docs: update model content and add keywords
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---
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title: Customize Engine Settings
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sidebar_position: 1
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description: A step-by-step guide to change your engine's settings.
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keywords:
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[
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Jan AI,
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Jan,
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ChatGPT alternative,
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local AI,
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private AI,
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conversational AI,
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no-subscription fee,
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large language model,
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import-models-manually,
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customize-engine-settings,
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]
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---
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# Customize Engine Settings
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A step-by-step guide to change your engine's settings.
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---
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In this guide, we'll walk you through the process of customizing your engine settings by tweaking the `nitro.json` file
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1. Navigate to the `App Settings` > `Advanced` > `Open App Directory` > `~/jan/engine` folder.
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@ -1,23 +1,28 @@
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---
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title: Manual Import
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sidebar_position: 3
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description: A step-by-step guide on how to perform manual import feature.
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keywords:
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[
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Jan AI,
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Jan,
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ChatGPT alternative,
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local AI,
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private AI,
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conversational AI,
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no-subscription fee,
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large language model,
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import-models-manually,
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absolute-filepath,
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]
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---
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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import janModel from './assets/jan-model-hub.png';
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# Manual Import
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A step-by-step guide on how to perform manual import feature.
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---
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:::warning
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This is currently under development.
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:::
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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.
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This guide 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.
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## Newer versions - nightly versions and v0.4.4+
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@ -25,18 +30,18 @@ This section will show you how to perform manual import. In this guide, we are u
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1. Navigate to the `App Settings` > `Advanced` > `Open App Directory` > `~/jan/models` folder.
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<Tabs>
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<TabItem value="mac" label="MacOS" default>
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<Tabs groupId = "operating-systems" >
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<TabItem value="mac" label = "MacOS" default>
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```sh
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cd ~/jan/models
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```
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</TabItem>
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<TabItem value="windows" label="Windows" default>
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<TabItem value = "windows" label = "Windows" default>
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```sh
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C:/Users/<your_user_name>/jan/models
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```
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</TabItem>
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<TabItem value="linux" label="Linux" default>
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<TabItem value = "linux" label = "Linux" default>
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```sh
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cd ~/jan/models
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```
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@ -57,24 +62,24 @@ Drag and drop your model binary into this folder, ensuring the `modelname.gguf`
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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).
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## Older versions - before v0.4.4
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## Older versions - before v0.44
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### 1. Create a Model Folder
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1. Navigate to the `App Settings` > `Advanced` > `Open App Directory` > `~/jan/models` folder.
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<Tabs>
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<TabItem value="mac" label="MacOS" default>
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<Tabs groupId = "operating-systems" >
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<TabItem value="mac" label = "MacOS" default>
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```sh
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cd ~/jan/models
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```
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</TabItem>
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<TabItem value="windows" label="Windows" default>
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<TabItem value = "windows" label = "Windows" default>
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```sh
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C:/Users/<your_user_name>/jan/models
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```
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</TabItem>
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<TabItem value="linux" label="Linux" default>
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<TabItem value = "linux" label = "Linux" default>
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```sh
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cd ~/jan/models
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```
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@ -93,20 +98,20 @@ Jan follows a folder-based, [standard model template](https://jan.ai/docs/engine
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This means that you can easily reconfigure your models, export them, and share your preferences transparently.
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<Tabs>
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<TabItem value="mac" label="MacOS" default>
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<Tabs groupId = "operating-systems" >
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<TabItem value="mac" label = "MacOS" default>
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```sh
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cd trinity-v1-7b
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touch model.json
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```
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</TabItem>
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<TabItem value="windows" label="Windows" default>
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<TabItem value = "windows" label = "Windows" default>
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```sh
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cd trinity-v1-7b
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echo {} > model.json
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```
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</TabItem>
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<TabItem value="linux" label="Linux" default>
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<TabItem value = "linux" label = "Linux" default>
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```sh
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cd trinity-v1-7b
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touch model.json
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@ -151,14 +156,15 @@ To update `model.json`:
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"engine": "nitro"
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}
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```
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#### Regarding `model.json`
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- In `settings`, two crucial values are:
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- `ctx_len`: Defined based on the model's context size.
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- `prompt_template`: Defined based on the model's trained template (e.g., ChatML, Alpaca).
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- To set up the `prompt_template`:
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1. Visit Hugging Face.
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2. Locate the model (e.g., [Gemma 7b it](https://huggingface.co/google/gemma-7b-it)).
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1. Visit [Hugging Face](https://huggingface.co/), an open-source machine learning platform.
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2. Find the current model that you're using (e.g., [Gemma 7b it](https://huggingface.co/google/gemma-7b-it)).
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3. Review the text and identify the template.
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- 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:
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@ -179,8 +185,8 @@ To update `model.json`:
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2. Locate your model.
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3. Click **Download** button to download the model binary.
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<div class="text--center">
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<img src={janModel} width={800} alt="jan-model-hub" />
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<div class="text--center" >
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<img src={ janModel } width = { 800} alt = "jan-model-hub" />
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</div>
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:::info[Assistance and Support]
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@ -1,19 +1,23 @@
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---
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title: Remote Server Integration
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sidebar_position: 2
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description: A step-by-step guide on how to set up Jan to connect with any remote or local API server.
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keywords:
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[
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Jan AI,
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Jan,
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ChatGPT alternative,
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local AI,
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private AI,
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conversational AI,
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no-subscription fee,
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large language model,
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import-models-manually,
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remote server,
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OAI compatible,
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]
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---
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# Remote Server Integration
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A step-by-step guide on how to set up Jan to connect with any remote or local API server.
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---
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:::warning
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This is currently under development.
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:::
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This guide will show you how to configure Jan as a client and point it to any remote & local (self-hosted) API server.
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## OpenAI Platform Configuration
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@ -156,11 +160,34 @@ Please note that currently, the code that supports any OpenAI-compatible endpoin
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},
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"engine": "openai"
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}
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```
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### Regarding `model.json`
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- In `settings`, two crucial values are:
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- `ctx_len`: Defined based on the model's context size.
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- `prompt_template`: Defined based on the model's trained template (e.g., ChatML, Alpaca).
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- To set up the `prompt_template`:
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1. Visit [Hugging Face](https://huggingface.co/), an open-source machine learning platform.
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2. Find the current model that you're using (e.g., [Gemma 7b it](https://huggingface.co/google/gemma-7b-it)).
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3. Review the text and identify the template.
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- 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:
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```json
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"parameters":{
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"temperature": 0.7,
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"top_p": 0.95,
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"stream": true,
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"max_tokens": 4096,
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"frequency_penalty": 0,
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"presence_penalty": 0
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}
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```
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### 3. Start the Model
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Restart Jan and navigate to the **Hub**. Locate your model and click the **Use** button.
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1. Restart Jan and navigate to the **Hub**.
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2. Locate your model and click the **Use** button.
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:::info[Assistance and Support]
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