language:
- en
tags:
- marketing
- reasoning
base_model:
- Qwen/Qwen3-8B
license: mit
library_name: transformers
pipeline_tag: text-generation
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
| Developed by | Marketeam |
|---|---|
| Base Model | Qwen/Qwen3-8B |
| Architecture | Decoder-only transformer |
| Parameters | 8B |
| Context Length | 32,768 tokens |
| Reasoning | Yes |
| Input | Text-only |
| Output | Text-only |
| Language | English |
| Knowledge Cutoff | December 2024 |
| License | Apache 2.0 |
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.
| Param | Value |
|---|---|
| Optimizer | adamw_torch_fused |
| Learning Rate | 4e-4 |
| Precision | bf16 |
| Gradient Accumulation | 64 steps |
| Epochs | 3 |
| Max Seq Length | 2500 |
| Scheduler | Cosine |
| Gradient Checkpointing | Enabled |
| FSDP | Full Shard + Transformer Layer Auto-Wrap |
| QLoRA | Enabled |
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 filteringabout– for tone and brand narrative modelingmarketing_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}, }