feat(inference): fix external links

pages/managed-inference/reference-content/supported-models.mdx

@@ -9,7 +9,7 @@ dates:
 
 Scaleway Managed Inference allows you to deploy various AI models, either from:
 
- * [Scaleway model catalog](#scaleway-model-catalog): A curated set of ready-to-deploy models available through the [Scaleway console](https:/console.scaleway.com/inference/deployments/) or the [Managed Inference models API](https:/www.scaleway.com/en/developers/api/inference/#path-models-list-models)
+ * [Scaleway model catalog](#scaleway-model-catalog): A curated set of ready-to-deploy models available through the [Scaleway console](https://console.scaleway.com/inference/deployments/) or the [Managed Inference models API](https://www.scaleway.com/en/developers/api/inference/#path-models-list-models)
  * [Custom models](#custom-models): Models that you import, typically from sources like Hugging Face.
 
 ## Scaleway model catalog
@@ -19,14 +19,14 @@ You can find a complete list of all models available in Scaleway's catalog on th
 ## Custom models
 
 <Message type="note">
-  Custom model support is currently in **beta**. If you encounter issues or limitations, please report them via our [Slack community channel](https:/scaleway-community.slack.com/archives/C01SGLGRLEA) or [customer support](https:/console.scaleway.com/support/tickets/create?for=product&productName=inference).
+  Custom model support is currently in **beta**. If you encounter issues or limitations, please report them via our [Slack community channel](https://scaleway-community.slack.com/archives/C01SGLGRLEA) or [customer support](https://console.scaleway.com/support/tickets/create?for=product&productName=inference).
 </Message>
 
 ### Prerequisites
 
 <Message type="tip">
   We recommend starting with a variation of a supported model from the Scaleway catalog.
-  For example, you can deploy a [quantized (4-bit) version of Llama 3.3](https:/huggingface.co/unsloth/Llama-3.3-70B-Instruct-bnb-4bit).
+  For example, you can deploy a [quantized (4-bit) version of Llama 3.3](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct-bnb-4bit).
   If deploying a fine-tuned version of Llama 3.3, make sure your file structure matches the example linked above.
   Examples whose compatibility has been tested are available in [tested models](#known-compatible-models).
 </Message>
@@ -37,7 +37,7 @@ To deploy a custom model via Hugging Face, ensure the following:
 
  * You must have access to the model using your Hugging Face credentials.
  * For gated models, request access through your Hugging Face account.
- * Credentials are not stored, but we recommend using [read or fine-grained access tokens](https:/huggingface.co/docs/hub/security-tokens).
+ * Credentials are not stored, but we recommend using [read or fine-grained access tokens](https://huggingface.co/docs/hub/security-tokens).
 
 #### Required files
 
@@ -46,7 +46,7 @@ Your model repository must include:
  * A `config.json` file containig:
    * An `architectures` array (see [supported architectures](#supported-models-architecture) for the exact list of supported values).
    * `max_position_embeddings`
- * Model weights in the [`.safetensors`](https:/huggingface.co/docs/safetensors/index) format
+ * Model weights in the [`.safetensors`](https://huggingface.co/docs/safetensors/index) format
  * A `tokenizer.json` file
    * If your are fine-tuning an existing model, we recommend you use the same `tokenizer.json` file from the base model.
  * A chat template included in either:
@@ -68,7 +68,7 @@ Your model must be one of the following types:
 
 <Message type="important">
   **Security Notice**<br />
-  Models using formats that allow arbitrary code execution, such as Python [`pickle`](https:/docs.python.org/3/library/pickle.html), are **not supported**.
+  Models using formats that allow arbitrary code execution, such as Python [`pickle`](https://docs.python.org/3/library/pickle.html), are **not supported**.
 </Message>
 
 ## API support