README.md

---
license: apache-2.0
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
library_name: transformers
pipeline_tag: text-generation

tags:
- mathematics
- reasoning
- algebra
- arithmetic
- geometry
- calculus
- statistics
- number-theory
- ai
- adaption-labs
- autoscientist
- lora
- education
- stem

language:
- en



metrics:
- Grade
- Overall Win Rate
- Math Win Rate
- Percentile


---
![Adaptive Math Demo](adaptive-math-demo.gif)

# Adaptive Math — Mixtral-8x7B

> A PEFT (LoRA) fine-tuned **Mixtral-8x7B-Instruct-v0.1** model specialized for mathematical reasoning, arithmetic, algebra, geometry, statistics and logical problem solving using the **Adaption Labs AutoScientist** workflow.

---

# Overview

Adaptive Math is a domain-adapted language model developed for mathematical reasoning and instruction following.

The model was fine-tuned using the **Adaption Labs AutoScientist** workflow on a mathematics reasoning dataset containing thousands of mathematical instruction-completion pairs.

Unlike a general language model, this model focuses on structured mathematical reasoning, symbolic manipulation and logical step-by-step solutions.

---

# Quick Facts

| Item | Value |
|------|-------|
| Domain | Mathematics |
| Task | Mathematical Reasoning |
| Base Model | Mixtral-8x7B-Instruct-v0.1 |
| Fine-tuning Method | PEFT (LoRA) |
| Training Framework | Adaption Labs AutoScientist |
| Dataset | adaption-single-integer-samples |
| Language | English |
| License | Apache-2.0 |

---

# Training Dataset

Dataset Repository

https://huggingface.co/datasets/Charley890/adaption-single-integer-samples

The training dataset contains mathematical reasoning examples covering:

- Arithmetic
- Algebra
- Number Theory
- Geometry
- Statistics
- Logical Reasoning
- Symbolic Mathematics
- Mathematical Word Problems

---

# Key Features

- Mathematical Question Answering
- Step-by-Step Reasoning
- Arithmetic Computation
- Algebraic Manipulation
- Geometry Reasoning
- Number Theory
- Statistics
- Symbolic Reasoning
- STEM Education
- AI Tutoring

---

# Base Model

| Property | Value |
|-----------|-------|
| Base Model | Mixtral-8x7B-Instruct-v0.1 |
| Fine-tuning | PEFT (LoRA) |
| Framework | Adaption Labs AutoScientist |
| Language | English |

---
![Adaptive Math Demo](adaptive-math-demo-2.gif)

# Evaluation Results

Evaluation performed using **Adaption Labs AutoScientist**

| Metric | Base Model | Adapted Model | Improvement |
|--------|-----------:|--------------:|------------:|
| Grade | E | A | Excellent |
| Overall Win Rate | 19 | 81 | +62 |
| Math Win Rate | 38 | 63 | +25 |
| Percentile | 0.0 | 57.7 | +57.7 |
| Relative Improvement | — | — | 82% |

---

# Performance Summary

```
✓ Training Completed Successfully

✓ Stable Optimization

✓ Mathematical Reasoning Improved

✓ Grade Improved from E → A

✓ Overall Win Rate Improved from 19 → 81

✓ Math Win Rate Improved from 38 → 63

✓ Percentile Improved from 0.0 → 57.7

✓ Relative Quality Improvement: 82%
```


![Adaptive Math Demo](output.gif)
---

# Mathematical Knowledge

This model is optimized to solve a broad range of mathematical problems through logical reasoning instead of memorization.

It performs best when explaining every intermediate step before arriving at the final answer.

---

## Supported Topics

| 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

| Formula | Equation |
|---------|----------|
| Quadratic Formula | $x=\frac{-b\pm\sqrt{b^2-4ac}}{2a}$ |
| Pythagorean Theorem | $a^2+b^2=c^2$ |
| Slope Formula | $m=\frac{y_2-y_1}{x_2-x_1}$,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! 🚀