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
π 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
π― 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
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