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---
base_model: unsloth/llama-3.1-8B-Instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
- text-generation
- math
- fine-tuning
- llama-3
license: apache-2.0
language:
- en
dataset:
- nivektk/math-augmented-dataset
task_categories:
- text-generation
- question-answering
size_categories:
- 1K<n<10K
model_name: BullSolve
---
# BullSolve: Fine-Tuned LLaMA 3 Model for Math Problem Solving
## Model Description
BullSolve is a fine-tuned version of `unsloth/llama-3.1-8B-Instruct-unsloth-bnb-4bit`, optimized for solving advanced math problems. The model was trained using LoRA adapters with the `nivektk/math-augmented-dataset`, which contains algebra problems and their solutions.
This model is optimized for low VRAM usage and efficient inference while maintaining high accuracy in mathematical problem-solving tasks.
# Training Data
The model was fine-tuned using a subset of the [MATH Dataset](https://arxiv.org/abs/2103.03874), specifically the **Algebra** category, containing **1,006 validated examples**. This dataset, originally developed by Dan Hendrycks et al., consists of mathematical problems structured in JSON format, with attributes:
- `problem`: Problem statement in text with LaTeX expressions.
- `level`: Difficulty level (1 to 5).
- `type`: Mathematical domain (e.g., Algebra, Geometry).
- `solution`: Step-by-step solution in English.
For fine-tuning, the dataset was preprocessed into ShareGPT format with the structure:
```
{question}[[
Solution:
{solution}
]]
```
Additionally, a chat template was applied for better inference compatibility.
## Training Configuration
The model was trained using **Unsloth** with LoRA, optimizing memory efficiency and inference speed. Key parameters:
- **Model**: `unsloth/llama-3.1-8B-Instruct-unsloth-bnb-4bit`
- **Max Sequence Length**: 2048 tokens
- **LoRA Config**:
- Rank (`r`): 16
- Alpha: 16
- Dropout: 0
- Target Modules: `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
- **Training Arguments**:
- Batch Size: 1
- Gradient Accumulation: 4
- Max Steps: 25
- Learning Rate: 1e-4
- Optimizer: AdamW (8-bit)
- Weight Decay: 0.01
- LR Scheduler: Linear
## Inference
BullSolve is optimized for fast inference and mathematical problem-solving. Example usage:
```python
from transformers import TextStreamer
from unsloth import FastLanguageModel
import torch
model, tokenizer = FastLanguageModel.from_pretrained("nivektk/BullSolve")
FastLanguageModel.for_inference(model)
messages = [{"role": "user", "content": "Evaluate $\\log_{5^2}5^4$."}]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
text_streamer = TextStreamer(tokenizer, skip_prompt=True)
_ = model.generate(input_ids, streamer=text_streamer, max_new_tokens=2000, pad_token_id=tokenizer.eos_token_id)
```
## Model Usage
This model is suitable for:
- Math tutoring and automated problem-solving
- AI-assisted mathematical reasoning
- Education-based chatbot assistants
## Limitations
- The model is trained only on algebra problems and may not generalize well to other areas of mathematics.
- It is optimized for inference efficiency rather than large-scale fine-tuning.
## Acknowledgments
- **Unsloth** for efficient LoRA fine-tuning
- **MATH Dataset** by Dan Hendrycks for problem-solving benchmarks
## Citation
If you use this model, please cite:
```bibtex
@article{BullSolve2025,
title={BullSolve: Fine-Tuned LLaMA 3 for Math Problems},
authors={Kevin Fabio Ramos López and Kevin Camilo Rincon Bohorquez and Nolhan Dumoulin},
year={2025},
journal={Hugging Face Models}
}
```
# Uploaded model
- **Developed by:** nivektk
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.1-8B-Instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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