# NanoGPT-Abstract-Generator

## Overview
`NanoGPT-Abstract-Generator` is a smaller, more efficient version of the GPT-2 architecture fine-tuned for generating concise, high-quality abstracts from a provided input sentence or a short document prompt. It is designed for low-latency inference on general-purpose text generation tasks.

This model is a strong choice for applications requiring quick, coherent, and contextually relevant text snippets without the massive computational overhead of larger models like GPT-3 or full-sized GPT-2 variants.

## Model Architecture
The model is based on the **GPT-2** decoder-only architecture, but significantly scaled down for efficiency (hence 'NanoGPT').
* **Base Model:** GPT-2 Decoder
* **Task:** Causal Language Modeling (`GPT2LMHeadModel`)
* **Size Reduction:** $n_{layer}=8$ (vs. 12 for GPT-2 Base), $n_{embd}=768$.
* **Parameters:** Approximately 100 Million parameters (highly optimized).
* **Context Window (`n_ctx`):** 512 tokens.
* **Tokenizer:** GPT-2 Tokenizer (BPE vocabulary, 50257 tokens).

## Intended Use
* **Abstractive Summarization:** Generating short, descriptive summaries (abstracts) for scientific papers, articles, or blog posts based on the first few sentences.
* **Creative Prompting:** Generating short stories, poem stanzas, or marketing copy from a seed phrase.
* **Chatbot Responses:** Providing fluent, contextualized, short-form responses in a conversational agent.
* **Rapid Prototyping:** Serving as a fast, accessible, and resource-friendly generator for local testing and development.

## Limitations
* **Coherence over Long Sequences:** Due to its reduced size and context window (512 tokens), coherence may degrade rapidly for generations exceeding 200 tokens.
* **Factual Accuracy (Hallucination):** Like all auto-regressive language models, it can generate text that sounds convincing but is factually incorrect or nonsensical.
* **Safety/Bias:** The model inherits biases present in its pre-training data. Care must be taken in deployment to filter or mitigate harmful outputs.

## Example Code (PyTorch/Transformers Pipeline)

```python
from transformers import pipeline

model_name = "NLP/NanoGPT-Abstract-Generator"
# The 'text-generation' pipeline handles the model and tokenizer automatically
generator = pipeline("text-generation", model=model_name)

prompt = "The recent advancements in quantum computing have shifted the paradigm"

# Generate text with specific decoding parameters
output = generator(
    prompt,
    max_length=50,
    num_return_sequences=1,
    temperature=0.7,      # Controls randomness
    top_k=50,             # Sampling top K tokens
    do_sample=True,       # Enable sampling
    pad_token_id=generator.tokenizer.eos_token_id # Set padding to EOS token
)

print(f"Prompt: {prompt}\n--- Abstract ---\n{output[0]['generated_text']}")

# Example Output:
# "The recent advancements in quantum computing have shifted the paradigm of theoretical cryptography, making several historically secure algorithms vulnerable to polynomial-time attacks. Researchers are now prioritizing the development of post-quantum cryptography protocols."