AI & ML interests

Open science and open source

Recent Activity

Sri-Vigneshwar-DJ 
posted an update 1 day 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
Sri-Vigneshwar-DJ 
posted an update 6 days 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-DJ 
posted an update 8 days 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-DJ 
posted an update 12 days ago
Sri-Vigneshwar-DJ 
posted an update 3 months ago
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353
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
  • 6 replies
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Sri-Vigneshwar-DJ 
posted an update 3 months ago
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4437
🚀 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 3 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
jeffboudier 
posted an update 5 months ago
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Quick 30s demo of the new Hub > Azure AI integration to deploy HF models in your own Azure account. Now with Py and CLI!

GG @alvarobartt @kramp @pagezyhf
jeffboudier 
posted an update 7 months ago
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AMD summer hackathons are here!
A chance to get hands-on with MI300X GPUs and accelerate models.
🇫🇷 Paris - Station F - July 5-6
🇮🇳 Mumbai - July 12-13
🇮🇳 Bengaluru - July 19-20

Hugging Face and GPU Mode will be on site and on July 6 in Paris @ror will share lessons learned while building new kernels to accelerate Llama 3.1 405B on ROCm

Register to Paris event: https://lu.ma/fmvdjmur?tk=KeAbiP
All dates: https://lu.ma/calendar/cal-3sxhD5FdxWsMDIz
jeffboudier 
posted an update 7 months ago
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Today we launched Training Cluster as a Service, to make the new DGX Cloud Lepton supercloud easily accessible to AI researchers.

Hugging Face will collaborate with NVIDIA to provision and set up GPU training clusters to make them available for the duration of training runs.

Hugging Face organizations can sign up here: https://huggingface.co/training-cluster
jeffboudier 
posted an update 8 months ago
jeffboudier 
posted an update 8 months ago
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Wrapping up a week of shipping and announcements with Dell Enterprise Hub now featuring AI Applications, on-device models for AI PCs, a new CLI and Python SDK... all you need for building AI on premises!

Blog post has all the details: https://huggingface.co/blog/dell-ai-applications
jeffboudier 
posted an update 8 months ago
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2613
Transcribing 1 hour of audio for less than $0.01 🤯

@mfuntowicz cooked with 8x faster Whisper speech recognition - whisper-large-v3-turbo transcribes at 100x real time on a $0.80/hr L4 GPU!

How they did it: https://huggingface.co/blog/fast-whisper-endpoints

1-click deploy with HF Inference Endpoints: https://endpoints.huggingface.co/new?repository=openai%2Fwhisper-large-v3-turbo&vendor=aws&region=us-east&accelerator=gpu&instance_id=aws-us-east-1-nvidia-l4-x1&task=automatic-speech-recognition&no_suggested_compute=true
jeffboudier 
posted an update 8 months ago
jeffboudier 
posted an update 9 months ago
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Llama4 is out and Scout is already on the Dell Enterprise Hub to deploy on Dell systems 👉 dell.huggingface.co
jeffboudier 
posted an update 9 months ago
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Enterprise orgs now enable serverless Inference Providers for all members
- includes $2 free usage per org member (e.g. an Enterprise org with 1,000 members share $2,000 free credit each month)
- admins can set a monthly spend limit for the entire org
- works today with Together, fal, Novita, Cerebras and HF Inference.

Here's the doc to bill Inference Providers usage to your org: https://huggingface.co/docs/inference-providers/pricing#organization-billing
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emre 
posted an update 10 months ago
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3779
having trouble with auto train
hello there this is the first time i am testing auto train with a 1.8k SFT dataset. Howevery i am not quite sure the training is going smooth. Logs seem quite confusing, token did not match can not auth, generates confusing train splits, do you know how i can check my running job properly?
what is being used for training as data?
any ideas?
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umarigan 
posted an update 12 months ago
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** Extracting Reasoning Prompts with DeepSeek-R1: A Step Towards Better AI Reasoning **

Hi everyone! 👋

I’m excited to share a small but impactful project I’ve been working on, where I extracted **reasoning prompts** using the **DeepSeek-R1 model**. Reasoning prompts are a powerful way to understand how AI models arrive at their answers, and they can be used to train smaller, more efficient models to generate reasoning. Let me walk you through the process and explain why this is important.

---

#### **The Code: Extracting Reasoning Prompts**

Here’s the code I used to extract reasoning prompts from the openaccess-ai-collective/oo-gpt4-filtered dataset:

from tqdm import tqdm
import time

reasoning_data = []

for example in tqdm(ds, desc="answering"):
    try:
        response = client.chat.completions.create(
            model='deepseek-reasoner',  # Using DeepSeek-R1 for reasoning
            messages=[
                {"role": "system", "content": example['system_prompt']},
                {"role": "user", "content": example['question']},
            ],
            stream=False,
            max_tokens=4096,
            temperature=0.7,
        )
        
        answer = response.choices[0].message.content
        reasoning = response.choices[0].message.reasoning_content

        reasonng_example = {
            "id": example['id'],
            "question": example['question'],
            'answer': answer,
            'reasoning': reasoning,
        }

        reasoning_data.append(reasonng_example)
    except Exception as e:
        print(f"Error translating example: {e}")
        time.sleep(3)  # Wait for 3 seconds before continuing
        continue  # Skip the current example and move to the next one

data: umarigan/deepseek-r1-reasoning-prompts