Hawky-AI H1 4B Performance Marketing (hawky-ai-H1-4b-PM)

The first open-source LLM fine-tuned specifically for Performance Marketing expertise

Model Description

Hawky-AI H1 4B PM is a specialized language model fine-tuned for performance marketing tasks. Built on Google's Gemma 3 4B architecture, this model was trained using knowledge distillation from Claude Opus 4.5, capturing expert-level reasoning for paid media optimization, creative strategy, and campaign management.

This model is developed by Hawky.ai, the Creative Intelligence Platform for Performance Marketing, serving major agencies (WPP, Madison, GroupM) and brands (TVS Motors, Tanishq, Bajaj Finserv).

Key Features

• 🎯 Domain-Specialized: Purpose-built for performance marketing, not a general-purpose model
• 🧠 Expert Reasoning: Distilled from Claude Opus 4.5 with chain-of-thought marketing expertise
• ⚡ Efficient: 4B parameters - runs on consumer GPUs (8GB+ VRAM)
• 📊 Practical: Trained on real-world scenarios from Meta Ads, Google Ads, TikTok, and more
• 🔓 Open Source: Fully open weights for the marketing community

Intended Use

Primary Use Cases

Use Case	Description
Campaign Troubleshooting	Diagnose ROAS drops, CTR declines, high CPAs
Strategy Recommendations	Campaign structure, budget allocation, scaling strategies
Creative Analysis	Hook rate optimization, fatigue detection, A/B testing
Platform Expertise	Meta Ads, Google Ads, TikTok, Performance Max guidance
Measurement & Attribution	ROAS vs MER analysis, incrementality, LTV optimization

Target Users

• Performance Marketers
• Media Buyers
• Growth Teams
• Marketing Agencies
• D2C Brand Teams

Training Details

Base Model

• Architecture: Gemma 3 4B Instruct
• Parameters: 4 Billion
• Context Length: 8,192 tokens

Fine-tuning Approach

• Method: QLoRA (4-bit quantization with LoRA adapters)
• Teacher Model: Claude Opus 4.5 (Anthropic)
• Technique: Knowledge Distillation with Chain-of-Thought reasoning
• Training Data: Curated performance marketing scenarios and expert responses

Training Configuration

LoRA Config:
  r: 64
  lora_alpha: 128
  target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
  lora_dropout: 0.05

Training Args:
  epochs: 3
  learning_rate: 2e-4
  batch_size: 2 (effective: 16 with gradient accumulation)
  optimizer: paged_adamw_8bit
  scheduler: cosine
  precision: bf16

Training Data Domains

Domain	Topics Covered
Meta Ads	Campaign structure, ASC vs manual, bidding strategies, retargeting, scaling, creative fatigue
Google Ads	Quality Score, Performance Max, lead gen, Search optimization
Creative Strategy	Hook rates, A/B testing, funnel-stage creative, TikTok native
Measurement	Attribution (ROAS/MER), incrementality testing, LTV:CAC, UTM tracking
Strategy	Budget allocation, competitive intelligence, landing page optimization

Usage

Quick Start with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Sri-Vigneshwar-DJ/hawky-ai-H1-4b-PM"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example: Diagnose a campaign issue
prompt = """<bos><start_of_turn>user
My Meta ads CTR dropped from 2.1% to 0.8% over two weeks. Frequency is at 4.5. What's happening and what should I do?<end_of_turn>
<start_of_turn>model
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

With 4-bit Quantization (Low VRAM)

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True
)

model = AutoModelForCausalLM.from_pretrained(
    "Sri-Vigneshwar-DJ/hawky-ai-H1-4b-PM",
    quantization_config=bnb_config,
    device_map="auto"
)

Example Prompts

# Campaign Troubleshooting
"A D2C brand's ROAS dropped from 3.5x to 1.8x over a month. They're running TOF broad, MOF retargeting, and BOF cart abandonment campaigns at $2000/day. Frequency is 2.1, same creatives for 6 weeks. Diagnose and provide an action plan."

# Strategy Question
"Should I use Advantage+ Shopping Campaign or manual campaigns for a new e-commerce brand with limited pixel data?"

# Creative Analysis
"Explain hook rate optimization for video ads. How do I diagnose and fix poor hook rates?"

# Measurement
"What's the difference between ROAS, MER, and blended metrics? When should I use each?"

# Scaling
"What's the best way to scale a Meta campaign from $500/day to $5000/day without killing performance?"

Evaluation

Qualitative Assessment

The model was evaluated on held-out performance marketing scenarios:

Capability	Assessment
Platform Mechanics Accuracy	✅ Strong - Correct Meta/Google feature knowledge
Strategic Reasoning	✅ Strong - Logical diagnostic frameworks
Actionable Recommendations	✅ Strong - Specific, implementable advice
Chain-of-Thought Quality	✅ Strong - Clear step-by-step reasoning
Edge Case Handling	⚡ Good - Handles most scenarios well

Comparison to Base Model

Aspect	Base Gemma 3 4B	Hawky-AI H1 4B PM
Marketing terminology	Generic	Domain-specific
Platform mechanics	Surface-level	Expert-level detail
Diagnostic frameworks	None	Structured approaches
Recommendations	Generic advice	Specific, actionable

Limitations

• Knowledge Cutoff: Training data reflects marketing best practices as of early 2025. Platform features may have changed.
• Platform Specifics: Strongest on Meta and Google Ads; other platforms have less coverage.
• No Real-Time Data: Cannot access live campaign data or current market conditions.
• Not Financial Advice: Recommendations are educational; always validate with your own testing.
• English Only: Optimized for English language queries.

Ethical Considerations

This model is designed to assist performance marketers with strategic and tactical decisions. Users should:

• Validate recommendations against platform documentation
• Test strategies at small scale before full implementation
• Consider privacy and data protection when implementing targeting strategies
• Follow platform advertising policies and guidelines

Citation

@misc{hawky-ai-h1-4b-pm,
  author = {Sri Vigneshwar DJ and Hawky.ai Team},
  title = {Hawky-AI H1 4B Performance Marketing: A Domain-Specialized LLM for Paid Media},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Sri-Vigneshwar-DJ/hawky-ai-H1-4b-PM}}
}

About Hawky.ai

Hawky.ai is a Creative Intelligence Platform for Performance Marketing, helping brands and agencies transform creative guesswork into data-driven decisions. Our platform provides:

• Creative Analyzer: AI pattern recognition using performance data
• Competitor 360: Competitive intelligence and strategy analysis
• Trend Analyzer: Emerging signal tracking and winning trend prediction

We serve major agencies (WPP, Madison, GroupM) and brands (TVS Motors, Tanishq, Bajaj Finserv) across India and beyond.

Links

• 🌐 Website: hawky.ai
• 🤗 Hugging Face: Hawky-ai
• 💼 LinkedIn: Hawky.ai
• 📧 Contact: team@hawky.ai

Acknowledgments

• Anthropic for Claude Opus 4.5 used as the teacher model
• Google for the Gemma 3 base model architecture
• Hugging Face for the transformers and PEFT libraries
• The open-source ML community

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