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docs/integrations/vscode.mdx 0 → 100644
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---
title: VS Code
---
## Install
Install [VSCode](https://code.visualstudio.com/download).
## Usage with Ollama
1. Open Copilot side bar found in top right window
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/vscode-sidebar.png"
alt="VSCode chat Sidebar"
width="75%"
/>
</div>
2. Select the model drowpdown > **Manage models**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/vscode-models.png"
alt="VSCode model picker"
width="75%"
/>
</div>
3. Enter **Ollama** under **Provider Dropdown** and select desired models (e.g `qwen3, qwen3-coder:480b-cloud`)
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/vscode-model-options.png"
alt="VSCode model options dropdown"
width="75%"
/>
</div>
docs/integrations/xcode.mdx 0 → 100644
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---
title: Xcode
---
## Install
Install [XCode](https://developer.apple.com/xcode/)
## Usage with Ollama
<Note> Ensure Apple Intelligence is setup and the latest XCode version is v26.0 </Note>
1. Click **XCode** in top left corner > **Settings**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/xcode-intelligence-window.png"
alt="Xcode Intelligence window"
width="50%"
/>
</div>
2. Select **Locally Hosted**, enter port **11434** and click **Add**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/xcode-locally-hosted.png"
alt="Xcode settings"
width="50%"
/>
</div>
3. Select the **star icon** on the top left corner and click the **dropdown**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/xcode-chat-icon.png"
alt="Xcode settings"
width="50%"
/>
</div>
4. Click **My Account** and select your desired model
## Connecting to ollama.com directly
1. Create an [API key](https://ollama.com/settings/keys) from ollama.com
2. Select **Internet Hosted** and enter URL as `https://ollama.com`
3. Enter your **Ollama API Key** and click **Add**
\ No newline at end of file
docs/integrations/zed.mdx 0 → 100644
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---
title: Zed
---
## Install
Install [Zed](https://zed.dev/download).
## Usage with Ollama
1. In Zed, click the **star icon** in the bottom-right corner, then select **Configure**.
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/zed-settings.png"
alt="Zed star icon in bottom right corner"
width="50%"
/>
</div>
2. Under **LLM Providers**, choose **Ollama**
3. Confirm the **Host URL** is `http://localhost:11434`, then click **Connect**
4. Once connected, select a model under **Ollama**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/zed-ollama-dropdown.png"
alt="Zed star icon in bottom right corner"
width="50%"
/>
</div>
## Connecting to ollama.com
1. Create an [API key](https://ollama.com/settings/keys) on **ollama.com**
2. In Zed, open the **star icon** → **Configure**
3. Under **LLM Providers**, select **Ollama**
4. Set the **API URL** to `https://ollama.com`
docs/linux.mdx 0 → 100644
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---
title: Linux
---
## Install
To install Ollama, run the following command:
```shell
curl -fsSL https://ollama.com/install.sh | sh
```
## Manual install
<Note>
If you are upgrading from a prior version, you should remove the old libraries
with `sudo rm -rf /usr/lib/ollama` first.
</Note>
Download and extract the package:
```shell
curl -fsSL https://ollama.com/download/ollama-linux-amd64.tgz \
| sudo tar zx -C /usr
```
Start Ollama:
```shell
ollama serve
```
In another terminal, verify that Ollama is running:
```shell
ollama -v
```
### AMD GPU install
If you have an AMD GPU, also download and extract the additional ROCm package:
```shell
curl -fsSL https://ollama.com/download/ollama-linux-amd64-rocm.tgz \
| sudo tar zx -C /usr
```
### ARM64 install
Download and extract the ARM64-specific package:
```shell
curl -fsSL https://ollama.com/download/ollama-linux-arm64.tgz \
| sudo tar zx -C /usr
```
### Adding Ollama as a startup service (recommended)
Create a user and group for Ollama:
```shell
sudo useradd -r -s /bin/false -U -m -d /usr/share/ollama ollama
sudo usermod -a -G ollama $(whoami)
```
Create a service file in `/etc/systemd/system/ollama.service`:
```ini
[Unit]
Description=Ollama Service
After=network-online.target
[Service]
ExecStart=/usr/bin/ollama serve
User=ollama
Group=ollama
Restart=always
RestartSec=3
Environment="PATH=$PATH"
[Install]
WantedBy=multi-user.target
```
Then start the service:
```shell
sudo systemctl daemon-reload
sudo systemctl enable ollama
```
### Install CUDA drivers (optional)
[Download and install](https://developer.nvidia.com/cuda-downloads) CUDA.
