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Update NOTES.txt to improve Azimuth UI help text
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‎charts/azimuth-chat/templates/NOTES.txt‎

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The LLM chatbot app allows users to deploy machine learning models from [HuggingFace](https://huggingface.co/models) and interact with them through a simple web interface.
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The models can also be interacted with via OpenAI API by other applications running on the same Kubernetes cluster using the in-cluster API address `http://{{ .Release.Name }}.{{ .Release.Namespace }}.svc` - see [here](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html) for available API endpoints.
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The target Kubernetes cluster must have a GPU worker node group configured, otherwise the app will remain in an 'Installing' state until a GPU node becomes available for scheduling.
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On deployment of a new model, the app must first download the model's weights from HuggingFace.
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This can take a significant amount of time depending on model choice and network speeds.
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Download progress can be monitored by inspecting the logs for the LLM API pod(s) via the Kubernetes Dashboard for the target cluster.
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The app uses [vLLM](https://docs.vllm.ai/en/latest/) as a model serving backend and [Gradio](https://github.com/gradio-app/gradio) + [LangChain](https://python.langchain.com/docs/get_started/introduction) to provide the web interface.
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The official list of HuggingFace models supported by vLLM can be found [here](https://docs.vllm.ai/en/latest/models/supported_models.html), though some of these may not be compatible with the LangChain prompt format.
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Release notes for the application can be found at https://github.com/stackhpc/azimuth-llm/releases

‎charts/azimuth-image-analysis/templates/NOTES.txt‎

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The LLM image analysis app allows users to deploy machine learning models from [HuggingFace](https://huggingface.co/models) and interact with them through a simple web interface.
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The models can also be interacted with via OpenAI API by other applications running on the same Kubernetes cluster using the in-cluster API address `http://{{ .Release.Name }}.{{ .Release.Namespace }}.svc` - see [here](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html) for available API endpoints.
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The target Kubernetes cluster must have a GPU worker node group configured, otherwise the app will remain in an 'Installing' state until a GPU node becomes available for scheduling.
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On deployment of a new model, the app must first download the model's weights from HuggingFace.
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This can take a significant amount of time depending on model choice and network speeds.
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Download progress can be monitored by inspecting the logs for the LLM API pod(s) via the Kubernetes Dashboard for the target cluster.
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The app uses [vLLM](https://docs.vllm.ai/en/latest/) as a model serving backend and [Gradio](https://github.com/gradio-app/gradio) to provide the web interface.
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The official list of HuggingFace models supported by vLLM can be found [here](https://docs.vllm.ai/en/latest/models/supported_models.html), though some of these may not be compatible with the LangChain prompt format.
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Release notes for the application can be found at https://github.com/stackhpc/azimuth-llm/releases

‎charts/azimuth-llm/templates/NOTES.txt‎

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The LLM chatbot app allows users to deploy machine learning models from [HuggingFace](https://huggingface.co/models) and interact with them through a simple web interface.
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The models can also be interacted with via OpenAI API by other applications running on the same Kubernetes cluster using the in-cluster API address `http://{{ .Release.Name }}.{{ .Release.Namespace }}.svc` - see [here](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html) for available API endpoints.
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The target Kubernetes cluster must have a GPU worker node group configured, otherwise the app will remain in an 'Installing' state until a GPU node becomes available for scheduling.
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Download progress can be monitored by inspecting the logs for the LLM API pod(s) via the Kubernetes Dashboard for the target cluster.
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The app uses [vLLM](https://docs.vllm.ai/en/latest/) as a model serving backend and [Gradio](https://github.com/gradio-app/gradio) + [LangChain](https://python.langchain.com/docs/get_started/introduction) to provide the web interface.
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The official list of HuggingFace models supported by vLLM can be found [here](https://docs.vllm.ai/en/latest/models/supported_models.html), though some of these may not be compatible with the LangChain prompt format.
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See [this documentation](https://github.com/stackhpc/azimuth-llm/) for a non-exhaustive list of languange models against which the app has been tested.
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Release notes for the application can be found at https://github.com/stackhpc/azimuth-llm/releases

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