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Sri-Vigneshwar-DJΒ 
posted an update about 6 hours ago
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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:

πŸ” Fatigue Signal Detection: CTR drops, CPM spikes, frequency analysis
🩺 Performance Diagnosis: Root cause analysis frameworks
πŸ“‹ Strategy: Creative refresh cadence, testing frameworks
πŸ“Š 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
prithivMLmodsΒ 
posted an update about 14 hours ago
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Introducing the Qwen-Image-Edit-3D-Lighting-Control app, featuring 8Γ— horizontal and 3Γ— elevational lighting positions for precise 3D lighting control. It enables studio-level lighting using fast Qwen Image Edit fast inference, paired with Multi-Angle-Lighting adapters. πŸ”¦

πŸ”₯ Space: prithivMLmods/Qwen-Image-Edit-3D-Lighting-Control
βœ… Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
πŸ“‚ GitHub: https://github.com/PRITHIVSAKTHIUR/Qwen-Image-Edit-3D-Lighting-Control
jorgemunozlΒ 
posted an update 5 days ago
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Test

I know that it was buggy, OMG
prithivMLmodsΒ 
posted an update 6 days ago
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Daggr UI version of the Qwen3-TTS demo.πŸ”₯
(custom voice, voice design, qwen3-asr and voice cloning) nodes.
No remote spaces used for API inference; all functions run in-app fn.
Powered by t4-m and built with daggr@0.5.2 and gradio@6.

πŸ‘‰Demo: prithivMLmods/Qwen3-TTS-Daggr-UI
⭐Github: https://github.com/PRITHIVSAKTHIUR/Qwen3-TTS-Daggr-UI
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prithivMLmodsΒ 
posted an update 9 days ago
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Qwen-Image-Edit-Object-Manipulator Space is now featured in Hugging Face Space of the Week. It enables object manipulation such as extracting objects, adding designs, and removing objects or designs from the red highlighted area using specialized adapters.

πŸ”₯Do enjoy the demo! ~ prithivMLmods/Qwen-Image-Edit-Object-Manipulator

Collections:
🧨Adapters-1: https://huggingface.co/collections/prithivMLmods/qwen-image-edit-exps
🧨Adapters-2: https://huggingface.co/collections/prithivMLmods/qie-jan-23-26
🧨Adapters-3: https://huggingface.co/collections/prithivMLmods/qwen-image-edit-object-manipulator

⭐Github: https://github.com/PRITHIVSAKTHIUR/Qwen-Image-Edit-Object-Manipulator

To learn more, visit the app page or the respective model pages.
ParveshiiiiΒ 
posted an update 9 days ago
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πŸš€ Wanna train your own AI Model or Tokenizer from scratch?

Building models isn’t just for big labs anymore β€” with the right data, compute, and workflow, you can create **custom AI models** and **tokenizers** tailored to any domain. Whether it’s NLP, domain‑specific datasets, or experimental architectures, training from scratch gives you full control over vocabulary, embeddings, and performance.

✨ Why train your own?
- Full control over vocabulary & tokenization
- Domain‑specific optimization (medical, legal, technical, etc.)
- Better performance on niche datasets
- Freedom to experiment with architectures

⚑ The best part?
- Tokenizer training (TikToken / BPE) can be done in **just 3 lines of code**.
- Model training runs smoothly on **Google Colab notebooks** β€” no expensive hardware required.

πŸ“‚ Try out my work:
- πŸ”— https://github.com/OE-Void/Tokenizer-from_scratch
- πŸ”— https://github.com/OE-Void/GPT
Sri-Vigneshwar-DJΒ 
posted an update 10 days ago
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πŸ™οΈ Hugging Face Community Post
Title: 🧬 Experimenting with "Dynamic Chaos" in Tamil SLMs

Hi everyone! I just published a new experimental study on Small Language Model (SLM) resilience.

I took the Qwen2.5-0.5B model and put it through a "Chaos Phase" to see how much weight data a tiny model can lose before its understanding of classical Tamil grammar breaks.

Key highlights of the study:

Target Data: Fine-tuned on the Thirukkural (1,330 couplets + modern explanations).
The Chaos Step: Applied 20% random weight pruning but implemented "Layer Protection" for the Token Embeddings and LM Head to keep the characters readable.
Compression: 4-bit (Q4_K_M) quantization for extreme efficiency.
Result: A surrealist classical Tamil model that is ultra-light (~300MB) and ultra-fast!

