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Nymbo 
posted an update 2 days ago
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5834
We should really have a release date range slider on the /models page. Tired of "trending/most downloaded" being the best way to sort and still seeing models from 2023 on the first page just because they're embedded in enterprise pipelines and get downloaded repeatedly. "Recently Created/Recently Updated" don't solve the discovery problem considering the amount of noise to sift through.

Slight caveat: Trending actually does have some recency bias, but it's not strong/precise enough.
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unmodeled-tyler 
posted an update 2 days ago
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5603
LINK: https://github.com/unmodeled-tyler/vessel-browser

Hey Hugging Face!

It's been quiet from me over here for the last few weeks, but I've been busy building! I just submitted my project to the Hermes Agent Hackathon, and wanted to share it with all of you.

This is Vessel Browser - an AI-native web browser that runs locally on Linux, and is operated by your personal AI agent via MCP server. Vessel is built from the ground up around the agent as first-class and visible UI for human-in-the-loop with 3 different levels of permissions.

Your agent finds, reads, and organizes the web for you, based on what you actually care about - not what a platform's algorithm thinks you care about.

Once your agent finds what it's looking for, it can organize bookmarked pages into custom folders with summaries for later browsing, take screenshots with highlighted text, and integrate with Obsidian for long-term browsing related-memory.

Check it out!
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danielhanchen 
posted an update about 12 hours ago
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363
Introducing Unsloth Studio ✨
A new open-source web UI to train and run LLMs.

• Run models locally on Mac, Windows, Linux
• Train 500+ models 2x faster with 70% less VRAM
• Supports GGUF, vision, audio, embedding models
• Auto-create datasets from PDF, CSV, DOCX
• Self-healing tool calling and code execution
• Compare models side by side + export to GGUF

GitHub: https://github.com/unslothai/unsloth
Blog and Guide: https://unsloth.ai/docs/new/studio

Available now on Hugging Face, NVIDIA, Docker and Colab.
Keeby-smilyai 
posted an update about 6 hours ago
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263
Hello everyone!
robtacconelli 
posted an update 1 day ago
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🧬 Midicoth: diffusion-based lossless compression — no neural net, no GPU, no training data

What if reverse diffusion could compress text — without a neural network?
Midicoth brings score-based denoising into classical compression. It treats prior smoothing as forward noise and reverses it with Tweedie's formula on a binary tree — 3 denoising steps, James-Stein shrinkage, applied after all model blending. ~2,000 lines of C, single CPU core.

Beats every dictionary compressor we tested:
enwik8 (100 MB) → 1.753 bpb (−11.9% vs xz, −15% vs Brotli, −24.5% vs bzip2)
alice29.txt → 2.119 bpb (−16.9% vs xz)
Outperforms xz, zstd, Brotli, bzip2, gzip on all inputs

PAQ/CMIX still win with hundreds of models + LSTMs. LLM compressors win with pre-trained knowledge. Midicoth closes the gap with pure statistics — no mixer, no gradient descent, just counting.
The Tweedie denoising layer adds 2.3–2.7% on every file tested — the most consistent component in the ablation. Adding SSE or logistic mixers made things worse. In the online setting, count-based beats gradient-based.
No external dependencies. Fully deterministic. Bit-exact encode/decode. ~60 KB/s throughput.
💻 Code: https://github.com/robtacconelli/midicoth
📄 Paper: Micro-Diffusion Compression -- Binary Tree Tweedie Denoising for Online Probability Estimation (2603.08771)
⭐ Space: robtacconelli/midicoth

If you ever wondered whether diffusion ideas belong in data compression — here's proof they do. ⭐ appreciated!
W8Yi 
posted an update 2 days ago
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2586
I built a **TCGA WSI feature dataset using UNI2-h**.

The official release currently has incomplete coverage (see discussion):
MahmoodLab/UNI2-h-features#2

To make the features easier to use for research, I generated a new dataset:

W8Yi/tcga-wsi-uni2h-features

Key differences from the official release:

• **All detected tissue tiles are encoded** (not a sampled subset)
• **Features can be downloaded per slide** instead of large ZIP archives
• **QC overlay images** are provided for visual inspection
• **UNI2-h 1536-D tile embeddings** stored in H5 format
• Organized by TCGA project for easier use in MIL / retrieval pipelines

Example layout:

TCGA-HNSC/
  features/*.h5
  vis/*__overlay.png


Hope this helps others working on computational pathology and TCGA WSI research.
OzTianlu 
posted an update 3 days ago
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5290
Arcade-3B — SmolReasoner
NoesisLab/Arcade-3B
Arcade-3B is a 3B instruction-following and reasoning model built on SmolLM3-3B. It is the public release from the ARCADE project at NoesisLab, which investigates the State–Constraint Orthogonality Hypothesis: standard Transformer hidden states conflate factual content and reasoning structure in the same subspace, and explicitly decoupling them improves generalization.
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prithivMLmods 
posted an update 3 days ago
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4924
QIE-2509-Object-Remover-Bbox-v3 is a more stable version of the Qwen Image Edit visual grounding–based object removal model. The app was previously featured in HF Spaces of the Week and is now updated with the latest Bbox-v3 LoRA adapter.

🤗 Demo: prithivMLmods/QIE-Object-Remover-Bbox
🤗 LoRA: prithivMLmods/QIE-2509-Object-Remover-Bbox-v3
🤗 Collection: https://huggingface.co/collections/prithivMLmods/qwen-image-edit-layout-bbox

To learn more, visit the app page or the respective model pages.
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DedeProGames 
posted an update about 10 hours ago
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249
Introducing GRM Family, a family of fine-tuned small models from the Qwen2.5 family for Long Cot and General Reasoning and Agentic Tasks.

GRM is available in 7b and 1.5b parameter sizes, these models being significantly relevant for complex tasks or local inference agents.
OrionLLM/GRM-7b
OrionLLM/GRM-1.5b
ajibawa-2023 
posted an update about 10 hours ago
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318
C-Code-Large
Dataset: ajibawa-2023/C-Code-Large

C-Code-Large is a large-scale corpus of C programming language source code comprising more than 4 million code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, and software engineering automation for the C ecosystem.

By offering a high-volume, language-focused dataset, C-Code-Large enables targeted experimentation in low-level programming, memory-constrained environments, and performance-critical systems, where C continues to be a dominant language.

C-Code-Large addresses the lack of large, curated, C-specific datasets, making it possible to conduct focused research on procedural programming paradigms, manual memory management, and system-level abstractions.

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