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.SYNC_INFO.md
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@@ -4,8 +4,8 @@ This is a mirror of the Ollama repository.
**Synced from:** https://github.com/ollama/ollama.git
**Branch:** main
**Commit:** 82f905cd9c06c6f0254d74c5326aa2a7f2f07e1f
**Sync Date:** 2026-07-12
**Commit:** 948f69330acf96a2310f1b53fdfc211731a386d8
**Sync Date:** 2026-08-12
**Content:** Paths: docs
---
docs/README.md
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@@ -19,5 +19,5 @@
### Resources
* [Troubleshooting Guide](https://docs.ollama.com/troubleshooting)
* [FAQ](https://docs.ollama.com/faq#faq)
* [FAQ](https://docs.ollama.com/faq)
* [Development guide](./development.md)
docs/api.md
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@@ -16,7 +16,6 @@
- [Generate Embeddings](#generate-embeddings)
- [List Running Models](#list-running-models)
- [Version](#version)
- [Experimental: Image Generation](#image-generation-experimental)
## Conventions
@@ -59,15 +58,6 @@ Advanced parameters (optional):
- `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
- `context` (deprecated): the context parameter returned from a previous request to `/generate`, this can be used to keep a short conversational memory
Experimental image generation parameters (for image generation models only):
> [!WARNING]
> These parameters are experimental and may change in future versions.
- `width`: width of the generated image in pixels
- `height`: height of the generated image in pixels
- `steps`: number of diffusion steps
#### Structured outputs
Structured outputs are supported by providing a JSON schema in the `format` parameter. The model will generate a response that matches the schema. See the [structured outputs](#request-structured-outputs) example below.
@@ -1189,7 +1179,7 @@ Create a model from:
- a safetensors directory; or
- a GGUF file.
If you are creating a model from a safetensors directory or from a GGUF file, you must [create a blob](#create-a-blob) for each of the files and then use the file name and SHA256 digest associated with each blob in the `files` field.
If you are creating a model from a safetensors directory or from a GGUF file, you must [push a blob](#push-a-blob) for each of the files and then use the file name and SHA256 digest associated with each blob in the `files` field.
### Parameters
@@ -1198,6 +1188,8 @@ If you are creating a model from a safetensors directory or from a GGUF file, yo
- `files`: (optional) a dictionary of file names to SHA256 digests of blobs to create the model from
- `adapters`: (optional) a dictionary of file names to SHA256 digests of blobs for LORA adapters
- `template`: (optional) the prompt template for the model
- `renderer`: (optional) the name of the renderer for the model
- `parser`: (optional) the name of the parser for the model
- `license`: (optional) a string or list of strings containing the license or licenses for the model
- `system`: (optional) a string containing the system prompt for the model
- `parameters`: (optional) a dictionary of parameters for the model (see [Modelfile](./modelfile.mdx#valid-parameters-and-values) for a list of parameters)
@@ -1878,55 +1870,3 @@ curl http://localhost:11434/api/version
"version": "0.5.1"
}
```
## Experimental Features
### Image Generation (Experimental)
> [!WARNING]
> Image generation is experimental and may change in future versions.
Image generation is now supported through the standard `/api/generate` endpoint when using image generation models. The API automatically detects when an image generation model is being used.
See the [Generate a completion](#generate-a-completion) section for the full API documentation. The experimental image generation parameters (`width`, `height`, `steps`) are documented there.
#### Example
##### Request
```shell
curl http://localhost:11434/api/generate -d '{
"model": "x/z-image-turbo",
"prompt": "a sunset over mountains",
"width": 1024,
"height": 768
}'
```
##### Response (streaming)
Progress updates during generation:
```json
{
"model": "x/z-image-turbo",
"created_at": "2024-01-15T10:30:00.000000Z",
"completed": 5,
"total": 20,
"done": false
}
```
##### Final Response
```json
{
"model": "x/z-image-turbo",
"created_at": "2024-01-15T10:30:15.000000Z",
"image": "iVBORw0KGgoAAAANSUhEUg...",
"done": true,
"done_reason": "stop",
"total_duration": 15000000000,
"load_duration": 2000000000
}
```
docs/api/introduction.mdx
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@@ -16,7 +16,7 @@ After installation, Ollama's API is served by default at:
http://localhost:11434/api
```
For running cloud models on **ollama.com**, the same API is available with the following base URL:
For running cloud models on [ollama.com](https://ollama.com), the same API is available with the following base URL:
```
https://ollama.com/api
@@ -40,7 +40,7 @@ Ollama has official libraries for Python and JavaScript:
- [Python](https://github.com/ollama/ollama-python)
- [JavaScript](https://github.com/ollama/ollama-js)
Several community-maintained libraries are available for Ollama. For a full list, see the [Ollama GitHub repository](https://github.com/ollama/ollama?tab=readme-ov-file#libraries-1).
