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+# 🚀 Speed Optimized Summarization with DistilBART
+
+The BART model is quite large (~1.6GB) and slow. I optimized it with a much faster, lighter model and better performance settings.
+
+---
+
+## 🚀 Major Speed Optimizations Applied
+
+### 1. Faster Model
+- **Switched from** `facebook/bart-large-cnn` (**~1.6GB**)  
+- **To** `sshleifer/distilbart-cnn-12-6` (**~400MB**)  
+- 🔥 **6x smaller model size** = Much faster loading and inference
+
+### 2. Processing Optimizations
+- **Smaller chunks:** 512 words vs 900 (faster processing)
+- **Limited chunks:** Max 5 chunks processed (prevents hanging on huge docs)
+- **Faster tokenization:** Word count instead of full tokenization for chunking
+- **Reduced beam search:** 2 beams instead of 4 (2x faster)
+
+### 3. Smart Summarization
+- **Shorter summaries:** Reduced max lengths across all modes
+- **Skip final summary:** For documents with ≤2 chunks (saves time)
+- **Early stopping:** Enabled for faster convergence
+- **Progress tracking:** Shows which chunk is being processed
+
+### 4. Memory & Performance
+- **Float16 precision:** Used when GPU is available (faster inference)
+- **Optimized pipeline:** Better model loading with fallback
+- **`optimum` library added:** For additional speed improvements
+
+---
+
+## ⚡ Expected Speed Improvements
+
+| Task              | Before               | After                        |
+|-------------------|----------------------|------------------------------|
+| Model loading     | ~30+ seconds         | ~10 seconds                  |
+| PDF processing    | Minutes              | ~5–15 seconds                |
+| Memory usage      | ~1.6GB               | ~400MB                       |
+| Overall speed     | Slow                 | 🚀 5–10x faster              |
+
+---
+
+## 🧬 What is DistilBART?
+
+**DistilBART** is a **compressed version of the BART model** designed to be **lighter and faster** while retaining most of BART’s performance. It’s the result of **model distillation**, where a smaller model (the *student*) learns from a larger one (the *teacher*), in this case, `facebook/bart-large`.
+
+| Attribute        | Description                                                         |
+|------------------|---------------------------------------------------------------------|
+| **Full Name**    | Distilled BART                                                      |
+| **Base Model**   | `facebook/bart-large`                                               |
+| **Distilled By** | Hugging Face 🤗                                                     |
+| **Purpose**      | Faster inference and smaller footprint for tasks like summarization |
+| **Architecture** | Encoder-decoder Transformer, like BART, but with fewer layers       |
+
+---
+
+## ⚙️ Key Differences: BART vs DistilBART
+
+| Feature        | BART (Large) | DistilBART            |
+|----------------|--------------|------------------------|
+| Encoder Layers | 12           | 6                      |
+| Decoder Layers | 12           | 6                      |
+| Parameters     | ~406M        | ~222M                  |
+| Model Size     | ~1.6GB       | ~400MB (~55% smaller)  |
+| Speed          | Slower       | ~2x faster             |
+| Performance    | Very high    | Slight drop (~1–2%)    |
+
+---
+
+## 🎯 Use Cases
+
+- ✅ **Text Summarization** (primary use case)
+- 🌐 **Translation** (basic use)
+- ⚡ Ideal for **edge devices** or **real-time systems** where speed & size matter
+
+---
+
+## 🧪 Example: Summarization with DistilBART
+
+You can easily use DistilBART with Hugging Face Transformers:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load pretrained DistilBART model
+tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-cnn-12-6")
+model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-cnn-12-6")
+
+# Input text
+ARTICLE = "The Indian Space Research Organisation (ISRO) launched a new satellite today from the Satish Dhawan Space Centre..."
+
+# Tokenize and summarize
+inputs = tokenizer([ARTICLE], max_length=1024, return_tensors="pt", truncation=True)
+summary_ids = model.generate(
+    inputs["input_ids"],
+    max_length=150,
+    min_length=40,
+    length_penalty=2.0,
+    num_beams=4,
+    early_stopping=True
+)
+
+print(tokenizer.decode(summary_ids[0], skip_special_tokens=True))
+````
+
+---
+
+## 📦 Available Variants
+
+| Model Name                        | Task                         | Description                              |
+| --------------------------------- | ---------------------------- | ---------------------------------------- |
+| `sshleifer/distilbart-cnn-12-6`   | Summarization                | Distilled from `facebook/bart-large-cnn` |
+| `philschmid/distilbart-xsum-12-6` | Summarization (XSUM dataset) | Short, abstractive summaries             |
+
+🔎 [Find more on Hugging Face Model Hub](https://huggingface.co/models?search=distilbart)
+
+---
+
+## 📘 Summary
+
+* 🧠 **DistilBART** is a distilled, faster version of **BART**
+* 🧩 Ideal for summarization tasks with lower memory and latency requirements
+* 💡 Trained using **knowledge distillation** from `facebook/bart-large`
+* ⚙️ Works well in apps needing faster performance without significant loss in quality
+
+---
+
+✅ **Try it now — it should be significantly faster!** 🏃‍♂️💨
+
+```
+
+
+
+
+Thank You
+```