diff --git a/docs/source/distributions/self_hosted_distro/nvidia.md b/docs/source/distributions/self_hosted_distro/nvidia.md
index 920ce950f..47e38f73d 100644
--- a/docs/source/distributions/self_hosted_distro/nvidia.md
+++ b/docs/source/distributions/self_hosted_distro/nvidia.md
@@ -74,22 +74,22 @@ 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 [NVIDIA Datasetio docs](/llama_stack/providers/remote/datasetio/nvidia/README.md) for supported features and example usage.
+See the {repopath}`NVIDIA Datasetio docs::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 [NVIDIA Eval docs](/llama_stack/providers/remote/eval/nvidia/README.md) for supported features and example usage.
+See the {repopath}`NVIDIA Eval docs::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 [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 {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.
 
-See the [NVIDIA Post-Training docs](/llama_stack/providers/remote/post_training/nvidia/README.md) for supported features and example usage.
+See the {repopath}`NVIDIA Post-Training docs::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 NVIDIA Safety docs for supported features and example usage.
+See the {repopath}`NVIDIA Safety docs::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`.
@@ -174,4 +174,4 @@ 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 `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 {repopath}`docs/notebooks/nvidia`.
diff --git a/llama_stack/templates/nvidia/doc_template.md b/llama_stack/templates/nvidia/doc_template.md
index 6138e2804..3cb8245df 100644
--- a/llama_stack/templates/nvidia/doc_template.md
+++ b/llama_stack/templates/nvidia/doc_template.md
@@ -46,22 +46,22 @@ 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 [NVIDIA Datasetio docs](/llama_stack/providers/remote/datasetio/nvidia/README.md) for supported features and example usage.
+See the {repopath}`NVIDIA Datasetio docs::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 [NVIDIA Eval docs](/llama_stack/providers/remote/eval/nvidia/README.md) for supported features and example usage.
+See the {repopath}`NVIDIA Eval docs::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 [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 {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.
 
-See the [NVIDIA Post-Training docs](/llama_stack/providers/remote/post_training/nvidia/README.md) for supported features and example usage.
+See the {repopath}`NVIDIA Post-Training docs::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 NVIDIA Safety docs for supported features and example usage.
+See the {repopath}`NVIDIA Safety docs::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`.
@@ -146,4 +146,4 @@ 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 `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 {repopath}`docs/notebooks/nvidia`.