| --- |
| 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 — 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 | |
|
|
| --- |
|  |
|
|
| # 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% |
| ``` |
|
|
|
|
|  |
| --- |
|
|
| # 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! 🚀 |
|
|
