victor (Victor Mustar)

posted an update about 5 hours ago

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Interesting article: use Claude Code to help open models write CUDA kernels (for eg) by turning CC traces into Skills. They made a library out of it 👀

https://huggingface.co/blog/upskill

reacted to danielhanchen's post with 🚀 1 day ago

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You can now run Kimi K2.5 locally! 🔥

We shrank the 1T model to 240GB (-60%) via Dynamic 1-bit.
Get >40 tok/s on 242GB or 622GB VRAM/RAM for near full precision.

GGUF: unsloth/Kimi-K2.5-GGUF

Guide: https://unsloth.ai/docs/models/kimi-k2.5

reacted to tegridydev's post with ❤️ 2 days ago

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Introducing OpenMALx

openmalx

Repository for Infosec and Machine Learning Resources

OpenMALx is an organization focused on the development of datasets and models for security analysis. The project objective is to provide structured data for training and evaluating large language models in a security context.

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Technical Focus

**Dataset Formatting:** Processing raw security tool logs into instruction/response pairs for model training.
**Local Execution:** Optimizing models for local hardware to ensure data remains on-premises.
**Response Logic:** Developing structured formats for explaining security vulnerabilities and remediation steps.

Active Projects

**infosec-tool-output:** A dataset mapping static and dynamic analysis tool outputs to technical summaries.
openmalx/infosec-tool-output

**open-malsec:** A collection of text-based security threats, including phishing and social engineering samples, for classification tasks.
openmalx/open-malsec

replied to kelsend's post 2 days ago

will it be open sourced?

replied to schuler's post 2 days ago

Cool! can I use from my CLI or must do some python scripting?

reacted to danieldk's post with 🔥 2 days ago

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kernels 0.12 is out! 🎉

Changes:

* Support for kernel version branches to gracefully roll out kernel API changes.
* Support for PyTorch 2.10.
* kernel-builder is now merged into the kernels repo.
* Initial support for standardized kernel benchmarks.

https://github.com/huggingface/kernels/releases/tag/v0.12.0

reacted to AbstractPhil's post with 👀 2 days ago

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AbstractPhil/tinyflux-experts
Introducing the "blot" expert, sd15-flow-sol. The twin sister flow-matching experts for tinyflux-lailah; sd15-flow-lune AND sd15-flow-sol will be used in tandem to train tinyflux-Lailah. sd15-flow-sol never managed to reach full flow-matching prediction, so epsilon vpred conversion is required. All experts will exist within the tinyflux-experts repo, including all the critical checkpoint sets.
Lune was heavily finetuned in the sd3-style and adapted shift timestep system after David's interpolation converted sd15 into geometric basis.
Sol was left abandoned after 50 epochs with David and was considered overcooked and rigid, until I noticed the geometric structure today. Lune doesn't produce geometric structure as solid as Sol, not even close. Lune produces improved fidelity and detail, but Sol produces something very very different, aligned to sd15's behavior, and fully representative of the 5point 4simplex structure that David brought to the table.

Sol is essentially a nearly perfect blob-forming geometric blotter. Sol is SD15, and yet SOL was trained using a specific pattern recognizing and timestep aligned David model. David was tasked with classifying timesteps and patterns using complex deep-recognition structural analysis layer-by-layer, determining full-scale opinions after watching the entirety of sd15's structure during training.

Even though the sd15-flow-sol was left abandoned, the structure of Sol is HIGHLY effective at understanding TIMESTEP blotting interpolation. I didn't realize how crucially important this was until Lailah started to show rigidity and compartmentalized behavior with sequence - which likely happens to ALL flow matching models.

AbstractPhil/sd15-flow-matching

AbstractPhil/geo-david-collective-sd15-distilled
AbstractPhil/geo-david-collective-sd15-base-e40

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reacted to Benedictat's post with 🔥 3 days ago

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Tencent HunyuanImage 3.0-Instruct is seriously impressive

skyrocketed to 2nd place globally on the LMArena leaderboard, only trailing Google Nano-banana Pro.

