Finetuned Academic Question-Answering Model for ICSE Physics (Class 9 & 10)

This specialized large language model (LLM) is finetuned to provide precise and accurate answers to ICSE Physics questions for Classes 9 and 10. It is designed to assist students, educators, and content creators in understanding and exploring fundamental physics concepts aligned with the ICSE curriculum.

Key Features

📚 Curriculum-Specific Training

Focused exclusively on ICSE Class 9 and 10 Physics topics, such as:

Motion Work, Energy, and Power Heat and Thermodynamics Electricity and Magnetism Light (Reflection and Refraction) Sound Modern Physics

🎯 Accurate and Concise Answers

Trained to deliver curriculum-aligned, student-friendly responses.

Contextual Understanding

Handles specific and multi-part questions effectively, ensuring relevance and precision.

Example Usage python Copy code from transformers import pipeline

Load the model from Hugging Face

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "pitangent-ds/academic_phy",
    load_in_4bit=True,  # Quantized model
    device_map="auto",
    # llm_int8_enable_fp32_cpu_offload=True
)
tokenizer = AutoTokenizer.from_pretrained("pitangent-ds/academic_phy")

Perform inference

text = "What are units ?"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs)
decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(decoded_output)

Training Details

Dataset: Curated ICSE Physics content for Classes 9 and 10 textbooks Loss Function: Cross-entropy loss Final Training Loss: 0.88 Training Framework: PyTorch, Hugging Face Transformers

pitangent-ds
/

academic_phy