Several community-maintained libraries are available for Ollama. For a full list, see the [Ollama GitHub repository](https://github.com/ollama/ollama?tab=readme-ov-file#libraries--sdks).
## Versioning
docs/api/openai-compatibility.mdx
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@@ -279,73 +279,6 @@ curl -X POST http://localhost:11434/v1/chat/completions \
- [x] `dimensions`
- [ ] `user`
### `/v1/images/generations` (experimental)
> Note: This endpoint is experimental and may change or be removed in future versions.
Generate images using image generation models.
<CodeGroup dropdown>
```python images.py
from openai import OpenAI
client = OpenAI(
base_url='http://localhost:11434/v1/',
api_key='ollama', # required but ignored
)
response = client.images.generate(
model='x/z-image-turbo',
prompt='A cute robot learning to paint',
size='1024x1024',
response_format='b64_json',
)
print(response.data[0].b64_json[:50] + '...')
```
```javascript images.js
import OpenAI from "openai";
const openai = new OpenAI({
baseURL: "http://localhost:11434/v1/",
apiKey: "ollama", // required but ignored
});
const response = await openai.images.generate({
model: "x/z-image-turbo",
prompt: "A cute robot learning to paint",
size: "1024x1024",
response_format: "b64_json",
});
console.log(response.data[0].b64_json.slice(0, 50) + "...");
```
```shell images.sh
curl -X POST http://localhost:11434/v1/images/generations \
-H "Content-Type: application/json" \
-d '{
"model": "x/z-image-turbo",
"prompt": "A cute robot learning to paint",
"size": "1024x1024",
"response_format": "b64_json"
}'
```
</CodeGroup>
#### Supported request fields
- [x] `model`
- [x] `prompt`
- [x] `size` (e.g. "1024x1024")
- [x] `response_format` (only `b64_json` supported)
- [ ] `n`
- [ ] `quality`
- [ ] `style`
- [ ] `user`
### `/v1/responses`
> Note: Added in Ollama v0.13.3
docs/cloud.mdx
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@@ -229,9 +229,9 @@ curl https://ollama.com/api/chat \
## Local only
Ollama can run in local-only mode by [disabling Ollama's cloud](./faq#how-do-i-disable-ollama-cloud) features.
Ollama can run in local-only mode by [disabling Ollama's cloud](./faq#how-do-i-disable-ollama-cloud-features) features.
## Deprecations
## Retirements
Ollama will occasionally deprecate and retire older cloud models as newer and better open-source models are released.
Tools and applications relying on Ollama Cloud models may need to be updated to keep working. Impacted users will be
@@ -240,32 +240,53 @@ Ollama website.
Ollama Cloud model retirement does not affect local models.
