Sync ollama docs from 12e04379 on 2026-06-12

.SYNC_INFO.md

@@ -4,8 +4,8 @@ This is a mirror of the Ollama repository.
 
 **Synced from:** https://github.com/ollama/ollama.git
 **Branch:** main
-**Commit:** 361d6c16c23b60f84eb1d2134914610c0ae5836b
-**Sync Date:** 2026-01-12
+**Commit:** 12e04379cd5a9a2f150104a02c5761213f4be777
+**Sync Date:** 2026-06-12
 **Content:** Paths: docs
 
 ---

docs/api.md

@@ -16,6 +16,7 @@
 - [Generate Embeddings](#generate-embeddings)
 - [List Running Models](#list-running-models)
 - [Version](#version)
+- [Experimental: Image Generation](#image-generation-experimental)
 
 ## Conventions
 
@@ -58,6 +59,15 @@ 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.
@@ -388,6 +398,7 @@ curl http://localhost:11434/api/generate -d '{
     "num_keep": 5,
     "seed": 42,
     "num_predict": 100,
+    "draft_num_predict": 4,
     "top_k": 20,
     "top_p": 0.9,
     "min_p": 0.0,
@@ -1867,3 +1878,55 @@ 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/anthropic-compatibility.mdx

@@ -4,16 +4,6 @@ title: Anthropic compatibility
 
 Ollama provides compatibility with the [Anthropic Messages API](https://docs.anthropic.com/en/api/messages) to help connect existing applications to Ollama, including tools like Claude Code.
 
-## Recommended models
-
-For coding use cases, models like `glm-4.7:cloud`, `minimax-m2.1:cloud`, and `qwen3-coder` are recommended.
-
-Pull a model before use:
-```shell
-ollama pull qwen3-coder
-ollama pull glm-4.7:cloud
-```
-
 ## Usage
 
 ### Environment variables
@@ -21,8 +11,8 @@ ollama pull glm-4.7:cloud
 To use Ollama with tools that expect the Anthropic API (like Claude Code), set these environment variables:
 
 ```shell
+export ANTHROPIC_AUTH_TOKEN=ollama  # required but ignored
 export ANTHROPIC_BASE_URL=http://localhost:11434
-export ANTHROPIC_API_KEY=ollama  # required but ignored
 ```
 
 ### Simple `/v1/messages` example
@@ -244,29 +234,54 @@ curl -X POST http://localhost:11434/v1/messages \
 
 ## Using with Claude Code
 
-[Claude Code](https://code.claude.com/docs/en/overview) can be configured to use Ollama as its backend:
+[Claude Code](https://code.claude.com/docs/en/overview) can be configured to use Ollama as its backend. 
+
+### Recommended models
+
+For coding use cases, models like `glm-4.7`, `minimax-m2.1`, and `qwen3-coder` are recommended.
+
+Download a model before use:
 
 ```shell
-ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY=ollama claude --model qwen3-coder
+ollama pull qwen3-coder
+```
+> Note: Qwen 3 coder is a 30B parameter model requiring at least 24GB of VRAM to run smoothly. More is required for longer context lengths. 
+
+```shell
+ollama pull glm-4.7:cloud
+```
+
+### Quick setup
+
+```shell
+ollama launch claude
+```
+
+This will prompt you to select a model, configure Claude Code automatically, and launch it. To configure without launching:
+
+```shell
+ollama launch claude --config
+```
+
+### Manual setup
+
+Set the environment variables and run Claude Code:
+
+```shell
+ANTHROPIC_AUTH_TOKEN=ollama ANTHROPIC_BASE_URL=http://localhost:11434 claude --model qwen3-coder
 ```
 
 Or set the environment variables in your shell profile:
 
 ```shell
+export ANTHROPIC_AUTH_TOKEN=ollama
 export ANTHROPIC_BASE_URL=http://localhost:11434
-export ANTHROPIC_API_KEY=ollama
 ```
 
 Then run Claude Code with any Ollama model:
 
 ```shell
-# Local models
 claude --model qwen3-coder
-claude --model gpt-oss:20b
-
-# Cloud models
-claude --model glm-4.7:cloud
-claude --model minimax-m2.1:cloud
 ```
 
 ## Endpoints

docs/api/errors.mdx

@@ -28,9 +28,9 @@ Errors are returned in the `application/json` format with the following structur
 If an error occurs mid-stream, the error will be returned as an object in the `application/x-ndjson` format with an `error` property. Since the response has already started, the status code of the response will not be changed.
 
 ```json
-{"model":"gemma3","created_at":"2025-10-26T17:21:21.196249Z","response":" Yes","done":false}
-{"model":"gemma3","created_at":"2025-10-26T17:21:21.207235Z","response":".","done":false}
-{"model":"gemma3","created_at":"2025-10-26T17:21:21.219166Z","response":"I","done":false}
-{"model":"gemma3","created_at":"2025-10-26T17:21:21.231094Z","response":"can","done":false}
+{"model":"gemma4","created_at":"2025-10-26T17:21:21.196249Z","response":" Yes","done":false}
+{"model":"gemma4","created_at":"2025-10-26T17:21:21.207235Z","response":".","done":false}
+{"model":"gemma4","created_at":"2025-10-26T17:21:21.219166Z","response":"I","done":false}
+{"model":"gemma4","created_at":"2025-10-26T17:21:21.231094Z","response":"can","done":false}
 {"error":"an error was encountered while running the model"}
 ```

docs/api/introduction.mdx

@@ -28,7 +28,7 @@ Once Ollama is running, its API is automatically available and can be accessed v
 
 ```shell
 curl http://localhost:11434/api/generate -d '{
-  "model": "gemma3",
+  "model": "gemma4",
   "prompt": "Why is the sky blue?"
 }'
 ```

docs/api/openai-compatibility.mdx

@@ -6,7 +6,7 @@ Ollama provides compatibility with parts of the [OpenAI API](https://platform.op
 
 ## Usage
 
-### Simple `v1/chat/completions` example
+### Simple `/v1/chat/completions` example
 
 <CodeGroup dropdown>
 
@@ -57,7 +57,7 @@ curl -X POST http://localhost:11434/v1/chat/completions \
 
 </CodeGroup>
 
-### Simple `v1/responses` example
+### Simple `/v1/responses` example
 
 <CodeGroup dropdown>
 
@@ -103,7 +103,7 @@ curl -X POST http://localhost:11434/v1/responses \
 
 </CodeGroup>
 
-### v1/chat/completions with vision example
+### `/v1/chat/completions` with vision example
 
 <CodeGroup dropdown>
 
@@ -184,6 +184,7 @@ curl -X POST http://localhost:11434/v1/chat/completions \
 - [x] Reproducible outputs
 - [x] Vision
 - [x] Tools
+- [x] Reasoning/thinking control (for thinking models)
 - [ ] Logprobs
 
 #### Supported request fields
@@ -207,6 +208,9 @@ curl -X POST http://localhost:11434/v1/chat/completions \
 - [x] `top_p`
 - [x] `max_tokens`
 - [x] `tools`
+- [x] `reasoning_effort` (`"high"`, `"medium"`, `"low"`, `"none"`)
+- [x] `reasoning`
+  - [x] `effort` (`"high"`, `"medium"`, `"low"`, `"none"`)
 - [ ] `tool_choice`
 - [ ] `logit_bias`
 - [ ] `user`
@@ -275,6 +279,73 @@ 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/api/streaming.mdx

