Upload README.md

README.md

@@ -1,11 +1,11 @@
 # Introduction
 
-DeepSeek-R1-INT4-FlagOS-Iluvatar  provides an all-in-one deployment solution, enabling execution of DeepSeek-R1-INT4 on Iluvatar GPUs. As the first-generation release for the ILUVATAR-BI150, this package delivers three key features:
+DeepSeek-R1-INT4-FlagOS-Iluvatar  provides an all-in-one deployment solution, enabling execution of DeepSeek-R1-INT4 on Iluvatar GPUs. As the first-generation release for the ILUVATAR, this package delivers three key features:
 
 1. Comprehensive Integration:
    - Integrated with FlagScale (https://github.com/FlagOpen/FlagScale).
    - Open-source inference execution code, preconfigured with all necessary software and hardware settings.
-   - Pre-built Docker image for rapid deployment on ILUVATAR-BI150.
+   - Pre-built Docker image for rapid deployment on ILUVATAR.
 2. Consistency Validation:
    - Evaluation tests verifying consistency of results between the official and ours.
 
@@ -93,9 +93,10 @@ Requested by Iluvatar, the file of docker image and model files should be applie
 ```bash
 
 pip install modelscope
-modelscope download --model <Model Name> --local_dir <Cache Path>
+modelscope download --model deepseek-ai/DeepSeek-R1 --local_dir /nfs/DeepSeek-R1
 
 ```
+contact services@iluvatar.comContact to obtain the quanted weights
 
 ### Download the FlagOS image
 
@@ -106,7 +107,7 @@ docker pull baai_v4
 ### Start the inference service
 
 ```bash
-docker run --shm-size="32g" -itd -v /dev:/dev -v /usr/src/:/usr/src -v /lib/modules/:/lib/modules -v /home:/home -v /mnt/share/:/data1 --privileged --cap-add=ALL --pid=host --net=host --name baai_v4 baai:v4
+docker run --shm-size="32g" -itd -v /dev:/dev -v /usr/src/:/usr/src -v /lib/modules/:/lib/modules -v /home:/home -v /nfs:/nfs -v /mnt/share/:/data1 --privileged --cap-add=ALL --pid=host --net=host --name baai_v4 baai:v4
 
 docker exec -it baai_v4 bash
 ```
@@ -162,5 +163,5 @@ send "FlagRelease"
 
 # License
 
-This project and related model weights are licensed under the MIT License.
+The weights of this model are based on deepseek-ai/DeepSeek-R1 and are open-sourced under the Apache 2.0 License: https://www.apache.org/licenses/LICENSE-2.0.txt.