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

python

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))

komsan/Qwen-Campaign-Concept

Model Card for Qwen3-8B Agent Marketing Campaign

Model Details

Model Description