open-acc (open/ acc)

posted an update 5 days ago

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4497

The Qwen3.5 Multimodal Understanding Demo, powered by Qwen3.5-2B, is now available on HF Spaces! It is a lightweight model designed for fast image and video reasoning. Built with Gradio, the demo showcases Image QA, Video QA, object detection, and 2D point tracking, along with real-time token streaming.

🤗 Demo: prithivMLmods/Qwen-3.5-HF-Demo
✅ Collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations
🔗 Qwen3.5-2B: Qwen/Qwen3.5-2B

To learn more, visit the app page or the respective model pages.

ronantakizawa

posted an update 6 days ago

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2592

Introducing the github-codereview dataset: A compilation of 200k+ human-written code reviews from top OSS projects (React, Tensorflow, VSCode...).

I finetuned a Qwen2.5-Coder-32B-Instruct model with this dataset and saw significant improvements in generating better code fixes and review comments (4x improved BLEU-4, ROUGE-L, SBERT scores compared to base model).

#codereview #code #datasets

ronantakizawa/github-codereview

MaziyarPanahi

posted an update 8 days ago

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4134

DNA, mRNA, proteins, AI. I spent the last year going deep into computational biology as an ML engineer. This is Part I of what I found. 🧬

In 2024, AlphaFold won the Nobel Prize in Chemistry.

By 2026, the open-source community had built alternatives that outperform it.

That's the story I find most interesting about protein AI right now. Not just the science (which is incredible), but the speed at which open-source caught up. Multiple teams, independently, reproduced and then exceeded AlphaFold 3's accuracy with permissive licenses. The field went from prediction to generation: we're not just modeling known proteins anymore, we're designing new ones.

I spent months mapping this landscape for ML engineers. What the architectures actually are (spoiler: transformers and diffusion models), which tools to use for what, and which ones you can actually ship commercially.

New post on the Hugging Face blog: https://huggingface.co/blog/MaziyarPanahi/protein-ai-landscape

Hope you all enjoy! 🤗

2 replies

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prithivMLmods

posted an update 9 days ago

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3947

QIE-Object-Remover-Bbox Demo removes objects and artifacts from selected regions using bounding box grounding. Built on Qwen-Image-Edit-2509 with Rapid Diffusers acceleration, it delivers fast 4-step inference via the QIE-2509 adapter. 🤗🔥

🔗Demo Space: prithivMLmods/QIE-Object-Remover-Bbox
🔗Qwen-Image-Edit-Rapid-AIO: prithivMLmods/Qwen-Image-Edit-Rapid-AIO-V4
🔗Adapter-(LoRA): prithivMLmods/QIE-2509-Object-Remover-Bbox

🔗Collection: https://huggingface.co/collections/prithivMLmods/qwen-image-edit-layout-bbox

To learn more, visit the app page or the respective model pages.

1 reply

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ronantakizawa

posted an update 10 days ago

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2356

Introducing the WebUI dataset: a compilation of screenshot to code pairs of modern websites detailing the styling, framework used, and box bounds for all viewports (Desktop, mobile, tablet).

This dataset showed signs of improved performance in web design LLM benchmarks for a finetuned QWEN 2.5 VL-7B!

#web #ui #datasets

ronantakizawa/webui

3 replies

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prithivMLmods

posted an update 15 days ago

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2500

FireRed-Image-Edit-1.0 (Rapid) Fast Experimental Demo is Out! 🚀🤗

Demo: prithivMLmods/FireRed-Image-Edit-1.0-Fast

-> Paired the EditPlusPipeline with the Diffusers-compatible transformer weights of Rapid AIO from Qwen-Image-Edit. (experimental)
-> This fusion delivers more accurate instruction following, higher image quality, and consistent visual coherence @ 4-step fast inference.
-> Better maintains text styles with high fidelity, along with high-quality old photo restoration, enhancement, and best-in-class virtual try-on.

Tonic

posted an update 19 days ago

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3179

🤔 Who would win ?

- a fully subsidized ai lab
OR
- 3 random students named

kurakurai ?

demo : Tonic/fr-on-device

if you like it give the demo a little star and send a shoutout to : @MaxLSB @jddqd and @GAD-cell for absolutely obliterating the pareto frontier of the french language understanding .

