added an image and fixed some linking

Author: aowen14

guides/deleteinstance.mdx

@@ -10,7 +10,7 @@ Stopping Instances helps save money on GPU deployments when you're not using the
 ### Setup
 Let's assume that you have a Shadeform API key from [here](https://platform.shadeform.ai/settings/api), 
 and an active instance. If you need an active instance, then 
-[this guide](https://docs.shadeform.ai/findingmostaffordablegpus) will help you get set up.
+[this guide](/guides/mostaffordablegpus) will help you get set up.
 
 ### Deleting an instance:
 

guides/mostaffordablegpus.mdx

@@ -15,7 +15,7 @@ In such cases, we provide an API that can help search for the most affordable GP
 
 ### Using the Instance Types API
 
-We can find the most affordable GPU instances by applying filtering and sorting query params to the [Instance Types API](/api-reference/endpoint/instances-types).
+We can find the most affordable GPU instances by applying filtering and sorting query params to the [Instance Types API](/api-reference/instances/instances-types).
 Without any query parameters, the API returns all of the instance types supported by Shadeform in a random order.
 By using the following query parameters, we can filter down the results and sort them as needed.
 
@@ -259,7 +259,7 @@ From array of results returned, we can see that the A6000 instance from RunPod i
 ```
 
 ### Launching the Instance
-After you have found the most affordable instance, you can launch the instance by using the [Create Instance API](/api-reference/endpoint/instances-create).
+After you have found the most affordable instance, you can launch the instance by using the [Create Instance API](/api-reference/instances/instances-create).
 In the example below, we will launch the most affordable A6000 GPU based on the data retrieved from the API call in the previous section.
 We will take the following properties from the previous response:
 

guides/serveembeddings.mdx

@@ -12,9 +12,9 @@ In this guide, we will show you how you can serve an embedding model using 'Text
 This framework runs inference very quicky and can handle large batch sizes.
 
 ### Setup
-This guide builds off of our others for [finding the best gpu](https://docs.shadeform.ai/findingmostaffordablegpus).
+This guide builds off of our others for [finding the best gpu](/guides/mostaffordablegpus).
 
-We have a python notebook already to go for you to deploy this model that you can [find here](https://github.com/shadeform/examples/blob/main/basic_serving_embeddigns_tei.ipynb).
+We have a python notebook already to go for you to deploy this model that you can [find here](https://github.com/shadeform/examples/blob/main/serving_embeddings_tei.ipynb).
 The requirements are simple, so in a python environment with `requests` installed:
 
 ```bash
@@ -71,7 +71,7 @@ print(response.text)
 
 Once we request it, Shadeform will provision the machine, and deploy a docker container based on the image, arguments, and environment variables that we selected.
 This might take 5-10 minutes depending on the machine chosen and the size of the model weights you choose.
-For more information on the API fields, check out the [Create Instance API Reference](https://docs.shadeform.ai/api-reference/endpoint/instances-create).
+For more information on the API fields, check out the [Create Instance API Reference](/api-reference/instances/instances-create).
 
 ### Checking on our server
 

guides/tgi.mdx

@@ -10,7 +10,7 @@ Model Deployment is a very common GPU use case. [Text-Generation-Inference](http
 
 With Shadeform, it's easy to deploy models right to the most affordable gpu's in the market with just a few commands.
 
-In this guide, we will deploy [Mistral-7b-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) with `TGI` onto an A6000. This guide is very similar to our [vLLM guide]() with a few changes to change the inference framework.
+In this guide, we will deploy [Mistral-7b-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) with `TGI` onto an A6000. This guide is very similar to our [vLLM guide](/guides/vllm) with a few changes to change the inference framework.
 
 ```bash
 git clone https://github.com/shadeform/examples.git
@@ -69,7 +69,7 @@ print(response.text)
 Once we request it, Shadeform will provision the machine, and deploy a docker container with the image, arguments, and environment variables provided. In this case, it will deploy an openai compatible server with `TGI` serving Mistral-7b-v0.1.
 This might take 5-10 minutes depending on the machine chosen and the size of the model weights you choose.
 
-For more information on the API fields, check out the [Create Instance API Reference](https://docs.shadeform.ai/api-reference/endpoint/instances-create).
+For more information on the API fields, check out the [Create Instance API Reference](/api-reference/instances/instances-create).
 
 ### Checking on our Model server
 

guides/vllm.mdx

@@ -11,7 +11,7 @@ In this guide, we will deploy [Mistral-7b-v0.1](https://huggingface.co/mistralai
 
 ### Setup
 
-This guide builds off of our others for [finding the best gpu](https://docs.shadeform.ai/findingmostaffordablegpus) and for [deploying gpu containers](https://docs.shadeform.ai/deployinggpucontainers).
+This guide builds off of our others for [finding the best gpu](/guides/mostaffordablegpus) and for [deploying gpu containers](/guides/dockercontainers).
 We have a python notebook already to go for you to deploy this model that you can [find here](https://github.com/shadeform/examples/blob/main/basic_serving_vllm.ipynb).
 The requirements are simple, so in a python environment with (`requests` + optionally `openai`) installed:
 
@@ -70,7 +70,7 @@ print(response.text)
 Once we request it, Shadeform will provision the machine, and deploy a docker container with the image, arguments, and environment variables provided. In this case, it will deploy an openai compatible server with `vLLM` serving Mistral-7b-v0.1.
 This might take 5-10 minutes depending on the machine chosen and the size of the model weights you choose.
 
-For more information on the API fields, check out the [Create Instance API Reference](https://docs.shadeform.ai/api-reference/endpoint/instances-create).
+For more information on the API fields, check out the [Create Instance API Reference](/api-reference/instances/instances-create).
 
 ### Checking on our Model server