@@ -5,13 +5,13 @@ title: Streaming
 Certain API endpoints stream responses by default, such as `/api/generate`. These responses are provided in the newline-delimited JSON format (i.e. the `application/x-ndjson` content type). For example:
 
 ```json
-{"model":"gemma3","created_at":"2025-10-26T17:15:24.097767Z","response":"That","done":false}
-{"model":"gemma3","created_at":"2025-10-26T17:15:24.109172Z","response":"'","done":false}
-{"model":"gemma3","created_at":"2025-10-26T17:15:24.121485Z","response":"s","done":false}
-{"model":"gemma3","created_at":"2025-10-26T17:15:24.132802Z","response":" a","done":false}
-{"model":"gemma3","created_at":"2025-10-26T17:15:24.143931Z","response":" fantastic","done":false}
-{"model":"gemma3","created_at":"2025-10-26T17:15:24.155176Z","response":" question","done":false}
-{"model":"gemma3","created_at":"2025-10-26T17:15:24.166576Z","response":"!","done":true, "done_reason": "stop"}
+{"model":"gemma4","created_at":"2025-10-26T17:15:24.097767Z","response":"That","done":false}
+{"model":"gemma4","created_at":"2025-10-26T17:15:24.109172Z","response":"'","done":false}
+{"model":"gemma4","created_at":"2025-10-26T17:15:24.121485Z","response":"s","done":false}
+{"model":"gemma4","created_at":"2025-10-26T17:15:24.132802Z","response":" a","done":false}
+{"model":"gemma4","created_at":"2025-10-26T17:15:24.143931Z","response":" fantastic","done":false}
+{"model":"gemma4","created_at":"2025-10-26T17:15:24.155176Z","response":" question","done":false}
+{"model":"gemma4","created_at":"2025-10-26T17:15:24.166576Z","response":"!","done":true, "done_reason": "stop"}
 ```
 
 ## Disabling streaming
@@ -19,7 +19,7 @@ Certain API endpoints stream responses by default, such as `/api/generate`. Thes
 Streaming can be disabled by providing `{"stream": false}` in the request body for any endpoint that support streaming. This will cause responses to be returned in the `application/json` format instead:
 
 ```json
-{"model":"gemma3","created_at":"2025-10-26T17:15:24.166576Z","response":"That's a fantastic question!","done":true}
+{"model":"gemma4","created_at":"2025-10-26T17:15:24.166576Z","response":"That's a fantastic question!","done":true}
 ```
 
 ## When to use streaming vs non-streaming

docs/api/usage.mdx

@@ -19,7 +19,7 @@ For endpoints that return usage metrics, the response body will include the usag
 
 ```json
 {
-  "model": "gemma3",
+  "model": "gemma4",
   "created_at": "2025-10-17T23:14:07.414671Z",
   "response": "Hello! How can I help you today?",
   "done": true,

docs/capabilities/structured-outputs.mdx

@@ -2,6 +2,10 @@
 title: Structured Outputs
 ---
 
+<Note>
+  Ollama's Cloud currently does not support structured outputs.
+</Note>
+
 Structured outputs let you enforce a JSON schema on model responses so you can reliably extract structured data, describe images, or keep every reply consistent.
 
 ## Generating structured JSON
@@ -96,12 +100,11 @@ Provide a JSON schema to the `format` field.
     ```
   </Tab>
   <Tab title="JavaScript">
-    Serialize a Zod schema with `zodToJsonSchema()` and parse the structured response:
+    Serialize a Zod schema with `z.toJSONSchema()` and parse the structured response:
 
     ```javascript
     import ollama from 'ollama'
-    import { z } from 'zod'
-    import { zodToJsonSchema } from 'zod-to-json-schema'
+    import * as z from 'zod'
 
     const Country = z.object({
       name: z.string(),
@@ -112,7 +115,7 @@ Provide a JSON schema to the `format` field.
     const response = await ollama.chat({
       model: 'gpt-oss',
       messages: [{ role: 'user', content: 'Tell me about Canada.' }],
-      format: zodToJsonSchema(Country),
+      format: z.toJSONSchema(Country),
     })
 
     const country = Country.parse(JSON.parse(response.message.content))
@@ -173,7 +176,7 @@ class ImageDescription(BaseModel):
   text_content: Optional[str] = None
 
 response = chat(
-  model='gemma3',
+  model='gemma4',
   messages=[{
     'role': 'user',
     'content': 'Describe this photo and list the objects you detect.',

docs/capabilities/vision.mdx

@@ -7,7 +7,7 @@ Vision models accept images alongside text so the model can describe, classify,
 ## Quick start
 
 ```shell
-ollama run gemma3 ./image.png whats in this image?
+ollama run gemma4 ./image.png whats in this image?
 ```
 
 
@@ -28,7 +28,7 @@ Provide an `images` array. SDKs accept file paths, URLs or raw bytes while the R
     curl -X POST http://localhost:11434/api/chat \
     -H "Content-Type: application/json" \
     -d '{
-        "model": "gemma3",
+        "model": "gemma4",
         "messages": [{
         "role": "user",
         "content": "What is in this image?",
@@ -52,7 +52,7 @@ Provide an `images` array. SDKs accept file paths, URLs or raw bytes while the R
     # img = Path(path).read_bytes()
 
     response = chat(
-      model='gemma3',
+      model='gemma4',
       messages=[
         {
           'role': 'user',
@@ -71,7 +71,7 @@ Provide an `images` array. SDKs accept file paths, URLs or raw bytes while the R
 
     const imagePath = '/absolute/path/to/image.jpg'
     const response = await ollama.chat({
-      model: 'gemma3',
+      model: 'gemma4',
       messages: [
         { role: 'user', content: 'What is in this image?', images: [imagePath] }
       ],

docs/capabilities/web-search.mdx

@@ -110,7 +110,7 @@ More Ollama [Python example](https://github.com/ollama/ollama-python/blob/main/e
 import { Ollama } from "ollama";
 
 const client = new Ollama();
-const results = await client.webSearch({ query: "what is ollama?" });
+const results = await client.webSearch("what is ollama?");
 console.log(JSON.stringify(results, null, 2));
 ```
 
@@ -213,7 +213,7 @@ models](https://ollama.com/models)\n\nAvailable for macOS, Windows, and Linux',
 import { Ollama } from "ollama";
 
 const client = new Ollama();
-const fetchResult = await client.webFetch({ url: "https://ollama.com" });
+const fetchResult = await client.webFetch("https://ollama.com");
 console.log(JSON.stringify(fetchResult, null, 2));
 ```
 

docs/cli.mdx

@@ -5,7 +5,49 @@ title: CLI Reference
 ### Run a model
 
 ```
-ollama run gemma3
+ollama run gemma4
+```
+
+### Launch integrations
+
+```
+ollama launch
+```
+
+Configure and launch external applications to use Ollama models. This provides an interactive way to set up and start integrations with supported apps.
+
+#### Supported integrations
+
+- **OpenCode** - Open-source coding assistant
+- **Claude Code** - Anthropic's agentic coding tool
+- **Codex** - OpenAI's coding assistant
+- **VS Code** - Microsoft's IDE with built-in AI chat
+- **Droid** - Factory's AI coding agent
+
+#### Examples
+
+Launch an integration interactively:
+
+```
+ollama launch
+```
+
+Launch a specific integration:
+
+```
+ollama launch claude
+```
+
+Launch with a specific model:
+
+```
+ollama launch claude --model qwen3.5
+```
+
+Configure without launching:
+
+```
+ollama launch droid --config
 ```
 
 #### Multiline input
@@ -22,7 +64,7 @@ I'm a basic program that prints the famous "Hello, world!" message to the consol
 #### Multimodal models
 
 ```
-ollama run gemma3 "What's in this image? /Users/jmorgan/Desktop/smile.png"
+ollama run gemma4 "What's in this image? /Users/jmorgan/Desktop/smile.png"
 ```
 
 ### Generate embeddings
@@ -40,13 +82,13 @@ echo "Hello world" | ollama run nomic-embed-text
 ### Download a model
 
 ```
-ollama pull gemma3
+ollama pull gemma4
 ```
 
 ### Remove a model
 
 ```
-ollama rm gemma3
+ollama rm gemma4
 ```
 
 ### List models
@@ -72,7 +114,7 @@ ollama signout
 First, create a `Modelfile`
 
 ```
-FROM gemma3
+FROM gemma4
 SYSTEM """You are a happy cat."""
 ```
 
@@ -91,7 +133,7 @@ ollama ps
 ### Stop a running model
 
