Update README.md

README.md

@@ -1,22 +1,133 @@
 ---
-base_model: unsloth/deepscaler-1.5b-preview-unsloth-bnb-4bit
+base_model: unsloth/deepscaler-1.5b-preview
 tags:
 - text-generation-inference
 - transformers
 - unsloth
 - qwen2
 - trl
+- reasoning 
+- finetune 
+- edge-device 
+- research 
 license: apache-2.0
 language:
 - en
+datasets:
+- bespokelabs/Bespoke-Stratos-17k
+- simplescaling/s1K
+- cognitivecomputations/dolphin-r1
+- openai/gsm8k
+- PrimeIntellect/NuminaMath-QwQ-CoT-5M
+library_name: transformers
 ---
 
-# Uploaded  model
+![image](./image.webp)
 
-- **Developed by:** Daemontatox
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/deepscaler-1.5b-preview-unsloth-bnb-4bit
+# **mini-Cogito-R1**
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+## **Overview**
+The **mini-Cogito-R1** is a lightweight, high-performance language model fine-tuned for **text generation**, **mathematical reasoning**, and **edge-device optimization**. Developed by **Daemontatox**, this model is based on the **unsloth/deepscaler-1.5b-preview** architecture and fine-tuned using the **Unsloth** framework and Huggingface's **TRL** library, achieving **2x faster training speeds** without compromising performance.
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+---
+
+## **Key Features**
+- **Efficient Training:** Leverages [Unsloth](https://github.com/unslothai/unsloth) for faster and more efficient fine-tuning.  
+- **Optimized for Edge Devices:** Designed to run efficiently on resource-constrained devices, making it ideal for edge computing applications.  
+- **Mathematical Reasoning:** Excels in tasks requiring logical and mathematical reasoning.  
+- **Text Generation:** Capable of generating high-quality, coherent text for a variety of applications.  
+- **Lightweight:** Despite its compact size (1.5B parameters), it delivers robust performance.  
+
+---
+
+## **Model Details**
+- **Developed by:** Daemontatox  
+- **Model Name:** mini-Cogito-R1  
+- **License:** Apache-2.0  
+- **Base Model:** unsloth/deepscaler-1.5b-preview  
+- **Fine-Tuned From:** unsloth/deepscaler-1.5b-preview-unsloth-bnb-4bit  
+- **Framework:** Unsloth + Huggingface TRL  
+- **Language:** English  
+
+---
+
+## **Training Datasets**
+The **mini-Cogito-R1** model was fine-tuned on a diverse set of high-quality datasets to enhance its reasoning, mathematical, and text-generation capabilities. These datasets include:
+
+1. **PrimeIntellect/NuminaMath-QwQ-CoT-5M**  
+   - A large-scale dataset focused on mathematical reasoning and chain-of-thought (CoT) problem-solving.  
+
+2. **openai/gsm8k**  
+   - A dataset of grade-school math problems designed to test mathematical reasoning and problem-solving skills.  
+
+3. **cognitivecomputations/dolphin-r1**  
+   - A dataset for instruction-following and reasoning tasks, enhancing the model's ability to understand and execute complex instructions.  
+
+4. **simplescaling/s1K**  
+   - A lightweight dataset for general-purpose text generation and reasoning tasks.  
+
+5. **bespokelabs/Bespoke-Stratos-17k**  
+   - A dataset tailored for edge-device optimization and efficient text generation.  
+
+---
+
+## **Use Cases**
+- **Edge Computing:** Deploy on edge devices for real-time text generation and reasoning tasks.  
+- **Educational Tools:** Assist in solving mathematical problems and logical reasoning exercises.  
+- **Content Creation:** Generate high-quality text for blogs, articles, and creative writing.  
+- **Research:** Explore efficient training techniques and lightweight model architectures.  
+
+---
+
+## **Performance**
+The **mini-Cogito-R1** was fine-tuned **2x faster** using Unsloth's optimized training pipeline, making it a cost-effective solution for developers and researchers. It maintains high accuracy and efficiency, particularly in mathematical reasoning and text generation tasks.
+
+---
+
+## **How to Use**
+You can load and use the model with Huggingface's `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Daemontatox/mini-Cogito-R1"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+inputs = tokenizer("Solve for x: 2x + 5 = 15", return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0]))
+```
+
+---
+
+## **Acknowledgments**
+- **Unsloth Team:** For their groundbreaking work on efficient model training.  
+- **Huggingface:** For providing the TRL library and ecosystem.  
+- **Open Source Community:** For continuous support and contributions.  
+
+---
+
+## **License**
+This model is licensed under the **Apache-2.0** license. For more details, see the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) file.
+
+---
+
+## **Connect with the Developer**
+- **GitHub:** [Daemontatox](https://github.com/Daemontatox)  
+- **Huggingface Model Hub:** [mini-Cogito-R1](https://huggingface.co/Daemontatox/mini-Cogito-R1)  
+
+---
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)  
+
+---
+
+### **Dataset References**
+- **NuminaMath-QwQ-CoT-5M:** [PrimeIntellect](https://huggingface.co/datasets/PrimeIntellect/NuminaMath-QwQ-CoT-5M)  
+- **GSM8K:** [OpenAI](https://huggingface.co/datasets/openai/gsm8k)  
+- **Dolphin-R1:** [Cognitive Computations](https://huggingface.co/datasets/cognitivecomputations/dolphin-r1)  
+- **S1K:** [Simple Scaling](https://huggingface.co/datasets/simplescaling/s1K)  
+- **Bespoke-Stratos-17k:** [Bespoke Labs](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k)  
+
+---