CerebriumAI · milo157 · Aug 26, 2026 · Aug 26, 2026
@@ -21,7 +21,7 @@ Deploying a model incurs two billable processes:
   on cold start (e.g., loading a model into GPU RAM, importing packages). This
   time is billed.
 - <u>Function runtime</u>: Code inside the request function, executed on every
-  request
+  request.
 
 **Example cost calculation**
 

@@ -3,7 +3,7 @@ title: "Custom Dockerfiles"
 description: "Run generic containerized applications on Cerebrium using your own custom Dockerfiles."
 ---
 
-Cerebrium supports deploying existing containerized apps — from standard Python apps to compiled Rust binaries - using a custom Dockerfile. This allows portable, locally reproducible deployment environments.
+Cerebrium supports deploying existing containerized apps — from standard Python apps to compiled Rust binaries — using a custom Dockerfile. This allows portable, locally reproducible deployment environments.
 
 ## Building Dockerized Python Apps
 

@@ -19,7 +19,7 @@ cerebrium init cog-migration-sdxl
 
 Cerebrium and Replicate both use a setup file: **cog.yaml** and **cerebrium.toml** for Replicate and Cerebrium respectively.
 
-Based on the cog.yaml, add/change the following in `cerebrium.toml`
+Based on the cog.yaml, add/change the following in `cerebrium.toml`:
 
 ```python
 [cerebrium.deployment]

@@ -77,7 +77,7 @@ After creating the domain, DNS configuration instructions will be displayed. Cre
 1. After configuring DNS, return to the Cerebrium dashboard
 2. Cerebrium will automatically attempt to validate DNS records every 30 minutes for up to 2 days
 3. To trigger an immediate validation attempt, click "Validation Status" -> "Validate Domain" (this works even after the 2-day window has elapsed)
-4. If validation fails, the dialog will show the last known error.
+4. If validation fails, the dialog will show the last known error
 5. Once validated, SSL certificates will be automatically provisioned
 
 ### Step 4: Start Using the Custom Domain

@@ -42,7 +42,7 @@ cerebrium init deepgram
 
 2. Create a self-hosted API key from the <b>Deepgram</b> dashboard. Navigate to the **Secrets** tab in the <b>Cerebrium</b> dashboard and add the API key with the name `DEEPGRAM_API_KEY`. This secret automatically becomes available as an environment variable in the deployment.
 
-3. Download model files from Deepgram's self-hosted section in the <b>Deepgram</b> dashboard using the guide available, [here](https://developers.deepgram.com/docs/deploy-deepgram-services#pull-deepgram-container-images). Select the 'license proxy' deployment type. Upload downloaded model files using the links provided by your Account Representative (with `.dg` extension) to persistent-storage in the `/deepgram-models` folder. This folder automatically attaches to the engine container. Use this command to upload the files:
+3. Download model files from Deepgram's self-hosted section in the <b>Deepgram</b> dashboard using the guide available [here](https://developers.deepgram.com/docs/deploy-deepgram-services#pull-deepgram-container-images). Select the 'license proxy' deployment type. Upload downloaded model files using the links provided by your Account Representative (with `.dg` extension) to persistent-storage in the `/deepgram-models` folder. This folder automatically attaches to the engine container. Use this command to upload the files:
 
 ```bash
 cerebrium cp <model>.dg deepgram-models/<model>.dg

@@ -31,7 +31,7 @@ The default ComfyUI workflow interface appears in this view. Use this locally ru
 
 ### Export ComfyUI Workflow
 
-The example GitHub repository contains a workflow.json file. Click the “Load” button on the right to load the workflow. It should appear populated
+The example GitHub repository contains a workflow.json file. Click the “Load” button on the right to load the workflow. It should appear populated.
 
 ![ComfyUI Workflow](/images/examples/comfyui1.png)
 
@@ -41,7 +41,7 @@ To export the workflow in API format, click the gear icon (settings) in the top-
 
 ![Save ComfyUI API format](/images/examples/comfyui2.png)
 
-A button appears in the right hover panel labeled “Save (API format)”. Use it to save the workflow as “workflow_api.json”
+A button appears in the right hover panel labeled “Save (API format)”. Use it to save the workflow as “workflow_api.json”.
 
 ### ComfyUI Application
 

@@ -29,7 +29,7 @@ Here are some frontend primitives for creating multi-step workflows:
 
 ![SGLang Architecture](/images/sglang-arch.png)
 
-Here is a summary of key advantages over traditional inference engines
+Here is a summary of key advantages over traditional inference engines:
 
 | Feature                 | Traditional Engines (vLLM, TGI)                  | SGLang                                                                     |
 | ----------------------- | ------------------------------------------------ | -------------------------------------------------------------------------- |
@@ -40,7 +40,7 @@ Here is a summary of key advantages over traditional inference engines
 | **Performance**         | Standard inference optimization                  | PyTorch-native with `torch.compile()`, quantization, sparse inference      |
 
 For more details, see this [article](https://huggingface.co/blog/paresh2806/sglang-efficient-llm-workflows).
-You can see the final code sample [here](https://github.com/CerebriumAI/examples/tree/master/5-large-language-models/7-vision-language-sglang)
+You can see the final code sample [here](https://github.com/CerebriumAI/examples/tree/master/5-large-language-models/7-vision-language-sglang).
 
