| | --- |
| | 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} |
| | } |
| | ``` |
