GPT-in-a-Box: Doc Updates (nutanix-cloud-native#54)

* GPT-in-a-Box Doc Updates:
* Replace "supported" with "validated"
* Reword parameter description to clarify that deployment metadata name is user-specified
* fix typos

docs/gpt-in-a-box/kubernetes/v0.2/generating_mar.md

@@ -6,7 +6,7 @@ Run the following command for downloading model files and generating MAR file:
 python3 $WORK_DIR/llm/generate.py [--hf_token <HUGGINGFACE_HUB_TOKEN> --repo_version <REPO_COMMIT_ID>] --model_name <MODEL_NAME> --output <NFS_LOCAL_MOUNT_LOCATION>
 ```
 
-* **model_name**:       Name of a [supported model](supported_models.md)
+* **model_name**:       Name of a [validated model](validated_models.md)
 * **output**:           Mount path to your nfs server to be used in the kube PV where model files and model archive file be stored
 * **repo_version**:     Commit ID of model's HuggingFace repository (optional, if not provided default set in model_config will be used)
 * **hf_token**:         Your HuggingFace token. Needed to download LLAMA(2) models.

docs/gpt-in-a-box/kubernetes/v0.2/huggingface_model.md

@@ -1,6 +1,6 @@
 # HuggingFace Model Support
 !!! Note
-    To start the inference server for the [**Supported Models**](supported_models.md), refer to the [**Deploying Inference Server**](inference_server.md) documentation.
+    To start the inference server for the [**Validated Models**](validated_models.md), refer to the [**Deploying Inference Server**](inference_server.md) documentation.
 
 We provide the capability to download model files from any HuggingFace repository and generate a MAR file to start an inference server using Kubeflow serving.<br />
 

docs/gpt-in-a-box/kubernetes/v0.2/inference_requests.md

@@ -1,4 +1,4 @@
-Kubeflow serving can be inferenced and managed through it's Inference APIs. Find out more about Kubeflow serving APIs in the official [Inference API](https://kserve.github.io/website/0.8/modelserving/v1beta1/torchserve/#model-inference) documentation.  
+Kubeflow serving can be inferenced and managed through its Inference APIs. Find out more about Kubeflow serving APIs in the official [Inference API](https://kserve.github.io/website/0.8/modelserving/v1beta1/torchserve/#model-inference) documentation.
 
 ### Set HOST and PORT  
 The first step is to [determine the ingress IP and ports](https://kserve.github.io/website/0.8/get_started/first_isvc/#4-determine-the-ingress-ip-and-ports) and set INGRESS_HOST and INGRESS_PORT.  
@@ -31,15 +31,15 @@ curl -v -H "Host: ${SERVICE_HOSTNAME}" -H "Content-Type: application/json" http:
 #### Examples:  
 Curl request for MPT-7B model
 ```
-curl -v -H "Host: ${SERVICE_HOSTNAME}" -H "Content-Type: application/json" http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/mpt_7b/infer -d @$WORK_DIR/data/qa/sample_test1.json
+curl -v -H "Host: ${SERVICE_HOSTNAME}" -H "Content-Type: application/json" http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/mpt_7b/infer -d @$WORK_DIR/data/qa/sample_text1.json
 ```
 Curl request for Falcon-7B model
 ```
-curl -v -H "Host: ${SERVICE_HOSTNAME}" -H "Content-Type: application/json" http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/falcon_7b/infer -d @$WORK_DIR/data/summarize/sample_test1.json
+curl -v -H "Host: ${SERVICE_HOSTNAME}" -H "Content-Type: application/json" http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/falcon_7b/infer -d @$WORK_DIR/data/summarize/sample_text1.json
 ```
 Curl request for Llama2-7B model
 ```
-curl -v -H "Host: ${SERVICE_HOSTNAME}" -H "Content-Type: application/json" http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/llama2_7b/infer -d @$WORK_DIR/data/translate/sample_test1.json
+curl -v -H "Host: ${SERVICE_HOSTNAME}" -H "Content-Type: application/json" http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/llama2_7b/infer -d @$WORK_DIR/data/translate/sample_text1.json
 ```
 
 ### Input data format

docs/gpt-in-a-box/kubernetes/v0.2/inference_server.md

@@ -5,12 +5,12 @@ Run the following command for starting Kubeflow serving and running inference on
 bash $WORK_DIR/llm/run.sh  -n <MODEL_NAME> -g <NUM_GPUS> -f <NFS_ADDRESS_WITH_SHARE_PATH> -m <NFS_LOCAL_MOUNT_LOCATION> -e <KUBE_DEPLOYMENT_NAME> [OPTIONAL -d <INPUT_PATH> -v <REPO_COMMIT_ID> -t <HUGGINGFACE_HUB_TOKEN>]
 ```
 
