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