Update README.md

README.md

@@ -133,7 +133,6 @@ This SFT approach enables Alpie-Core to deliver reliable, aligned, and context-a
 ## 7. Training Details
 
 - **Hardware**: 8× NVIDIA H100-80GB GPUs
-- **Training Duration**: 408 hours
 - **Fine-tuning Method**: LoRA/QLoRA with the following configuration:
   - LoRA Alpha: 8
   - LoRA Dropout: 0.05
@@ -144,6 +143,8 @@ This SFT approach enables Alpie-Core to deliver reliable, aligned, and context-a
 
 ## 8. Environmental Impact
 
+![Carbon Footprint](carbon_footprint.png)
+
 **Carbon Footprint**: We estimated the environmental impact of training Alpie-Core (32B) on 8× NVIDIA H100-80GB GPUs by calculating carbon emissions from GPU energy consumption. The calculation follows the formula:
 CO₂e (kg) = Grid CO₂ Factor (kg/kWh) × Runtime (hours) × Power per GPU (kW) × Number of GPUs
 
@@ -280,7 +281,6 @@ with torch.no_grad():
 ### Deployment Options
 - **Transformers**: Python, PyTorch integration
 - **vLLM**: High-throughput inference
-- **LMDeploy/Ollama/TensorRT-LLM**: Production deployments
 
 ## 12. Citation
 
@@ -297,6 +297,10 @@ with torch.no_grad():
 
 Apache 2.0 – Free for research and commercial use
 
+## 14. Acknowledgements / Credits
+
+We would like to thank **DeepSeek** for their original model, which served as the foundation for this work. Our team fine-tuned the model and implemented **4-bit quantization**, achieving improved efficiency and accuracy for downstream tasks. This model is built with respect to the contributions of the original authors and aims to provide a safe, high-performance solution for reasoning and inference.
+
 ---
 
 *For technical details, training methodology, and comprehensive evaluation results, please refer to our technical report.*