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README.md

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+---
+language: en
+license: cc-by-4.0
+tags:
+  - gemma
+  - rag
+  - pdf
+  - question-answering
+---
+
+# RAG-DSE-PAST-PAPER-2012-ICT
+
+Gemma 3 4B model fine-tuned for answering questions about DSE ICT 2012 past paper using RAG
+
+## Model Description
+
+This model is based on Gemma 3 4B and has been optimized for answering questions about PDF documents using Retrieval Augmented Generation (RAG).
+
+## Usage
+
+You can use this model with the Transformers library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("khysam2022/RAG-DSE-PAST-PAPER-2012-ICT")
+model = AutoModelForCausalLM.from_pretrained("khysam2022/RAG-DSE-PAST-PAPER-2012-ICT")
+
+# Example usage with RAG
+prompt = "What is the main topic of this document?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## Training Details
+
+This model was adapted from Google's Gemma 3 4B model and fine-tuned for PDF question-answering.
+
+## Limitations and Biases
+
+This model inherits the limitations and biases of the base Gemma 3 model.

config.json

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+{
+  "model_type": "gemma",
+  "architectures": [
+    "GemmaForCausalLM"
+  ],
+  "name": "RAG-DSE-PAST-PAPER-2012-ICT",
+  "description": "Gemma 3 4B model fine-tuned for answering questions about DSE ICT 2012 past paper using RAG"
+}