🏙️ 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!
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
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
🦅 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:
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.
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
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:
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?
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
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.
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.
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.
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.
