Update README.md

README.md

@@ -4,4 +4,59 @@ datasets:
 - GAIR/LIMO
 base_model:
 - Josephgflowers/Tinyllama-STEM-Cinder-Agent-v1
----
+---
+
+## Model Overview
+**TinyLlama-R1-LIMO** is a small, efficient transformer-based model designed to improve mathematical reasoning with minimal but high-quality training data. It was fine-tuned on the **LIMO** dataset, which emphasizes the principle that "Less Is More" for reasoning tasks. The model is part of ongoing research to enhance instruction-following and reasoning capabilities using a dataset of only 817 curated samples.
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6328952f798f8d122ce62a44/kW4PCQ387on6BErRz_Fnn.png)
+
+Model Name: `Josephgflowers/Tinyllama-R1-LIMO-Agent`
+
+This model was made possible by the generous support of www.cherryrepublic.com
+
+---
+
+## Key Features
+- **Data Efficiency**: Achieves competitive reasoning performance using the **LIMO** dataset with only 817 training samples.
+- **Mathematical Reasoning Focus**: Tailored for tasks requiring logical and numerical problem-solving.
+- **Instruction Adaptation**: The model shows improved chain-of-thought (CoT) reasoning but may require further refinement for handling complex, multi-step prompts.
+- **Training Pipeline**: Built using the LLaMA-Factory framework with dataset-specific optimizations.
+
+---
+
+## Model Details
+- **Model Type**: Transformer-based (TinyLlama architecture)
+- **Parameter Count**: 1.1B
+- **Training Framework**: Unsloth 8k context / Hugging Face Transformers
+- **Primary Use Cases**:
+  - Mathematical and logical reasoning
+  - STEM education and problem-solving
+  - Instruction-following conversations
+
+---
+
+## Training Data
+This model was fine-tuned using the **LIMO** dataset, which emphasizes the power of high-quality data over quantity. 
+
+### Dataset Highlights
+- **Name**: LIMO (Less Is More for Reasoning)
+- **Size**: 817 samples
+
+Acknowledgments
+
+Thanks to the creators of the LIMO dataset and contributors to the LLaMA-Factory training framework. Special thanks to Joseph Flowers for model fine-tuning and experimentation.
+Citation
+
+If you use this model or dataset, please cite the following paper:
+
+@misc{ye2025limoreasoning,
+      title={LIMO: Less is More for Reasoning}, 
+      author={Yixin Ye and Zhen Huang and Yang Xiao and Ethan Chern and Shijie Xia and Pengfei Liu},
+      year={2025},
+      eprint={2502.03387},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.03387}, 
+}