AdaptiveMath API & Inference Endpoint

Supported Topics

Table with columns: Category, Examples
Category	Examples
Arithmetic	Addition, Subtraction, Multiplication, Division
Algebra	Linear Equations, Quadratic Equations, Polynomial Factorization
Statistics	Mean, Median, Mode, Variance, Standard Deviation,etc.

Fundamental Mathematical Formulae

Table with columns: Formula, Equation
Formula	Equation
Quadratic Formula	$x = 2 a - b \pm b 2 - 4 a c $
Pythagorean Theorem	$a 2 + b 2 = c 2$
Slope Formula	$m = x 2 - x 1 y 2 - y$ ,etc.

Example Usage

Example 1

Input

text
Solve:
2x+7=19

Output

text
Subtract 7 from both sides.
2x=12
Divide both sides by 2.
x=6

Example 2

Input

text
Find the derivative of
f(x)=x²+5x+2

Output

text
Using the power rule,
f'(x)=2x+5

Example 3

Input

text
Find the area of a circle with radius 7.

Output

text
A=\pi r^2
A=49\pi
≈153.94


y
10 |                          ●
 9 |                      ●
 8 |                  ●
 7 |              ●
 6 |          ●
 5 |      ●
 4 |   ●
 3 | ●
   +--------------------------------→ x
     1   2   3   4   5   6   7   8

text
Frequency

10 |                    █
 9 |                    █
 8 |               █    █
 7 |               █    █
 6 |          █    █    █
 5 |     █    █    █    █
 4 |     █    █    █    █
 3 | █   █    █    █    █
 2 | █   █    █    █    █
 1 | █   █    █    █    █
   +----------------------------
      A    B    C    D    E

Inference with Transformers

python
from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM
model_name = "Charlie890/Adaptive-Math-Reasoner-Mixtral-8x7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
prompt = "Solve: 3x + 5 = 20"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=256
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Applications

This model is designed for a wide variety of mathematical and scientific applications.

AI Tutors
STEM Education
Mathematics Chatbots
Scientific Computing
Homework Assistance
Research
Educational Software and more.

Limitations

Although highly capable, the model has several limitations.

May occasionally make arithmetic mistakes on extremely long calculations.
Complex symbolic manipulations should always be verified.
Does not replace professional Computer Algebra Systems (CAS) such as Mathematica or Maple.
Performance depends heavily on prompt quality.
Mathematical proofs may require human verification.
May hallucinate unsupported mathematical identities if prompted incorrectly.
Numerical approximations can accumulate rounding errors.
High-stakes scientific applications should always be independently validated.
Performance may decrease on very large expressions or lengthy derivations.

Future Improvements

Future releases may include:

Better symbolic reasoning
Olympiad-level mathematics
Interactive tutoring
Diagram understanding
Mathematical OCR support
Scientific equation solving and more.

Acknowledgements

Special thanks to:

Adaption Labs
Mixtral Team
Hugging Face and Kaggle
Transformers Community
Open-source AI Community
Mathematical research contributors

Citation

If you use this model in your research, please cite:

bibtex
@misc{adaptive_math_reasoner,
  title        = {Adaptive Math Reasoner},
  author       = {Edidiong Charlie},
  year         = {2026},
  publisher    = {Hugging Face},
  model        = {Mixtral-8x7B-Instruct},
  framework    = {Adaption Labs + AutoScientist},
  license      = {Apache-2.0},
  url          = {https://huggingface.co/Charlie890/Adaptive-Math-Reasoner-Mixtral-8x7B}
}

License

This project is released under the Apache License 2.0.

You are free to:

✅ Use commercially
✅ Modify
✅ Distribute
✅ Private use
✅ Research
✅ Education

Subject to the terms and conditions of the Apache License 2.0.

Performance Goals

The model is optimized to:

Produce step-by-step mathematical reasoning.
Solve algebraic equations accurately.
Handle advanced calculus problems.
Solve geometry and trigonometry questions.
Perform statistical computations.
Explain mathematical concepts clearly.
Generate clean LaTeX mathematical expressions.
Assist students, educators, engineers, and researchers.

Example Prompt Ideas

text
Solve:
3x² + 7x - 10 = 0

text
Differentiate:

f(x)=sin(x)e^x

text
Integrate:

∫x²cos(x)dx

text
Find the determinant of

|2 4|
|1 5|

text
Prove the Binomial Theorem.

text
Explain Bayes' Theorem with a practical example.

text
Find the eigenvalues of the matrix:

[[4,2],
 [1,3]]

Mission

The goal of Adaptive Math Reasoner is to provide accurate, explainable, and accessible mathematical reasoning powered by modern Large Language Models.

The project aims to make advanced mathematics easier to learn, explore, and apply across education, engineering, science, finance, and research.

Version

Version: 1.0.0

Base Model: Mixtral-8x7B-Instruct

Framework: Transformers

License: Apache-2.0

Primary Domain: Mathematical Reasoning

Author: Edidiong Charlie

Thank You

Thank you for using Adaptive Math Reasoner.

We hope this model helps students, educators, researchers, developers, engineers, and the open-source community solve mathematical problems more effectively.

Happy building with AI and Mathematics! 🚀

Supported Topics

Table with columns: Category, Examples
Category	Examples
Arithmetic	Addition, Subtraction, Multiplication, Division
Algebra	Linear Equations, Quadratic Equations, Polynomial Factorization
Statistics	Mean, Median, Mode, Variance, Standard Deviation,etc.

