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Readme(Qweb-math).md

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+---
+license: Apache-2.0
+language:
+- en
+base_model:
+- Qwen2.5-1.5B
+tags:
+- mathematics
+- reasoning
+- problem-solving
+- education
+- transformer
+---
+
+# Qwen2.5-Math-1.5B Model
+
+This repository hosts the **Qwen2.5-Math-1.5B** language model an optimized transformer designed to handle advanced mathematical reasoning, symbolic problem solving, and step-by-step solution generation. Built for educational assistance, competitive mathematics settings, and research in formal reasoning, the model offers strong performance while maintaining efficient deployment requirements.
+
+
+## Model Overview
+
+- **Base-Model**: Qwen2.5-1.5B
+- **Original-Model**: Qwen2.5-Math-1.5B
+- **Architecture**: Decoder-only transformer
+- **Quantized Versions**: 
+  - Q4_K_M (4-bit quantization)
+  - Q5_K_M (5-bit quantization)
+- **Modalities**: Text
+- **Developer**: Qwen
+- **Language**: English
+- **License**: [Apache 2.0](https://choosealicense.com/licenses/apache-2.0/)
+- **Input/Output Format**: Instruction-tuned conversational format
+
+
+## Quantization Details
+
+### Q4_K_M Version
+- Approx. ~70% size reduction
+- Lower memory footprint (~940 MB)
+- Best suited for deployment on edge devices or low-resource GPUs
+- Slight performance degradation in complex reasoning scenarios
+
+### Q5_K_M Version
+- Approx. ~66% size reduction
+- Higher fidelity (~1.04 GB)
+- Better performance retention, recommended when quality is a priority
+
+
+### Dataset & Training
+
+- The model is trained on curated mathematics-focused datasets consisting of:
+ - Textbooks & structured solutions
+ - Problem-answer pairs and mathematical explanations
+ - High-difficulty reasoning tasks used in competitive examination preparation
+
+
+## Key Strengths
+
+- Strong capability for multi-step reasoning and deriving structured solutions
+- Generates stepwise explanations rather than single-answer outputs
+- Suitable for high-performance inference on GPUs and high-end CPUs
+- Rich instruction-following behavior for math problem sets and tutoring systems
+- Works effectively with chain-of-thought prompting strategies
+
+
+## Intended Use
+
+This model is designed for scenarios where mathematical reasoning is critical, such as:
+
+- **Learning platforms & tutoring assistants** : Automated step-by-step math explainer systems
+- **Academic research** : Algorithms and experiments involving symbolic reasoning
+- **STEM educational tools** : Training models targeted at competitive exam preparation
+- **Conversational reasoning agents** : Math-focused dialog systems for structured question answering
+
+
+### Usage
+
+This model is meant for mathematical guidance and should not replace expert professional judgement in scientific or financial applications.
+
+**llama.cpp (text-only)**
+```sh
+./llama-cli -hf SandLogicTechnologies/Qwen2.5-Math-1.5B-GGUF -p "Explain Taylor series"
+```
+
+## Acknowledgments
+
+These quantized models are based on the original work by **Qwen** development team.
+
+Special thanks to:
+- The [Qwen](https://huggingface.co/Qwen) team for developing and releasing the [Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) model.
+- **Georgi Gerganov** and the entire [`llama.cpp`](https://github.com/ggerganov/llama.cpp) open-source community for enabling efficient model quantization and inference via the GGUF format.
+
+
+## Contact
+For any inquiries or support, please contact us at support@sandlogic.com or visit our [Website](https://www.sandlogic.com/).