Update README.md

README.md

@@ -41,24 +41,23 @@ Choose the variant that best fits your deployment constraints:
 
 | Model | Pruned | Kept | Size Reduction | Performance Trade-off |
 |-------|--------|------|----------------|----------------------|
-| **REAP-10** | 10% | 90% | Small | Minimal |
-| **REAP-20** | 20% | 80% | Moderate | Small |
-| **REAP-30** | 30% | 70% | Significant | Moderate |
-| **REAP-40** | 40% | 60% | Large | Noticeable |
+| **REAP-19** | 19 | 81% | Moderate | Small |
+| **REAP-29** | 29% | 71% | Significant | Moderate |
+| **REAP-39** | 39% | 61% | Large | Noticeable |
 | **REAP-50** | 50% | 50% | Very Large | Significant |
 
 **Repository Links:**
-- [`Akicou/MiniMax-M2.5-REAP-19`](https://huggingface.co/Akicou/MiniMax-M2.5-REAP-19)
-- [`Akicou/MiniMax-M2.5-REAP-29`](https://huggingface.co/Akicou/MiniMax-M2.5-REAP-29)
-- [`Akicou/MiniMax-M2.5-REAP-39`](https://huggingface.co/Akicou/MiniMax-M2.5-REAP-39)
-- [`Akicou/MiniMax-M2.5-REAP-50`](https://huggingface.co/Akicou/MiniMax-M2.5-REAP-50)
+- [`Akicou/MiniMax-M2-5-REAP-19`](https://huggingface.co/Akicou/MiniMax-M2-5-REAP-19)
+- [`Akicou/MiniMax-M2-5-REAP-29`](https://huggingface.co/Akicou/MiniMax-M2-5-REAP-29)
+- [`Akicou/MiniMax-M2-5-REAP-39`](https://huggingface.co/Akicou/MiniMax-M2-5-REAP-39)
+- [`Akicou/MiniMax-M2-5-REAP-50`](https://huggingface.co/Akicou/MiniMax-M2-5-REAP-50)
 
 ## Quick Start
 
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-model_name = "Akicou/MiniMax-M2.5-REAP-19"
+model_name = "Akicou/MiniMax-M2-5-REAP-19"
 
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
@@ -111,7 +110,7 @@ Quantized GGUF variants optimized for `llama.cpp`, `Ollama`, and similar backend
 
 ### REAP Framework
 
-Pruning was performed using the [REAP framework](https://github.com/CerebrasResearch/reap) (implementation: [Akicou/reap](https://github.com/Akicou/reap)) with the following configuration:
+Pruning was performed using the [REAP framework](https://github.com/CerebrasResearch/reap) (implementation: [Akicuo/reap](https://github.com/Akicuo/reap)) with the following configuration:
 
 **Calibration Settings:**
 - **Dataset:** Mixed-domain calibration corpus (150 samples per category)
@@ -196,7 +195,7 @@ If you use these pruned models in your research or applications, please cite bot
 Pruning large MoE models requires substantial computational resources (multi-GPU H100 clusters). If you find these models useful:
 
 - ☕ [Buy me a coffee](https://www.buymeacoffee.com/Akicou) to help offset GPU rental costs
-- ⭐ Star the [GitHub repository](https://github.com/Akicou/reap)
+- ⭐ Star the [GitHub repository](https://github.com/Akicuo/reap)
 - 📢 Share with others who might benefit
 - 🐛 Report issues and contribute improvements
 
@@ -204,7 +203,7 @@ Your support enables continued development and release of efficient model varian
 
 ## 📞 Contact & Feedback
 
-- **Issues & Requests:** Open an issue on [GitHub](https://github.com/Akicou/reap/issues)
+- **Issues & Requests:** Open an issue on [GitHub](https://github.com/Akicuo/reap/issues)
 - **Discussions:** Use the HuggingFace Community tab above
 - **Custom Pruning:** Reach out for specific pruning ratios or other MoE models
 
@@ -220,6 +219,6 @@ This model inherits the MIT license from the original MiniMax-M2.5 model. See [L
 
 **Made with ❤️ by Akicou | Powered by REAP**
 
-[🤗 Model Hub](https://huggingface.co/Akicou) | [💻 GitHub](https://github.com/Akicou) | [☕ Support](https://www.buymeacoffee.com/Akicou)
+[🤗 Model Hub](https://huggingface.co/Akicou) | [💻 GitHub](https://github.com/Akicuo) | [☕ Support](https://www.buymeacoffee.com/Akicou)
 
 </div>