Update README.md

README.md

@@ -191,13 +191,13 @@ The implementation of these tasks within RAG systems can significantly improve o
 
 ## Environmental Impact
 
-GRAG-PHI-SFT, running on NVIDIA A100 with 8 GPUs for 5 days, has an approximate power consumption as follows:
+GRAG-PHI-SFT, running on NVIDIA A100 with 40 GPUs for 5 days, has an approximate power consumption as follows:
 
 It's important to note that the actual power consumption may vary depending on the specific workload and operational conditions. For accurate power consumption measurements, using dedicated power monitoring tools is recommended.
 
 | Model          | GPU Type            | Power Consumption From GPUs | 
 |----------------|---------------------|-----------------------------|
-| GRAG-PHI-SFT   | A100 ([Hessian AI supercomputer](https://hessian.ai/de/)) | 0.288 MWh                    |
+| GRAG-PHI-SFT   | A100 ([Hessian AI supercomputer](https://hessian.ai/de/)) | 0.0144 MWh                    |
 ## Bias, Risks, and Limitations
 
 Like any base language model or fine-tuned model without safety filtering, it is relatively easy for a user to prompt these models to generate harmful and generally sensitive content.