What excites me most is its newly launched image editing and multi-image fusion capabilities

its semantic understanding is rock-solid this Instruct-following capability basically enables one-sentence end-to-end workflows, delivering a dimensionality-reducing boost in efficiency.

Frankly, it nails the pain points of frontline creators: old photo restoration, text modification, even extracting people from multiple images to create group shots. Previously, tweaking the fusion quality took tons of effort, but now the out-of-the-box realism and emotional expression are top-tier zero cheap AI artifacts

👉 Repo: https://hunyuan.tencent.com/chat/HunyuanDefault?from=modelSquare&modelId=Hunyuan-Image-3.0-Instruct

technical report：https://arxiv.org/abs/2509.23951

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reacted to consome2's post with ❤️ 3 days ago

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We’ve released two conversational speech datasets from oto on Hugging Face 🤗
Both are based on real, casual, full-duplex conversations, but with slightly different focuses.

Dataset 1: Processed / curated subset
otoearth/otoSpeech-full-duplex-processed-141h
* Full-duplex, spontaneous multi-speaker conversations
* Participants filtered for high audio quality
* PII removal and audio enhancement applied
* Designed for training and benchmarking S2S or dialogue models

Dataset 2: Larger raw(er) release
otoearth/otoSpeech-full-duplex-280h
* Same collection pipeline, with broader coverage
* More diversity in speakers, accents, and conversation styles
* Useful for analysis, filtering, or custom preprocessing experiments

We intentionally split the release to support different research workflows:
clean and ready-to-use vs. more exploratory and research-oriented use.

The datasets are currently private, but we’re happy to approve access requests — feel free to request access if you’re interested.

If you’re working on speech-to-speech (S2S) models or are curious about full-duplex conversational data, we’d love to discuss and exchange ideas together.

Feedback and ideas are very welcome!

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reacted to raincandy-u's post with 🔥 3 days ago

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🤗 Just released Rain-100M, an experimental ~97M-parameter Qwen3-style language model trained from random initialization.

Repo: raincandy-u/Rain-100M

Data: HuggingFaceFW/fineweb-edu, ~3B tokens, English only

Tokenizer: custom 16k BPE, context length 4096

Architecture: 12 Transformer layers, hidden size 768, 12 heads, MLP 2048, SiLU, bf16

Rain-100M is a raw base model (not instruction-tuned or safety-aligned), aimed at small-scale research, debugging training pipelines, and CPU/edge experiments. If you run evaluations, finetunes, or visualizations with it, I would be very interested in your results!

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reacted to FreshmanD's post with 👀 3 days ago

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Really thanks to the community for their help! LoongFlow now has its first Chinese hands-on video!

Version 2.0 is under intensive development. The next version will support Skills, making it easier for users to create their own expert agents to solve various challenging and complex real-world problems.

Meanwhile, we are also exploring whether we can automatically generate high-quality expert Skills after a task is completed, reducing the difficulty of writing Skills and letting the LoongFlow framework automatically output the best Skills for challenging scenarios!

reacted to ovi054's post with ❤️ 3 days ago

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ovi054/LTX-2-19b-Squish-LoRA ⚡

I trained a Squish LoRA for LTX-2. Upload an image and give prompt "squish it" to get the squish video.

Demo output videos are attached.

👉Try it now:
ovi054/LTX-2-19b-Squish-LoRA
ovi054/ltx-2-Audio-to-Video

reacted to danielhanchen's post with 🔥 9 days ago

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Run GLM-4.7-Flash locally on your device with 24GB RAM!🔥

It's the best performing 30B model on SWE-Bench and GPQA. With 200K context, it excels at coding, agents, chat & reasoning.

