Update README.md

README.md

@@ -1,51 +1,62 @@
 ---
+language:
+- en
+- code
+pipeline_tag: text-generation
 license: apache-2.0
 tags:
-- unsloth
-- trl
-- sft
+- coderion
 - code
+- coding
 - reasoning
+- small-language-model
+- 0.6b
+- chronological-reasoning
+- high-reasoning
+- compact-model
+library_name: transformers
 datasets:
 - nvidia/OpenCodeReasoning
-language:
-- en
 base_model:
 - Qwen/Qwen3-0.6B
-pipeline_tag: text-generation
-library_name: transformers
 ---
 
-# Qwen3-0.6B-Code-Expert
+<p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/685ea8ff7b4139b6845ce395/1z7OO6Xv_EWEHUDemqSL1.png" alt="logo" width="200">
+</p>
 
-This project performs full fine-tuning on the **Qwen3-0.6B** language model to enhance its code reasoning and generation capabilities. Training was conducted exclusively on the `nvidia/OpenCodeReasoning` dataset, and the model was optimized using the bfloat16 (bf16) data type.
+<h1 align="center">Coderion</h1>
 
-## Training Procedure
+<p align="center"><b>A compact 0.6B coding model built for strong reasoning efficiency.</b></p>
 
-1. **Dataset Preparation**
+---
 
-   * `nvidia/OpenCodeReasoning` dataset was used.
-   * Each example consists of code snippets paired with detailed step-by-step reasoning in Chain-of-Thought (CoT) style.
+**Coderion** is a **small 0.6B parameter coding-focused language model** designed for **high and xhigh chronological reasoning** in programming tasks.
 
-2. **Model Loading and Configuration**
+It is built to deliver **surprisingly strong structured reasoning and coding performance for its size**, focusing on consistency, logical step progression, and efficient problem solving.
 
-   * Qwen3-0.6B base model weights were loaded via the `unsloth` library in bf16 precision.
-   * Full fine-tuning (`full_finetuning=True`) was applied to all layers for optimal adaptation to code reasoning.
+While **Coderion is not intended to be a general everyday assistant**, it is a **small but capable specialist model** that performs well within its class and remains **reliable for compact code reasoning workloads**.
 
-3. **Supervised Fine-Tuning**
+---
 
-   * Employed the Hugging Face TRL library with the Supervised Fine-Tuning (SFT) approach.
-   * The model was trained to generate correct code solutions along with the corresponding reasoning chains.
+## Key Characteristics
 
-## Purpose and Outcome
+- **0.6B parameters**
+- **Dedicated to code**
+- **Optimized for high reasoning intensity**
+- **Chronological reasoning style**
+- **Strong consistency for a compact model**
+- **Designed for efficient performance despite its small size**
 
-* The model’s capacity for understanding, reasoning about, and generating code was significantly improved through specialized, single-dataset training in bf16 precision.
-* Outputs include both intermediate reasoning steps and final code solutions, enabling transparent and interpretable code generation.
+---
 
-## License
+## Limitations
 
-This project is licensed under the Apache License 2.0. See the [LICENSE](./LICENSE) file for details.
+Coderion is a **small specialized model**.
 
-## Support
+Because of that:
 
-<a href="https://www.buymeacoffee.com/suayptalha" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
+- It may not match larger models on broad real-world assistant tasks
+- It is not primarily designed for daily casual use
+- It performs best when used for **focused coding and reasoning workloads**
+- Its main strength is **efficiency, consistency, and reasoning quality relative to size**