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---
language: en
license: apache-2.0
tags:
- gpt2
- distilgpt2
- knowledge-distillation
- tally
- accounting
- conversational
- business
- transformer
- language-model
- safetensors
model_type: gpt2
library_name: transformers
datasets: custom
pipeline_tag: text-generation
base_model:
- openai-community/gpt2-large
---
# 💼 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.
---
## 🧠 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
---
## 🚀 Example Usage
```python
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)) |