---
license: apache-2.0  # Change if you have a different one; apache-2.0 is common for open models
base_model: meta-llama/Llama-3-8B  # ← IMPORTANT: Replace with your ACTUAL base model (e.g. mistralai/Mistral-7B-Instruct-v0.3, Qwen/Qwen2-7B-Instruct, google/gemma-2-9b-it, etc.)
tags:
  - peft
  - lora
  - text-generation
  - mathematics
  - math-reasoning
  - mathematical-olympiad
  - transformers
library_name: peft
pipeline_tag: text-generation
inference: false  # Set to true later if you deploy it
---

# ShineMath: Mathematical Olympiad Language Model

ShineMath is a custom-trained **LoRA adapter** designed to assist with mathematical olympiad problems, reasoning, step-by-step solution generation, and proof writing.  
It was fine-tuned for challenging math tasks using efficient PEFT methods.

**Author:** Shine Gupta (@shine_gupta17)  
**Repository:** [Shinegupta/ShineMath](https://huggingface.co/Shinegupta/ShineMath)

### Model Details
- **Type:** PEFT LoRA adapter (not a full model – load on top of a base LLM)
- **Files included:** adapter_model.safetensors, adapter_config.json, tokenizer files, chat_template.jinja, generation_config.json
- **Size:** ~82.5 MB (lightweight and easy to share/load)
- **Intended use:** Solving/generating IMO-style problems, AMC/AIME prep, mathematical reasoning, explanations

### Usage (with PEFT + Transformers)

Since this is a LoRA adapter, load it **on top of the base model**:

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

base_model_name = "meta-llama/Llama-3-8B"  # ← Replace with your actual base model!
adapter_name = "Shinegupta/ShineMath"

tokenizer = AutoTokenizer.from_pretrained(adapter_name)
model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    torch_dtype=torch.bfloat16,  # or "auto"
    device_map="auto"
)
model = PeftModel.from_pretrained(model, adapter_name)

# Example
prompt = "Solve: Let x² + y² = 1. Find the maximum value of x + y under the constraint x, y ≥ 0."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

Simpler with pipeline (auto-handles adapter):

```python
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model=base_model_name,
    peft_model=adapter_name,  # Loads the LoRA automatically
    device_map="auto"
)

result = pipe("Prove by induction that the sum of the first n natural numbers is n(n+1)/2.")
print(result[0]["generated_text"])
```

Tip: Use the chat_template.jinja for chat/instruct formats if your base model supports it (e.g., apply_chat_template).

### Applications
- Solving and generating mathematical olympiad problems (IMO, AIME, AMC, etc.)
- Step-by-step solution explanations
- Mathematical reasoning, theorem proving, and algebraic manipulations

### License
See the LICENSE file or specify here (e.g., Apache-2.0 for open use).

### Citation
If you use ShineMath in research or projects, please cite:  

  author = Shine Gupta,
  title = ShineMath: Mathematical Olympiad Language Model,
  publisher = Hugging Face,
  howpublished = https://huggingface.co/Shinegupta/ShineMath

For questions, collaborations, or issues — open a discussion on the model page! Happy math solving!