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Model: r-karra/Gemma-2-9B-JEE-Socratic-Final
Source: Original Platform

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README.md

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+---
+base_model: r-karra/Gemma-2-9B-JEE-Socratic-Final
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- gemma2
+license: apache-2.0
+language:
+- en
+library_name: transformers
+---
+
+# Uploaded finetuned  model
+
+- **Developed by:** r-karra
+- **License:** apache-2.0
+- **Finetuned from model :** r-karra/Gemma-2-9B-JEE-Socratic-Final
+
+This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+# JEE Advanced Socratic Tutor Project
+
+> An AI-powered Socratic tutor for IIT-JEE Advanced aspirants, designed to encourage conceptual understanding through guided questioning rather than direct solutions.
+
+---
+
+## 🎯 Objective
+
+This project focuses on building an AI-driven educational assistant specifically for students preparing for the **IIT-JEE Advanced** examination.
+
+Unlike conventional AI tutors that directly provide answers, this model follows the **Socratic Method**, encouraging students to think critically by asking carefully designed questions that guide them toward the solution.
+
+The goal is to promote:
+
+- 🧠 Deep conceptual understanding
+- 📈 Self-paced learning
+- 🎓 Strong problem-solving skills
+- 🔍 Reasoning instead of memorization
+
+---
+
+# 📚 Pedagogical Approach
+
+The tutor acts as a **learning facilitator**, not merely an answer generator.
+
+For every student question, the model:
+
+1. Analyzes the student's query.
+2. Identifies the underlying JEE Advanced concept.
+3. Breaks the concept into smaller reasoning steps.
+4. Asks guiding questions instead of revealing the answer.
+5. Encourages students to derive the final result independently.
+
+### Example Topics
+
+- Thermodynamics
+- Rotational Dynamics
+- Electrostatics
+- Organic Chemistry
+- Coordinate Geometry
+- Calculus
+- Mechanics
+- Chemical Equilibrium
+- Physical Chemistry
+- Modern Physics
+
+---
+
+# 🛠️ Project Structure
+
+```
+.
+├── Dataset
+│   ├── JEE Advanced Papers
+│   ├── Chemistry Syllabus
+│   ├── Physics Notes
+│   └── Mathematics Problems
+│
+├── Data Processing
+│   ├── Cleaning
+│   ├── Formatting
+│   └── Socratic Prompt Creation
+│
+├── Fine-Tuning
+│   ├── Unsloth
+│   ├── Hugging Face Transformers
+│   └── Gemma-2-9B
+│
+├── Model
+│   └── Gemma-2-9B-JEE-Socratic-Final
+│
+└── Inference
+    └── Hugging Face Transformers
+```
+
+---
+
+# 📂 Dataset
+
+The dataset consists of carefully curated **IIT-JEE Advanced** educational resources.
+
+## Question Papers
+
+- JEE Advanced 2013
+- JEE Advanced 2015
+- JEE Advanced 2017
+- JEE Advanced 2018
+- JEE Advanced 2019
+- JEE Advanced 2022
+
+Subjects include:
+
+- Physics
+- Chemistry
+- Mathematics
+
+---
+
+## Support Material
+
+Additional curated educational resources include:
+
+- Rotational Motion
+- Gaseous State
+- Liquid State
+- Official Chemistry Syllabus (2026)
+
+These resources help improve conceptual understanding and expand the tutoring capability beyond previous examination questions.
+
+---
+
+# 🚀 Fine-Tuning Pipeline
+
+## 1. Environment Setup
+
+The project uses **Unsloth** for efficient GPU-based fine-tuning.
+
+Supported environments:
+
+- Kaggle
+- Google Colab
+
+Benefits:
+
+- Faster training
+- Lower GPU memory usage
+- Easy Hugging Face integration
+
+---
+
+## 2. Dataset Preparation
+
+The original JEE Advanced questions are transformed into conversational teaching examples.
+
+Each example teaches the model to:
+
+- Understand the student's intent
+- Avoid giving direct answers
+- Generate Socratic prompts
+- Encourage reasoning
+
+---
+
+## 3. Instruction Tuning
+
+The Gemma model is instruction-tuned to recognize pedagogical patterns.
+
+Instead of:
+
+> "Here is the answer."
+
+The model responds like:
+
+> "Which thermodynamic quantity determines spontaneity?"
+
+> "What happens to ΔG when entropy increases?"
+
+This encourages active learning.
+
+---
+
+## 4. Deployment
+
+After fine-tuning, the model is published on Hugging Face.
+
+**Model Repository**
+
+```
+r-karra/Gemma-2-9B-JEE-Socratic-Final
+```
+
+---
+
+# 💻 Inference Guide
+
+You **do not** need to convert the model to GGUF format.
+
+The model can be loaded directly using the Hugging Face `transformers` library.
+
+## Installation
+
+```bash
+pip install transformers accelerate torch
+```
+
+---
+
+## Python Example
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from google.colab import userdata
+
+# Configuration
+model_id = "r-karra/Gemma-2-9B-JEE-Socratic-Final"
+token = userdata.get("HF_TOKEN")
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load tokenizer
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(
+    "google/gemma-2-9b-it",
+    token=token
+)
+
+# Load model
+print("Loading model...")
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    token=token,
+    device_map="auto",
+    torch_dtype=torch.float16
+).to(device)
+
+def ask_tutor(question):
+    prompt = (
+        "System: You are an expert IIT-JEE Advanced tutor "
+        "using the Socratic method.\n"
+        f"User: {question}\n"
+        "Model:"
+    )
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=500
+        )
+
+    response = tokenizer.decode(
+        outputs[0][inputs["input_ids"].shape[1]:],
+        skip_special_tokens=True
+    )
+
+    return response
+
+print(
+    ask_tutor(
+        "How can I determine if a reaction is spontaneous using Gibbs free energy?"
+    )
+)
+```
+
+---
+
+# 🧪 Example Interaction
+
+### Student
+
+> How can I determine whether a reaction is spontaneous using Gibbs free energy?
+
+### Tutor
+
+> Before thinking about spontaneity, can you recall the mathematical expression relating Gibbs free energy to enthalpy and entropy?
+
+> What does a negative value of ΔG imply about the direction of a process?
+
+> How would increasing temperature affect the entropy contribution?
+
+---
+
+# 🎓 Educational Philosophy
+
+Rather than replacing teachers, this project aims to become an intelligent learning companion that:
+
+- Encourages curiosity
+- Promotes conceptual reasoning
+- Builds confidence
+- Helps students think like problem solvers
+
+The emphasis is on **learning the process**, not memorizing the answer.
+
+---
+
+# 🧰 Technology Stack
+
+- Python
+- PyTorch
+- Hugging Face Transformers
+- Hugging Face Hub
+- Unsloth
+- Google Colab
+- Kaggle
+- Gemma-2-9B
+
+---
+
+# 📦 Model
+
+**Model Name**
+
+```
+Gemma-2-9B-JEE-Socratic-Final
+```
+
+**Hugging Face Repository**
+
+```
+r-karra/Gemma-2-9B-JEE-Socratic-Final
+```
+
+---
+
+# 📄 License
+
+All IIT-JEE Advanced question papers remain the intellectual property of their respective examination authorities.
+
+This repository is intended solely for:
+
+- Educational purposes
+- Research
+- AI-assisted learning
+
+No copyright over the original examination content is claimed.
+
+---
+
+# 🙏 Acknowledgements
+
+Special thanks to:
+
+- Google DeepMind
+- Hugging Face
+- Unsloth
+- Kaggle
+- Google Colab
+- The Full-Stack GenAI Bootcamp
+
+for providing the tools and ecosystem that made this project possible.
+
+---
+
+# ⭐ Citation
+
+If you find this project useful, please consider starring the repository and citing it in your educational or research work.
+
+```
+@misc{jee-socratic-tutor,
+  author = {Rajesh Kumar Karra},
+  title = {Gemma-2-9B JEE Advanced Socratic Tutor},
+  year = {2026},
+  publisher = {Hugging Face},
+  model = {r-karra/Gemma-2-9B-JEE-Socratic-Final}
+}
+```

