Qwen3.5-4B CRM Specialist

This model is a fine-tuned version of unsloth/Qwen3.5-4B, meticulously optimized for Customer Relationship Management (CRM) and Customer Behavior Analysis.

It has been trained on the yothinS/Customer_Behavior_Analysis dataset to decode customer insights, predict consumer behaviors, and deliver high-impact, professional CRM recommendations.

🚀 Key Capabilities

CRM Chatbot: Engages customers with context-aware, professional, and personalized interactions.
CRM Agent: Automates routine customer management tasks and streamlines workflows.
CRM Analyzer: Evaluates customer data to uncover deep insights and predict future behavioral trends.

Model Description

Developed by: Me
Model type: Causal Language Model
Language(s) (NLP): Thai, English
License: Apache-2.0
Finetuned from model: unsloth/Qwen3.5-4B

Intended Uses & Limitations

This model is designed to act as a CRM Specialist AI. It can be integrated into CRM platforms, customer support workflows, and marketing analytics tools.

Recommended Tasks:

Analyzing customer feedback and sentiment.
Providing data-driven insights based on purchase history and behavior.
Generating personalized customer retention responses and email marketing copy.
Assisting support agents with context-aware recommendations.

Limitations:

The model's outputs depend heavily on the quality and context provided in the prompt.
It should not be used as the sole decision-maker for critical financial or legal customer disputes without human oversight.

Training Details

Training Dataset

The model was fine-tuned on the yothinS/Customer_Behavior_Analysis dataset, which contains structured or unstructured data regarding customer interactions, patterns, and behaviors.

Training Procedure

Optimized using Unsloth for faster training and lower memory consumption.

Framework: PyTorch, Hugging Face Transformers, Unsloth
Method: LoRA (Low-Rank Adaptation) / QLoRA

How to Use

You can load and run this model using the following Python snippet (requires unsloth or transformers):

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "yothinS/Qwen3.5-4B-CRM"

model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)

prompt = "Analyze the following customer behavior and suggest a retention strategy: [Insert Customer Data]"

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))