Model Card for Qwen3-8B Agent Marketing Campaign
Model Details
Model Description
This model is a fine-tuned version of Qwen3-8B on a custom dataset of Agent Marketing Campaigns.
The dataset simulates an AI Agent System for Marketing Campaign Design, where agents represent different roles in a marketing agency such as:
- Strategy Agent – analyzes consumer insight, market context, and campaign direction.
- Creative Concept Agent – generates big ideas, key messages, campaign themes, and storytelling hooks.
- Channel Planner Agent – proposes media mix, influencer strategies, challenges, gamification, and activities.
- KPI & Performance Agent – defines measurable KPIs such as awareness, engagement, lead generation, and conversion.
This fine-tuning process enables the base model to better understand marketing workflows and generate structured campaign concepts.
- Developed by: komsan suwanjarern
- Model type: Causal Language Model
- Language(s): English & Thai
- License: Apache 2.0
- Finetuned from model: Qwen/Qwen3-8B
Model Sources
- Repository: [More Information Needed]
- Paper (Qwen base): Qwen Technical Report
Uses
Direct Use
- Generating marketing campaign proposals
- Brainstorming creative ideas for advertisements
- Simulating multi-agent collaboration in marketing workflows
Downstream Use
- Can be further fine-tuned for specific industries (e.g., automotive, F&B, finance)
- Adaptable for customer-facing campaign assistants
Out-of-Scope Use
- Not intended for general-purpose chit-chat
- Not suitable for financial/legal decision-making without human review
Bias, Risks, and Limitations
- The model is specialized in marketing campaign text; outputs outside this domain may be unreliable.
- May reflect biases present in the dataset, e.g., overemphasis on certain campaign strategies.
- Generated ideas should be validated by human marketing experts before real-world use.
Recommendations
- Use the model as a co-pilot for brainstorming, not as a sole campaign planner.
- Always cross-check KPIs and strategies against real-world market data.
How to Get Started with the Model
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "komsan/Qwen3-8B-Agent-Marketing-Campaign"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
prompt = "Generate a campaign concept for launching a new electric car in Thailand."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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