DivyaRani/TallyAssistant

💼 TallyPrimeAssistant — Distilled GPT-2 Model

This is a distilled GPT-2-based conversational model fine-tuned on FAQs and navigation instructions from TallyPrime, a leading business accounting software used widely in India. The model is designed to help users get quick and accurate answers about using features in TallyPrime like GST, e-invoicing, payroll, and more.

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🧠 Model Summary

• Teacher Model: gpt2-large
• Student Model: distilgpt2
• Distillation Method: Knowledge Distillation using Hugging Face's Transformers and custom training pipeline
• Training Dataset: Internal dataset of Q&A pairs and system navigation steps from TallyPrime documentation and usage
• Format: safetensors (secure and fast)
• Tokenizer: Byte-Pair Encoding (BPE), same as GPT-2

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🚀 Example Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("Jayanthram/TallyPrimeAssistant")
tokenizer = AutoTokenizer.from_pretrained("Jayanthram/TallyPrimeAssistant")

prompt = "How to enable GST in Tally Prime?"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=60)
print(tokenizer.decode(output[0], skip_special_tokens=True))