--- 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. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65e468008629cedec7980db6/ovQ-yH2xvmRkkxMmEnrhh.png) **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](https://www.marketeam.ai/) | |---------------|------------| | 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 ```python from transformers import pipeline pipe = pipeline("text-generation", model="marketeam/Qwen-Marketing") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages) ``` ### Load model directly ```python 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`](https://huggingface.co/datasets/marketeam/offtopic) – for irrelevant content filtering - [`about`](https://huggingface.co/datasets/marketeam/about) – for tone and brand narrative modeling - [`marketing_user_prompts`](https://huggingface.co/datasets/marketeam/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](https://www.linkedin.com/in/sahar-millis/) [Coby Benveniste](https://www.linkedin.com/in/coby-benveniste/)