Check out the model and the experiment logic here: Sri-Vigneshwar-DJ/qwen-tamil-chaos-v1
prithivMLmodsΒ 
posted an update 12 days ago
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Introducing QIE-2511-Zoom-Master for highlight-guided area zoom-in, enabling lossless zooming within a drawn square area, and QIE-2511-Object-Remover-v2 for precise object or highlight-guided area cleanup. These experimental adapters are trained based on QIE-2511. Find the adapters below.

πŸ•ΉοΈQIE-2511-Zoom-Master : prithivMLmods/QIE-2511-Zoom-Master
πŸ•ΉοΈQIE-2511-Object-Remover-v2: prithivMLmods/QIE-2511-Object-Remover-v2

πŸ€—Demo: prithivMLmods/Qwen-Image-Edit-Object-Manipulator

πŸ“‚Collection: https://huggingface.co/collections/prithivMLmods/qwen-image-edit-exps

To learn more, visit the app page or the respective model pages.
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ParveshiiiiΒ 
posted an update 15 days ago
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πŸ“’ The Announcement
Subject: XenArcAI is now Modotte – A New Chapter Begins! πŸš€

Hello everyone,

We are thrilled to announce that XenArcAI is officially rebranding to Modotte!

Since our journey began, we’ve been committed to pushing the boundaries of AI through open-source innovation, research, and high-quality datasets. As we continue to evolve, we wanted a name that better represents our vision for a modern, interconnected future in the tech space.

What is changing?

The Name: Moving forward, all our projects, models, and community interactions will happen under the Modotte banner.

The Look: You’ll see our new logo and a fresh color palette appearing across our platforms.

What is staying the same?

The Core Team: It’s still the same people behind the scenes, including our founder, Parvesh Rawal.

Our Mission: We remain dedicated to releasing state-of-the-art open-source models and datasets.

Our Continuity: All existing models, datasets, and projects will remain exactly as they areβ€”just with a new home.

This isn’t just a change in appearance; it’s a commitment to our next chapter of growth and discovery. We are so grateful for your ongoing support as we step into this new era.

Welcome to the future. Welcome to Modotte.

Best regards, The Modotte Team
Sri-Vigneshwar-DJΒ 
posted an update 18 days ago
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Performance Marketing meets "Thinking Mode" 🧠

I’m excited to release hawky-ai-Qwen3-0.6B-Marketing-MoT, a specialized SLM designed for deep strategic reasoning in performance marketing.

While small at 0.6B parameters, this model punches way above its weight class by utilizing a Mixture of Thoughts (MoT) framework. It doesn't just give you an answer; it thinks through the logic of Meta Ads scaling, GA4 attribution, and unit economics before providing a strategic recommendation.

Key Features:

Thinking-First: Trained on 1,500+ critical thinking scenarios.
MoT Framework: 5 distinct reasoning styles (Linear, Exploratory, Critical, Deconstructive, Analogical).
SLM Speed: Perfect for low-latency, high-precision marketing audits.
Check it out on Hugging Face: πŸ”— Sri-Vigneshwar-DJ/hawky-ai-Qwen3-0.6B-Marketing-MoT
prithivMLmodsΒ 
posted an update 24 days ago
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LTX-2 Camera-Control LoRA demo with dolly-in/out and dolly-left/right is now available on Hugging Face, paired with ltx-2-19b-distilled-lora for fast inference. It also includes dynamic GPU duration adjustments for long video generations. Click the related Space links below.

πŸ€—Try it now on : prithivMLmods/LTX-2-LoRAs-Camera-Control-Dolly
⭐Github: https://github.com/PRITHIVSAKTHIUR/LTX-2-LoRAs-Camera-Control-Dolly
πŸ•ΉοΈCollection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection

To learn more, visit the app page or the respective model pages.
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Sri-Vigneshwar-DJΒ 
posted an update 24 days ago
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Introducing Hawky-AI H1 4B PM: The First Open-Source LLM for Performance Marketing 🎯

Hey HF Community! πŸ‘‹

Just released the first LLM fine-tuned specifically for Performance Marketing.
What is it?
Gemma 3 4B distilled from Claude Opus 4.5 with expert-level marketing knowledge.
Covers:
πŸ“± Meta Ads (campaign structure, bidding, scaling, creative fatigue)
πŸ” Google Ads (Quality Score, Performance Max, lead gen)
πŸ“Š Measurement (ROAS vs MER, incrementality, LTV:CAC)
🎨 Creative Strategy (hook rates, A/B testing, funnel creative)
Why we built it:
Generic LLMs say "optimize your targeting" β€” not helpful. This model gives specific frameworks like "frequency at 4.5 + CTR drop = creative fatigue, here's the fix..."
Technical:

Base: Gemma 3 4B
Method: QLoRA (r=64)
Teacher: Claude Opus 4.5

πŸ”— Model: Sri-Vigneshwar-DJ/hawky-ai-H1-4b-PM
Built by Hawky.ai

Try it and let us know what you think! πŸš€
Sri-Vigneshwar-DJΒ 
posted an update 27 days ago
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πŸ¦… Introducing Hawky AI H1 Mini 4B: A Domain-Specific Model for Performance Marketing

Hey HuggingFace community! πŸ‘‹

We're excited to share our first open-source release: **Hawky AI H1 Mini 4B Experimental** - a Gemma 3 4B model fine-tuned specifically for Meta advertising and performance marketing strategy.

