like a teacher) to distill to earlier layers to make early layers more performant.

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reacted to mrfakename's post with 👍🤗 5 months ago

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Hi everyone,

I just launched TTS Arena V2 - a platform for benchmarking TTS models by blind A/B testing. The goal is to make it easy to compare quality between open-source and commercial models, including conversational ones.

What's new in V2:

- **Conversational Arena**: Evaluate models like CSM-1B, Dia 1.6B, and PlayDialog in multi-turn settings
- **Personal Leaderboard**: Optional login to see which models you tend to prefer
- **Multi-speaker TTS**: Random voices per generation to reduce speaker bias
- **Performance Upgrade**: Rebuilt from Gradio → Flask. Much faster with fewer failed generations.
- **Keyboard Shortcuts**: Vote entirely via keyboard

Also added models like MegaTTS 3, Cartesia Sonic, and ElevenLabs' full lineup.

I'd love any feedback, feature suggestions, or ideas for models to include.

TTS-AGI/TTS-Arena-V2

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reacted to merve's post with 🚀👍 5 months ago

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you can easily fine-tune, quantize, play with sota vision LM InternVL3 now 🔥
we have recently merged InternVL3 to Hugging Face transformers and released converted checkpoints 🤗

collection for converted checkpoints: merve/internvl3-hf-6814be2943b2ae0e711c92a5
notebook: https://colab.research.google.com/drive/1wAQ7cyjyaCwLXbMA_OjXZe7aCxCFm6sI?usp=sharing 📖

reacted to merterbak's post with 🔥 5 months ago

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Microsoft released their new fine-tuned phi-4 models with reasoning data yesterday. They outperform/rival much larger models . Check out them if you haven't yet. 🚀

Phi4 mini reasoning(SFT): microsoft/Phi-4-mini-reasoning
Phi-4 reasoning(SFT): microsoft/Phi-4-reasoning
Phi-4 reasoning plus (SFT + RL): microsoft/Phi-4-reasoning-plus
Demo: https://github.com/marketplace/models/azureml/Phi-4-reasoning/playground
Articles: https://arxiv.org/pdf/2504.21318
https://arxiv.org/pdf/2504.21233
Blog: https://azure.microsoft.com/en-us/blog/one-year-of-phi-small-language-models-making-big-leaps-in-ai/

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reacted to as-cle-bert's post with ❤️👍 7 months ago

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I just released a fully automated evaluation framework for your RAG applications!📈

GitHub 👉 https://github.com/AstraBert/diRAGnosis
PyPi 👉 https://pypi.org/project/diragnosis/

It's called 𝐝𝐢𝐑𝐀𝐆𝐧𝐨𝐬𝐢𝐬 and is a lightweight framework that helps you 𝗱𝗶𝗮𝗴𝗻𝗼𝘀𝗲 𝘁𝗵𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗼𝗳 𝗟𝗟𝗠𝘀 𝗮𝗻𝗱 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝘀 𝗶𝗻 𝗥𝗔𝗚 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀.

You can launch it as an application locally (it's Docker-ready!🐋) or, if you want more flexibility, you can integrate it in your code as a python package📦

The workflow is simple:
🧠 You choose your favorite LLM provider and model (supported, for now, are Mistral AI, Groq, Anthropic, OpenAI and Cohere)
🧠 You pick the embedding models provider and the embedding model you prefer (supported, for now, are Mistral AI, Hugging Face, Cohere and OpenAI)
📄 You prepare and provide your documents
⚙️ Documents are ingested into a Qdrant vector database and transformed into a synthetic question dataset with the help of LlamaIndex
📊 The LLM is evaluated for the faithfulness and relevancy of its retrieval-augmented answer to the questions
📊 The embedding model is evaluated for hit rate and mean reciprocal ranking (MRR) of the retrieved documents

And the cool thing is that all of this is 𝗶𝗻𝘁𝘂𝗶𝘁𝗶𝘃𝗲 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱: you plug it in, and it works!🔌⚡

Even cooler? This is all built on top of LlamaIndex and its integrations: no need for tons of dependencies or fancy workarounds🦙
And if you're a UI lover, Gradio and FastAPI are there to provide you a seamless backend-to-frontend experience🕶️

So now it's your turn: you can either get diRAGnosis from GitHub 👉 https://github.com/AstraBert/diRAGnosis
or just run a quick and painless:

uv pip install diragnosis

To get the package installed (lightning-fast) in your environment🏃‍♀️

Have fun and feel free to leave feedback and feature/integrations requests on GitHub issues✨

reacted to MonsterMMORPG's post with 🔥 7 months ago

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Wan 2.1 Ultra Advanced Gradio APP for - Works as low as 4GB VRAM - 1-Click Installers for Windows, RunPod, Massed Compute - Batch Processing - T2V - I2V - V2V

Installer and APP : https://www.patreon.com/posts/123105403

Download from here : https://www.patreon.com/posts/123105403

I have been working 14 hours today to make this APP before sleeping for you guys :)

We have all the features of Wan 2.1 model

Text to Video 1.3B (as low as 3.5 GB VRAM) - Really fast - 480x832px or 832x480px

Video to Video 1.3B (as low as 3.5 GB VRAM) - Really fast - 480x832px or 832x480px

Text to Video 14B (as low as 17 GB VRAM) - still may work at below VRAM but slower - 720x1280px or 1280x720px

Image to Video 14B (as low as 17 GB VRAM) - still may work at below VRAM but slower - 720x1280px or 1280x720px

When you analyze the above and below images
First video is animated from the input image with following prompt

A hooded wraith stands motionless in a torrential downpour, lightning cracking across the stormy sky behind it. Its face is an impenetrable void of darkness beneath the tattered hood. Rain cascades down its ragged, flowing cloak, which appears to disintegrate into wisps of shadow at the edges. The mysterious figure holds an enormous sword of pure energy, crackling with electric blue lightning that pulses and flows through the blade like liquid electricity. The weapon drags slightly on the wet ground, sending ripples of power across the puddles forming at the figure's feet. Three glowing blue gems embedded in its chest pulse in rhythm with the storm's lightning strikes, each flash illuminating the decaying, ancient fabric of its attire. The rain intensifies around the figure, droplets seemingly slowing as they near the dark entity, while forks of lightning repeatedly illuminate its imposing silhouette. The atmosphere grows heavier with each passing moment as the wraith slowly raises its crackling blade, the blue energy intensifying and casting eerie shadows

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reacted to their post with 🤗 8 months ago

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Welcome back,

Small Language Models Enthusiasts and GPU Poor oss enjoyers lets connect.
Just created an organization which main target is to have fun with smaller models tuneable on consumer range GPUs, feel free to join and lets have some fun, much love ;3

SmolTuners

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s3nh

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