codewithdark
/

deepmath-7b-l

@@ -1,199 +1,86 @@
 ---
 library_name: transformers
-tags: []
 ---
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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}
+}
+```