-* **n**:    Name of a [supported model](supported_models.md)
+* **n**:    Name of a [validated model](validated_models.md)
 * **d**:    Absolute path of input data folder (Optional)
 * **g**:    Number of gpus to be used to execute (Set 0 to use cpu)
 * **f**:    NFS server address with share path information
 * **m**:    Mount path to your nfs server to be used in the kube PV where model files and model archive file be stored
-* **e**:    Name of the deployment metadata
+* **e**:    Desired name of the deployment metadata (will be created)
 * **v**:    Commit ID of model's HuggingFace repository (optional, if not provided default set in model_config will be used)
 * **t**:    Your HuggingFace token. Needed for LLAMA(2) model.
 

docs/gpt-in-a-box/kubernetes/v0.2/supported_models.md → docs/gpt-in-a-box/kubernetes/v0.2/validated_models.md

@@ -1,8 +1,8 @@
-# Supported Models for Kubernetes Version
+# Validated Models for Kubernetes Version
 
-GPT-in-a-Box currently supports a curated set of HuggingFace models Information pertaining to these models is stored in the ```llm/model_config.json``` file.
+GPT-in-a-Box has been validated on a curated set of HuggingFace models Information pertaining to these models is stored in the ```llm/model_config.json``` file.
 
-The Supported Models are :
+The Validated Models are :
 
 | Model Name | HuggingFace Repository ID |
 | --- | --- |

docs/gpt-in-a-box/vm/v0.3/generating_mar.md

@@ -12,7 +12,7 @@ python3 $WORK_DIR/llm/generate.py [--skip_download --repo_version <REPO_VERSION>
 ```
 Where the arguments are : 
 
-- **model_name**:      Name of a [supported model](supported_models.md)
+- **model_name**:      Name of a [validated model](validated_models.md)
 - **repo_version**:    Commit ID of model's HuggingFace repository (optional, if not provided default set in model_config will be used)
 - **model_path**:      Absolute path of model files (should be empty if downloading)
 - **mar_output**:      Absolute path of export of MAR file (.mar)

docs/gpt-in-a-box/vm/v0.3/huggingface_model.md

@@ -1,6 +1,6 @@
 # HuggingFace Model Support
 !!! Note
-    To start the inference server for the [**Supported Models**](supported_models.md), refer to the [**Deploying Inference Server**](inference_server.md) documentation.
+    To start the inference server for the [**Validated Models**](validated_models.md), refer to the [**Deploying Inference Server**](inference_server.md) documentation.
 
 We provide the capability to download model files from any HuggingFace repository and generate a MAR file to start an inference server using it with Torchserve.
 

docs/gpt-in-a-box/vm/v0.3/inference_server.md

@@ -6,7 +6,7 @@ bash $WORK_DIR/llm/run.sh -n <MODEL_NAME> -a <MAR_EXPORT_PATH> [OPTIONAL -d <INP
 ```
 Where the arguments are :
 
-- **n**:    Name of a [supported model](supported_models.md)
+- **n**:    Name of a [validated model](validated_models.md)
 - **v**:    Commit ID of model's HuggingFace repository (optional, if not provided default set in model_config will be used)
 - **d**:    Absolute path of input data folder (optional)
 - **a**:    Absolute path to the Model Store directory

docs/gpt-in-a-box/vm/v0.3/supported_models.md → docs/gpt-in-a-box/vm/v0.3/validated_models.md

@@ -1,8 +1,8 @@
-# Supported Models for Virtual Machine Version
+# Validated Models for Virtual Machine Version
 
-GPT-in-a-Box currently supports a curated set of HuggingFace models. Information pertaining to these models is stored in the ```llm/model_config.json``` file.
+GPT-in-a-Box has been validated on a curated set of HuggingFace models. Information pertaining to these models is stored in the ```llm/model_config.json``` file.
 
-The Supported Models are :
+The Validated Models are :
 
 | Model Name | HuggingFace Repository ID |
 | --- | --- |

mkdocs.yml

@@ -123,7 +123,7 @@ nav:
         - "Deploy on Virtual Machine":
             - "v0.3":
                 - "Getting Started": "gpt-in-a-box/vm/v0.3/getting_started.md"
-                - "Supported Models": "gpt-in-a-box/vm/v0.3/supported_models.md"
+                - "Validated Models": "gpt-in-a-box/vm/v0.3/validated_models.md"
                 - "Generating Model Archive File": "gpt-in-a-box/vm/v0.3/generating_mar.md"
                 - "Deploying Inference Server": "gpt-in-a-box/vm/v0.3/inference_server.md"
                 - "Inference Requests": "gpt-in-a-box/vm/v0.3/inference_requests.md"
@@ -142,7 +142,7 @@ nav:
         - "Deploy on Kubernetes":
             - "v0.2":
                 - "Getting Started": "gpt-in-a-box/kubernetes/v0.2/getting_started.md"
-                - "Supported Models": "gpt-in-a-box/kubernetes/v0.2/supported_models.md"
+                - "Validated Models": "gpt-in-a-box/kubernetes/v0.2/validated_models.md"
                 - "Generating Model Archive File": "gpt-in-a-box/kubernetes/v0.2/generating_mar.md"
                 - "Deploying Inference Server": "gpt-in-a-box/kubernetes/v0.2/inference_server.md"
                 - "Inference Requests": "gpt-in-a-box/kubernetes/v0.2/inference_requests.md"