Markethinking

QWEN3-Marketing: Reasoning-LLM for Marketing

Markethinking is a domain-specific large language model, adapted from Qwen/Qwen3-8B through finetuning on over 10 billion tokens of curated marketing data.
It is the first in our line of models to inherit and preserve reasoning capabilities for domain-specific applications.
This early checkpoint is released for research, experimentation, and continued development by the community.

Qwen-Marketing is a reasoning-optimized language model fine-tuned for marketing tasks.

Unlike general-purpose LLMs, Qwen-Marketing specializes in understanding marketing contexts, strategies, and tone. It was trained on proprietary data combined with curated open datasets to ensure performance across real-world business scenarios.

Fine-tuned from Qwen1.5 (or relevant base model) Trained on real marketing tasks, prompts, and responses

Use Case & Applications

Qwen-Marketing is designed for marketers, brand strategists, product managers, and marketing analysts.

Example use cases:

• Writing product descriptions in brand voice
• Generating campaign ideas or messaging variants
• Summarizing customer feedback
• Answering marketing-related questions with context-specific reasoning

Model Description

Markethinking blends the powerful general capabilities of Qwen3-8B with deep domain knowledge from the marketing world. It supports:

• Reasoning-driven content generation

• Domain-specific language modeling in marketing

• Long-context handling (up to 32,768 tokens natively)

This checkpoint is instruction-tuned and should be used for research purposes. Use in high-stakes or production settings is not advised.

Model Details

Intended Use

Markethinking is intended for:

• Domain-specific Q&A in marketing contexts

• Strategic idea development (customer personas, campaign planning)

• Marketing content generation (product copy, email sequences, landing pages, ...)

⚠️ This early checkpoint isn't aligned for production. Use in controlled environments only.

Training Details

Markethinking was adapted from Qwen3-8B through marketing-specific reasoning tasks.

We used syntetic data and real-world data. Grounding the model around information and tasks around:

• Ad campaigns

• Email campaigns

• Meetings, Podcasts

• Landing pages, Newsletters

• Blogs, Books, Websites, Articles

• Social Media Posts, Press Releases, Trends

• ...

~5% general corpus was retained to avoid catastrophic forgetting.

Optimization techniques:

• Fine-tuning via supervised instruction-following
• Prompt-based format with marketing-specific structure
• Negative prompt formats to teach safety and relevance

Training

We used AWS SageMaker (p4de.24xlarge), with 4× NVIDIA A100 (80GB) GPUs.

How to use

Use a pipeline as a high-level helper

from transformers import pipeline

pipe = pipeline("text-generation", model="marketeam/Qwen-Marketing")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("marketeam/Qwen-Marketing")
model = AutoModelForCausalLM.from_pretrained("marketeam/Qwen-Marketing")

The model was trained on proprietary marketing data, as well as open datasets curated by our team:

• offtopic – for irrelevant content filtering
• about – for tone and brand narrative modeling
• marketing_user_prompts – for supervised prompt training

Safety & Bias

To reduce hallucinations and improve safety:

• Negative prompts were included during training (showing the model what not to do)
• Fine-tuning was applied on real-world, domain-specific data to ensure appropriate outputs in context

Performance & Benchmarking

While there is no formal academic benchmark yet, our internal tests ("Marketeam Benchmarketing") show:

• Higher relevance and brand tone accuracy
• Lower hallucination rate on product-focused queries
• Better performance than GPT-4o & DeepSeek & Qwen & LLama on marketing prompts

Deployment & Integration

Qwen-Marketing is available as a Hugging Face model and can be deployed:

• Via API.
• In your own marketing GenAI pipelines.
• Embedded in CRM, analytics, or content tools.

Clone it, run it locally, or use inference widgets to test.

Prompt Example

User Prompt:
Write a product launch email for a new AI-based skincare analyzer. Keep it confident, science-driven, and friendly.

Qwen-Marketing Output:

Subject: Meet Your Skin’s New Best Friend 🧪✨  
Body: Discover personalized skincare backed by real science. Our AI Skin Analyzer scans your skin in seconds and gives you the exact ingredients it craves. Say goodbye to guesswork — and hello to glowing confidence.

License & Attribution

• License: Apache 2.0
• Base model: Qwen/Qwen3-8B

Citation @misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388}, }

Contributers

Sahar Millis Coby Benveniste