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shivs28/jee_nujan_math_expert
jee_nujan_math_expert is a machine learning model from shivs28. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
The Ultimate JEE Mathematics AI Tutor - Fine-tuned Specialist
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Updated Aug 10, 2025
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From the Hugging Face model README
The Ultimate JEE Mathematics AI Tutor - Fine-tuned Specialist
This is a fine-tuned version of JEE NUJAN Mix v2 Base specifically trained on JEE-style mathematics problems to excel at Indian competitive exam mathematics.
shivs28/jee_nujan_mix_v2_baseThis model excels at:
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "shivs28/jee_nujan_math_expert"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
# JEE problem format
jee_prompt = '''<|problem|>
Find the number of real solutions of the equation xยณ - 3xยฒ + 2x - 1 = 0 in the interval [0, 3].
<|solution|>'''
inputs = tokenizer(jee_prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_length=800,
temperature=0.1, # Low temperature for mathematical accuracy
do_sample=True,
pad_token_id=tokenizer.pad_token_id,
repetition_penalty=1.05
)
solution = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(solution)
complex_problem = '''<|problem|>
In triangle ABC, if a = 7, b = 8, c = 9, find:
1. The area of triangle ABC
2. The radius of the circumscribed circle
3. The radius of the inscribed circle
<|solution|>'''
# Generate comprehensive solution
inputs = tokenizer(complex_problem, return_tensors="pt")
outputs = model.generate(
**inputs,
max_length=1200,
temperature=0.05, # Very low for multi-step problems
top_p=0.95,
do_sample=True,
pad_token_id=tokenizer.pad_token_id
)
generation_config = {
"max_length": 800,
"temperature": 0.1,
"top_p": 0.95,
"do_sample": True,
"repetition_penalty": 1.05,
"pad_token_id": tokenizer.pad_token_id
}
advanced_config = {
"max_length": 1200, # Longer for complex solutions
"temperature": 0.05, # Very low for accuracy
"top_p": 0.9,
"do_sample": True,
"repetition_penalty": 1.1,
"pad_token_id": tokenizer.pad_token_id
}
<|problem|> and <|solution|> tagsIf you use this model in your research or educational content, please cite:
@model{jee_nujan_math_expert,
title={JEE NUJAN Math Expert: Fine-tuned Mathematics Specialist},
author={shivs28},
year={2025},
url={https://huggingface.co/shivs28/jee_nujan_math_expert}
}
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