transformers safetensors qwen2 text-generation math conversational en dataset:openai/gsm8k base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B license:mit autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
datasets:
- openai/gsm8k
language:
- en
library_name: transformers
license: mit
pipeline_tag: text-generation
tags:
- math
---


# DeepMath-7B-M

## Model Overview
DeepMath-7B-M is a fine-tuned version 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). This model is 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)
- **Repository:** [codewithdark/deepmath-7b-m](https://huggingface.co/codewithdark/deepmath-7b-m)
- **Commit Message:** "Full merged model for math QA"

## 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-M excels in:
- Solving word problems with step-by-step reasoning
- Performing algebraic and arithmetic computations
- Understanding complex problem structures
- Generating structured solutions with explanations

## Usage
You can load and use the model via the Hugging Face `transformers` library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

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

input_text = "A farmer has 5 chickens and each lays 3 eggs a day. How many eggs in total after a week?"
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 mit License.

## Citation
If you use this model, please cite:
```bibtex
@misc{DeepMath-7B-M,
  author = {Ahsan},
  title = {DeepMath-7B-M: Fine-Tuned DeepSeek-R1-Distill-Qwen-1.5B on GSM8K},
  year = {2025},
  url = {https://huggingface.co/codewithdark/deepmath-7b-m}
}
```