GGUF: unsloth/GLM-4.7-Flash-GGUF

Guide: https://unsloth.ai/docs/models/glm-4.7-flash

reacted to sergiopaniego's post with 🔥 14 days ago

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New REPL environment in OpenEnv available! ✨
Used in the Recursive Language Models (RLM) paper by Alex Zhang.

Ready for inference & post-training using trajectories. Handles long contexts:

> Run Python code in a sandbox
> Make recursive calls to LMs
> Explore data programmatically
> Return final result

Docs: https://meta-pytorch.org/OpenEnv/environments/repl/
Inference script: https://github.com/meta-pytorch/OpenEnv/blob/main/examples/repl_oolong_simple.py

reacted to MikeDoes's post with 👀 about 1 month ago

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What if an AI agent could be tricked into stealing your data, just by reading a tool's description? A new paper reports it's possible.

The "Attractive Metadata Attack" paper details this stealthy new threat. To measure the real-world impact of their attack, the researchers needed a source of sensitive data for the agent to leak. We're proud that the AI4Privacy corpus was used to create the synthetic user profiles containing standardized PII for their experiments.

This is a perfect win-win. Our open-source data helped researchers Kanghua Mo, 龙昱丞, Zhihao Li from Guangzhou University and The Hong Kong Polytechnic University to not just demonstrate a new attack, but also quantify its potential for harm. This data-driven evidence is what pushes the community to build better, execution-level defenses for AI agents.

🔗 Check out their paper to see how easily an agent's trust in tool metadata could be exploited: https://arxiv.org/pdf/2508.02110

#OpenSource
#DataPrivacy
#LLM
#Anonymization
#AIsecurity
#HuggingFace
#Ai4Privacy
#Worldslargestopensourceprivacymaskingdataset

reacted to csabakecskemeti's post with 🔥 about 1 month ago

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Just sharing a result of a homelab infrastructure experiment:

I've managed to setup a distributed inference infra at home using a DGX Spark (128GB unified gddr6) and a linux workstation with an RTX 6000 Pro (96GB gddr7) connected via 100Gbps RoCEv2. The model I've used (https://lnkd.in/gx6J7YuB) is about 140GB so could not fit either of the GPU. Full setup and tutorial soon on devquasar.com

Screen recording:
https://lnkd.in/gKM9H5GJ

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reacted to prithivMLmods's post with ❤️ about 1 month ago

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Introducing the Qwen-Image-Edit-2511-LoRAs-Fast demo, featuring image property comparison and contrast, built on top of Gradio and the combined Rerun SDK. It supports single and multi-image edits with existing LoRAs that are lazily loaded. (Note: This is still an experimental Space for Qwen-Image-Edit-2511.)

⭐ Space Demo: prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast
⭐ GitHub: https://github.com/PRITHIVSAKTHIUR/Qwen-Image-Edit-2511-LoRAs-Fast-Multi-Image-Rerun
⭐ Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection

To know more about it, visit the app page or the respective model page!

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reacted to prithivMLmods's post with 👍🤗 about 1 month ago

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Update: TRELLIS.2 (Text to 3D, Image to 3D) Gradio with Rerun Embedded demo with improved visualization of the 3D model previewer is now available on Hugging Face. Generate assets and view them in the 3D viewer, powered and streamlined with Microsoft’s TRELLIS.2 and Tongyi-MAI’s Z-Image-Turbo models.

🤗 TRELLIS.2 (Demo): prithivMLmods/TRELLIS.2-Text-to-3D
🕹️ GitHub: https://github.com/PRITHIVSAKTHIUR/TRELLIS.2-Text-to-3D-RERUN
🕹️ Collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations

To know more about it, visit the app page or the respective model page!

reacted to MohamedRashad's post with ❤️ about 1 month ago

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I have update my https://huggingface.co/collections/MohamedRashad/arabic-speech-datasets
with new datasets, making the full audio data more than 3000 hours of good arabic speech.

Feel Free to use it in your new innovations, And happy new year!

Victor Mustar PRO

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Victor Mustar PRO

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