 ```
-ollama stop gemma3
+ollama stop gemma4
 ```
 
 ### Start Ollama

docs/cloud.mdx

@@ -3,8 +3,6 @@ title: Cloud
 sidebarTitle: Cloud
 ---
 
-<Info>Ollama's cloud is currently in preview.</Info>
-
 ## Cloud Models
 
 Ollama's cloud models are a new kind of model in Ollama that can run without a powerful GPU. Instead, cloud models are automatically offloaded to Ollama's cloud service while offering the same capabilities as local models, making it possible to keep using your local tools while running larger models that wouldn't fit on a personal computer.
@@ -228,3 +226,29 @@ curl https://ollama.com/api/chat \
 
   </Tab>
 </Tabs>
+
+## Local only
+
+Ollama can run in local-only mode by [disabling Ollama's cloud](./faq#how-do-i-disable-ollama-cloud) features.
+
+## Deprecations
+
+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
+notified in advance of model deprecation and retirement. Deprecations will be communicated through email and on the
+Ollama website.
+
+Ollama Cloud model retirement does not affect local models.
+
+### Upcoming deprecations
+
+| 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` |

docs/context-length.mdx

@@ -5,10 +5,13 @@ title: Context length
 Context length is the maximum number of tokens that the model has access to in memory.  
 
 <Note>
-  The default context length in Ollama is 4096 tokens.
+  Ollama defaults to the following context lengths based on VRAM:
+    - < 24 GiB VRAM: 4k context
+    - 24-48 GiB VRAM: 32k context
+    - &gt;= 48 GiB VRAM: 256k context
 </Note>
 
-Tasks which require large context like web search, agents, and coding tools should be set to at least 32000 tokens.
+Tasks which require large context like web search, agents, and coding tools should be set to at least 64000 tokens.
 
 ## Setting context length
 
@@ -24,7 +27,7 @@ Change the slider in the Ollama app under settings to your desired context lengt
 ### CLI
 If editing the context length for Ollama is not possible, the context length can also be updated when serving Ollama.  
 ```
-OLLAMA_CONTEXT_LENGTH=32000 ollama serve
+OLLAMA_CONTEXT_LENGTH=64000 ollama serve
 ```
 
 ### Check allocated context length and model offloading
@@ -34,5 +37,5 @@ ollama ps
 ```
 ```
 NAME             ID              SIZE      PROCESSOR    CONTEXT    UNTIL
-gemma3:latest    a2af6cc3eb7f    6.6 GB    100% GPU     65536      2 minutes from now
+gemma4:latest    c6eb396dbd59    9.6 GB    100% GPU     131072     2 minutes from now
 ```

docs/development.md

@@ -3,9 +3,11 @@
 Install prerequisites:
 
 - [Go](https://go.dev/doc/install)
-- C/C++ Compiler e.g. Clang on macOS, [TDM-GCC](https://github.com/jmeubank/tdm-gcc/releases/latest) (Windows amd64) or [llvm-mingw](https://github.com/mstorsjo/llvm-mingw) (Windows arm64), GCC/Clang on Linux.
+- [CMake](https://cmake.org/download/) 3.24 or newer
+- C/C++ compiler: Clang on macOS, Visual Studio 2022 C++ tools on Windows, or GCC/Clang on Linux
+- [Ninja](https://github.com/ninja-build/ninja/releases) in `PATH` is recommended, especially on Windows
 
-Then build and run Ollama from the root directory of the repository:
+For pure Go iteration against an existing native payload, run Ollama from the repository root:
 
 ```shell
 go run . serve
@@ -14,50 +16,73 @@ go run . serve
 > [!NOTE]
 > Ollama includes native code compiled with CGO.  From time to time these data structures can change and CGO can get out of sync resulting in unexpected crashes.  You can force a full build of the native code by running `go clean -cache` first. 
 
+## Native build model
+
+For a fresh checkout, or after changing native code, build from the repository root. On macOS arm64, this builds Metal inference. On all other platforms this builds CPU-only inference. It builds the Go binary at the repository root and installs the native runtime payload under `build/lib/ollama`.
+
+```shell
+cmake -B build .
+cmake --build build --parallel 8
+./ollama serve
+```
+
+To install into a standard prefix layout:
+
+```shell
+cmake --install build --prefix /path/to/install
+```
+
+On all platforms except macOS arm64, to build GPU backends select the backends explicitly:
+
+```shell
+cmake -B build . -DOLLAMA_LLAMA_BACKENDS="cuda_v13;vulkan"
+cmake --build build --parallel 8
+```
+
+Supported backend values are `cuda_v12`, `cuda_v13`, `rocm_v7_1`, `rocm_v7_2`, `vulkan`, `cuda_jetpack5`, and `cuda_jetpack6`.
+
+Use standard CMake architecture overrides to narrow GPU builds for local hardware:
+
+```shell
+# CUDA
+cmake -B build . -DOLLAMA_LLAMA_BACKENDS=cuda_v13 -DCMAKE_CUDA_ARCHITECTURES=native
+
+# ROCm / HIP
+cmake -B build . -DOLLAMA_LLAMA_BACKENDS=rocm_v7_2 -DCMAKE_HIP_ARCHITECTURES=gfx1100
+```
+
+You can tune GGML build options by setting `GGML_*` values during configure. For example, to build CUDA v12 for Pascal without flash attention kernels:
+
+```shell
+cmake -B build . -DOLLAMA_LLAMA_BACKENDS=cuda_v12 -DCMAKE_CUDA_ARCHITECTURES=61 -DGGML_CUDA_FA=OFF
+```
 
 ## macOS (Apple Silicon)
 
-macOS Apple Silicon supports Metal which is built-in to the Ollama binary. No additional steps are required.
+Additional prerequisites:
 
-## macOS (Intel)
-
-Install prerequisites:
-
-- [CMake](https://cmake.org/download/) or `brew install cmake`
-
-Then, configure and build the project:
+MLX Metal requires the Metal toolchain. Install [Xcode](https://developer.apple.com/xcode/) first, then:
 
 ```shell
-cmake -B build
-cmake --build build
-```
-
-Lastly, run Ollama:
-
-```shell
-go run . serve
+xcodebuild -downloadComponent MetalToolchain
 ```
 
 ## Windows
 
-Install prerequisites:
+Additional prerequisites:
 
-- [CMake](https://cmake.org/download/)
 - [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/) including the Native Desktop Workload
 - (Optional) AMD GPU support
     - [ROCm](https://rocm.docs.amd.com/en/latest/)
-    - [Ninja](https://github.com/ninja-build/ninja/releases)
 - (Optional) NVIDIA GPU support
-    - [CUDA SDK](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=11&target_type=exe_network)
-- (Optional) VULKAN GPU support
-    - [VULKAN SDK](https://vulkan.lunarg.com/sdk/home) - useful for AMD/Intel GPUs
+    - [CUDA SDK](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_type=exe_network)
+- (Optional) Vulkan GPU support
+    - [Vulkan SDK](https://vulkan.lunarg.com/sdk/home) - useful for AMD/Intel GPUs
+- (Optional) MLX engine support
+    - [CUDA 13+ SDK](https://developer.nvidia.com/cuda-downloads)
+    - [cuDNN 9+](https://developer.nvidia.com/cudnn)
 
-Then, configure and build the project:
-
-```shell
-cmake -B build
-cmake --build build --config Release
-```
+For Ninja builds, run CMake from a Developer PowerShell/Command Prompt or another shell where the Visual Studio compiler is available.
 
 > Building for Vulkan requires VULKAN_SDK environment variable:
 > 
@@ -70,52 +95,64 @@ cmake --build build --config Release
 > set VULKAN_SDK=C:\VulkanSDK\<version>
 > ```
 
-> [!IMPORTANT]
-> Building for ROCm requires additional flags:
-> ```
-> cmake -B build -G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++
-> cmake --build build --config Release
-> ```
-
-
-
-Lastly, run Ollama:
-
-```shell
-go run . serve
-```
-
 ## Windows (ARM)
 
-Windows ARM does not support additional acceleration libraries at this time.  Do not use cmake, simply `go run` or `go build`.
+Windows ARM does not support additional acceleration libraries at this time.
 
 ## Linux
 
-Install prerequisites:
+Additional prerequisites:
 
-- [CMake](https://cmake.org/download/) or `sudo apt install cmake` or `sudo dnf install cmake`
 - (Optional) AMD GPU support
     - [ROCm](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/quick-start.html)
 - (Optional) NVIDIA GPU support
     - [CUDA SDK](https://developer.nvidia.com/cuda-downloads)
-- (Optional) VULKAN GPU support
-    - [VULKAN SDK](https://vulkan.lunarg.com/sdk/home) - useful for AMD/Intel GPUs
+- (Optional) Vulkan GPU support
+    - [Vulkan SDK](https://vulkan.lunarg.com/sdk/home) - useful for AMD/Intel GPUs
     - Or install via package manager: `sudo apt install vulkan-sdk` (Ubuntu/Debian) or `sudo dnf install vulkan-sdk` (Fedora/CentOS)
+- (Optional) MLX engine support
+    - [CUDA 13+ SDK](https://developer.nvidia.com/cuda-downloads)
+    - [cuDNN 9+](https://developer.nvidia.com/cudnn)
+    - OpenBLAS/LAPACK: `sudo apt install libopenblas-dev liblapack-dev liblapacke-dev` (Ubuntu/Debian)
 > [!IMPORTANT]
 > Ensure prerequisites are in `PATH` before running CMake.
 
+## MLX Engine (Optional)
 
-Then, configure and build the project:
+The MLX engine enables running safetensor based models. On macOS arm64, MLX is enabled by default. On other platforms, MLX backends are selected with `OLLAMA_MLX_BACKENDS`.
+
+### CUDA
+
+Requires CUDA 13+ and [cuDNN](https://developer.nvidia.com/cudnn) 9+.
 