4 replies

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prithivMLmods

posted an update 20 days ago

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3210

Try the demo for Qwen3-VL-abliterated-MAX-Fast: prithivMLmods/Qwen3-VL-abliterated-MAX-Fast (Unredacted: Ask Anything with Near-Zero Refusal Rates). The full model series collection is available here: https://huggingface.co/collections/prithivMLmods/unredacted-max-vl

MaziyarPanahi

authored a paper 20 days ago

Arcee Trinity Large Technical Report

Paper • 2602.17004 • Published 21 days ago • 17

ronantakizawa

posted an update 20 days ago

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2114

Introducing the github-top-code dataset: A curated dataset of 1.3M+ source code files from GitHub's top ranked developers.

I collected the best source code files from Github's highest trending developers of all time, and compiled a dataset to train LLMs to write well-structured, production-grade code.

#dataset #codedataset #pretraining

ronantakizawa/github-top-code

Tonic

posted an update 23 days ago

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3232

🙋🏻‍♂️hello my lovelies ,

it is with great pleasure i present to you my working one-click deploy 16GB ram completely free huggingface spaces deployment.

repo : Tonic/hugging-claw (use git clone to inspect)
literally the one-click link : Tonic/hugging-claw

you can also run it locally and see for yourself :

docker run -it -p 7860:7860 --platform=linux/amd64 \
-e HF_TOKEN="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_TRUSTED_PROXIES="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_PASSWORD="YOUR_VALUE_HERE" \
-e OPENCLAW_CONTROL_UI_ALLOWED_ORIGINS="YOUR_VALUE_HERE" \
registry.hf.space/tonic-hugging-claw:latest

just a few quite minor details i'll take care of but i wanted to share here first

2 replies

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prithivMLmods

posted an update 24 days ago

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Dropping the Qwen3 VL Series of Unredacted MAX-VL models. These models have undergone multi-stage training to minimize refusal rates through continuous abliterated optimization. You can find the models in BF16, FP8-Dynamic, and GGUF formats at the links below.🔥🚀

Unredacted MAX - VL:
➜ prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX
➜ prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX
➜ prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX
➜ prithivMLmods/Qwen3-VL-8B-Thinking-Unredacted-MAX

Unredacted MAX - VL [FP8]
➜ prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-FP8
➜ prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-FP8
➜ prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX-FP8
➜ prithivMLmods/Qwen3-VL-8B-Thinking-Unredacted-MAX-FP8

Unredacted MAX - VL [GGUF]
➜ prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF
➜ prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF
➜ prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX-GGUF
➜ prithivMLmods/Qwen3-VL-8B-Thinking-Unredacted-MAX-GGUF

Unredacted MAX - VL [Collection]
➜ https://huggingface.co/collections/prithivMLmods/unredacted-max-vl-fp8
➜ https://huggingface.co/collections/prithivMLmods/unredacted-max-vl
➜ https://huggingface.co/collections/prithivMLmods/unredacted-max-vl-gguf

To learn more, visit the app page or the respective model pages.

ronantakizawa

posted an update 24 days ago

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283

Introducing the LeetCode Assembly Dataset: a dataset of 400+ LeetCode problem solutions in assembly across x86-64, ARM64, MIPS64, and RISC-V using GCC & Clang at -O0/-O1/-O2/-O3 optimizations.

This dataset is perfect for teaching LLMs complex compiler behavior!

#dataset #leetcode #assembly

ronantakizawa/leetcode-assembly

MaziyarPanahi

posted an update 29 days ago

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2226

Announcing: OpenMed Multilingual PII Detection Models

Today I am releasing 105 open-source models for Personally Identifiable Information (PII) detection in French, German, and Italian.

All Apache 2.0 licensed. Free for commercial use. No restrictions.

Performance:

- French: 97.97% F1 (top model)
- German: 97.61% F1 (top model)
- Italian: 97.28% F1 (top model)

All top-10 models per language exceed 96% F1

Coverage:

55+ PII entity types per language
Native ID formats: NSS (French), Sozialversicherungsnummer (German), Codice Fiscale (Italian)
Language-specific address, phone, and name patterns

Training Data:

French: 49,580 samples
German: 42,250 samples
Italian: 40,944 samples

Why Multilingual?

European healthcare operates in European languages. Clinical notes, patient records, and medical documents are generated in French, German, Italian, and other languages.

Effective de-identification requires:

- Native language understanding — not translation
- Local ID format recognition — each country has unique patterns
- Cultural context awareness — names, addresses, and formats vary
- These models deliver production-ready accuracy without requiring data to leave your infrastructure or language.

