README.md

---
library_name: transformers
tags:
- math
license: apache-2.0
datasets:
- openai/gsm8k
language:
- en
metrics:
- accuracy
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
pipeline_tag: text-generation
---

# DeepMath-7B-L

## Model Overview
DeepMath-7B-L are fine-tuned versions of [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) on the [GSM8K dataset](https://huggingface.co/datasets/gsm8k). These models are designed for mathematical reasoning and problem-solving, excelling in arithmetic, algebra, and word problems.

## Model Details
- **Base Model:** DeepSeek-R1-Distill-Qwen-1.5B
- **Fine-Tuning Dataset:** GSM8K
- **Parameters:** 1.5 Billion
- **Task:** Mathematical Question Answering (Math QA)
- **Repositories:**
  - [DeepMath-7B-L](https://huggingface.co/codewithdark/deepmath-7b-l) (LoRA adapter-enhanced model)
- **Commit Messages:**
  - "Full merged model for math QA"
  - "Added LoRA adapters for math reasoning"

## Training Details
- **Dataset:** GSM8K (Grade School Math 8K) - a high-quality dataset for mathematical reasoning
- **Fine-Tuning Framework:** Hugging Face Transformers & PyTorch
- **Optimization Techniques:**
  - AdamW Optimizer
  - Learning rate scheduling
  - Gradient accumulation
  - Mixed precision training (FP16)
- **Training Steps:** Multiple epochs on a high-performance GPU cluster

## Capabilities & Performance
DeepMath-7B-L excel in:
- Solving word problems with step-by-step reasoning
- Performing algebraic and arithmetic computations
- Understanding complex problem structures
- Generating structured solutions with explanations



### DeepMath-7B-L (LoRA Adapter-Enhanced Model)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("codewithdark/deepmath-7b-l")
model = AutoModelForCausalLM.from_pretrained("codewithdark/deepmath-7b-l")

input_text = "Solve: 2x + 3 = 7"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Limitations
- May struggle with extremely complex mathematical proofs
- Performance is limited to the scope of GSM8K-type problems
- Potential biases in training data

## Future Work
- Extending training to more diverse math datasets
- Exploring larger models for improved accuracy
- Fine-tuning on physics and higher-level mathematical reasoning datasets

## License
This model is released under the Apache 2.0 License.

## Citation
If you use these models, please cite:
```
@misc{DeepMath-7B-L,
  author = {Ahsan},
  title = {DeepMath-7B-L: LoRA Adapter Enhanced Model for Math Reasoning},
  year = {2025},
  url = {https://huggingface.co/codewithdark/deepmath-7b-l}
}
```