 ```shell
-cmake -B build
-cmake --build build
+cmake -B build . -DOLLAMA_MLX_BACKENDS=cuda_v13
+cmake --build build --parallel 8
 ```
 
-Lastly, run Ollama:
+### Local MLX source overrides
+
+To build against a local checkout of MLX and/or MLX-C (useful for development), set environment variables before running CMake:
 
 ```shell
-go run . serve
+export OLLAMA_MLX_SOURCE=/path/to/mlx
+export OLLAMA_MLX_C_SOURCE=/path/to/mlx-c
+```
+
+On macOS arm64:
+
+```shell
+OLLAMA_MLX_SOURCE=../mlx OLLAMA_MLX_C_SOURCE=../mlx-c cmake -B build .
+cmake --build build --parallel 8
+```
+
+For CUDA:
+
+```powershell
+$env:OLLAMA_MLX_SOURCE="../mlx"
+$env:OLLAMA_MLX_C_SOURCE="../mlx-c"
+cmake -B build . -DOLLAMA_MLX_BACKENDS=cuda_v13
+cmake --build build --parallel 8
 ```
 
 ## Docker
@@ -138,42 +175,13 @@ To run tests, use `go test`:
 go test ./...
 ```
 
-> NOTE: In rare circumstances, you may need to change a package using the new
-> "synctest" package in go1.24.
->
-> If you do not have the "synctest" package enabled, you will not see build or
-> test failures resulting from your change(s), if any, locally, but CI will
-> break.
->
-> If you see failures in CI, you can either keep pushing changes to see if the
-> CI build passes, or you can enable the "synctest" package locally to see the
-> failures before pushing.
->
-> To enable the "synctest" package for testing, run the following command:
->
-> ```shell
-> GOEXPERIMENT=synctest go test ./...
-> ```
->
-> If you wish to enable synctest for all go commands, you can set the
-> `GOEXPERIMENT` environment variable in your shell profile or by using:
->
-> ```shell
-> go env -w GOEXPERIMENT=synctest
-> ```
->
-> Which will enable the "synctest" package for all go commands without needing
-> to set it for all shell sessions.
->
-> The synctest package is not required for production builds.
-
 ## Library detection
 
-Ollama looks for acceleration libraries in the following paths relative to the `ollama` executable:
+Ollama looks for native helper binaries and acceleration libraries in installed and local development layouts:
 
-* `./lib/ollama` (Windows)
-* `../lib/ollama` (Linux)
-* `.` (macOS)
-* `build/lib/ollama` (for development)
+* `../lib/ollama` for standard installs where `ollama` is under `bin/`
+* `./lib/ollama` for Windows release-style payloads and local dist output
+* `.` for macOS release artifacts that colocate helpers with `ollama`
+* `build/lib/ollama` and `dist/<platform>/lib/ollama` for local development builds
 
 If the libraries are not found, Ollama will not run with any acceleration libraries.

docs/docker.mdx

@@ -70,12 +70,16 @@ docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 114
 
 ## Vulkan Support
 
-Vulkan is bundled into the `ollama/ollama` image.  
+Vulkan is bundled into the `ollama/ollama` image and is enabled by default when
+the container can access the GPU devices.
 
 ```shell
-docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 11434:11434 -e OLLAMA_VULKAN=1 --name ollama ollama/ollama
+docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
 ```
 
+Use `OLLAMA_VULKAN=0` to disable Vulkan, or `GGML_VK_VISIBLE_DEVICES=<ids>` to
+select specific Vulkan devices.
+
 
 ## Run model locally
 
@@ -88,4 +92,3 @@ docker exec -it ollama ollama run llama3.2
 ## Try different models
 
 More models can be found on the [Ollama library](https://ollama.com/library).
-

docs/docs.json

@@ -71,6 +71,14 @@
     {
       "source": "/api",
       "destination": "/api/introduction"
+    },
+    {
+      "source": "/integrations/clawdbot",
+      "destination": "/integrations/openclaw"
+    },
+    {
+      "source": "/integrations/poolside",
+      "destination": "/integrations/pool"
     }
   ],
   "navigation": {
@@ -101,23 +109,75 @@
           {
             "group": "Integrations",
             "pages": [
-              "/integrations/claude-code",
-              "/integrations/vscode",
-              "/integrations/jetbrains",
-              "/integrations/codex",
-              "/integrations/cline",
-              "/integrations/droid",
-              "/integrations/goose",
-              "/integrations/zed",
-              "/integrations/roo-code",
-              "/integrations/n8n",
-              "/integrations/xcode"
+              "/integrations/index",
+              {
+                "group": "Assistants",
+                "expanded": true,
+                "pages": [
+                  "/integrations/openclaw",
+                  "/integrations/hermes",
+                  "/integrations/hermes-desktop"
+                ]
+              },
+              {
+                "group": "Coding",
+                "expanded": true,
+                "pages": [
+                  "/integrations/claude-code",
+                  "/integrations/codex-app",
+                  "/integrations/codex",
+                  "/integrations/copilot-cli",
+                  "/integrations/cline-cli",
+                  "/integrations/opencode",
+                  "/integrations/droid",
+                  "/integrations/goose",
+                  "/integrations/oh-my-pi",
+                  "/integrations/pi",
+                  "/integrations/pool"
+                ]
+              },
+              {
+                "group": "IDEs & Editors",
+                "expanded": true,
+                "pages": [
+                  "/integrations/cline",
+                  "/integrations/jetbrains",
+                  "/integrations/roo-code",
+                  "/integrations/vscode",
+                  "/integrations/xcode",
+                  "/integrations/zed"
+                ]
+              },
+              {
+                "group": "Chat & RAG",
+                "pages": [
+                  "/integrations/onyx"
+                ]
+              },
+              {
+                "group": "Automation",
+                "pages": [
+                  "/integrations/n8n"
+                ]
+              },
+              {
+                "group": "Notebooks",
+                "pages": [
+                  "/integrations/marimo"
+                ]
+              }
             ]
           },
           {
             "group": "More information",
             "pages": [
               "/cli",
+              {
+                "group": "Assistant Sandboxing",
+                "pages": [
+                  "/integrations/nemoclaw"
+                ]
+              },
               "/modelfile",
               "/context-length",
               "/linux",

docs/faq.mdx

@@ -14,15 +14,15 @@ curl -fsSL https://ollama.com/install.sh | sh
 
 ## How can I view the logs?
 
-Review the [Troubleshooting](./troubleshooting) docs for more about using logs.
+Review the [Troubleshooting](./troubleshooting.mdx) docs for more about using logs.
 
 ## Is my GPU compatible with Ollama?
 
-Please refer to the [GPU docs](./gpu).
+Please refer to the [GPU docs](./gpu.mdx).
 
 ## How can I specify the context window size?
 
-By default, Ollama uses a context window size of 2048 tokens.
+By default, Ollama uses a context window size of 4096 tokens.
 
