Imrozkhan007
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programming-tutor-gemma-2b

@@ -1,202 +1,74 @@
 ---
 base_model: google/gemma-2b-it
-library_name: peft
 ---
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.10.0

 ---
+language:
+- en
+license: apache-2.0
 base_model: google/gemma-2b-it
+tags:
+- peft
+- qlora
+- tutor
+- python
+- sql
+- dsa
 ---
+# AI Programming Tutor (Gemma 2B - Fine-Tuned)
+This model is a fine-tuned version of `google/gemma-2b-it` designed to act as an expert programming tutor. It was developed as part of an AI/ML Engineer assessment for Purple Merit Technologies.
+Rather than simply giving users the answer, this model is trained to teach concepts using a strict pedagogical structure.
+## 🧠 Pedagogical Structure
+The model enforces the following flow for every response:
+1. **Goal**: States what the student will learn.
+2. **Concept**: Explains the intuition behind the topic.
+3. **Worked Example**: Provides step-by-step code with comments.
+4. **Common Mistakes**: Highlights typical errors students make.
+5. **Checkpoint**: Asks a guiding question to verify understanding.
+It is also trained to gracefully redirect out-of-scope requests (e.g., calculus or diet advice) back to programming topics.
+## 🛠️ Training Details
+* **Base Model:** `google/gemma-2b-it`
+* **Technique:** QLoRA (4-bit NF4 quantization)
+* **Dataset:** A curated mix of synthetic tutoring conversations and a filtered Python/SQL subset of CodeAlpaca-20k.
+* **Infrastructure:** Trained on a single NVIDIA T4 GPU.
+## 📊 Evaluation
+The model was evaluated on a held-out set of 30 prompts (25 in-domain, 5 out-of-domain).
+* **Perplexity Improvement:** The fine-tuned model achieved a perplexity of **2.09**, a **95.1% improvement** over the base model's 43.05 on the same tutoring dataset.
+## 💻 How to Use (Inference)
+You can load this model using `transformers` and `peft`. *Note: Ensure you load the model in `float16` if running on a T4 GPU to prevent memory access errors.*
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
+import torch
+BASE = "google/gemma-2b-it"
+ADAPTER = "Imrozkhan007/programming-tutor-gemma-2b"
+# Use float16 for T4 compatibility
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type='nf4',
+    bnb_4bit_compute_dtype=torch.float16
+)
+tokenizer = AutoTokenizer.from_pretrained(BASE)
+base_model = AutoModelForCausalLM.from_pretrained(BASE, quantization_config=bnb_config, device_map={"": 0})
+model = PeftModel.from_pretrained(base_model, ADAPTER)
+model.eval()
+# Example Prompt
+prompt = "Explain binary search step by step"
+messages = [
+    {"role": "user", "content": f"You are an expert programming tutor...\n\nStudent Question: {prompt}"}
+]
+formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(formatted_prompt, return_tensors='pt').to(model.device)
+out = model.generate(**inputs, max_new_tokens=512, temperature=0.3)
+print(tokenizer.decode(out[0], skip_special_tokens=True))