HIPAA & GDPR Compliance
Built for US and European privacy regulations:

- On-premise deployment: Process data locally with zero external dependencies
- Data sovereignty: No API calls, no cloud services, no cross-border transfers
- Air-gapped capable: Deploy in fully isolated environments if required
- Regulatory-grade accuracy: Supporting Expert Determination standards
- HIPAA and GDPR compliance across languages, without compliance gaps.

Use Cases
- Hospital EHR systems: Automated patient record de-identification
- Clinical research: Multilingual dataset preparation for studies
- Insurance companies: Claims processing across

https://huggingface.co/collections/OpenMed/multilingual-pii-and-de-identification

1 reply

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ronantakizawa

posted an update about 1 month ago

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240

Hit 10,000+ downloads across my models and datasets on Hugging Face!

Follow for more @ronantakizawa !

#building #datasets #huggingface

prithivMLmods

posted an update about 1 month ago

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2993

Introducing FLUX.2-Klein-LoRA-Studio, a demo for image editing using specialized LoRA adapters built for the FLUX.2-Klein-Distilled model. It features an edit-style gallery for multi-style image editing, including de-light, face swap, mannequin, and more. Try the demo below.

🤗Demo: prithivMLmods/FLUX.2-Klein-LoRA-Studio
🤗Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
🤗GitHub: https://github.com/PRITHIVSAKTHIUR/FLUX.2-Klein-LoRA-Studio

To learn more, visit the app page or the respective model pages.

MaziyarPanahi

posted an update about 1 month ago

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1255

From Golden Gate Bridge to Broken JSON: Why Anthropic's SAE Steering Fails for Structured Output

I ran 6 experiments trying to use Anthropic's SAE steering for JSON generation.

- Base model: 86.8% valid JSON
- Steering only: 24.4%
- Fine-tuned: 96.6%
- FSM constrained: 100%

Steering is for semantics, not syntax.

https://huggingface.co/blog/MaziyarPanahi/sae-steering-json

MaziyarPanahi

posted an update about 1 month ago

Post

3996

🚨 Day 8/8: OpenMed Medical Reasoning Dataset Release - THE GRAND FINALE

Today I complete my 8-day release series with Medical-Reasoning-SFT-Mega.
The largest open medical reasoning dataset, combining 7 state-of-the-art AI models with fair distribution deduplication.

THE 7 SOURCE MODELS (Original Sample Counts):

1. Trinity-Mini: 810,284 samples
2. Qwen3-Next-80B: 604,249 samples
3. GPT-OSS-120B: 506,150 samples
4. Nemotron-Nano-30B: 444,544 samples
5. GLM-4.5-Air: 225,179 samples
6. MiniMax-M2.1: 204,773 samples
7. Baichuan-M3-235B: 124,520 samples

TOTAL BEFORE DEDUPLICATION: 2,919,699 samples

TOKEN COUNTS:
- Content tokens: 2.22 Billion
- Reasoning tokens: 1.56 Billion
- Total tokens: 3.78 Billion
- Samples with chain-of-thought: 100%

Quick Start:

from datasets import load_dataset
ds = load_dataset("OpenMed/Medical-Reasoning-SFT-Mega")

6 replies

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prithivMLmods

posted an update about 1 month ago

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877

GLM OCR, a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture. It delivers high accuracy and strong generalization with a blazing-fast inference pipeline. The demo is live . Try it now. 🤗🚀

✨ Demo: prithivMLmods/GLM-OCR-Demo
✨ Multimodal Implementations: https://huggingface.co/collections/prithivMLmods/multimodal-implementations
✨ GitHub: https://github.com/PRITHIVSAKTHIUR/GLM-OCR-Demo

Sri-Vigneshwar-DJ

posted an update about 1 month ago

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1403

Just released a new dataset designed for training reasoning models on Meta (Facebook/Instagram) advertising fatigue detection!

What is it? A GRPO (Group Relative Policy Optimization) training dataset with 200+ carefully crafted scenarios covering:

🔍 Fatigue Signal Detection: CTR drops, CPM spikes, frequency analysis
🩺 Performance Diagnosis: Root cause analysis frameworks
📋 Strategy: Creative refresh cadence, testing frameworks
📊 Analysis: ROI calculations, metric interpretation
Why GRPO? GRPO training helps models learn structured reasoning. Each response follows the <thinking> and <answer> format.

Check it out here: Sri-Vigneshwar-DJ/meta-fatigue-grpo-dataset

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