 This can be overridden with the `OLLAMA_CONTEXT_LENGTH` environment variable. For example, to set the default context window to 8K, use:
 
@@ -66,7 +66,7 @@ llama3:70b  bcfb190ca3a7  42 GB   100% GPU    4 minutes from now
 ```
 </Info>
 
-The `Processor` column will show which memory the model was loaded in to:
+The `Processor` column will show which memory the model was loaded into:
 
 - `100% GPU` means the model was loaded entirely into the GPU
 - `100% CPU` means the model was loaded entirely in system memory
@@ -158,7 +158,27 @@ docker run -d -e HTTPS_PROXY=https://my.proxy.example.com -p 11434:11434 ollama-
 
 ## Does Ollama send my prompts and answers back to ollama.com?
 
-No. Ollama runs locally, and conversation data does not leave your machine.
+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?
+
+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. 
+
+Set `disable_ollama_cloud` in `~/.ollama/server.json`:
+
+```json
+{
+  "disable_ollama_cloud": true
+}
+```
+
+You can also set the environment variable:
+
+```shell
+OLLAMA_NO_CLOUD=1
+```
+
+Restart Ollama after changing configuration. Once disabled, Ollama's logs will show `Ollama cloud disabled: true`. 
 
 ## How can I expose Ollama on my network?
 
@@ -183,7 +203,7 @@ server {
 
 ## How can I use Ollama with ngrok?
 
-Ollama can be accessed using a range of tools for tunneling tools. For example with Ngrok:
+Ollama can be accessed using a range of tunneling apps. For example with Ngrok:
 
 ```shell
 ngrok http 11434 --host-header="localhost:11434"
@@ -240,7 +260,7 @@ GPU acceleration is not available for Docker Desktop in macOS due to the lack of
 
 This can impact both installing Ollama, as well as downloading models.
 
-Open `Control Panel > Networking and Internet > View network status and tasks` and click on `Change adapter settings` on the left panel. Find the `vEthernel (WSL)` adapter, right click and select `Properties`.
+Open `Control Panel > Networking and Internet > View network status and tasks` and click on `Change adapter settings` on the left panel. Find the `vEthernet (WSL)` adapter, right click and select `Properties`.
 Click on `Configure` and open the `Advanced` tab. Search through each of the properties until you find `Large Send Offload Version 2 (IPv4)` and `Large Send Offload Version 2 (IPv6)`. _Disable_ both of these
 properties.
 
@@ -299,7 +319,7 @@ The `keep_alive` API parameter with the `/api/generate` and `/api/chat` API endp
 
 ## How do I manage the maximum number of requests the Ollama server can queue?
 
-If too many requests are sent to the server, it will respond with a 503 error indicating the server is overloaded. You can adjust how many requests may be queue by setting `OLLAMA_MAX_QUEUE`.
+If too many requests are sent to the server, it will respond with a 503 error indicating the server is overloaded. You can adjust how many requests may be queued by setting `OLLAMA_MAX_QUEUE`.
 
 ## How does Ollama handle concurrent requests?
 
@@ -312,10 +332,10 @@ Parallel request processing for a given model results in increasing the context
 The following server settings may be used to adjust how Ollama handles concurrent requests on most platforms:
 
 - `OLLAMA_MAX_LOADED_MODELS` - The maximum number of models that can be loaded concurrently provided they fit in available memory. The default is 3 \* the number of GPUs or 3 for CPU inference.
-- `OLLAMA_NUM_PARALLEL` - The maximum number of parallel requests each model will process at the same time. The default will auto-select either 4 or 1 based on available memory.
+- `OLLAMA_NUM_PARALLEL` - The maximum number of parallel requests each model will process at the same time, default 1.  Required RAM will scale by `OLLAMA_NUM_PARALLEL` * `OLLAMA_CONTEXT_LENGTH`.
 - `OLLAMA_MAX_QUEUE` - The maximum number of requests Ollama will queue when busy before rejecting additional requests. The default is 512
 
-Note: Windows with Radeon GPUs currently default to 1 model maximum due to limitations in ROCm v5.7 for available VRAM reporting. Once ROCm v6.2 is available, Windows Radeon will follow the defaults above. You may enable concurrent model loads on Radeon on Windows, but ensure you don't load more models than will fit into your GPUs VRAM.
+Note: Windows with Radeon GPUs currently default to 1 model maximum due to limitations in ROCm v5.7 for available VRAM reporting. Once ROCm v6.2 is available, Windows Radeon will follow the defaults above. You may enable concurrent model loads on Radeon on Windows, but ensure you don't load more models than will fit into your GPU's VRAM.
 
 ## How does Ollama load models on multiple GPUs?
 
@@ -382,7 +402,7 @@ ollama signin
   Replace &lt;username&gt; with your actual Windows user name.
 </Note>
 
-## How can I stop Ollama from starting when I login to my computer
+## How can I stop Ollama from starting when I login to my computer?
 
 Ollama for Windows and macOS register as a login item during installation.  You can disable this if you prefer not to have Ollama automatically start.  Ollama will respect this setting across upgrades, unless you uninstall the application.
 
@@ -390,4 +410,4 @@ Ollama for Windows and macOS register as a login item during installation.  You
 - In `Task Manager` go to the `Startup apps` tab, search for `ollama` then click `Disable`
 
 **MacOS**
-- Open `Settings` and search for "Login Items", find the `Ollama` entry under "Allow in the Background`, then click the slider to disable.
+- Open `Settings` and search for "Login Items", find the `Ollama` entry under `Allow in the Background`, then click the slider to disable.

docs/gpu.mdx

@@ -4,12 +4,14 @@ title: Hardware support
 
 ## Nvidia
 Ollama supports Nvidia GPUs with compute capability 5.0+ and driver version 531 and newer.
+Nvidia GPUs with compute capability 5.0 through 6.2 require driver version 570 or newer.
 
 Check your compute compatibility to see if your card is supported:
 [https://developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus)
 
 | Compute Capability | Family              | Cards                                                                                                                          |
 | ------------------ | ------------------- | ------------------------------------------------------------------------------------------------------------------------------ |
+| 12.1               | NVIDIA              | `GB10 (DGX Spark)`                                                                                                             |
 | 12.0               | GeForce RTX 50xx    | `RTX 5060` `RTX 5060 Ti` `RTX 5070` `RTX 5070 Ti` `RTX 5080` `RTX 5090`                                                        |
 |                    | NVIDIA Professional | `RTX PRO 4000 Blackwell` `RTX PRO 4500 Blackwell` `RTX PRO 5000 Blackwell` `RTX PRO 6000 Blackwell`                            |
 | 9.0                | NVIDIA              | `H200` `H100`                                                                                                                  |
@@ -60,15 +62,21 @@ Ollama supports the following AMD GPUs via the ROCm library:
 
 ### Linux Support
 
-| Family         | Cards and accelerators                                                                                                                         |
-| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
-| AMD Radeon RX  | `7900 XTX` `7900 XT` `7900 GRE` `7800 XT` `7700 XT` `7600 XT` `7600` `6950 XT` `6900 XTX` `6900XT` `6800 XT` `6800` `Vega 64`                  |
-| AMD Radeon PRO | `W7900` `W7800` `W7700` `W7600` `W7500` `W6900X` `W6800X Duo` `W6800X` `W6800` `V620` `V420` `V340` `V320` `Vega II Duo` `Vega II` `SSG`       |
-| AMD Instinct   | `MI300X` `MI300A` `MI300` `MI250X` `MI250` `MI210` `MI200` `MI100` `MI60`                                                                      |
+Ollama requires the AMD ROCm v7 driver on Linux. You can install or upgrade
+using the `amdgpu-install` utility from
+[AMD's ROCm documentation](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/).
+
+| Family         | Cards and accelerators                                                                                                                                         |
+| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| AMD Radeon RX  | `9070 XT` `9070 GRE` `9070` `9060 XT` `9060 XT LP` `9060` `7900 XTX` `7900 XT` `7900 GRE` `7800 XT` `7700 XT` `7700` `7600 XT` `7600` `6950 XT` `6900 XTX` `6900XT` `6800 XT` `6800` `5700 XT` `5700` `5600 XT` `5500 XT` |
+| AMD Radeon AI PRO | `R9700` `R9600D`                                                                                                                                            |
+| AMD Radeon PRO | `W7900` `W7800` `W7700` `W7600` `W7500` `W6900X` `W6800X Duo` `W6800X` `W6800` `V620`                                                                        |
+| AMD Ryzen AI   | `Ryzen AI Max+ 395` `Ryzen AI Max 390` `Ryzen AI Max 385` `Ryzen AI 9 HX 475` `Ryzen AI 9 HX 470` `Ryzen AI 9 465` `Ryzen AI 9 HX 375` `Ryzen AI 9 HX 370` `Ryzen AI 9 365` |
+| AMD Instinct   | `MI350X` `MI300X` `MI300A` `MI250X` `MI250` `MI210` `MI100`                                                                                                   |
 
