---
library_name: transformers
tags:
- math
- qwen2
- aimo
license: mit
datasets:
- Floppanacci/QWQ-LongCOT-AIMO
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
language:
- en
---

# DeepSeek-R1-Distill-Qwen-7B Fine-tuned for AIMO Math Problems

This model is a fine-tuned version of `deepseek-ai/DeepSeek-R1-Distill-Qwen-7B` on the [`Floppanacci/QWQ-LongCOT-AIMO`](https://huggingface.co/datasets/Floppanacci/QWQ-LongCOT-AIMO) dataset.

## Model Description

The model was fine-tuned to improve performance on mathematical reasoning tasks, particularly those involving step-by-step solutions (Chain-of-Thought) similar to problems found in the [AI Mathematical Olympiad (AIMO)](https://www.kaggle.com/competitions/ai-mathematical-olympiad-progress-prize-2) competition.

It's trained on a dataset containing ~30k math questions paired with detailed solutions.

An [AWQ quantized version](https://huggingface.co/Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci-AWQ) is also available for faster inference and reduced memory usage.

## How to Use

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

model_id = "Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16, # or torch.float16
    device_map="auto"
)

# Example Prompt (adjust based on how the model expects input)
prompt = "Question: What is the value of $2+2$? Answer:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# Generate
outputs = model.generate(**inputs, max_new_tokens=8192, temperature=0.7, do_sample=True)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
```

## Training Data

The model was fine-tuned on the train split of the [`Floppanacci/QWQ-LongCOT-AIMO`](https://huggingface.co/datasets/Floppanacci/QWQ-LongCOT-AIMO) dataset (29.5k examples).