supportlm-qwen2.5-3b/README.md

language, tags, base_model, pipeline_tag

language	tags	base_model	pipeline_tag
hi ta en	customer-support hinglish tanglish fine-tuned qwen2.5 qlora	Qwen/Qwen2.5-3B-Instruct	text-generation

SupportLM — Hinglish/Tanglish Customer Support Model

A fine-tuned Qwen2.5-3B model for Indian e-commerce customer support in code-switched language (Hinglish and Tanglish).

What it does

Given a customer support message in Hinglish or Tanglish, the model:

1. Classifies the issue type into one of 8 categories
2. Extracts the order ID if present in the message
3. Generates an empathetic response in the same language the customer used

Benchmark Results

Evaluated on 159 held-out test examples never seen during training.

Metric	GPT-4o-mini	SFT Model	DPO Model
Classification accuracy	74.8%	79.9%	78.0%
Order ID extraction	98.7%	99.4%	99.4%
Language match rate	96.2%	99.4%	100.0%

SFT model chosen for deployment — best classification accuracy.

Issue Types

order_status, return_request, payment_issue, delivery_complaint, product_defect, cancellation_request, wrong_item, discount_query

Training Details

• Base model: Qwen/Qwen2.5-3B-Instruct
• Training data: 1277 synthetic Hinglish/Tanglish examples
• Training method: QLoRA (r=16, alpha=32) + DPO (beta=0.1)
• Hardware: Google Colab T4 GPU (free tier)
• Training time: ~40 minutes total (SFT + DPO)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import json

model = AutoModelForCausalLM.from_pretrained("GuruVarshini/supportlm-qwen2.5-3b")
tokenizer = AutoTokenizer.from_pretrained("GuruVarshini/supportlm-qwen2.5-3b")

message = "bhai mere order ka kya hua yaar 3 din ho gaye"

messages = [
    {"role": "system", "content": "You are a customer support agent for an Indian e-commerce platform. Given a customer message, respond with a JSON object containing issue_type, order_id, and response."},
    {"role": "user", "content": message}
]

input_ids = tokenizer.apply_chat_template(
    messages,
    return_tensors="pt",
    add_generation_prompt=True
)
output = model.generate(input_ids, max_new_tokens=200)
print(tokenizer.decode(output[0][input_ids.shape[1]:], skip_special_tokens=True))