diff --git a/llama_stack/distributions/nvidia/doc_template.md b/llama_stack/distributions/nvidia/doc_template.md
index 56e99e523..fbee17ef8 100644
--- a/llama_stack/distributions/nvidia/doc_template.md
+++ b/llama_stack/distributions/nvidia/doc_template.md
@@ -49,22 +49,22 @@ The deployed platform includes the NIM Proxy microservice, which is the service
 ### Datasetio API: NeMo Data Store
 The NeMo Data Store microservice serves as the default file storage solution for the NeMo microservices platform. It exposts APIs compatible with the Hugging Face Hub client (`HfApi`), so you can use the client to interact with Data Store. The `NVIDIA_DATASETS_URL` environment variable should point to your NeMo Data Store endpoint.
 
-See the {repopath}`NVIDIA Datasetio docs::llama_stack/providers/remote/datasetio/nvidia/README.md` for supported features and example usage.
+See the [NVIDIA Datasetio docs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/datasetio/nvidia/README.md) for supported features and example usage.
 
 ### Eval API: NeMo Evaluator
 The NeMo Evaluator microservice supports evaluation of LLMs. Launching an Evaluation job with NeMo Evaluator requires an Evaluation Config (an object that contains metadata needed by the job). A Llama Stack Benchmark maps to an Evaluation Config, so registering a Benchmark creates an Evaluation Config in NeMo Evaluator. The `NVIDIA_EVALUATOR_URL` environment variable should point to your NeMo Microservices endpoint.
 
-See the {repopath}`NVIDIA Eval docs::llama_stack/providers/remote/eval/nvidia/README.md` for supported features and example usage.
+See the [NVIDIA Eval docs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/eval/nvidia/README.md) for supported features and example usage.
 
 ### Post-Training API: NeMo Customizer
-The NeMo Customizer microservice supports fine-tuning models. You can reference {repopath}`this list of supported models::llama_stack/providers/remote/post_training/nvidia/models.py` that can be fine-tuned using Llama Stack. The `NVIDIA_CUSTOMIZER_URL` environment variable should point to your NeMo Microservices endpoint.
+The NeMo Customizer microservice supports fine-tuning models. You can reference [this list of supported models](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/post_training/nvidia/models.py) that can be fine-tuned using Llama Stack. The `NVIDIA_CUSTOMIZER_URL` environment variable should point to your NeMo Microservices endpoint.
 
-See the {repopath}`NVIDIA Post-Training docs::llama_stack/providers/remote/post_training/nvidia/README.md` for supported features and example usage.
+See the [NVIDIA Post-Training docs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/post_training/nvidia/README.md) for supported features and example usage.
 
 ### Safety API: NeMo Guardrails
 The NeMo Guardrails microservice sits between your application and the LLM, and adds checks and content moderation to a model. The `GUARDRAILS_SERVICE_URL` environment variable should point to your NeMo Microservices endpoint.
 
-See the {repopath}`NVIDIA Safety docs::llama_stack/providers/remote/safety/nvidia/README.md` for supported features and example usage.
+See the [NVIDIA Safety docs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/safety/nvidia/README.md) for supported features and example usage.
 
 ## Deploying models
 In order to use a registered model with the Llama Stack APIs, ensure the corresponding NIM is deployed to your environment. For example, you can use the NIM Proxy microservice to deploy `meta/llama-3.2-1b-instruct`.
@@ -138,4 +138,4 @@ llama stack run ./run.yaml \
 ```
 
 ## Example Notebooks
-For examples of how to use the NVIDIA Distribution to run inference, fine-tune, evaluate, and run safety checks on your LLMs, you can reference the example notebooks in {repopath}`docs/notebooks/nvidia`.
+For examples of how to use the NVIDIA Distribution to run inference, fine-tune, evaluate, and run safety checks on your LLMs, you can reference the example notebooks in [docs/notebooks/nvidia](https://github.com/meta-llama/llama-stack/tree/main/docs/notebooks/nvidia).
diff --git a/scripts/provider_codegen.py b/scripts/provider_codegen.py
index 88776cba8..207840d49 100755
--- a/scripts/provider_codegen.py
+++ b/scripts/provider_codegen.py
@@ -229,11 +229,21 @@ def generate_provider_docs(progress, provider_spec: Any, api_name: str) -> str:
 
             # Handle multiline default values and escape problematic characters for MDX
             if "\n" in default:
-                default = default.replace("\n", "<br/>").replace("<", "&lt;").replace(">", "&gt;").replace("{", "&#123;").replace("}", "&#125;")
+                default = (
+                    default.replace("\n", "<br/>")
+                    .replace("<", "&lt;")
+                    .replace(">", "&gt;")
+                    .replace("{", "&#123;")
+                    .replace("}", "&#125;")
+                )
             else:
-                default = default.replace("<", "&lt;").replace(">", "&gt;").replace("{", "&#123;").replace("}", "&#125;")
+                default = (
+                    default.replace("<", "&lt;").replace(">", "&gt;").replace("{", "&#123;").replace("}", "&#125;")
+                )
 
             description_text = field_info["description"] or ""
+            # Escape curly braces in description text for MDX compatibility
+            description_text = description_text.replace("{", "&#123;").replace("}", "&#125;")
 
             md_lines.append(f"| `{field_name}` | `{field_type}` | {required} | {default} | {description_text} |")