---
license: apache-2.0
language: en
pipeline_tag: text-generation
tags:
- gemma3
- multitask
- qlora
- customer-service
- fashion
- complaint-analysis
---



![image/png](https://cdn-uploads.huggingface.co/production/uploads/66ad89f2685fc4c1c2397398/JJJCAzMpxOmvDxsKnItYv.png)

# Fine-tuned Gemma-3 4B for Multi-Task Customer Service Complaint Analysis

This repository contains a `google/gemma-3-4b-it` model that has been fine-tuned using QLoRA for a comprehensive, multi-task customer service application. The model was trained on a synthetic dataset of fashion-related customer complaints to perform both causal language modeling (generating a structured JSON response) and several classification tasks simultaneously via specialized classification heads.

This model is designed to act as an "agent" that can ingest a customer complaint and its surrounding context, then output a complete analysis covering multiple business-critical dimensions.



![image/png](https://cdn-uploads.huggingface.co/production/uploads/66ad89f2685fc4c1c2397398/N5zvIkKPO107nKtMT2SS5.png)

## Model Capabilities


![image/png](https://cdn-uploads.huggingface.co/production/uploads/66ad89f2685fc4c1c2397398/ARLiJ9Vw9x6lfqsQ4p-4Q.png)

This model is trained to perform 8 classification tasks simultaneously based on the input complaint:
1.  **`is_actionable`**: Determines if the complaint requires a direct action (boolean).
2.  **`complaint_category`**: Classifies the complaint into one of 11 categories (e.g., "Sizing Issue", "Damaged Item").
3.  **`decision_recommendation`**: Recommends a course of action from 11 options (e.g., "Full_Refund_With_Return").
4.  **`info_complete`**: Assesses if all necessary information is present to resolve the issue (boolean).
5.  **`tone`**: Classifies the required tone for a formal response (e.g., "Empathetic_Standard").
6.  **`refund_percentage`**: Suggests a specific refund percentage (0-100).
7.  **`sentiment`**: Detects the customer's sentiment (e.g., "negative", "very_negative").
8.  **`aggression`**: Detects the level of aggression in the customer's message.


![image/png](https://cdn-uploads.huggingface.co/production/uploads/66ad89f2685fc4c1c2397398/JbaH_J37D4KC5wXnFkZfA.png)

## How to Use (for Classification)

This model uses custom classification heads and requires the `GemmaComplaintResolver` wrapper class from the training notebook to be used correctly.

```python
import torch
from transformers import AutoTokenizer, AutoConfig
from peft import PeftModel
from huggingface_hub import hf_hub_download
import os

# You must have the GemmaComplaintResolver class definition in your environment.
# Assuming it's defined as it was in the training notebook...

# --- Configuration ---
repo_id = "ShovalBenjer/gemma-3-4b-fashion-multitask_A4000_v7"
device = "cuda" if torch.cuda.is_available() else "cpu"

# --- 1. Load Tokenizer and Model Config ---
tokenizer = AutoTokenizer.from_pretrained(repo_id)
config = AutoConfig.from_pretrained("google/gemma-3-4b-it", trust_remote_code=True)

# Define the label structure the model was trained with
num_labels_dict = {
    "is_actionable": 2, "complaint_category": 11, "decision_recommendation": 11,
    "info_complete": 2, "tone": 7, "refund_percentage": 13,
    "sentiment": 6, "aggression": 5
}

# --- 2. Instantiate the Custom Model Wrapper ---
# IMPORTANT: This assumes the GemmaComplaintResolver class is defined.
model = GemmaComplaintResolver(
    base_model_name_or_path="google/gemma-3-4b-it",
    num_labels_dict=num_labels_dict,
    model_config_for_base_loading=config,
)

# --- 3. Load the Fine-Tuned Weights ---
# a) Load the classification head weights
weights_path = hf_hub_download(repo_id=repo_id, filename="classification_heads.pth")
model.load_state_dict(torch.load(weights_path, map_location='cpu'), strict=False)


# b) Apply the LoRA adapter
model = PeftModel.from_pretrained(model, repo_id)

# --- 4. Prepare for Inference ---
# Cast to appropriate dtype and move to device
compute_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
model.to(dtype=compute_dtype).to(device).eval()

# --- 5. Run Inference ---
customer_complaint = "The t-shirt I ordered arrived with a huge hole in it! I'm very angry and want a full refund immediately."
# The model expects the full prompt structure used during training.
# In this notebook, the pre-processed column was 'text_for_lm'.
# The structure inside 'text_for_lm' was: <start_of_turn>user\n{complaint_details}<end_of_turn>\n<start_of_turn>model\n{json_output}<eos>
# For inference on just the classification heads, we only need the prompt part.
input_text = f"<start_of_turn>user\\n{customer_complaint}<end_of_turn>\\n<start_of_turn>model\\n"

inputs = tokenizer(input_text, return_tensors="pt").to(device)

with torch.no_grad():
    outputs = model(**inputs)

# --- 6. Decode a Prediction ---
# Example: Get the predicted complaint category
category_logits = outputs['logits_complaint_category']
predicted_category_id = torch.argmax(category_logits, dim=-1).item()
complaint_categories = ["Sizing Issue", "Damaged Item", "Not as Described", "Shipping Problem", "Policy Inquiry", "Late Delivery", "Wrong Item Received", "Quality Issue", "Return Process Issue", "Other", "N/A"]
predicted_category = complaint_categories[predicted_category_id]

print(f"Customer Complaint: '{customer_complaint}'")
print(f"Predicted Complaint Category: {predicted_category}")