Deploy this TheBloke/vicuna-13B-v1.5-GGUF model on AWS

Deploy this TheBloke/vicuna-13B-v1.5-GGUF model on AWS

I want to use this model as an endpoint in my web application in this format:

Chatbot Requirements
1. Scope: Chatbot (Encoder/Decoder for Text Inference or Conversational)
2. Input via API (JSON): Chatgpt Style – The template can be see below

The JSON will contain 25 user messages, and the response should be the system response.
Please use this guidelines to understand API consumption: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html

3. Prompt Template for the system:
a. template = '''
You are going to be my education assistant.
System:{System}
Question:{question}'''

4. LLM Model Parameters: max_new_tokens=512, temperature=0.7, top_p=0.9
5. If possible use a AutomodelforCausalLM otherwise train a LLM model.
6. It will be deployed on AWS Sagemaker using S3 buckets.
7. The GGUF should be saved on a S3 Bucket.
8. Chat Buffer should store 25 conversations and create a session ID (No need to send this to the End point).
9. The quantized model is contained here https://huggingface.co/TheBloke/vicuna-13B-v1.5-GGUF/blob/main/vicuna-13b-v1.5.Q4_K_M.gguf
10. Use HuggingFace/Langchain when possible.
11. Deliverables: Jupyter notebook/Code – 2 Hours should be used to set up the model in AWS with the customer.

Provide me with complete source code that I can use in my jupyter notebook on aws to make an endpoint.
I need it asap.

No specific repository file, test, or entry point is named. Start by reviewing the repository's SageMaker deployment notebooks and the linked SageMaker InvokeEndpoint API, then determine whether GGUF and the requested chat buffer fit an existing example. Done would require a working notebook and endpoint implementation matching the listed model, prompt, parameters, API input, and session requirements.

Written by the indexing model from the issue text.