SandLogicTechnologies
/

Qwen2.5-Math-1.5B-GGUF

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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/).