🎯 Why We Built This

At [Hawky.ai](https://hawky.ai), we build AI-powered creative intelligence tools for performance marketers. We work with major agencies (WPP, Madison, GroupM) and brands (TVS Motors, Tanishq, Bajaj Finserv) on campaign optimization.

We wanted to explore: Can a small, domain-specific model provide expert-level guidance on performance marketing?

Specifically, we focused on Meta's Andromeda algorithm - the AI system that now powers ad delivery across Facebook and Instagram. Understanding Andromeda is crucial for modern media buying, but the knowledge is scattered and constantly evolving.

🧠 What Makes This Different

Chain-of-Thought Reasoning
The model doesn't just answer - it **thinks through problems** step-by-step:

Sri-Vigneshwar-DJ/hawky-ai-h1-mini-4b-experimental
NymboΒ 
posted an update 28 days ago
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Genuine recommendation: You should really use this AutoHotKey macro. Save the file as macros.ahk and run it. Before sending a prompt to your coding agent, press Ctrl + Alt + 1 and paste your prompt to any regular chatbot. Then send the output to the agent. This is the actual, boring, real way to "10x your prompting". Use the other number keys to avoid repeating yourself over and over again. I use this macro prolly 100-200 times per day. AutoHotKey isn't as new or hype as a lot of other workflows, but there's a reason it's still widely used after 17 years. Don't overcomplicate it.

; Requires AutoHotkey v1.1+

; All macros are `Ctrl + Alt + <variable>`

^!1::
    Send, Please help me more clearly articulate what I mean with this message (write the message in a code block):
return

^!2::
    Send, Please make the following changes:
return

^!3::
    Send, It seems you got cut off by the maximum response limit. Please continue by picking up where you left off.
return


In my experience the past few months, Ctrl + Alt + 1 works best with Instruct models (non-thinking). Reasoning causes some models to ramble and miss the point. I've just been using GPT-5.x for this.
MaziyarPanahiΒ 
posted an update 29 days ago
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πŸŽ‰ OpenMed 2025 Year in Review: 6 Months of Open Medical AI

I'm thrilled to share what the OpenMed community has accomplished since our July 2025 launch!

πŸ“Š The Numbers

29,700,000 downloads Thank you! πŸ™

- 481 total models (475 medical NER models + 6 fine-tuned LLMs)
- 475 medical NER models in [OpenMed](
OpenMed ) organization
- 6 fine-tuned LLMs in [openmed-community](
openmed-community )
- 551,800 PyPI downloads of the [openmed package](https://pypi.org/project/openmed/)
- 707 followers on HuggingFace (you!)
- 97 GitHub stars on the [toolkit repo](https://github.com/maziyarpanahi/openmed)

πŸ† Top Models by Downloads

1. [OpenMed-NER-PharmaDetect-SuperClinical-434M]( OpenMed/OpenMed-NER-PharmaDetect-SuperClinical-434M) β€” 147,305 downloads
2. [OpenMed-NER-ChemicalDetect-ElectraMed-33M]( OpenMed/OpenMed-NER-ChemicalDetect-ElectraMed-33M) β€” 126,785 downloads
3. [OpenMed-NER-BloodCancerDetect-TinyMed-65M]( OpenMed/OpenMed-NER-BloodCancerDetect-TinyMed-65M) β€” 126,465 downloads

πŸ”¬ Model Categories

Our 481 models cover comprehensive medical domains:

- Disease Detection (~50 variants)
- Pharmaceutical Detection (~50 variants)
- Oncology Detection (~50 variants)
- Genomics/DNA Detection (~80 variants)
- Chemical Detection (~50 variants)
- Species/Organism Detection (~60 variants)
- Protein Detection (~50 variants)
- Pathology Detection (~50 variants)
- Blood Cancer Detection (~30 variants)
- Anatomy Detection (~40 variants)
- Zero-Shot NER (GLiNER-based)


OpenMed
OpenMed NER: Open-Source, Domain-Adapted State-of-the-Art Transformers for Biomedical NER Across 12 Public Datasets (2508.01630)
https://huggingface.co/collections/OpenMed/medical-and-clinical-ner
https://huggingface.co/collections/OpenMed/zeroshot-medical-and-clinical-ner
OpenMed/Medical-Reasoning-SFT-GPT-OSS-120B
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pcuenqΒ 
posted an update about 1 month ago
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πŸ‘‰ What happened in AI in 2025? πŸ‘ˆ

We prepared the 2025 version of the HF AI Timeline Grid, highlighting open vs API-based model releases, and allowing you to browse and filter by access, modality, and release type!

