diff --git a/docs/docs/quickstart/models/customize-engine.mdx b/docs/docs/quickstart/models/customize-engine.mdx
index a44060cb0..ec38b0790 100644
--- a/docs/docs/quickstart/models/customize-engine.mdx
+++ b/docs/docs/quickstart/models/customize-engine.mdx
@@ -1,62 +1,184 @@
---
-sidebar_position: 1
+sidebar_position: 3
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
+import janModel from './assets/jan-model-hub.png';
-# Customize Engine Settings
+# Manual Import
-In this guide, we'll walk you through the process of customizing your engine settings by tweaking the `nitro.json` file
+:::warning
-1. Navigate to the `App Settings` > `Advanced` > `Open App Directory` > `~/jan/engine` folder.
+This is currently under development.
+
+:::
+
+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/engines
+ cd ~/jan/models
```
```sh
- C:/Users//jan/engines
+ C:/Users//jan/models
```
```sh
- cd ~/jan/engines
+ cd ~/jan/models
```
-2. Modify the `nitro.json` file based on your needs. The default settings are shown below.
+2. In the `models` folder, create a folder with the name of the model.
-```json title="~/jan/engines/nitro.json"
+```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"
{
- "ctx_len": 2048,
- "ngl": 100,
- "cpu_threads": 1,
- "cont_batching": false,
- "embedding": false
+ "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
}
```
-The table below describes the parameters in the `nitro.json` file.
+### 3. Download the Model
-| Parameter | Type | Description |
-| --------- | ---- | ----------- |
-| `ctx_len` | **Integer** | The context length for the model operations. |
-| `ngl` | **Integer** | The number of GPU layers to use. |
-| `cpu_threads` | **Integer** | The number of threads to use for inferencing (CPU mode only) |
-| `cont_batching` | **Boolean** | Whether to use continuous batching. |
-| `embedding` | **Boolean** | Whether to use embedding in the model. |
+1. Restart Jan and navigate to the Hub.
+2. Locate your model.
+3. Click **Download** button to download the model binary.
-:::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).
-
-:::
+
+
+