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Nymbo 
posted an update 2 days ago
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1728
Anthropic gave me six months of Claude Max 20x through the Claude for Open Source program, granted based on my Hugging Face work. Thank you
Anthropic for supporting open source.

So far I've been pointing it at Markdown Minimap, an Obsidian plugin that adds a scrollable IDE-style minimap to your notes. This week I've been clearing a backlog of user-reported issues on it, with Claude often handling them end to end.

https://github.com/Nymbo/Markdown-Minimap — issues and PRs welcome.
Nymbo 
posted an update 18 days ago
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5627
Introducing Inflect-v2, two exceptionally small, open-weight English TTS models at just 3.9M and 9.3M parameters. Both generate speech multiple times faster than real-time on CPU. Despite their size, Inflect-v2 delivers quality that is competitive with much larger lightweight TTS systems, including KittenTTS, Piper, and Supertonic-3.

CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.

See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2

Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
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eienmojiki 
posted an update about 2 months ago
Aurelien-Morgan 
posted an update 3 months ago
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1107
@retrain-pipelines v0.2.0 is out !
I'm at Station F at My booth with GOSIM Paris 2026 today & tomorrow.
Come meet me for a live in-person demo and a chat !
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Sri-Vigneshwar-DJ 
posted an update 3 months ago
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![Feather DB LongMemEval Results]( Hawky-ai/longmemeval-results)

We ran Feather DB v0.8.0 on LongMemEval (ICLR 2025) — 500 questions across real multi-session conversations, up to 115K tokens each.

**Score: 0.693** · GPT-4o full-context baseline: 0.640
Full 500-question run with Gemini-Flash: **$2.40**

Per-axis breakdown:
→ Info-extraction: **0.942**
→ Knowledge-update: **0.714**
→ Multi-session: **0.606**
→ Temporal: **0.477** ← the hard one, Phase 9 addresses this

Architecture: Hybrid BM25+dense · adaptive temporal decay · embedded (no server) · p50 = 0.19ms · MIT

pip install feather-db

Raw results + audit JSONs: Hawky-ai/longmemeval-results
Aurelien-Morgan 
posted an update 4 months ago
Nymbo 
posted an update 5 months ago
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7903
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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efecelik 
posted an update 6 months ago
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The moment we've been waiting for — ACE-Step dropped their new model: Ace-Step 1.5 🎉
🔗 ACE-Step/Ace-Step1.5
And the best part? It's released under the MIT license.
We've already started integrating it into our project. Let's go 🚀
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Sri-Vigneshwar-DJ 
posted an update 6 months 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:

🔍 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
Sri-Vigneshwar-DJ 
posted an update 7 months ago
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🏙️ 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
turhancan97 
authored a paper 7 months ago
efecelik 
posted an update 7 months ago
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🎮 Introducing: Paper Popularity Game

Think you know which AI papers go viral? Test your instincts!
I built a little game where you try to guess the popularity of AI research papers from the Hugging Face Daily Papers feed.

How it works:
You'll see two papers side by side—read the titles, check the abstracts, and pick which one you think got more upvotes from the HF community.

It's a great way to discover trending AI research while having fun.
Tests your intuition about what the ML community finds interesting.

Try it out:
efecelik/paper-popularity-game
Would love to hear your high scores and feedback!
efecelik 
posted an update 7 months ago
efecelik 
posted an update 7 months ago
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Having multiple perspectives helps me create more diverse, innovative projects but without deep mastery in one area, I never feel truly satisfied.

What's the better investment: going deep in one field, or staying broad across many?
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Sri-Vigneshwar-DJ 
posted an update 7 months 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: 🔗 Sri-Vigneshwar-DJ/hawky-ai-Qwen3-0.6B-Marketing-MoT
efecelik 
posted an update 7 months ago
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My First MCP Server: DataView
Browse HuggingFace datasets directly from your AI assistant.
-Search & filter datasets
-View rows & stats
-SQL queries & Parquet export
efecelik/dataview-mcp
efecelik 
posted an update 7 months ago
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We Built a Music App with ACE-Step – Looking for Feedback

Hey everyone,

We've been building AceSteps – a platform where anyone can create music using the ACE-Step model ( ACE-Step/ACE-Step-v1-3.5B). You can mint your tracks as NFTs, tokenize them into 100,000 fractional shares, and trade them on Uniswap V4. When your song gets popular, token holders earn from ad revenue automatically. It's a Farcaster Mini-App on Base Network.

But we want to make it better, and we'd love your input:

What's the one feature that would make you actually use an AI music tool regularly?
Andd any suggestions on how we can make this model better? Actually sharing here for this question. 🤗

Any feedback, ideas, or critiques are welcome.
🔗 https://docs.acesteps.com/
🔗 https://docs.acesteps.com/pitch-deck.html
🔗 https://farcaster.xyz/?launchFrameUrl=https%3A%2F%2Fwww.acesteps.com%2F
🔗 https://www.acesteps.com
efecelik 
posted an update 7 months ago
Sri-Vigneshwar-DJ 
posted an update 7 months ago
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Introducing Hawky-AI H1 4B PM: The First Open-Source LLM for Performance Marketing 🎯

Hey HF Community! 👋

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:
📱 Meta Ads (campaign structure, bidding, scaling, creative fatigue)
🔍 Google Ads (Quality Score, Performance Max, lead gen)
📊 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" — 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

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

Try it and let us know what you think! 🚀
Sri-Vigneshwar-DJ 
posted an update 7 months ago
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🦅 Introducing Hawky AI H1 Mini 4B: A Domain-Specific Model for Performance Marketing

Hey HuggingFace community! 👋

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