Update README.md

README.md

@@ -1,22 +1,64 @@
 ---
-base_model: unsloth/qwen2.5-14b-instruct-bnb-4bit
 tags:
-- text-generation-inference
+- long-cot-reasoning
 - transformers
-- unsloth
-- qwen2
-- trl
+- mamba2
+- llms
+- chain-of-thought
 license: apache-2.0
 language:
 - en
+datasets:
+- Daemontatox/LongCOT-Reason
+- Daemontatox/alpaca_reasoning_COT
+base_model:
+- Qwen/Qwen2.5-14B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
 ---
 
-# Uploaded  model
+![Sphinx of Reasoning](./Sphinx.jpg)
 
-- **Developed by:** Daemontatox
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/qwen2.5-14b-instruct-bnb-4bit
+# **Sphinx: A Long Chain-of-Thought Reasoning Model**  
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+- **Developed by:** Daemontatox  
+- **License:** Apache-2.0  
+- **Base Model:** Fine-tuned from `unsloth/qwen2.5-14b-instruct-bnb-4bit`  
+- **Accelerated by:** [Unsloth Framework](https://github.com/unslothai/unsloth)  
+- **TRL-Optimized:** Integrated with Huggingface's TRL library for enhanced performance.  
+
+## **Overview**
+Sphinx is a state-of-the-art Long Chain-of-Thought (CoT) reasoning model designed to address complex, multi-step reasoning tasks with precision and clarity. Built on the Qwen2.5 architecture, Sphinx excels in generating coherent, logical thought processes while maintaining high levels of interpretability and explainability.  
+
+> _"Decoding complexity into clarity."_  
+
+### **Key Features**
+- **Enhanced CoT Reasoning:** Fine-tuned for generating multi-step solutions with deep logical consistency.  
+- **Efficient Performance:** Powered by Unsloth, achieving 2x faster training without compromising accuracy.  
+- **4-bit Quantization:** Optimized for resource-constrained environments while maintaining robust performance.  
+- **Multi-Task Versatility:** Excels in diverse domains, including mathematical proofs, legal reasoning, and advanced scientific problem-solving.  
+- **TRL Integration:** Employs reinforcement learning to improve generation quality through continuous feedback loops.  
+
+## **Model Details**
+### **Architecture**
+- **Base Model:** Qwen2.5-14B  
+- **Parameters:** 14 billion  
+- **Quantization:** 4-bit precision using BitsAndBytes (bnb).  
+- **Token Window:** Supports long-form inputs with a context window of up to 16k tokens, ideal for extensive reasoning tasks.  
+
+### **Training Details**
+- **Frameworks:** Huggingface Transformers + TRL + Unsloth.  
+- **Data Sources:** Curated datasets emphasizing reasoning tasks, including academic, legal, and logical contexts.  
+- **Optimization:** LoRA for parameter-efficient fine-tuning; RLHF for enhanced response alignment.  
+
+### **Capabilities**
+1. **Long-CoT Generation:** Capable of breaking down and solving complex, multi-layered problems.  
+2. **Explainable AI (XAI):** Provides clear, step-by-step reasoning for outputs.  
+3. **Customizability:** Easily adaptable to niche reasoning tasks via lightweight fine-tuning.  
+
+## **Applications**
+- **Academic Research:** Generating detailed, structured analyses for scientific problems.  
+- **Legal Assistance:** Drafting and explaining multi-step legal arguments.  
+- **STEM Education:** Guiding students through intricate mathematical and logical problems.  
+- **Cognitive AI Systems:** Seamless integration into systems requiring transparent decision-making.  
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)