### 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

```python
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
```python
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