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---
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}
}
``` |