 ### Windows Support
 
-With ROCm v6.1, the following GPUs are supported on Windows.
+Ollama requires an AMD ROCm v7 / HIP7-capable driver stack on Windows.
 
 | Family         | Cards and accelerators                                                                                               |
 | -------------- | -------------------------------------------------------------------------------------------------------------------- |
@@ -96,17 +104,20 @@ This table shows some example GPUs that map to these LLVM targets:
 | **LLVM Target** | **An Example GPU** |
 |-----------------|---------------------|
 | gfx908 | Radeon Instinct MI100 |
-| gfx90a | Radeon Instinct MI210 |
-| gfx940 | Radeon Instinct MI300 |
-| gfx941 | |
-| gfx942 | |
+| gfx90a | Radeon Instinct MI210/MI250 |
+| gfx942 | Radeon Instinct MI300X/MI300A |
+| gfx950 | Radeon Instinct MI350X |
+| gfx1010 | Radeon RX 5700 XT |
+| gfx1012 | Radeon RX 5500 XT |
 | gfx1030 | Radeon PRO V620 |
 | gfx1100 | Radeon PRO W7900 |
 | gfx1101 | Radeon PRO W7700 |
 | gfx1102 | Radeon RX 7600 |
-
-AMD is working on enhancing ROCm v6 to broaden support for families of GPUs in a
-future release which should increase support for more GPUs.
+| gfx1103 | Radeon 780M |
+| gfx1150 | Ryzen AI 9 HX 375 |
+| gfx1151 | Ryzen AI Max+ 395 |
+| gfx1200 | Radeon RX 9070 |
+| gfx1201 | Radeon RX 9070 XT |
 
 Reach out on [Discord](https://discord.gg/ollama) or file an
 [issue](https://github.com/ollama/ollama/issues) for additional help.
@@ -132,12 +143,9 @@ Ollama supports GPU acceleration on Apple devices via the Metal API.
 
 ## Vulkan GPU Support
 
-> **NOTE:**
-> Vulkan is currently an Experimental feature.  To enable, you must set OLLAMA_VULKAN=1 for the Ollama server as
-described in the [FAQ](faq#how-do-i-configure-ollama-server)
-
 Additional GPU support on Windows and Linux is provided via
-[Vulkan](https://www.vulkan.org/). On Windows most GPU vendors drivers come
+[Vulkan](https://www.vulkan.org/). Vulkan is enabled by default when the
+backend is installed. On Windows most GPU vendors drivers come
 bundled with Vulkan support and require no additional setup steps. Most Linux
 distributions require installing additional components, and you may have
 multiple options for Vulkan drivers between Mesa and GPU Vendor specific packages
@@ -163,4 +171,9 @@ To select specific Vulkan GPU(s), you can set the environment variable
 `GGML_VK_VISIBLE_DEVICES` to one or more numeric IDs on the Ollama server as
 described in the [FAQ](faq#how-do-i-configure-ollama-server). If you
 encounter any problems with Vulkan based GPUs, you can disable all Vulkan GPUs
-by setting `GGML_VK_VISIBLE_DEVICES=-1` 
+by setting `OLLAMA_VULKAN=0` or `GGML_VK_VISIBLE_DEVICES=-1`.
+
+On mixed iGPU/dGPU systems where the Vulkan iGPU is unstable, keep Vulkan
+enabled and set `GGML_VK_VISIBLE_DEVICES` to the discrete GPU index. For
+example, use `GGML_VK_VISIBLE_DEVICES=1` when `Vulkan1` is the discrete
+GPU.

docs/import.mdx

@@ -134,22 +134,12 @@ success
 
 ### Supported Quantizations
 
-- `q4_0`
-- `q4_1`
-- `q5_0`
-- `q5_1`
 - `q8_0`
 
 #### K-means Quantizations
 
-- `q3_K_S`
-- `q3_K_M`
-- `q3_K_L`
 - `q4_K_S`
 - `q4_K_M`
-- `q5_K_S`
-- `q5_K_M`
-- `q6_K`
 
 ## Sharing your model on ollama.com
 

docs/index.mdx

@@ -5,11 +5,11 @@ sidebarTitle: Welcome
 
 <img src="/images/welcome.png" noZoom className="rounded-3xl" />
 
-[Ollama](https://ollama.com) is the easiest way to get up and running with large language models such as gpt-oss, Gemma 3, DeepSeek-R1, Qwen3 and more.
+[Ollama](https://ollama.com) is the easiest way to get up and running with large language models such as gpt-oss, Gemma 4, DeepSeek-R1, Qwen3 and more.
 
 <CardGroup cols={2}>
   <Card title="Quickstart" icon="rocket" href="/quickstart">
-    Get up and running with your first model
+    Get up and running with your first model or integrate Ollama with your favorite tools
   </Card>
   <Card
     title="Download Ollama"

docs/integrations/claude-code.mdx

@@ -2,6 +2,12 @@
 title: Claude Code
 ---
 
+Claude Code is Anthropic's agentic coding tool that can read, modify, and execute code in your working directory. 
+
+Open models can be used with Claude Code through Ollama's Anthropic-compatible API, enabling you to use models such as `qwen3.5`, `glm-5:cloud`, `kimi-k2.5:cloud`.
+
+![Claude Code with Ollama](https://files.ollama.com/claude-code.png)
+
 ## Install
 
 Install [Claude Code](https://code.claude.com/docs/en/overview):
@@ -20,50 +26,111 @@ irm https://claude.ai/install.ps1 | iex
 
 ## Usage with Ollama
 
+### Quick setup
+
+```shell
+ollama launch claude
+```
+
+### Run directly with a model
+```shell
+ollama launch claude --model kimi-k2.5:cloud
+```
+
+## Recommended Models
+
+- `kimi-k2.5:cloud`
+- `glm-5:cloud`
+- `minimax-m2.7:cloud`
+- `qwen3.5:cloud`
+- `glm-4.7-flash`
+- `qwen3.5`
+
+Cloud models are also available at [ollama.com/search?c=cloud](https://ollama.com/search?c=cloud).
+
+## Non-interactive (headless) mode
+
+Run Claude Code without interaction for use in Docker, CI/CD, or scripts:
+
+```shell
+ollama launch claude --model kimi-k2.5:cloud --yes -- -p "how does this repository work?"
+```
+
+The `--yes` flag auto-pulls the model, skips selectors, and requires `--model` to be specified. Arguments after `--` are passed directly to Claude Code.
+
+## Web search
+
+Claude Code can search the web through Ollama's web search API. See the [web search documentation](/capabilities/web-search) for setup and usage.
+
+## Scheduled Tasks with `/loop`
+
+The `/loop` command runs a prompt or slash command on a recurring schedule inside Claude Code. This is useful for automating repetitive tasks like checking PRs, running research, or setting reminders.
+
+```
+/loop <interval> <prompt or /command>
+```
+
+### Examples
+
+**Check in on your PRs**
+
+```
+/loop 30m Check my open PRs and summarize their status
+```
+
+**Automate research tasks**
+
+```
+/loop 1h Research the latest AI news and summarize key developments
+```
+
+**Automate bug reporting and triaging**
+
+```
+/loop 15m Check for new GitHub issues and triage by priority
+```
+
+**Set reminders**
+
+```
+/loop 1h Remind me to review the deploy status
+```
+
+## Telegram
+
+Chat with Claude Code from Telegram by connecting a bot to your session. Install the [Telegram plugin](https://github.com/anthropics/claude-plugins-official), create a bot via [@BotFather](https://t.me/BotFather), then launch with the channel flag:
+
+```shell
+ollama launch claude -- --channels plugin:telegram@claude-plugins-official
+```
+
+Claude Code will prompt for permission on most actions. To allow the bot to work autonomously, configure [permission rules](https://code.claude.com/docs/en/permissions) or pass `--dangerously-skip-permissions` in isolated environments.
+
+See the [plugin README](https://github.com/anthropics/claude-plugins-official/tree/main/external_plugins/telegram) for full setup instructions including pairing and access control.
+
+## Manual setup
+
 Claude Code connects to Ollama using the Anthropic-compatible API.
 
