Create README.md

README.md

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+# 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."