🦅 Hawky AI H1 Mini 1B (Experimental)

📖 Model Description

Hawky AI H1 Mini 1B Experimental is a domain-specific fine-tuned version of Google's Gemma 3 1B Instruct, trained specifically for Meta Andromeda algorithm expertise and creative-first performance marketing strategy.

This experimental model was created to measure the effectiveness of domain-specific fine-tuning for performance marketing knowledge, particularly around Meta's latest advertising AI systems.

🎯 What This Model Knows

Domain	Capabilities
Meta Andromeda Algorithm	Deep understanding of Meta's AI-powered ad retrieval system, how it matches creatives to users, and optimization strategies
Creative Strategy	Best practices for ad creative diversity, video hooks, creative fatigue, and format selection
Campaign Structure	Guidance on Advantage+ Shopping, CBO vs ABO, budget allocation, and campaign consolidation
Performance Metrics	Interpretation of CPM, CPA, ROAS signals and diagnostic frameworks
Audience Strategy	Broad vs narrow targeting under Andromeda, audience signals, and creative-as-targeting

🧠 Chain-of-Thought Reasoning

The model was trained with Chain-of-Thought (CoT) reasoning, meaning it thinks through problems step-by-step before providing answers. Responses include <think> blocks showing the reasoning process.

📊 Training Details

Base Model

Property	Value
Model	Google Gemma 3 1B Instruct
Parameters	~1 Billion
Architecture	Decoder-only Transformer

Fine-tuning Configuration

Property	Value
Method	QLoRA (4-bit Quantization + LoRA)
LoRA Rank (r)	16
LoRA Alpha	32
LoRA Dropout	0.05
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Trainable Parameters	~0.5% of total

Training Hyperparameters

Property	Value
Epochs	3
Effective Batch Size	16
Learning Rate	2e-4
LR Scheduler	Cosine with warmup
Warmup Ratio	0.1
Precision	FP16
Max Sequence Length	1024
Optimizer	Paged AdamW 8-bit

Training Data

Property	Value
Dataset	Hawky Andromeda Dataset
Samples	1,000 Q&A pairs
Format	Chain-of-Thought with `<think>` reasoning blocks
Topics	Meta Andromeda, creative strategy, campaign structure, performance optimization

Training Results

Metric	Value
Final Training Loss	0.48485241896576353
Training Hardware	NVIDIA Tesla T4 (Google Colab)
Training Duration	~45-60 minutes

🚀 Quick Start

Installation

pip install transformers peft accelerate bitsandbytes torch

Load the Model

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
import torch

# Quantization config (optional, for memory efficiency)
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-3-1b-it",
    quantization_config=bnb_config,
    device_map="auto",
    torch_dtype=torch.float16,
)

# Load fine-tuned LoRA adapter
model = PeftModel.from_pretrained(
    base_model, 
    "Ashwath21/hawky-ai-h1-mini-1b-experimental"
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("Ashwath21/hawky-ai-h1-mini-1b-experimental")

Generate Responses

def ask_hawky(question):
    """Ask the Hawky AI model a performance marketing question."""
    
    prompt = f"<start_of_turn>user\n{question}<end_of_turn>\n<start_of_turn>model\n"
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=512,
            temperature=0.7,
            top_p=0.9,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id,
        )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.split("<start_of_turn>model")[-1].strip()

# Example
response = ask_hawky("How many creatives should I use in my Advantage+ campaign?")
print(response)

💬 Example Conversations

Creative Diversity

Q: How many distinct creative concepts should I use in my Advantage+ campaign?

<think>
This is a creative strategy question about Andromeda's diversity requirements.

Key considerations:
- Andromeda treats similar variations as one ad
- Genuine diversity means different hooks, angles, formats
- The algorithm needs variety to optimize effectively

Research indicates 8-15 genuinely distinct concepts is optimal.
</think>

You should aim for 8-15 genuinely distinct creative concepts per campaign. 
"Genuinely distinct" means different hooks, angles, formats, or visual styles - 
not just minor copy variations. Five product shots with slightly different text 
are treated as ONE ad by Andromeda. True diversity gives the algorithm more 
tools to match the right creative to each user.

Video Hooks

Q: What makes an effective video hook under Andromeda?

<think>
This is about the critical first 1-3 seconds of video ads.

Under Andromeda, hooks serve two purposes:
1. Stop the scroll (user engagement)
2. Signal audience relevance (algorithm matching)

Effective hooks share certain characteristics.
</think>

Effective video hooks capture attention in the first 1-3 seconds through:
- Pattern interrupts (unexpected visuals)
- Direct benefits ("This saved me $800")
- Compelling questions
- Social proof ("500,000 customers")
- Before/after reveals

Design for sound-off with text overlays. Test 3-5 different hooks per core 
video concept - hooks are the highest-impact creative element and fatigue 
fastest, so refresh weekly.

Performance Diagnosis

Q: My CPM is rising but conversions are stable. What does this indicate?

<think>
Let me analyze this performance pattern.

CPM rising + conversions stable means:
- Cost per impression is increasing
- But conversion volume hasn't dropped

This is actually a positive signal under Andromeda.
</think>

This is actually a healthy signal! Rising CPM with stable conversions indicates 
Andromeda is finding higher-value users who cost more to reach but convert at 
better rates. Your effective CPA is likely improving. 

Monitor your CPA and ROAS - if those metrics are stable or improving, don't 
intervene. The algorithm is optimizing for outcomes, not reach efficiency.

⚠️ Limitations

Limitation	Description
Experimental	This is a research model, not production-ready
Domain-Specific	Optimized for Meta advertising; may not perform well on general tasks
Knowledge Cutoff	Training data reflects best practices as of January 2026
Model Size	1B parameters limits complexity of reasoning
No Real-Time Data	Cannot access current campaign data or live metrics

🎯 Intended Use Cases

✅ Recommended Uses:

Research on domain-specific fine-tuning effectiveness
Learning about Meta Andromeda and creative strategy
Prototyping AI-assisted marketing tools
Educational demonstrations
Brainstorming creative strategies

❌ Not Recommended For:

Production deployment without further evaluation
Financial or business-critical decisions
Replacing human marketing expertise
Real-time campaign management

🏢 About Hawky.ai

Hawky.ai is an AI-powered creative intelligence platform for digital marketing, providing:

🔍 Automated Campaign Analysis - Deep insights into ad performance
🎨 Creative Performance Optimization - Data-driven creative recommendations
📊 Competitive Intelligence - Track and analyze competitor strategies
🤖 AI-Powered Insights - Actionable recommendations for performance marketers

Clients include: WPP, Madison, GroupM, and leading performance marketing agencies.

📚 Citation

@misc{hawky-ai-h1-mini-1b-2026,
  author = {Hawky.ai},
  title = {Hawky AI H1 Mini 1B Experimental: A Fine-tuned Model for Performance Marketing},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Ashwath21/hawky-ai-h1-mini-1b-experimental}}
}

📄 License

This model is released under the Gemma License.

The fine-tuning code and dataset methodology are released under Apache 2.0.

🙏 Acknowledgments

Google for the Gemma 3 1B base model
Hugging Face for the transformers and PEFT libraries
Meta for publicly sharing Andromeda algorithm insights

Built with ❤️ by Hawky.ai

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