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asigalov61 
posted an update 12 days ago
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3366
🔥🎵 ➕ 🖹 🔥Check out my new large-scale MIDI + Lyrics dataset!!!

asigalov61/Lyrics-MIDI-Dataset

~179k MIDIs with corresponding Lyrics to play with!!! 🤗

If you liked the dataset, please ❤️

Any feedback and/or suggestions are also appreciated 🤗
Sri-Vigneshwar-DJ 
posted an update 2 months ago
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Do you think domain-specific embedding fine-tuners are needed?
I've been working with embeddings for marketing use cases and noticed something: most embeddings don't get marketing concepts very well. They're trained in general-purpose ways.
The Issue I'm Seeing
When I search marketing content with general embeddings:

"organic growth" returns farming articles
"conversion funnel" matches industrial equipment
"brand lift" doesn't connect to campaign effectiveness
Marketing jargon like CAC, ROAS, CTR aren't properly understood

My Question
Do you think domain-specific embeddings are needed for marketing?
Some thoughts:

Marketing has its own vocabulary and concept relationships
General models trained on Wikipedia/web crawl miss these nuances
But is fine-tuning worth the effort vs just using more retrieval tricks?

Quick Example
I fine-tuned all-mpnet-base-v2 on ~1000 marketing concept pairs and saw 15-20% better retrieval accuracy. But I'm curious:

Has anyone else tried this for marketing or other domains?
When do you think domain-specific embeddings are actually necessary vs overkill?
Are there better approaches I'm missing?

https://huggingface.co/blog/Sri-Vigneshwar-DJ/why-your-marketing-rag-system-needs-domain-specifi
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Sri-Vigneshwar-DJ 
posted an update 2 months ago
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🚀 Exciting News! We've released a Performance Marketing Expert Dataset from Hawky.ai [www.hawky.ai] Hawky-ai


This dataset empowers AI models with cutting-edge strategies for Meta, Google Ads, and TikTok campaigns. It includes:
1. Multi-platform strategies for e-commerce, DTC, B2B, and more
2. Creative optimization and audience targeting insights
3. ROI-driven recommendations based on 2025 best practices

Sri-Vigneshwar-DJ/Performance-Marketing-Data
Sri-Vigneshwar-DJ 
posted an update 2 months ago
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🚀 Qwen3-Omni for Marketing: A Game-Changer

Just wanted to share something exciting I've been exploring—Qwen3-Omni and how it's transforming marketing workflows.

What makes it special? At Hawky.ai we are started experimenting with Qwen3 recently for Analysis and Optimization.

Unlike traditional tools that look at text, images, or audio separately, Qwen3-Omni analyzes everything together. It handles 119 languages, processes 40-minute audio sequences, and understands both images and videos—all at once.

The cool part? It's 2-3x faster than similar models thanks to its MoE architecture.

Real applications I'm seeing:
Ad Analysis: It scores video ads by combining visual elements, audio tone, and text—giving 25% better CTR predictions than single-mode tools.
Campaign Localization: Drop in one ad, get 10 localized versions with native voiceovers in under a minute. Perfect for testing across markets.

Market Research: Feed it competitor content, podcasts, or UGC videos. It extracts actionable insights like "3-second hooks boost retention by 15%" and saves about 70% of analysis time.

Quality Checks: Automatically catches lip-sync errors and audio-visual mismatches.

Full technical breakdown: https://huggingface.co/blog/Sri-Vigneshwar-DJ/hawky-aiqwen3-omni-advanced-architecture-and-marke

Has anyone else been experimenting with multimodal models for marketing? Would love to hear what you're building!

#MultimodalAI #MarTech #OpenSource
asigalov61 
posted an update 4 months ago
1024m 
authored 2 papers 4 months ago
asigalov61 
posted an update 5 months ago
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Hey guys!

I wanted to invite all of you who are interested in symbolic music AI to check out my Orpheus Music Transformer

IMHO the model turned out very well and it plays very well too.

I would really appreciate any feedback and likes. It helps a lot.

Here are the links for your convenience:

1) Orpheus Music Transformer main demo space asigalov61/Orpheus-Music-Transformer

2) Orpheus Music Transformer Collection asigalov61/orpheus-music-transformer-685c3c8e59ed1414c02bb8cd

3) Orpheus Music Transformer Models Repo asigalov61/Orpheus-Music-Transformer

I hope you will enjoy it :)

Sincerely,

Alex
1024m 
authored a paper 7 months ago
Felguk 
posted an update 7 months ago
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2221
Where gone streamlit in huggingface?
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1024m 
authored 3 papers 8 months ago
not-lain 
posted an update 9 months ago
not-lain 
posted an update 10 months ago
not-lain 
posted an update 11 months ago
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1820
we now have more than 2000 public AI models using ModelHubMixin🤗
not-lain 
posted an update 11 months ago
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4165
Published a new blogpost 📖
In this blogpost I have gone through the transformers' architecture emphasizing how shapes propagate throughout each layer.
🔗 https://huggingface.co/blog/not-lain/tensor-dims
some interesting takeaways :
Sri-Vigneshwar-DJ 
posted an update 11 months ago
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882
Checkout phi-4 from Microsoft, dropped a day ago... If you ❤️ the Phi series, then here is the GGUF - Sri-Vigneshwar-DJ/phi-4-GGUF. phi-4 is a 14B highly efficient open LLM that beats much larger models at math and reasoning - check out evaluations on the Open LLM.

Technical paper - https://arxiv.org/pdf/2412.08905 ; The Data Synthesis approach is interesting
Sri-Vigneshwar-DJ 
posted an update 11 months ago
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Just sharing a thought: I started using DeepSeek V3 a lot, and an idea struck me about agents "orchestrating during inference" on a test-time compute model like DeepSeek V3 or the O1 series.

Agents (Instruction + Function Calls + Memory) execute during inference, and based on the output decision, a decision is made to scale the time to reason or perform other tasks.
Sri-Vigneshwar-DJ 
posted an update 11 months ago
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Combining smolagents with Anthropic’s best practices simplifies building powerful AI agents:

1. Code-Based Agents: Write actions as Python code, reducing steps by 30%.
2. Prompt Chaining: Break tasks into sequential subtasks with validation gates.
3. Routing: Classify inputs and direct them to specialized handlers.
4. Fallback: Handle tasks even if classification fails.

https://huggingface.co/blog/Sri-Vigneshwar-DJ/building-effective-agents-with-anthropics-best-pra