review updates

Author: nullfunc

docs/concepts/managed-llms/openai-access-gateway.md

@@ -37,7 +37,7 @@ The `x-defang-llm` extension is used to configure the appropriate roles and perm
 
 ## Model Mapping
 
-Defang supports model mapping through the openai-access-gateway on AWS and GCP. This takes a model with a Docker naming convention (e.g. ai/lama3.3) and maps it to the closest matching model name on the target platform. If no such match can be found it can fallback onto a known existing model (e.g. ai/mistral). These environment variables are USE_MODEL_MAPPING (default to true) and FALLBACK_MODEL (no default), respectively.
+Defang supports model mapping through the [openai-access-gateway](https://github.com/DefangLabs/openai-access-gateway) on AWS and GCP. This takes a model with a Docker naming convention (e.g. ai/lama3.3) and maps it to the closest matching model name on the target platform. If no such match can be found it can fallback onto a known existing model (e.g. ai/mistral). These environment variables are USE_MODEL_MAPPING (default to true) and FALLBACK_MODEL (no default), respectively.
 
 ## Current Support
 

docs/tutorials/deploy-openai-apps-aws-bedrock.mdx

@@ -110,11 +110,11 @@ Choose the correct `MODEL` depending on which cloud provider you are using.
 - For **AWS Bedrock**, use a Bedrock model ID (e.g., `anthropic.claude-3-sonnet-20240229-v1:0`) [See available Bedrock models](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html).
 :::
 
-Alternatively, Defang supports model mapping through the openai-access-gateway. This takes a model with a Docker naming convention (e.g. ai/lama3.3) and maps it to
+Alternatively, Defang supports model mapping through the openai-access-gateway. This takes a model with a Docker naming convention (e.g. `ai/lama3.3`) and maps it to
 the closest equilavent on the target platform. If no such match can be found a fallback can be defined to use a known existing model (e.g. ai/mistral). These environment
 variables are USE_MODEL_MAPPING (default to true) and FALLBACK_MODEL (no default), respectively.
 
-
+  
 :::info
 # Complete Example Compose File
 
@@ -151,13 +151,13 @@ services:
 | Variable           | AWS Bedrock |
 |--------------------|-------------|
 | `REGION`            | Required|
-| `MODEL`             | Bedrock model ID / Docker model name |
+| `MODEL`             | Bedrock model ID or Docker model name, for example `meta.llama3-3-70b-instruct-v1:0` or `ai/lama3.3` |
 
 ---
 
 You now have a single app that can:
 
-- Talk to **GCP Vertex AI**
+- Talk to **AWS Bedrock**
 - Use the same OpenAI-compatible client code
 - Easily switch cloud providers by changing a few environment variables
 :::

docs/tutorials/deploy-openai-apps-gcp-vertex.mdx

@@ -117,7 +117,7 @@ To do this, you can check your [AWS Bedrock model access](https://docs.aws.amazo
 
 Alternatively, Defang supports model mapping through the openai-access-gateway. This takes a model with a Docker naming convention (e.g. ai/lama3.3) and maps it to
 the closest matching one on the target platform. If no such match can be found it can fallback onto a known existing model (e.g. ai/mistral). These environment
-variables are USE_MODEL_MAPPING (default to true) and FALLBACK_MODEL (no default), respectively.
+variables are `USE_MODEL_MAPPING` (default to true) and `FALLBACK_MODEL` (no default), respectively.
 
 
 :::info
@@ -158,7 +158,7 @@ services:
 |--------------------|---------------|
 | `GCP_PROJECT_ID`    | Required      |
 | `REGION`            | Required      |
-| `MODEL`             | Vertex model / Docker model name |
+| `MODEL`             | Vertex model or Docker model name, for example `publishers/meta/models/llama-3.3-70b-instruct-maas` or `ai/llama3.3` |
 
 ---
 

docs/tutorials/deploy-openai-apps.mdx

@@ -9,7 +9,3 @@ Defang currently supports LLM using AWS Bedrock and GCP Vertex AI. Follow the li
 
 - [AWS Bedrock](/docs/tutorials/deploy-openai-apps-aws-bedrock/)
 - [GCP Vertex AI](/docs/tutorials/deploy-openai-apps-gcp-vertex/).
-
-
-
-