Hap4114/boostpad-llm-v1

BoostPad LLM v1 — LoRA Adapter

Fine-tuned Qwen 2.5 7B Instruct for AI-powered Indian ad copy generation

This is a LoRA adapter trained on Indian small business advertising data. It generates emotionally engaging, hyper-local, platform-appropriate ad copy for Meta and Google campaigns targeting Indian audiences.

Model Details

• Base Model: Qwen/Qwen2.5-7B-Instruct
• Adapter Type: LoRA (Low-Rank Adaptation)
• Training Framework: PEFT 0.12.0
• Quantization: 4-bit (NF4) for efficient inference
• Domain: Advertising copywriting for Indian SMBs
• Languages: English, Hinglish (Hindi-English code-mixing)

Capabilities

This adapter enables 5 specialized functions:

1. Generate Ad Copy — Creates headline, description, CTA for Meta/Google ads
2. Score Variations — Rates existing ad copy 0-10 with reasoning
3. Evaluate Live Ads — Real-time kill/scale/continue decisions based on ROAS, CPC, CTR
4. Weekly Digests — Plain-language campaign summaries for business owners
5. Fix Underperforming Ads — Rewrites low-scoring ads with improvements

Training Data

• Dataset: Proprietary Indian ad copy corpus
• Business Types: Restaurants, gyms, dental clinics, tiffin services, sweet shops, coaching centers
• Platforms: Meta Feed, Meta Reels, Google Search
• Geographic Focus: Major Indian cities (Mumbai, Bangalore, Hyderabad, Pune, Indore, etc.)

Use Cases

• Generate platform-specific ad copy (respects character limits, emoji rules)
• Score A/B test variations before spending budget
• Automate ad performance decisions in real-time
• Provide non-technical campaign insights to business owners

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-7B-Instruct",
    load_in_4bit=True,
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "YOUR_USERNAME/boostpad-llm-v1")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")

# Generate ad copy
prompt = """Generate ad for a family restaurant in Mumbai targeting families with kids on Meta Reels. Use Hinglish. Return JSON with headline, description, cta."""

messages = [
    {"role": "system", "content": "You are BoostPad's expert Indian ad copywriter..."},
    {"role": "user", "content": prompt}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))

Evaluation

Tested on 16 scenarios across all 5 capabilities:

• Pass Rate: 16/16 (100%)
• Avg Latency: 5.47s per call (Kaggle T4 GPU)
• JSON Reliability: 100% parseable outputs

See results_test.pdf in training repo for full test results.

Limitations

• Trained specifically for Indian market — may not generalize to other regions
• Optimized for small business categories in training set
• Requires 4-bit quantization for consumer GPUs (full precision needs 28GB VRAM)
• No built-in content moderation — assumes responsible use

License

MIT

Framework Versions

• PEFT 0.12.0
• Transformers 4.46.0
• PyTorch 2.4.1