### Upcoming deprecations
### Upcoming retirements
| Retirement date | Model | Recommended alternative |
| --- | --- | --- |
| June 16, 2026 | `kimi-k2-thinking` | `kimi-k2.6` |
| June 16, 2026 | `kimi-k2:1t` | `kimi-k2.6` |
| June 16, 2026 | `minimax-m2` | `minimax-m3` |
| June 16, 2026 | `glm-4.6` | `glm-5.1` |
| June 16, 2026 | `qwen3-next:80b` | `qwen3.5` |
| June 16, 2026 | `qwen3-vl:235b` | `qwen3.5` |
| June 16, 2026 | `qwen3-vl:235b-instruct` | `qwen3.5` |
| June 16, 2026 | `cogito-2.1:671b` | `deepseek-v4-flash` |
| June 30, 2026 | `rnj-1:8b` | |
| July 15, 2026 | `deepseek-v3.1:671b` | `deepseek-v4-flash` |
| July 15, 2026 | `deepseek-v3.2` | `deepseek-v4-flash` |
| July 15, 2026 | `devstral-2:123b` | `mistral-large-3:675b` |
| July 15, 2026 | `devstral-small-2:24b` | |
| July 15, 2026 | `ministral-3:14b` | |
| July 15, 2026 | `ministral-3:3b` | |
| July 15, 2026 | `ministral-3:8b` | |
| July 15, 2026 | `gemini-3-flash-preview` | `minimax-m3` |
| July 15, 2026 | `gemma3:12b` | `gemma4:31b` |
| July 15, 2026 | `gemma3:27b` | `gemma4:31b` |
| July 15, 2026 | `gemma3:4b` | `gemma4:31b` |
| July 15, 2026 | `glm-4.7` | `glm-5.2` |
| July 15, 2026 | `glm-5` | `glm-5.2` |
| July 15, 2026 | `minimax-m2.1` | `minimax-m3` |
| July 15, 2026 | `qwen3-coder-next` | `qwen3.5:397b` |
| July 15, 2026 | `qwen3-coder:480b` | `qwen3.5:397b` |
| July 31, 2026 | `minimax-m2.5` | `minimax-m2.7` |
| July 31, 2026 | `kimi-k2.5` | `kimi-k2.6` |
### Past retirements
<AccordionGroup>
<Accordion title="July 15, 2026">
| Model | Recommended alternative |
| --- | --- |
| `deepseek-v3.1:671b` | `deepseek-v4-flash` |
| `deepseek-v3.2` | `deepseek-v4-flash` |
| `devstral-2:123b` | `mistral-large-3:675b` |
| `devstral-small-2:24b` | |
| `ministral-3:14b` | |
| `ministral-3:3b` | |
| `ministral-3:8b` | |
| `gemini-3-flash-preview` | `minimax-m3` |
| `gemma3:12b` | `gemma4:31b` |
| `gemma3:27b` | `gemma4:31b` |
| `gemma3:4b` | `gemma4:31b` |
| `glm-4.7` | `glm-5.2` |
| `glm-5` | `glm-5.2` |
| `minimax-m2.1` | `minimax-m3` |
| `qwen3-coder-next` | `qwen3.5:397b` |
| `qwen3-coder:480b` | `qwen3.5:397b` |
</Accordion>
<Accordion title="June 30, 2026">
| Model | Recommended alternative |
| --- | --- |
| `rnj-1:8b` | |
</Accordion>
<Accordion title="June 16, 2026">
| Model | Recommended alternative |
| --- | --- |
| `kimi-k2-thinking` | `kimi-k2.6` |
| `kimi-k2:1t` | `kimi-k2.6` |
| `minimax-m2` | `minimax-m3` |
| `glm-4.6` | `glm-5.1` |
| `qwen3-next:80b` | `qwen3.5` |
| `qwen3-vl:235b` | `qwen3.5` |
| `qwen3-vl:235b-instruct` | `qwen3.5` |
| `cogito-2.1:671b` | `deepseek-v4-flash` |
</Accordion>
</AccordionGroup>
docs/docs.json
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@@ -60,6 +60,26 @@
}
},
"redirects": [
{
"source": "/development.md",
"destination": "/development"
},
{
"source": "/api/openai-compatibility.mdx",
"destination": "/api/openai-compatibility"
},
{
"source": "/gpu.mdx",
"destination": "/gpu"
},
{
"source": "/integrations/vscode.mdx",
"destination": "/integrations/vscode"
},
{
"source": "/troubleshooting.mdx",
"destination": "/troubleshooting"
},
{
"source": "/openai",
"destination": "/api/openai-compatibility"
docs/examples.md
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@@ -11,4 +11,4 @@ Ollama JavaScript examples at [ollama-js/examples](https://github.com/ollama/oll
## OpenAI compatibility examples
Ollama OpenAI compatibility examples at [ollama/examples/openai](../docs/openai.md)
Ollama OpenAI compatibility examples at [ollama/examples/openai](./api/openai-compatibility.mdx)
docs/faq.mdx
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@@ -160,7 +160,7 @@ docker run -d -e HTTPS_PROXY=https://my.proxy.example.com -p 11434:11434 ollama-
Ollama runs locally. We don't see your prompts or data when you run locally. When using cloud-hosted models, we process your prompts and responses to provide the service but do not store or log that content and never train on it. We collect basic account info and limited usage metadata to provide the service that does not include prompt or response content. We don't sell your data. You can delete your account anytime.
