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ShahDhruv/
distillgpt2_accountant

Name: distillgpt2_accountant
Availability: InStock

text-generation-inference

endpoints_compatible

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🚀 Fine-Tuned GPT-2 for Question Answering

🌟 Model Overview

Welcome to the Fine-Tuned GPT-2 QA Model! This model is a specialized version of GPT-2, fine-tuned on a high-quality question-answering dataset to provide accurate and concise answers to a wide range of questions. Whether you're building a chatbot, a knowledge base, or an interactive QA system, this model is designed to deliver reliable responses with the power of the Hugging Face transformers library.

🔑 Key Features:

📚 Fine-tuned for question-answering tasks
🤖 Based on the GPT-2 architecture
⚡ Supports GPU acceleration for faster inference
🌐 Hosted on Hugging Face Hub for easy access

🛠️ Usage

Get started with this model in just a few lines of code! Below is an example of how to load the model and tokenizer using the transformers library and perform question answering.

python

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ShahDhruv/distillgpt2_accountant")
model = AutoModelForCausalLM.from_pretrained("ShahDhruv/distillgpt2_accountant")

question = "What is double-entry bookkeeping?"

input_text = f"Question: {question}\nAnswer: "
inputs = tokenizer(input_text, return_tensors="pt")

# Generate answer
outputs = model.generate(
    **inputs,
    max_length=200,
    num_return_sequences=1,
    do_sample=True,
    temperature=0.4,
    top_k=40,
    top_p=0.95,
    repetition_penalty=1.2,
    min_length = 50,
    length_penalty = 1.2
)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(answer)