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