Fundamental Mathematical Formulae

Table with columns: Formula, Equation
Formula	Equation
Quadratic Formula	$x = 2 a - b \pm b 2 - 4 a c $
Pythagorean Theorem	$a 2 + b 2 = c 2$
Slope Formula	$m = x 2 - x 1 y 2 - y$ ,etc.

Example Usage

Example 1

Input

text
Solve:
2x+7=19

Output

text
Subtract 7 from both sides.
2x=12
Divide both sides by 2.
x=6

Example 2

Input

text
Find the derivative of
f(x)=x²+5x+2

Output

text
Using the power rule,
f'(x)=2x+5

Example 3

Input

text
Find the area of a circle with radius 7.

Output

text
A=\pi r^2
A=49\pi
≈153.94


y
10 |                          ●
 9 |                      ●
 8 |                  ●
 7 |              ●
 6 |          ●
 5 |      ●
 4 |   ●
 3 | ●
   +--------------------------------→ x
     1   2   3   4   5   6   7   8

text
Frequency

10 |                    █
 9 |                    █
 8 |               █    █
 7 |               █    █
 6 |          █    █    █
 5 |     █    █    █    █
 4 |     █    █    █    █
 3 | █   █    █    █    █
 2 | █   █    █    █    █
 1 | █   █    █    █    █
   +----------------------------
      A    B    C    D    E

Inference with Transformers

python
from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM
model_name = "Charlie890/Adaptive-Math-Reasoner-Mixtral-8x7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
prompt = "Solve: 3x + 5 = 20"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=256
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Applications

This model is designed for a wide variety of mathematical and scientific applications.

AI Tutors
STEM Education
Mathematics Chatbots
Scientific Computing
Homework Assistance
Research
Educational Software and more.

Limitations

Although highly capable, the model has several limitations.

May occasionally make arithmetic mistakes on extremely long calculations.
Complex symbolic manipulations should always be verified.
Does not replace professional Computer Algebra Systems (CAS) such as Mathematica or Maple.
Performance depends heavily on prompt quality.
Mathematical proofs may require human verification.
May hallucinate unsupported mathematical identities if prompted incorrectly.
Numerical approximations can accumulate rounding errors.
High-stakes scientific applications should always be independently validated.
Performance may decrease on very large expressions or lengthy derivations.

Future Improvements

Future releases may include:

Better symbolic reasoning
Olympiad-level mathematics
Interactive tutoring
Diagram understanding
Mathematical OCR support
Scientific equation solving and more.

Acknowledgements

Special thanks to:

Adaption Labs
Mixtral Team
Hugging Face and Kaggle
Transformers Community
Open-source AI Community
Mathematical research contributors

Citation

If you use this model in your research, please cite:

bibtex
@misc{adaptive_math_reasoner,
  title        = {Adaptive Math Reasoner},
  author       = {Edidiong Charlie},
  year         = {2026},
  publisher    = {Hugging Face},
  model        = {Mixtral-8x7B-Instruct},
  framework    = {Adaption Labs + AutoScientist},
  license      = {Apache-2.0},
  url          = {https://huggingface.co/Charlie890/Adaptive-Math-Reasoner-Mixtral-8x7B}
}

License

This project is released under the Apache License 2.0.

You are free to:

✅ Use commercially
✅ Modify
✅ Distribute
✅ Private use
✅ Research
✅ Education

Subject to the terms and conditions of the Apache License 2.0.

Performance Goals

The model is optimized to:

Produce step-by-step mathematical reasoning.
Solve algebraic equations accurately.
Handle advanced calculus problems.
Solve geometry and trigonometry questions.
Perform statistical computations.
Explain mathematical concepts clearly.
Generate clean LaTeX mathematical expressions.
Assist students, educators, engineers, and researchers.

Example Prompt Ideas

text
Solve:
3x² + 7x - 10 = 0

text
Differentiate:

f(x)=sin(x)e^x

text
Integrate:

∫x²cos(x)dx

text
Find the determinant of

|2 4|
|1 5|

text
Prove the Binomial Theorem.

text
Explain Bayes' Theorem with a practical example.

text
Find the eigenvalues of the matrix:

[[4,2],
 [1,3]]

Mission

The goal of Adaptive Math Reasoner is to provide accurate, explainable, and accessible mathematical reasoning powered by modern Large Language Models.

The project aims to make advanced mathematics easier to learn, explore, and apply across education, engineering, science, finance, and research.

Version

Version: 1.0.0

Base Model: Mixtral-8x7B-Instruct

Framework: Transformers

License: Apache-2.0

Primary Domain: Mathematical Reasoning

Author: Edidiong Charlie

Thank You

Thank you for using Adaptive Math Reasoner.

We hope this model helps students, educators, researchers, developers, engineers, and the open-source community solve mathematical problems more effectively.

Happy building with AI and Mathematics! 🚀

AdaptiveMath

README

Supported Topics

Fundamental Mathematical Formulae

Example Usage

Example 1

Example 2

Example 3

Inference with Transformers

Applications

Limitations

Future Improvements

Acknowledgements

Citation

License

Performance Goals

Example Prompt Ideas

Mission

Version

Thank You

Explore FriendliAI today

README

Supported Topics

Fundamental Mathematical Formulae

Example Usage

Example 1

Example 2

Example 3

Inference with Transformers

Applications

Limitations

Future Improvements

Acknowledgements

Citation

License

Performance Goals

Example Prompt Ideas

Mission

Version

Thank You