## How do I disable Ollama's cloud features?
## How do I disable Ollama Cloud features?
Ollama can run in local only mode by disabling Ollama's cloud features. By turning off Ollama's cloud features, you will lose the ability to use Ollama's cloud models and web search.
@@ -248,7 +248,7 @@ Refer to the section [above](#how-do-i-configure-ollama-server) for how to set e
## How can I use Ollama in Visual Studio Code?
There is already a large collection of plugins available for VS Code as well as other editors that leverage Ollama. See the list of [extensions & plugins](https://github.com/ollama/ollama#extensions--plugins) at the bottom of the main repository readme.
Install the [Ollama extension](https://marketplace.visualstudio.com/items?itemName=Ollama.ollama) to use Ollama models in VS Code Chat. See the [VS Code integration guide](./integrations/vscode.mdx) for setup and troubleshooting.
## How do I use Ollama with GPU acceleration in Docker?
docs/gpu.mdx
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@@ -35,7 +35,7 @@ Check your compute compatibility to see if your card is supported:
| 5.0 | GeForce GTX | `GTX 750 Ti` `GTX 750` `NVS 810` |
| | Quadro | `K2200` `K1200` `K620` `M1200` `M520` `M5000M` `M4000M` `M3000M` `M2000M` `M1000M` `K620M` `M600M` `M500M` |
For building locally to support older GPUs, see [developer](./development#linux-cuda-nvidia)
For building locally to support older GPUs, see [development](./development.md).
### GPU Selection
docs/import.mdx
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@@ -4,10 +4,10 @@ title: Importing a Model
## Table of Contents
- [Importing a Safetensors adapter](#Importing-a-fine-tuned-adapter-from-Safetensors-weights)
- [Importing a Safetensors model](#Importing-a-model-from-Safetensors-weights)
- [Importing a GGUF file](#Importing-a-GGUF-based-model-or-adapter)
- [Sharing models on ollama.com](#Sharing-your-model-on-ollamacom)
- [Importing a Safetensors adapter](#importing-a-fine-tuned-adapter-from-safetensors-weights)
- [Importing a Safetensors model](#importing-a-model-from-safetensors-weights)
- [Importing a GGUF file](#importing-a-gguf-based-model-or-adapter)
- [Sharing models on ollama.com](#sharing-your-model-on-ollama-com)
## Importing a fine tuned adapter from Safetensors weights
docs/integrations/goose.mdx
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@@ -4,7 +4,7 @@ title: Goose
## Goose Desktop
Install [Goose](https://block.github.io/goose/docs/getting-started/installation/) Desktop.
Install [Goose](https://goose-docs.ai/docs/getting-started/installation/) Desktop.
### Usage with Ollama
1. In Goose, open **Settings** → **Configure Provider**.
@@ -27,7 +27,7 @@ Install [Goose](https://block.github.io/goose/docs/getting-started/installation/
## Goose CLI
Install [Goose](https://block.github.io/goose/docs/getting-started/installation/) CLI
Install [Goose](https://goose-docs.ai/docs/getting-started/installation/) CLI
### Usage with Ollama
1. Run `goose configure`
docs/integrations/hermes-desktop.mdx
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@@ -14,7 +14,7 @@ ollama launch hermes-desktop
Ollama handles the setup flow automatically:
1. **Install** - If Hermes Desktop isn't installed, Ollama prompts to install it
1. **Install** - If Hermes isn't installed, Ollama prompts to install the Hermes command-line agent. On first desktop launch, Hermes builds its packaged desktop app.
2. **Model** - Pick a model from the selector
3. **Configure** - Ollama configures Hermes Desktop to use your selected Ollama model
4. **Launch** - Ollama opens Hermes Desktop
@@ -26,3 +26,13 @@ ollama launch hermes-desktop --model <model>
```
Run `ollama launch hermes-desktop` again to switch models later.
