Update README.md

README.md

@@ -1,3 +1,4 @@
+
 ---
 language:
 - ar
@@ -13,20 +14,22 @@ base_model:
 library_name: transformers
 tags:
 - code
+- open-source
 ---
-# M3-V2: A State-of-the-Art Commercial Language Model
+# M3-V2: An Open Source Model for State-of-the-Art Code Generation
 
-[![License](https://img.shields.io/badge/License-Custom_Commercial-red.svg)](https://opensource.org/licenses/Apache-2.0)
+[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Support via PayPal](https://img.shields.io/badge/Support-PayPal-blue.svg)](https://www.paypal.me/moelanobyzedev)
 
-M3-V2 is a state-of-the-art causal language model featuring a proprietary architecture that enables advanced reasoning and self-correction. This model is **not open source** and is available for commercial licensing.
+M3-V2 is a state-of-the-art causal language model featuring a novel architecture that enables advanced reasoning and self-correction. This model is **fully open source** under the Apache 2.0 license, making it available for academic, personal, and commercial use.
 
-The model achieves a groundbreaking **98.17% Pass@1 score on the HumanEval benchmark**, placing it at the absolute cutting edge of AI code generation and making it one of the most powerful code generation engines available today.
+The model achieves a groundbreaking **98.17% Pass@1 score on the HumanEval benchmark**, placing it at the cutting edge of AI code generation and making it one of the most powerful open-source code generation engines available today.
 
 ---
 
 ## Benchmark Performance
 
-The benchmark results demonstrate a level of performance that significantly surpasses publicly available models.
+The benchmark results demonstrate a level of performance that significantly surpasses many publicly available models.
 
 ![HumanEval Benchmark Chart](wawa.png)
 
@@ -34,44 +37,36 @@ The benchmark results demonstrate a level of performance that significantly surp
 
 | Model                               | HumanEval Pass@1 Score | Note                   |
 | :---------------------------------- | :--------------------: | :--------------------- |
-| **moelanoby/phi3-M3-V2 (This Model)** |       **95.12%/98.17%/98.56%**       | **Commercial License** and they are ordered with 0,1,2 self corrections with 1 being the default |
+| **moelanoby/phi3-M3-V2 (This Model)** |       **95.12% / 98.17% / 98.56%**       | **Apache 2.0 License**. Scores correspond to 0, 1, and 2 self-correction passes, with 1 being the default. |
 | GPT-4.5 / "Orion"                   |       `~96.00%`        | Projected (Late 2025)  |
 | Gemini 2.5 Pro                      |       `~95.00%`        | Projected (Late 2025)  |
 | Claude 4                            |       `~94.00%`        | Projected (Late 2025)  |
 
 ---
 
-## License and Terms of Use
-
-This model is proprietary and is governed by the following custom terms. By accessing or using this model, you agree to be bound by these rules.
+## Support the Project
 
-1.  **Architecture Non-Derivability:** The underlying code and architectural design, including the `architecture.py` file, are proprietary and represent a trade secret. You are strictly prohibited from reverse-engineering, copying, or integrating this architecture or its components into any other model or software.
+M3-V2 is an open-source project, free for everyone to use. I am passionate about creating powerful and accessible AI tools for the community.
 
-2.  **Commercial License Required:** Access to and use of this model require a paid commercial license. Unauthorized use, distribution, or access is strictly forbidden and will be subject to legal action.
+If you find this model helpful in your work, research, or personal projects, please consider supporting its development. Your contribution helps cover server costs, allows me to dedicate more time to improvements, and fuels the creation of new open-source models. Every little bit helps and is greatly appreciated!
 
-3.  **Ethical Use and Finetuning Restriction:** You may not finetune, train, or adapt this model on any dataset intended to remove ethical safeguards, promote illegal acts, or generate uncensored content. The model must be used in accordance with safety and ethical best practices.
+[**Support via PayPal**](https://www.paypal.me/moelanobyzedev)
 
 ---
 
-## How to Get Access
-
-This model is available for commercial use via a paid license.
-
-To purchase a license and gain access to the model, please contact our licensing team:
+## License
 
-**Email:** `elanoby69@email.com`
+This model is licensed under the **Apache 2.0 License**. You are free to use, modify, and distribute this model and its source code for any purpose, including commercial applications, subject to the terms of the license. You can find a copy of the license in the repository.
 