Verify that the drivers are installed by running the following command, which should print details about your GPU:
```shell
nvidia-smi
```
### Install AMD ROCm drivers (optional)
[Download and Install](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html) ROCm v6.
### Start Ollama
Start Ollama and verify it is running:
```shell
sudo systemctl start ollama
sudo systemctl status ollama
```
<Note>
While AMD has contributed the `amdgpu` driver upstream to the official linux
kernel source, the version is older and may not support all ROCm features. We
recommend you install the latest driver from
https://www.amd.com/en/support/linux-drivers for best support of your Radeon
GPU.
</Note>
## Customizing
To customize the installation of Ollama, you can edit the systemd service file or the environment variables by running:
```shell
sudo systemctl edit ollama
```
Alternatively, create an override file manually in `/etc/systemd/system/ollama.service.d/override.conf`:
```ini
[Service]
Environment="OLLAMA_DEBUG=1"
```
## Updating
Update Ollama by running the install script again:
```shell
curl -fsSL https://ollama.com/install.sh | sh
```
Or by re-downloading Ollama:
```shell
curl -fsSL https://ollama.com/download/ollama-linux-amd64.tgz \
| sudo tar zx -C /usr
```
## Installing specific versions
Use `OLLAMA_VERSION` environment variable with the install script to install a specific version of Ollama, including pre-releases. You can find the version numbers in the [releases page](https://github.com/ollama/ollama/releases).
For example:
```shell
curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.5.7 sh
```
## Viewing logs
To view logs of Ollama running as a startup service, run:
```shell
journalctl -e -u ollama
```
## Uninstall
Remove the ollama service:
```shell
sudo systemctl stop ollama
sudo systemctl disable ollama
sudo rm /etc/systemd/system/ollama.service
```
Remove ollama libraries from your lib directory (either `/usr/local/lib`, `/usr/lib`, or `/lib`):
```shell
sudo rm -r $(which ollama | tr 'bin' 'lib')
```
Remove the ollama binary from your bin directory (either `/usr/local/bin`, `/usr/bin`, or `/bin`):
```shell
sudo rm $(which ollama)
```
Remove the downloaded models and Ollama service user and group:
```shell
sudo userdel ollama
sudo groupdel ollama
sudo rm -r /usr/share/ollama
```
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docs/modelfile.mdx 0 → 100644
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---
title: Modelfile Reference
---
A Modelfile is the blueprint to create and share customized models using Ollama.
## Table of Contents
- [Format](#format)
- [Examples](#examples)
- [Instructions](#instructions)
- [FROM (Required)](#from-required)
- [Build from existing model](#build-from-existing-model)
- [Build from a Safetensors model](#build-from-a-safetensors-model)
- [Build from a GGUF file](#build-from-a-gguf-file)
- [PARAMETER](#parameter)
- [Valid Parameters and Values](#valid-parameters-and-values)
- [TEMPLATE](#template)
- [Template Variables](#template-variables)
- [SYSTEM](#system)
- [ADAPTER](#adapter)
- [LICENSE](#license)
- [MESSAGE](#message)
- [Notes](#notes)
## Format
The format of the `Modelfile`:
```
# comment
INSTRUCTION arguments
```
| Instruction | Description |
| ----------------------------------- | -------------------------------------------------------------- |
| [`FROM`](#from-required) (required) | Defines the base model to use. |
| [`PARAMETER`](#parameter) | Sets the parameters for how Ollama will run the model. |
| [`TEMPLATE`](#template) | The full prompt template to be sent to the model. |
| [`SYSTEM`](#system) | Specifies the system message that will be set in the template. |
| [`ADAPTER`](#adapter) | Defines the (Q)LoRA adapters to apply to the model. |
| [`LICENSE`](#license) | Specifies the legal license. |
| [`MESSAGE`](#message) | Specify message history. |
## Examples
### Basic `Modelfile`
An example of a `Modelfile` creating a mario blueprint:
```
FROM llama3.2
# sets the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1
# sets the context window size to 4096, this controls how many tokens the LLM can use as context to generate the next token
PARAMETER num_ctx 4096
# sets a custom system message to specify the behavior of the chat assistant
SYSTEM You are Mario from super mario bros, acting as an assistant.
```
To use this:
1. Save it as a file (e.g. `Modelfile`)
2. `ollama create choose-a-model-name -f <location of the file e.g. ./Modelfile>`
3. `ollama run choose-a-model-name`
4. Start using the model!
To view the Modelfile of a given model, use the `ollama show --modelfile` command.
