Update README.md

README.md

@@ -36,6 +36,52 @@ This model was fine-tuned as a learning project to demonstrate the full workflow
 | **Hardware** | Google Colab (NVIDIA T4, 16 GB VRAM) |
 | **Training time** | ~33 minutes |
 
+## What Is This — A Model or an Adapter?
+
+This repository contains a **LoRA adapter**, not a standalone model. Understanding the difference matters for how you load and use it.
+
+### The Two Artifacts
+
+| | **Base Model** | **LoRA Adapter (this repo)** |
+|---|---|---|
+| **What it is** | The full pretrained neural network | A small set of trained weights that modify the base |
+| **Size** | ~3 GB | ~74 MB |
+| **Who made it** | The Qwen team | Me (SathishKumar89) |
+| **Repo** | `Qwen/Qwen2.5-Coder-1.5B-Instruct` | `SathishKumar89/my-python-coder` |
+| **Contains** | All model weights, tokenizer, config | Only adapter weights + config + tokenizer copy |
+| **Loadable alone?** | ✅ Yes | ❌ No — needs the base model |
+
+### Why This Design?
+
+Instead of retraining all ~1.5 billion parameters of the base model, **LoRA (Low-Rank Adaptation)** freezes the base model and only trains a tiny number of new parameters. This gives several advantages:
+
+- **Tiny file size** — 74 MB vs. ~3 GB (a ~40× reduction)
+- **Fast training** — minutes to hours instead of days
+- **Runs on modest hardware** — a free Google Colab T4 GPU is enough
+- **Easy to swap** — you can keep the same base model and load different adapters for different tasks
+
+### How to Load It Correctly
+
+Because this repo is an adapter, you must load **two** things — the base model first, then the adapter on top:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+
+# Step 1: Load the base model
+base = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2.5-Coder-1.5B-Instruct",
+    dtype=torch.float16,
+    device_map="auto",
+)
+
+# Step 2: Attach the LoRA adapter
+model = PeftModel.from_pretrained(base, "SathishKumar89/my-python-coder")
+
+# Step 3: Load the tokenizer (included in this repo)
+tokenizer = AutoTokenizer.from_pretrained("SathishKumar89/my-python-coder")
+
 ## Prompt Format
 
 This model was trained with the following instruction format. Using the same format at inference time will give the best results: