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Model: GuruVarshini/supportlm-qwen2.5-3b
Source: Original Platform

README.md

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+---
+language:
+- hi
+- ta
+- en
+tags:
+- customer-support
+- hinglish
+- tanglish
+- fine-tuned
+- qwen2.5
+- qlora
+base_model: Qwen/Qwen2.5-3B-Instruct
+pipeline_tag: 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
+
+```python
+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))