```shell
ollama show --modelfile llama3.2
```
```
# Modelfile generated by "ollama show"
# To build a new Modelfile based on this one, replace the FROM line with:
# FROM llama3.2:latest
FROM /Users/pdevine/.ollama/models/blobs/sha256-00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29
TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
{{ .Response }}<|eot_id|>"""
PARAMETER stop "<|start_header_id|>"
PARAMETER stop "<|end_header_id|>"
PARAMETER stop "<|eot_id|>"
PARAMETER stop "<|reserved_special_token"
```
## Instructions
### FROM (Required)
The `FROM` instruction defines the base model to use when creating a model.
```
FROM <model name>:<tag>
```
#### Build from existing model
```
FROM llama3.2
```
<Card title="Base Models" href="https://github.com/ollama/ollama#model-library">
A list of available base models
</Card>
<Card title="Base Models" href="https://ollama.com/library">
Additional models can be found at
</Card>
#### Build from a Safetensors model
```
FROM <model directory>
```
The model directory should contain the Safetensors weights for a supported architecture.
Currently supported model architectures:
- Llama (including Llama 2, Llama 3, Llama 3.1, and Llama 3.2)
- Mistral (including Mistral 1, Mistral 2, and Mixtral)
- Gemma (including Gemma 1 and Gemma 2)
- Phi3
#### Build from a GGUF file
```
FROM ./ollama-model.gguf
```
The GGUF file location should be specified as an absolute path or relative to the `Modelfile` location.
### PARAMETER
The `PARAMETER` instruction defines a parameter that can be set when the model is run.
```
PARAMETER <parameter> <parametervalue>
```
#### Valid Parameters and Values
| Parameter | Description | Value Type | Example Usage |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | -------------------- |
| mirostat | Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | int | mirostat 0 |
| mirostat_eta | Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1) | float | mirostat_eta 0.1 |
| mirostat_tau | Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0) | float | mirostat_tau 5.0 |
| num_ctx | Sets the size of the context window used to generate the next token. (Default: 2048) | int | num_ctx 4096 |
| repeat_last_n | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx) | int | repeat_last_n 64 |
| repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1) | float | repeat_penalty 1.1 |
| temperature | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8) | float | temperature 0.7 |
| seed | Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. (Default: 0) | int | seed 42 |
| stop | Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Multiple stop patterns may be set by specifying multiple separate `stop` parameters in a modelfile. | string | stop "AI assistant:" |
| num_predict | Maximum number of tokens to predict when generating text. (Default: -1, infinite generation) | int | num_predict 42 |
| top_k | Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40) | int | top_k 40 |
| top_p | Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9) | float | top_p 0.9 |
| min_p | Alternative to the top*p, and aims to ensure a balance of quality and variety. The parameter \_p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with _p_=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out. (Default: 0.0) | float | min_p 0.05 |
### TEMPLATE
`TEMPLATE` of the full prompt template to be passed into the model. It may include (optionally) a system message, a user's message and the response from the model. Note: syntax may be model specific. Templates use Go [template syntax](https://pkg.go.dev/text/template).
#### Template Variables
| Variable | Description |
| ----------------- | --------------------------------------------------------------------------------------------- |
| `{{ .System }}` | The system message used to specify custom behavior. |
| `{{ .Prompt }}` | The user prompt message. |
| `{{ .Response }}` | The response from the model. When generating a response, text after this variable is omitted. |
```
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
"""
```
### SYSTEM
The `SYSTEM` instruction specifies the system message to be used in the template, if applicable.
```
SYSTEM """<system message>"""
```
### ADAPTER
The `ADAPTER` instruction specifies a fine tuned LoRA adapter that should apply to the base model. The value of the adapter should be an absolute path or a path relative to the Modelfile. The base model should be specified with a `FROM` instruction. If the base model is not the same as the base model that the adapter was tuned from the behaviour will be erratic.
#### Safetensor adapter
```
ADAPTER <path to safetensor adapter>
```
Currently supported Safetensor adapters:
- Llama (including Llama 2, Llama 3, and Llama 3.1)
- Mistral (including Mistral 1, Mistral 2, and Mixtral)
- Gemma (including Gemma 1 and Gemma 2)
#### GGUF adapter
```
ADAPTER ./ollama-lora.gguf
```
### LICENSE
The `LICENSE` instruction allows you to specify the legal license under which the model used with this Modelfile is shared or distributed.
