Mistral-small

README.md

@@ -164,10 +164,10 @@ to implement production-ready inference pipelines.
 
 **_Installation_**
 
-Make sure you install [`vLLM >= 0.8.1`](https://github.com/vllm-project/vllm/releases/tag/v0.8.1):
+Make sure you install [`vLLM nightly`](https://github.com/vllm-project/vllm/):
 
 ```
-pip install vllm --upgrade
+pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly --upgrade
 ```
 
 Doing so should automatically install [`mistral_common >= 1.5.4`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.4).
@@ -177,7 +177,7 @@ To check:
 python -c "import mistral_common; print(mistral_common.__version__)"
 ```
 
-You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39).
+You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39) followed by a nightly install of vllm as shown above.
 
 #### Server