chat_template.jinja

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+{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{'<start_of_turn>user
+' + messages[0]['content'] | trim + ' ' + messages[1]['content'] | trim + '<end_of_turn>
+'}}{% set messages = messages[2:] %}{% endif %}{% for message in messages %}{% if message['role'] == 'user' %}{{'<start_of_turn>user
+' + message['content'] | trim + '<end_of_turn>
+'}}{% elif message['role'] == 'assistant' %}{{'<start_of_turn>model
+' + message['content'] | trim + '<end_of_turn>
+' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<start_of_turn>model
+' }}{% endif %}

config.json

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+{
+    "architectures": [
+        "Gemma2ForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "attn_logit_softcapping": 50.0,
+    "bos_token_id": 2,
+    "cache_implementation": "hybrid",
+    "torch_dtype": "float16",
+    "eos_token_id": 1,
+    "final_logit_softcapping": 30.0,
+    "head_dim": 256,
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_activation": "gelu_pytorch_tanh",
+    "hidden_size": 3584,
+    "initializer_range": 0.02,
+    "intermediate_size": 14336,
+    "layer_types": [
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention"
+    ],
+    "max_position_embeddings": 8192,
+    "model_type": "gemma2",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 42,
+    "num_key_value_heads": 8,
+    "pad_token_id": 0,
+    "query_pre_attn_scalar": 256,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+        "rope_theta": 10000.0,
+        "rope_type": "default"
+    },
+    "sliding_window": 4096,
+    "sliding_window_size": 4096,
+    "tie_word_embeddings": true,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.6.9",
+    "use_bidirectional_attention": null,
+    "use_cache": true,
+    "vocab_size": 256000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": [
+    1
+  ],
+  "max_length": 8192,
+  "pad_token_id": 0,
+  "transformers_version": "5.5.0"
+}

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