## Install Hermes Desktop directly
On macOS and Windows, the Hermes Desktop installer is the recommended upstream installation path. It installs the desktop app and Hermes Agent together. If you prefer the command line, `ollama launch hermes-desktop` remains the explicit Ollama-managed path and uses the same Hermes configuration, sessions, skills, and memory as the CLI.
To force Hermes to rebuild its packaged desktop app:
```bash
ollama launch hermes-desktop -- --force-build
```
docs/integrations/hermes.mdx
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@@ -14,7 +14,7 @@ ollama launch hermes
Ollama handles everything automatically:
1. **Install** — If Hermes isn't installed, Ollama prompts to install it via the Nous Research install script
1. **Install** — If Hermes isn't installed, Ollama prompts to install the Hermes command-line agent
2. **Model** — Pick a model from the selector (local or cloud)
3. **Onboarding** — Ollama configures the Ollama provider, points Hermes at `http://127.0.0.1:11434/v1`, and sets your model as the primary
4. **Gateway** — Optionally connects a messaging platform (Telegram, Discord, Slack, WhatsApp, Signal, Email) and launches the Hermes chat
@@ -45,10 +45,10 @@ hermes gateway setup
## Reconfigure
Re-run the full setup wizard at any time:
Use Hermes's model picker to change providers or models later:
```bash
hermes setup
hermes model
```
## Manual setup
@@ -106,7 +106,7 @@ Optionally connect a messaging platform during setup:
Connect a messaging platform? (Telegram, Discord, etc.)
→ Set up messaging now (recommended)
Skip — set up later with 'hermes setup gateway'
Skip — set up later with 'hermes gateway setup'
```
### Launch
@@ -114,4 +114,3 @@ Connect a messaging platform? (Telegram, Discord, etc.)
```
Launch hermes chat now? [Y/n]: Y
```
docs/integrations/index.mdx
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@@ -39,6 +39,6 @@ Use Ollama models inside your editor.
<CardGroup cols={2}>
<Card title="VS Code" icon="/images/launch-icons/vscode.svg" href="/integrations/vscode">
Select Ollama models from the Copilot Chat model picker in VS Code.
Use Ollama models in VS Code Chat.
</Card>
</CardGroup>
docs/integrations/vscode.mdx
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@@ -2,84 +2,57 @@
title: VS Code
---
VS Code includes built-in AI chat through GitHub Copilot Chat. Ollama models can be used directly in the Copilot Chat model picker.
Use Ollama models in VS Code Chat with the [Ollama extension](https://marketplace.visualstudio.com/items?itemName=Ollama.ollama).
## Requirements
![VS Code with Ollama](/images/vscode.png)
- [Visual Studio Code 1.127 or newer](https://code.visualstudio.com/download)
- Ollama installed and running
- At least one local or cloud model available in Ollama
Earlier VS Code versions do not reliably cancel requests from language model providers.
## Prerequisites
Ollama 0.17.6 or newer is recommended for cloud model sign-in and richer model metadata. Older versions may still work with local models.
- Ollama v0.18.3+
- [VS Code 1.113+](https://code.visualstudio.com/download)
- [GitHub Copilot Chat extension 0.41.0+](https://marketplace.visualstudio.com/items?itemName=GitHub.copilot-chat)
## Install the extension
<Note> VS Code requires you to be logged in to use its model selector, even for custom models. This doesn't require a paid GitHub Copilot account; GitHub Copilot Free will enable model selection for custom models.</Note>
1. Install the [Ollama extension](https://marketplace.visualstudio.com/items?itemName=Ollama.ollama) from the VS Code Marketplace.
2. Open Chat in VS Code.
3. Open the model picker at the bottom of the chat input.
4. Choose a model from the **Ollama** section.
## Quick setup
The extension discovers models from `http://127.0.0.1:11434` by default.
## Add a model
Pull a local model:
```shell
ollama launch vscode
ollama pull qwen3.6
```
Recommended models will be shown after running the command. See the latest models at [ollama.com](https://ollama.com/search?c=tools).