 ## Tutorial
 
@@ -239,7 +239,7 @@ Once deployed, test with a sample request:
 curl -X POST "https://api.cerebrium.ai/v4/p-xxxxxxxx/7-vision-language-sglang/analyze" \
   -H "Content-Type: application/json" \
   -d '{
-    "company_description": "Nike is a global leader in athletic footwear, apparel, and sports equipment known for its innovative designs and the iconic “swoosh” logo. The brand embodies performance, style, and inspiration, empowering athletes worldwide to Just Do It."",
+    "company_description": "Nike is a global leader in athletic footwear, apparel, and sports equipment known for its innovative designs and the iconic “swoosh” logo. The brand embodies performance, style, and inspiration, empowering athletes worldwide to Just Do It.",
     "image_base64": "<BASE64_ENCODED_IMAGE>",
     "dimensions": ["Effectiveness","Clarity", "Appeal","Credibility"]
   }'
@@ -263,7 +263,3 @@ curl -X POST "https://api.cerebrium.ai/v4/p-xxxxxxxx/7-vision-language-sglang/an
 This example demonstrates how to leverage SGLang's structured generation capabilities to build an ad analysis system, using features like `fork()` for parallel processing and SGLang's built-in output control.
 
 You can find the complete code for this tutorial in our [examples repository](https://github.com/CerebriumAI/examples/tree/master/5-large-language-models/7-vision-language-sglang).
-
-```
-
-```
@@ -3,7 +3,7 @@ title: "Serving GPT-OSS with vLLM"
 description: "Deploy OpenAI's Latest Open Source Model"
 ---
 
-GPT recently released GPT-OSS ([gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) and [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b)) two state-of-the-art open-weight language models that deliver strong real-world performance at low cost. Available under the flexible Apache 2.0 license, these models outperform similarly sized open models on reasoning tasks, demonstrate strong tool use capabilities, and are optimized for efficient deployment on consumer hardware.
+GPT recently released GPT-OSS ([gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) and [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b)), two state-of-the-art open-weight language models that deliver strong real-world performance at low cost. Available under the flexible Apache 2.0 license, these models outperform similarly sized open models on reasoning tasks, demonstrate strong tool use capabilities, and are optimized for efficient deployment on consumer hardware.
 
 ## What Makes GPT-OSS Special?
 
@@ -53,7 +53,6 @@ max_replicas = 5
 cooldown = 30
 replica_concurrency = 32
 scaling_metric = "concurrency_utilization"
-scaling
 
 [cerebrium.runtime.custom]
 port = 8000

@@ -354,7 +354,7 @@ Set up LangSmith monitoring:
 2. Create a free LangSmith account [here](https://smith.langchain.com/)
 3. Generate API key (click gear icon in bottom left)
 
-Set the following environment variables at the top of main.py. Add the API key to Cerebrium secrets
+Set the following environment variables at the top of main.py. Add the API key to Cerebrium secrets.
 
 ```python
 import os
@@ -379,7 +379,7 @@ After a successful test run, data populates in LangSmith:
 
 The Runs tab shows all runs (invocations/API requests).
 
-In 1 above, the function name appears, with input set to the Cerebrium RunID (set to “test”. The input and total latency of the run are also visible.
+In 1 above, the function name appears, with input set to the Cerebrium RunID (set to “test”). The input and total latency of the run are also visible.
 
 LangSmith supports various data automations:
 

@@ -125,14 +125,14 @@ Each service offers a generous free tier:
 
 - **Deepgram**
 
-You can signup for a Deepgram account [here](https://www.notion.so/Livekit-Outbound-agent-13ebfd664fb180c5b02bf9c71f72b23d?pvs=21). Straight from the dashboard, you can create a API key.
-Store this value for later
+You can signup for a Deepgram account [here](https://www.notion.so/Livekit-Outbound-agent-13ebfd664fb180c5b02bf9c71f72b23d?pvs=21). Straight from the dashboard, you can create an API key.
+Store this value for later.
 
 ![Deepgram Dashboard](/images/examples/livekit-agent/deepgram-dashboard.png)
 
 - **OpenAI**
 
-You can signup for a OpenAI account [here](https://platform.openai.com/signup). You can then click “Dashboard” top right and then “API keys” in the left sidebar.
+You can signup for an OpenAI account [here](https://platform.openai.com/signup). You can then click “Dashboard” top right and then “API keys” in the left sidebar.
 Create an API key and store this value to use later.
 
 ![OpenAI Dashboard](/images/examples/livekit-agent/openai-dashboard.png)
@@ -226,7 +226,7 @@ async def entrypoint(ctx: JobContext):
             temperature=0.5,
         ),
         tts=cartesia.TTS(),
-        # intial ChatContext with system prompt
+        # initial ChatContext with system prompt
         chat_ctx=initial_ctx,
         # whether the agent can be interrupted
         allow_interruptions=True,

@@ -464,7 +464,7 @@ The endpoints are used in the frontend interface below.
 
 A public fork of the PipeCat frontend demonstrates this application. Clone the repo [here](https://github.com/CerebriumAI/web-client-ui).
 
-Follow the instructions in the README.md and then populate the following variables in your .env.development.local
+Follow the instructions in the README.md and then populate the following variables in your .env.development.local.
 
 ```
 VITE_SERVER_URL=https://api.cerebrium.ai/v4/p-xxxxxxxx/<APP_NAME> #This is the base url of your pipecat-agent. Do not include the function names