Play with it here:
2025-ai-timeline/2025-ai-timeline

Here's my personal quarterly TL;DR:

1️⃣ Q1 β€” Learning to Reason
Deepseek not only releases a top-notch reasoning model, but shows how to train them and compete with closed frontier models. OpenAI debuts Deep Research.

Significant milestones: DeepSeek R1 & R1-Zero, Qwen 2.5 VL, OpenAI Deep Research, Gemini 2.5 Pro (experimental)

2️⃣ Q2 β€” Multimodality and Coding
More LLMs embrace multimodality by default, and there's a surge in coding agents. Strong vision, audio, and generative models emerge.

Significant milestones: Llama 4, Qwen 3, Imagen 4, OpenAI Codex, Google Jules, Claude 4

3️⃣ Q3 β€” "Gold" rush, OpenAI opens up, the community goes bananas
Flagship models get gold in Math olympiads and hard benchmarks. OpenAI releases strong open source models and Google releases the much anticipated nano-banana for image generation and editing. Agentic workflows become commonplace.

Significant milestones: Gemini and OpenAI IMO Gold, gpt-oss, Gemini 2.5 Flash Image, Grok 4, Claude Sonnet 4.5

4️⃣ Q4 β€” Mistral returns, leaderboard hill-climbing
Mistral is back with updated model families. All labs release impressive models to wrap up the year!

Significant milestones: Claude Opus 4.5, DeepSeek Math V2, FLUX 2, GPT 5.1, Kimi K2 Thinking, Nano Banana Pro, GLM 4.7, Gemini 3, Mistral 3, MiniMax M2.1 🀯

Credits
πŸ™ NHLOCAL for the source data https://github.com/NHLOCAL/AiTimeline

🫑 @reach-vb for the original idea, design and recipe

πŸ™Œ @ariG23498 and yours truly for compiling and verifying the 2025 edition

πŸ₯³ Here's to 2026, wishing it becomes the best year ever for open releases and on-device-first use-cases! πŸ₯‚
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prithivMLmodsΒ 
posted an update about 1 month ago
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Dropping Image Edit (Object Manipulator): Add or remove specified objects/designs, with flexible support for both single-image and multi-image modes.

πŸ€— Demo: prithivMLmods/Qwen-Image-Edit-Object-Manipulator

Qwen-Image-Edit-2511-Object-Remover is an adapter (LoRA) developed for Qwen’s Qwen-Image-Edit-2511 image-to-image model. It is specifically designed for precise object removal from images.

⭐ Model: prithivMLmods/Qwen-Image-Edit-2511-Object-Remover

Qwen-Image-Edit-2511-Object-Adder is an adapter (LoRA) developed for Qwen’s Qwen-Image-Edit-2511 image-to-image model. It is specifically designed for precise object addition to images.

⭐ Model: prithivMLmods/Qwen-Image-Edit-2511-Object-Adder

πŸ•ΉοΈ Collection: https://huggingface.co/collections/prithivMLmods/qwen-image-edit-object-manipulator
πŸ•ΉοΈ github: https://github.com/PRITHIVSAKTHIUR/Qwen-Image-Edit-Object-Manipulator

To learn more, visit the app page or the respective model pages.
Sri-Vigneshwar-DJΒ 
posted an update about 1 month ago
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Domain-specific reasoning is crucial when working with big-budget campaigns on Meta. That's why we've launched an experimental Chain-of-Thought (CoT) reasoning model for critical thinking, tailored to Meta's Andromeda algorithm-based campaign structuring and optimization.

Sri-Vigneshwar-DJ/hawky-ai-h1-mini-1b-experimental
Sri-Vigneshwar-DJΒ 
posted an update about 1 month ago
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The recent update to Meta's ad algorithm is very difficult to crack, and even the latest models struggle to keep up with it. To address this, we've created a small experimental dataset for fine-tuning models to better tackle Meta's Andromeda algorithm: Sri-Vigneshwar-DJ/hawky-ai-andromeda-dataset
Sri-Vigneshwar-DJΒ 
posted an update about 1 month ago