diff --git a/docs/docs/quickstart/models/customize-engine.mdx b/docs/docs/quickstart/models/customize-engine.mdx
index ec38b0790..d56fde2d8 100644
--- a/docs/docs/quickstart/models/customize-engine.mdx
+++ b/docs/docs/quickstart/models/customize-engine.mdx
@@ -1,185 +1,63 @@
---
-sidebar_position: 3
+sidebar_position: 1
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-import janModel from './assets/jan-model-hub.png';
-# Manual Import
+# Customize Engine Settings
-:::warning
+In this guide, we'll walk you through the process of customizing your engine settings by tweaking the `nitro.json` file
-This is currently under development.
+1. Navigate to the `App Settings` > `Advanced` > `Open App Directory` > `~/jan/engine` folder.
+
+
+
+ ```sh
+ cd ~/jan/engines
+ ```
+
+
+ ```sh
+ C:/Users//jan/engines
+ ```
+
+
+ ```sh
+ cd ~/jan/engines
+ ```
+
+
+
+2. Modify the `nitro.json` file based on your needs. The default settings are shown below.
+
+```json title="~/jan/engines/nitro.json"
+{
+ "ctx_len": 2048,
+ "ngl": 100,
+ "cpu_threads": 1,
+ "cont_batching": false,
+ "embedding": false
+}
+```
+
+The table below describes the parameters in the `nitro.json` file.
+
+| Parameter | Type | Description |
+| --------- | ---- | ----------- |
+| `ctx_len` | **Integer** | Typically set at `2048`, `ctx_len` provides ample context for model operations like `GPT-3.5`. (*Maximum*: `4096`, *Minimum*: `1`) |
+| `ngl` | **Integer** | Defaulted at `100`, `ngl` determines GPU layer usage. |
+| `cpu_threads` | **Integer** | Determines CPU inference threads, limited by hardware and OS. (*Maximum* determined by system) |
+| `cont_batching` | **Integer** | Controls continuous batching, enhancing throughput for LLM inference. |
+| `embedding` | **Integer** | Enables embedding utilization for tasks like document-enhanced chat in RAG-based applications. |
+
+:::tip
+ - By default, the value of `ngl` is set to 100, which indicates that it will offload all. If you wish to offload only 50% of the GPU, you can set `ngl` to 15 because most models on Mistral or Llama are around ~ 30 layers.
+ - To utilize the embedding feature, include the JSON parameter `"embedding": true`. It will enable Nitro to process inferences with embedding capabilities. Please refer to the [Embedding in the Nitro documentation](https://nitro.jan.ai/features/embed) for a more detailed explanation.
+ - To utilize the continuous batching feature for boosting throughput and minimizing latency in large language model (LLM) inference, include `cont_batching: true`. For details, please refer to the [Continuous Batching in the Nitro documentation](https://nitro.jan.ai/features/cont-batch).
:::
-This section will show you how to perform manual import. In this guide, we are using a GGUF model from [HuggingFace](https://huggingface.co/) and our latest model, [Trinity](https://huggingface.co/janhq/trinity-v1-GGUF), as an example.
-
-## Newer versions - nightly versions and v0.4.4+
-
-### 1. Create a Model Folder
-
-1. Navigate to the `App Settings` > `Advanced` > `Open App Directory` > `~/jan/models` folder.
-
-
-
- ```sh
- cd ~/jan/models
- ```
-
-
- ```sh
- C:/Users//jan/models
- ```
-
-
- ```sh
- cd ~/jan/models
- ```
-
-
-
-2. In the `models` folder, create a folder with the name of the model.
-
-```sh
-mkdir trinity-v1-7b
-```
-
-### 2. Drag & Drop the Model
-
-Drag and drop your model binary into this folder, ensuring the `modelname.gguf` is the same name as the folder name, e.g. `models/modelname`.
-
-### 3. Done!
-
-If your model doesn't show up in the **Model Selector** in conversations, **restart the app** or contact us via our [Discord community](https://discord.gg/Dt7MxDyNNZ).
-
-## Older versions - before v0.4.4
-
-### 1. Create a Model Folder
-
-1. Navigate to the `App Settings` > `Advanced` > `Open App Directory` > `~/jan/models` folder.
-
-
-
- ```sh
- cd ~/jan/models
- ```
-
-
- ```sh
- C:/Users//jan/models
- ```
-
-
- ```sh
- cd ~/jan/models
- ```
-
-
-
-2. In the `models` folder, create a folder with the name of the model.
-
-```sh
-mkdir trinity-v1-7b
-```
-
-### 2. Create a Model JSON
-
-Jan follows a folder-based, [standard model template](https://jan.ai/docs/engineering/models/) called a `model.json` to persist the model configurations on your local filesystem.
-
-This means that you can easily reconfigure your models, export them, and share your preferences transparently.
-
-
-
- ```sh
- cd trinity-v1-7b
- touch model.json
- ```
-
-
- ```sh
- cd trinity-v1-7b
- echo {} > model.json
- ```
-
-
- ```sh
- cd trinity-v1-7b
- touch model.json
- ```
-
-
-
-To update `model.json`:
-
- - Match `id` with folder name.
- - Ensure GGUF filename matches `id`.
- - Set `source.url` to direct download link ending in `.gguf`. In HuggingFace, you can find the direct links in the `Files and versions` tab.
- - Verify that you are using the correct `prompt_template`. This is usually provided in the HuggingFace model's description page.
-
-```json title="model.json"
-{
- "sources": [
- {
- "filename": "trinity-v1.Q4_K_M.gguf",
- "url": "https://huggingface.co/janhq/trinity-v1-GGUF/resolve/main/trinity-v1.Q4_K_M.gguf"
- }
- ],
- "id": "trinity-v1-7b",
- "object": "model",
- "name": "Trinity-v1 7B Q4",
- "version": "1.0",
- "description": "Trinity is an experimental model merge of GreenNodeLM & LeoScorpius using the Slerp method. Recommended for daily assistance purposes.",
- "format": "gguf",
- "settings": {
- "ctx_len": 4096,
- "prompt_template": "{system_message}\n### Instruction:\n{prompt}\n### Response:",
- "llama_model_path": "trinity-v1.Q4_K_M.gguf"
- },
- "parameters": {
- "max_tokens": 4096
- },
- "metadata": {
- "author": "Jan",
- "tags": ["7B", "Merged"],
- "size": 4370000000
- },
- "engine": "nitro"
-}
-```
-#### Regarding `model.json`
-
-- In `settings`, two crucial values are:
- - `ctx_len`: Defined based on the model's context size.
- - `prompt_template`: Defined based on the model's trained template (e.g., ChatML, Alpaca).
- - To set up the `prompt_template`:
- 1. Visit Hugging Face.
- 2. Locate the model (e.g., [Gemma 7b it](https://huggingface.co/google/gemma-7b-it)).
- 3. Review the text and identify the template.
-- In `parameters`, consider the following options. The fields in `parameters` are typically general and can be the same across models. An example is provided below:
-
-```json
-"parameters":{
- "temperature": 0.7,
- "top_p": 0.95,
- "stream": true,
- "max_tokens": 4096,
- "frequency_penalty": 0,
- "presence_penalty": 0
-}
-```
-
-### 3. Download the Model
-
-1. Restart Jan and navigate to the Hub.
-2. Locate your model.
-3. Click **Download** button to download the model binary.
-
-
-
-