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update_ima
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6a705ad3d4 |
@@ -41,7 +41,7 @@ services:
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environment:
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TTS_ENDPOINT: ${TTS_ENDPOINT}
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tgi-service:
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image: ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu
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image: ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu
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container_name: tgi-service
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ports:
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- "3006:80"
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@@ -103,7 +103,7 @@ services:
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HF_HUB_ENABLE_HF_TRANSFER: 0
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restart: unless-stopped
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tgi-service:
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image: ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu
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image: ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu
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container_name: tgi-service
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ports:
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- "9009:80"
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@@ -102,7 +102,7 @@ services:
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HF_HUB_ENABLE_HF_TRANSFER: 0
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restart: unless-stopped
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tgi-service:
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image: ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu
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image: ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu
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container_name: tgi-service
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ports:
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- "6042:80"
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@@ -70,7 +70,7 @@ services:
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HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
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restart: unless-stopped
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tgi-service:
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image: ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu
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image: ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu
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container_name: tgi-service
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ports:
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- "9009:80"
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@@ -20,7 +20,7 @@ The ChatQnA uses the below prebuilt images if you choose a Xeon deployment
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- retriever: opea/retriever-redis:latest
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- tei_xeon_service: ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
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- reranking: opea/reranking-tei:latest
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- tgi-service: ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu
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- tgi-service: ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu
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- llm: opea/llm-tgi:latest
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- chaqna-xeon-backend-server: opea/chatqna:latest
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@@ -1122,7 +1122,7 @@ spec:
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name: chatqna-tgi-config
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securityContext:
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{}
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image: "ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu"
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image: "ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu"
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imagePullPolicy: IfNotPresent
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volumeMounts:
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- mountPath: /data
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@@ -3,7 +3,7 @@
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services:
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tgi-service:
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image: ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu
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image: ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu
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container_name: tgi-service
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ports:
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- "8028:80"
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@@ -239,7 +239,7 @@ spec:
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name: codegen-tgi-config
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securityContext:
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{}
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image: "ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu"
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image: "ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu"
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imagePullPolicy: IfNotPresent
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volumeMounts:
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- mountPath: /data
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@@ -126,7 +126,7 @@ spec:
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- name: no_proxy
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value:
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securityContext: {}
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image: "ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu"
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image: "ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu"
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imagePullPolicy: IfNotPresent
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volumeMounts:
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- mountPath: /data
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@@ -3,7 +3,7 @@
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services:
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tgi-service:
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image: ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu
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image: ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu
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container_name: codetrans-tgi-service
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ports:
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- "8008:80"
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@@ -239,7 +239,7 @@ spec:
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name: codetrans-tgi-config
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securityContext:
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{}
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image: "ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu"
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image: "ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu"
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imagePullPolicy: IfNotPresent
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volumeMounts:
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- mountPath: /data
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@@ -3,7 +3,7 @@
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services:
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tgi-service:
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image: ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu
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image: ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu
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container_name: tgi-service
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ports:
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- "8008:80"
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@@ -8,7 +8,7 @@ Install GMC in your Kubernetes cluster, if you have not already done so, by foll
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The DocSum application is defined as a Custom Resource (CR) file that the above GMC operator acts upon. It first checks if the microservices listed in the CR yaml file are running, if not it starts them and then proceeds to connect them. When the DocSum RAG pipeline is ready, the service endpoint details are returned, letting you use the application. Should you use "kubectl get pods" commands you will see all the component microservices, in particular embedding, retriever, rerank, and llm.
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The DocSum pipeline uses prebuilt images. The Xeon version uses the prebuilt image llm-docsum-tgi:latest which internally leverages the
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the image ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu. The service is called tgi-svc. Meanwhile, the Gaudi version launches the
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the image ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu. The service is called tgi-svc. Meanwhile, the Gaudi version launches the
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service tgi-gaudi-svc, which uses the image ghcr.io/huggingface/tgi-gaudi:1.2.1. Both TGI model services serve the model specified in the LLM_MODEL_ID variable that is exported by you. In the below example we use Intel/neural-chat-7b-v3-3.
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[NOTE]
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@@ -239,7 +239,7 @@ spec:
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name: docsum-tgi-config
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securityContext:
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{}
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image: "ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu"
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image: "ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu"
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imagePullPolicy: IfNotPresent
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volumeMounts:
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- mountPath: /data
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@@ -126,7 +126,7 @@ spec:
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- name: no_proxy
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value:
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securityContext: {}
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image: "ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu"
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image: "ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu"
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imagePullPolicy: IfNotPresent
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volumeMounts:
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- mountPath: /data
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@@ -3,7 +3,7 @@
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services:
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tgi-service:
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image: ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu
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image: ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu
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container_name: tgi-xeon-server
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ports:
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- "8008:80"
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@@ -126,7 +126,7 @@ spec:
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- name: no_proxy
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value:
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securityContext: {}
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image: "ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu"
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image: "ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu"
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imagePullPolicy: IfNotPresent
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volumeMounts:
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- mountPath: /data
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@@ -73,7 +73,7 @@ services:
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HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
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restart: unless-stopped
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tgi-service:
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image: ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu
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image: ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu
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container_name: tgi-service
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ports:
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- "3006:80"
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@@ -3,7 +3,7 @@
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services:
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tgi-service:
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image: ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu
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image: ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu
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container_name: tgi-service
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ports:
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- "8008:80"
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@@ -71,12 +71,12 @@ cd ../../../..
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### 4. Pull TGI Xeon Image
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```bash
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docker pull ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu
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docker pull ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu
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```
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Then run the command `docker images`, you will have the following 4 Docker Images:
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1. `ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu`
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1. `ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu`
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2. `opea/lvm-tgi:latest`
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3. `opea/visualqna:latest`
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4. `opea/visualqna-ui:latest`
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@@ -3,7 +3,7 @@
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services:
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llava-tgi-service:
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image: ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu
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image: ghcr.io/huggingface/text-generation-inference:sha-8f99f16-intel-cpu
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container_name: tgi-llava-xeon-server
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ports:
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- "8399:80"
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Reference in New Issue
Block a user