 1. Set the environment variables:
 
 ```shell
+export ANTHROPIC_AUTH_TOKEN=ollama
+export ANTHROPIC_API_KEY=""
 export ANTHROPIC_BASE_URL=http://localhost:11434
-export ANTHROPIC_API_KEY=ollama
 ```
 
 2. Run Claude Code with an Ollama model:
 
 ```shell
-claude --model qwen3-coder
+claude --model qwen3.5
 ```
 
 Or run with environment variables inline:
 
 ```shell
-ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY=ollama claude --model qwen3-coder
+ANTHROPIC_AUTH_TOKEN=ollama ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY="" claude --model glm-5:cloud
 ```
 
-## Connecting to ollama.com
+**Note:** Claude Code requires a large context window. We recommend at least 64k tokens. See the [context length documentation](/context-length) for how to adjust context length in Ollama.
 
-1. Create an [API key](https://ollama.com/settings/keys) on ollama.com
-2. Set the environment variables:
-
-```shell
-export ANTHROPIC_BASE_URL=https://ollama.com
-export ANTHROPIC_API_KEY=<your-api-key>
-```
-
-3. Run Claude Code with a cloud model:
-
-```shell
-claude --model glm-4.7:cloud
-```
-
-## Recommended Models
-
-### Cloud models
-- `glm-4.7:cloud` - High-performance cloud model
-- `minimax-m2.1:cloud` - Fast cloud model
-- `qwen3-coder:480b` - Large coding model
-
-### Local models
-- `qwen3-coder` - Excellent for coding tasks
-- `gpt-oss:20b` - Strong general-purpose model

docs/integrations/claude-desktop.mdx

@@ -0,0 +1,13 @@
+---
+title: Claude Desktop
+---
+
+Claude Desktop is no longer supported by `ollama launch`.
+
+Existing installations can be restored to the usual Claude profile:
+
+```shell
+ollama launch claude-desktop --restore
+```
+
+Use [Claude Code](/integrations/claude-code) for Anthropic-compatible coding workflows with Ollama.

docs/integrations/cline-cli.mdx

@@ -0,0 +1,98 @@
+---
+title: Cline CLI
+---
+
+Cline CLI is an autonomous coding agent for interactive terminal sessions.
+
+<img
+  src="/images/cline-cli.png"
+  alt="Cline CLI launched with Ollama selected as the provider"
+  style={{ borderRadius: "12px" }}
+/>
+
+## Install
+
+Install the [Cline CLI](https://docs.cline.bot/usage/cli-overview). For the IDE extension, see [Cline](/integrations/cline).
+
+```bash
+npm install -g cline
+```
+
+<Note>If Cline CLI is not installed and `npm` is available, `ollama launch cline` will prompt to install `cline@latest`.</Note>
+
+## Usage with Ollama
+
+### Quick setup
+
+```bash
+ollama launch cline
+```
+
+When launched through `ollama launch cline`, Ollama sets Cline's provider to Ollama, points it at the local Ollama endpoint, and selects the model you choose.
+
+To configure without launching:
+
+```shell
+ollama launch cline --config
+```
+
+### Run directly with a model
+
+```shell
+ollama launch cline --model qwen3.5
+```
+
+To use a cloud model:
+
+```shell
+ollama launch cline --model kimi-k2.6:cloud
+```
+
+
+### Pass a prompt to Cline
+
+Arguments after `--` are passed directly to Cline:
+
+```shell
+ollama launch cline -- "summarize this repository"
+```
+
+To open Cline's Kanban board:
+
+```shell
+ollama launch cline -- kanban
+```
+
+<img
+  src="/images/cline-kanban.png"
+  alt="Cline Kanban board opened from the CLI"
+  style={{ borderRadius: "12px" }}
+/>
+
+### Manual setup
+
+To configure Cline CLI manually, first make sure Ollama is running and the model you want to use is available:
+
+```shell
+ollama pull qwen3.5
+```
+
+Then run Cline's interactive auth flow:
+
+```shell
+cline auth
+```
+
+Select Ollama as the provider, use `http://localhost:11434` as the base URL if prompted, and choose a model such as `qwen3.5` or `kimi-k2.6:cloud`.
+
+To check the current Cline configuration:
+
+```shell
+cline config
+```
+
+To start an interactive session:
+
+```shell
+cline
+```

docs/integrations/codex-app.mdx

@@ -0,0 +1,82 @@
+---
+title: Codex App
+---
+
+Codex App is OpenAI's desktop coding agent for macOS and Windows. Ollama configures the app to use Ollama's OpenAI-compatible endpoint, so Codex can work with local models and Ollama Cloud models in the desktop app.
+
+<img
+  src="/images/codex-app-home.png"
+  alt="Codex App with Ollama selected"
+  style={{ borderRadius: "12px" }}
+/>
+
+## Install
+
+Install the [Codex App](https://developers.openai.com/codex/quickstart/) for macOS or Windows.
+
+<Note>Codex App support is available in Ollama v0.24.0 and newer.</Note>
+
+
+## Quick setup
+
+```shell
+ollama launch codex-app
+```
+
+Once Codex App opens, start a task or open a repository as usual.
+
+## Built-in browser
+
+Codex App can open local servers and sites in its built-in browser. Annotate directly on the page to request changes.
+
+<img
+  src="/images/codex-app-annotate.png"
+  alt="Codex App browser annotations"
+  style={{ borderRadius: "12px" }}
+/>
+
+## Review mode
+
+Use review mode to inspect code changes, leave comments, and iterate on fixes without leaving the app.
+
+<img
+  src="/images/codex-app-review.png"
+  alt="Codex App review comments"
+  style={{ borderRadius: "12px" }}
+/>
+
+### Run directly with a model
+
+```shell
+ollama launch codex-app --model kimi-k2.6:cloud
+```
+
+Use a local model by passing its model name:
+
+```shell
+ollama launch codex-app --model gemma4:31b
+```
+
+Running `ollama launch codex-app` is persistent and will have your model selected next time you open Codex.
+
+
+### Restore Codex App
+
+To switch Codex App back to the profile you were using before `ollama launch codex-app`, run:
+
+```shell
+ollama launch codex-app --restore
+```
+
+Ollama restores Codex App's settings and configs. If Codex App is open, Ollama asks before restarting it.
+
+
+The Codex CLI profile managed by `ollama launch codex` is left separate from the Codex App profile.
+
+Before overwriting Codex App config files, Ollama Launch saves backups under `~/.ollama/backup/codex-app/`. On Windows, `~` resolves to your user profile directory.
+
+## Troubleshooting
+
+If Codex App does not open after setup, open Codex manually once and run `ollama launch codex-app` again.
+
+If Codex App is already running and does not switch models, allow Ollama to restart it when prompted, or quit Codex App and run `ollama launch codex-app` again.

docs/integrations/codex.mdx

@@ -1,11 +1,11 @@
 ---
-title: Codex
+title: Codex CLI
 ---
 
 
 ## Install
 
-Install the [Codex CLI](https://developers.openai.com/codex/cli/):
+Install the [Codex CLI](https://developers.openai.com/codex/cli/). For the desktop app, see [Codex App](/integrations/codex-app).
 
 ```
 npm install -g @openai/codex
@@ -13,7 +13,30 @@ npm install -g @openai/codex
 
 ## Usage with Ollama
 
-<Note>Codex requires a larger context window. It is recommended to use a context window of at least 32K tokens.</Note>
+<Note>Codex requires a larger context window. It is recommended to use a context window of at least 64k tokens.</Note>
+
+### Quick setup
+
+```
+ollama launch codex
+```
+
+When launched through `ollama launch codex`, Ollama refreshes the model catalog
+and uses a dedicated Codex profile for that session.
+
+To configure without launching:
+
+```shell
+ollama launch codex --config
+```
+
+To remove the Ollama launch profile and generated model catalog:
+
+```shell
+ollama launch codex --restore
+```
+
+### Manual setup
 
 To use `codex` with Ollama, use the `--oss` flag:
 
@@ -21,36 +44,35 @@ To use `codex` with Ollama, use the `--oss` flag:
 codex --oss
 ```
 