-**Payment:**  [PAY WITH PAYPAL](https://www.paypal.me/moelanobyzedev/15USD)
+## Ethical Considerations
 
-**WARNING:** ***YOU MUST GIVE ME YOUR USERNAME IN HUGGING FACE AND IN PAYPAL*** (for preventing scams)
-
-You will be provided with access credentials and usage instructions upon completion of the licensing agreement.
+While this model is open source, users are encouraged to use it responsibly. Finetuning the model to generate harmful, illegal, or unethical content is strongly discouraged. We advocate for the use of this technology to build positive and safe applications.
 
 ---
 
-## Technical Usage (For Licensed Users)
+## How to Use
 
-**Note:** The following instructions are for licensed users only. Running this code without a valid commercial license is a violation of the terms of use.
+This model is publicly available on the Hugging Face Hub.
 
 ### Installation
 
@@ -83,51 +78,51 @@ pip install torch transformers accelerate
 
 ### Python Implementation
 
-After gaining access, you can integrate the model into your application. You **must** use `trust_remote_code=True` for the proprietary architecture to load correctly.
+You can easily integrate the model into your application. You **must** use `trust_remote_code=True` for the custom architecture to load correctly from the Hub.
 
 ```python
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-# Use the private model ID and token provided with your license
 MODEL_ID = "moelanoby/phi3-M3-V2"
-# AUTH_TOKEN = "YOUR_HF_ACCESS_TOKEN_HERE" # Required for private models
 
 print("Loading tokenizer and model...")
 tokenizer = AutoTokenizer.from_pretrained(
     MODEL_ID, 
     trust_remote_code=True, 
-    # token=AUTH_TOKEN
 )
 model = AutoModelForCausalLM.from_pretrained(
     MODEL_ID,
     trust_remote_code=True,
     torch_dtype=torch.bfloat16,
     device_map="auto",
-    # token=AUTH_TOKEN
 )
 print("Model loaded successfully.")
 
-# --- Controlling the model's proprietary reasoning feature ---
-# This feature is a key part of your license.
-# Default is 1 pass.
+# --- Controlling the model's self-correction feature ---
+# Default is 1 pass. You can adjust it for different performance profiles.
 try:
     target_layer_path = "model.layers.15.mlp.gate_up_proj" 
     custom_layer = model
     for part in target_layer_path.split('.'):
         custom_layer = getattr(custom_layer, part)
         
-    custom_layer.num_correction_passes = 3 
-    print(f"✅ Number of reasoning passes set to: {custom_layer.num_correction_passes}")
+    # Set the number of self-correction passes (e.g., 0, 1, 2, or 3)
+    custom_layer.num_correction_passes = 2 
+    print(f"✅ Number of self-correction passes set to: {custom_layer.num_correction_passes}")
 except AttributeError:
     print("⚠️ Could not access the custom layer. The model will run with its default settings.")
 
 # (Example generation code would follow here)
 ```
-## HUGE NOTES
-- downside: the model might grow more incoherent and less accurate as you add more self corrections
-- recommendations: you could use 1,2,3 self corrections if needed and 2 self corrections is the most recommended 
+## Important Notes
+- **Downside:** The model might become more incoherent or less accurate as you add more self-correction passes. Experiment to find the best balance for your use case.
+- **Recommendations:** You can use 1, 2, or 3 self-correction passes if needed. **2 passes** is the most recommended setting for a balance of performance and coherence.
+
+---
+
 ## Acknowledgements
 
 -   The base of this model utilizes the **Phi-3** architecture developed by Microsoft.
--   The benchmark results were obtained using the **HumanEval** dataset from OpenAI.
+-   The benchmark results were obtained using the **HumanEval** dataset from OpenAI.
+-   We thank the open-source community for their continuous contributions to AI research.