```
LICENSE """
<license text>
"""
```
### MESSAGE
The `MESSAGE` instruction allows you to specify a message history for the model to use when responding. Use multiple iterations of the MESSAGE command to build up a conversation which will guide the model to answer in a similar way.
```
MESSAGE <role> <message>
```
#### Valid roles
| Role | Description |
| --------- | ------------------------------------------------------------ |
| system | Alternate way of providing the SYSTEM message for the model. |
| user | An example message of what the user could have asked. |
| assistant | An example message of how the model should respond. |
#### Example conversation
```
MESSAGE user Is Toronto in Canada?
MESSAGE assistant yes
MESSAGE user Is Sacramento in Canada?
MESSAGE assistant no
MESSAGE user Is Ontario in Canada?
MESSAGE assistant yes
```
## Notes
- the **`Modelfile` is not case sensitive**. In the examples, uppercase instructions are used to make it easier to distinguish it from arguments.
- Instructions can be in any order. In the examples, the `FROM` instruction is first to keep it easily readable.
[1]: https://ollama.com/library
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---
title: n8n
---
## Install
Install [n8n](https://docs.n8n.io/choose-n8n/).
## Using Ollama Locally
1. In the top right corner, click the dropdown and select **Create Credential**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/n8n-credential-creation.png"
alt="Create a n8n Credential"
width="75%"
/>
</div>
2. Under **Add new credential** select **Ollama**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/n8n-ollama-form.png"
alt="Select Ollama under Credential"
width="75%"
/>
</div>
3. Confirm Base URL is set to `http://localhost:11434` and click **Save**
<Note> If connecting to `http://localhost:11434` fails, use `http://127.0.0.1:11434`</Note>
4. When creating a new workflow, select **Add a first step** and select an **Ollama node**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/n8n-chat-node.png"
alt="Add a first step with Ollama node"
width="75%"
/>
</div>
5. Select your model of choice (e.g. `qwen3-coder`)
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/n8n-models.png"
alt="Set up Ollama credentials"
width="75%"
/>
</div>
## Connecting to ollama.com
1. Create an [API key](https://ollama.com/settings/keys) on **ollama.com**.
2. In n8n, click **Create Credential** and select **Ollama**
4. Set the **API URL** to `https://ollama.com`
5. Enter your **API Key** and click **Save**
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</svg>
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---
title: Quickstart
---
This quickstart will walk your through running your first model with Ollama. To get started, download Ollama on macOS, Windows or Linux.
<a
href="https://ollama.com/download"
target="_blank"
className="inline-block px-6 py-2 bg-black rounded-full dark:bg-neutral-700 text-white font-normal border-none"
>
Download Ollama
</a>
## Run a model
<Tabs>
<Tab title="CLI">
Open a terminal and run the command:
```
ollama run gemma3
```
</Tab>
<Tab title="cURL">
```
ollama pull gemma3
```
Lastly, chat with the model:
```shell
curl http://localhost:11434/api/chat -d '{
"model": "gemma3",
"messages": [{
"role": "user",
"content": "Hello there!"
}],
"stream": false
}'
```
</Tab>
<Tab title="Python">
Start by downloading a model:
```
ollama pull gemma3
```
Then install Ollama's Python library:
```
pip install ollama
```
Lastly, chat with the model:
```python
from ollama import chat
from ollama import ChatResponse
response: ChatResponse = chat(model='gemma3', messages=[
{
'role': 'user',
'content': 'Why is the sky blue?',
},
])
print(response['message']['content'])
# or access fields directly from the response object
print(response.message.content)
```
</Tab>
<Tab title="JavaScript">
Start by downloading a model:
```
ollama pull gemma3
```
Then install the Ollama JavaScript library:
```
npm i ollama
```
Lastly, chat with the model:
```shell
import ollama from 'ollama'
const response = await ollama.chat({
model: 'gemma3',
messages: [{ role: 'user', content: 'Why is the sky blue?' }],
})
console.log(response.message.content)
```
</Tab>
</Tabs>
See a full list of available models [here](https://ollama.com/models).
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body {
font-family: ui-sans-serif, system-ui, sans-serif, Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji;
}
pre, code, .font-mono {
font-family: ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;
}
.nav-logo {
height: 44px;
}
.eyebrow {
color: #666;
font-weight: 400;
}
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---
title: Text generation
---
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---
title: Vision
description: Provide images to models
---
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