Make sure **Local** is selected at the bottom of the Copilot Chat panel to use your Ollama models.
<div style={{ display: "flex", justifyContent: "center" }}>
<img
src="/images/local.png"
alt="Ollama Local Models"
width="60%"
style={{ borderRadius: "4px", marginTop: "10px", marginBottom: "10px" }}
/>
</div>
## Run directly with a model
To use a cloud model, pull it and sign in:
```shell
ollama launch vscode --model qwen3.5:cloud
ollama pull kimi-k2.6:cloud
ollama signin
```
Cloud models are also available at [ollama.com](https://ollama.com/search?c=cloud).
## Manual setup
Local models do not require sign-in.
To configure Ollama manually without `ollama launch`:
## Context length
1. Open the **Copilot Chat** side bar from the top right corner
<div style={{ display: "flex", justifyContent: "center" }}>
<img
src="/images/vscode-sidebar.png"
alt="VS Code chat Sidebar"
width="75%"
style={{ borderRadius: "4px" }}
/>
</div>
2. Click the **settings gear icon** (<Icon icon="gear" />) to bring up the Language Models window
<div style={{ display: "flex", justifyContent: "center" }}>
<img
src="/images/vscode-other-models.png"
alt="VS Code model picker"
width="75%"
style={{ borderRadius: "4px" }}
/>
</div>
3. Click **Add Models** and select **Ollama** to load all your Ollama models into VS Code
<div style={{ display: "flex", justifyContent: "center" }}>
<img
src="/images/vscode-add-ollama.png"
alt="VS Code model options dropdown to add ollama models"
width="75%"
style={{ borderRadius: "4px" }}
/>
</div>
VS Code may show a model's maximum supported context length even when Ollama allocates a smaller context at runtime.
4. Click the **Unhide** button in the model picker to show your Ollama models
<div style={{ display: "flex", justifyContent: "center" }}>
<img
src="/images/vscode-unhide.png"
alt="VS Code unhide models button"
width="75%"
style={{ borderRadius: "4px" }}
/>
</div>
For local models, open Ollama **Settings**, set the context length to at least 64k, reload the VS Code window, and resend your prompt. See [Context length](/context-length) for more information.
## Troubleshooting
If Ollama models do not appear in the model picker:
1. Make sure Ollama is running.
2. Run `ollama list` and confirm that models are available.
3. Run **Ollama: Refresh Models** from the Command Palette.
4. Run **Ollama: Diagnose Models** and check the **Ollama** output channel.
If a cloud model asks you to sign in, run `ollama signin`.
docs/linux.mdx
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@@ -116,7 +116,7 @@ sudo systemctl status ollama
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
https://www.amd.com/en/support/download/linux-drivers.html for best support of your Radeon
GPU.
</Note>
docs/modelfile.mdx
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@@ -107,12 +107,8 @@ FROM <model name>:<tag>
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 title="Model library" href="https://ollama.com/library">
Browse available models
</Card>
#### Build from a Safetensors model
docs/openapi.yaml
+6
View File
@@ -483,6 +483,12 @@ components:
template:
type: string
description: Prompt template to use for the model
renderer:
type: string
description: Name of the renderer for the model
parser:
type: string
description: Name of the parser for the model
license:
oneOf:
- type: string
docs/styling.css
+13
View File
@@ -115,3 +115,16 @@ pre, code, .font-mono {
grid-template-columns: 1fr;
}
}
.accordion-group {
border-style: none;
border-radius: 0;
}
.accordion-group div[id*="-accordion-children"] {
margin-inline: calc(var(--spacing) * 1);
}
.accordion-group details summary {
padding-inline: 0;
}
docs/troubleshooting.mdx
+1 -1
View File
@@ -137,7 +137,7 @@ After upgrading, reboot and restart Ollama.
If you experience gibberish responses when models load across multiple AMD GPUs on Linux, see the following guide.
- https://rocm.docs.amd.com/projects/radeon/en/latest/docs/install/native_linux/mgpu.html#mgpu-known-issues-and-limitations
- https://rocm.docs.amd.com/projects/radeon-ryzen/en/latest/docs/install/installrad/native_linux/mgpu.html#mgpu-known-issues-and-limitations
## Windows Terminal Errors