-### Changing Models
-
-By default, codex will use the local `gpt-oss:20b` model. However, you can specify a different model with the `-m` flag:
+To use a specific model, pass the `-m` flag:
 
 ```
 codex --oss -m gpt-oss:120b
 ```
 
-### Cloud Models
+To use a cloud model:
 
 ```
 codex --oss -m gpt-oss:120b-cloud
 ```
 
+### Profile-based setup
 
-## Connecting to ollama.com
-
-
-Create an [API key](https://ollama.com/settings/keys) from ollama.com and export it as `OLLAMA_API_KEY`.
-
-To use ollama.com directly, edit your `~/.codex/config.toml` file to point to ollama.com.
+For a persistent Codex CLI configuration, create `~/.codex/ollama-launch.config.toml`:
 
 ```toml
 model = "gpt-oss:120b"
-model_provider = "ollama"
+model_provider = "ollama-launch"
+model_catalog_json = "/Users/you/.codex/model.json"
 
-[model_providers.ollama]
+[model_providers.ollama-launch]
 name = "Ollama"
-base_url = "https://ollama.com/v1"
-env_key = "OLLAMA_API_KEY"
+base_url = "http://localhost:11434/v1/"
+wire_api = "responses"
 ```
 
-Run `codex` in a new terminal to load the new settings.
+Then run:
+
+```
+codex --profile ollama-launch
+```

docs/integrations/copilot-cli.mdx

@@ -0,0 +1,93 @@
+---
+title: Copilot CLI
+---
+
+GitHub Copilot CLI is GitHub's AI coding agent for the terminal. It can understand your codebase, make edits, run commands, and help you build software faster.
+
+Open models can be used with Copilot CLI through Ollama, enabling you to use models such as `qwen3.5`, `glm-5.1:cloud`, `kimi-k2.5:cloud`.
+
+## Install
+
+Install [Copilot CLI](https://github.com/features/copilot/cli/):
+
+<CodeGroup>
+
+```shell macOS / Linux (Homebrew)
+brew install copilot-cli
+```
+
+```shell npm (all platforms)
+npm install -g @github/copilot
+```
+
+```shell macOS / Linux (script)
+curl -fsSL https://gh.io/copilot-install | bash
+```
+
+```powershell Windows (WinGet)
+winget install GitHub.Copilot
+```
+
+</CodeGroup>
+
+## Usage with Ollama
+
+### Quick setup
+
+```shell
+ollama launch copilot
+```
+
+### Run directly with a model
+
+```shell
+ollama launch copilot --model kimi-k2.5:cloud
+```
+
+## Recommended Models
+
+- `kimi-k2.5:cloud`
+- `glm-5:cloud`
+- `minimax-m2.7:cloud`
+- `qwen3.5:cloud`
+- `glm-4.7-flash`
+- `qwen3.5`
+
+Cloud models are also available at [ollama.com/search?c=cloud](https://ollama.com/search?c=cloud).
+
+## Non-interactive (headless) mode
+
+Run Copilot CLI without interaction for use in Docker, CI/CD, or scripts:
+
+```shell
+ollama launch copilot --model kimi-k2.5:cloud --yes -- -p "how does this repository work?"
+```
+
+The `--yes` flag auto-pulls the model, skips selectors, and requires `--model` to be specified. Arguments after `--` are passed directly to Copilot CLI.
+
+## Manual setup
+
+Copilot CLI connects to Ollama using the OpenAI-compatible API via environment variables.
+
+1. Set the environment variables:
+
+```shell
+export COPILOT_PROVIDER_BASE_URL=http://localhost:11434/v1
+export COPILOT_PROVIDER_API_KEY=
+export COPILOT_PROVIDER_WIRE_API=responses
+export COPILOT_MODEL=qwen3.5
+```
+
+1. Run Copilot CLI:
+
+```shell
+copilot
+```
+
+Or run with environment variables inline:
+
+```shell
+COPILOT_PROVIDER_BASE_URL=http://localhost:11434/v1 COPILOT_PROVIDER_API_KEY= COPILOT_PROVIDER_WIRE_API=responses COPILOT_MODEL=glm-5:cloud copilot
+```
+
+**Note:** Copilot requires a large context window. We recommend at least 64k tokens. See the [context length documentation](/context-length) for how to adjust context length in Ollama.

docs/integrations/droid.mdx

@@ -11,10 +11,24 @@ Install the [Droid CLI](https://factory.ai/):
 curl -fsSL https://app.factory.ai/cli | sh
 ```
 
-<Note>Droid requires a larger context window. It is recommended to use a context window of at least 32K tokens. See [Context length](/context-length) for more information.</Note>
+<Note>Droid requires a larger context window. It is recommended to use a context window of at least 64k tokens. See [Context length](/context-length) for more information.</Note>
 
 ## Usage with Ollama
 
+### Quick setup
+
+```bash
+ollama launch droid
+```
+
+To configure without launching:
+
+```shell
+ollama launch droid --config
+```
+
+### Manual setup
+
 Add a local configuration block to `~/.factory/config.json`:
 
 ```json
@@ -73,4 +87,4 @@ Add the cloud configuration block to `~/.factory/config.json`:
    }
    ```
 
-Run `droid` in a new terminal to load the new settings.
+Run `droid` in a new terminal to load the new settings.

docs/integrations/hermes-desktop.mdx

@@ -0,0 +1,28 @@
+---
+title: Hermes Desktop
+---
+
+Hermes Desktop is a native AI assistant app by Nous Research. It provides a desktop chat interface for Hermes Agent, an AI agent that can work with models, run tools, manage projects, use memory and skills, and connect to messaging gateways.
+
+![Hermes Desktop with Ollama](http://files.ollama.com/hermes-agent.png)
+
+## Quick start
+
+```bash
+ollama launch hermes-desktop
+```
+
+Ollama handles the setup flow automatically:
+
+1. **Install** - If Hermes Desktop isn't installed, Ollama prompts to install it
+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
+
+## Run directly with a model
+
+```bash
+ollama launch hermes-desktop --model <model>
+```
+
+Run `ollama launch hermes-desktop` again to switch models later.

docs/integrations/hermes.mdx

@@ -0,0 +1,117 @@
+---
+title: Hermes Agent
+---
+
+Hermes Agent is a self-improving AI agent built by Nous Research. It features automatic skill creation, cross-session memory, and 70+ skills that it ships with by default. 
+
+![Hermes Agent with Ollama](/images/hermes.png)
+
+## Quick start
+
+```bash
+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
+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
+
+## Recommended models
+
+**Cloud models**:
+
+- `kimi-k2.5:cloud` — Multimodal reasoning with subagents
+- `glm-5.1:cloud` — Reasoning and code generation
+- `qwen3.5:cloud` — Reasoning, coding, and agentic tool use with vision
+- `minimax-m2.7:cloud` — Fast, efficient coding and real-world productivity
+
+**Local models:**
+
+- `gemma4` — Reasoning and code generation locally (~16 GB VRAM)
+- `qwen3.6` — Reasoning, coding, and visual understanding locally (~24 GB VRAM)
+
+More models at [ollama.com/search](https://ollama.com/search?c=cloud).
+
+## Connect messaging apps
+
+Link Telegram, Discord, Slack, WhatsApp, Signal, or Email to chat with your models from anywhere:
+
+```bash
+hermes gateway setup
+```
+
+## Reconfigure
+
+Re-run the full setup wizard at any time:
+
+```bash
+hermes setup
+```
+
+## Manual setup
+
+If you'd rather drive Hermes's own wizard instead of `ollama launch hermes`, install it directly:
+
+```bash
+curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
+```
+
+Hermes launches the setup wizard automatically. Choose **Quick setup**:
+
+```
+How would you like to set up Hermes?
+
+ →  Quick setup — provider, model & messaging (recommended)
+    Full setup — configure everything
+```
+
+### Connect to Ollama
+
+1. Select **More providers...**
+2. Select **Custom endpoint (enter URL manually)**
+3. Set the API base URL to the Ollama OpenAI-compatible endpoint:
+
+   ```
+   API base URL [e.g. https://api.example.com/v1]: http://127.0.0.1:11434/v1
+   ```
+
+4. Leave the API key blank (not required for local Ollama):
+
+   ```
+   API key [optional]:
+   ```
+
+5. Hermes auto-detects downloaded models, confirm the one you want:
+
+   ```
+   Verified endpoint via http://127.0.0.1:11434/v1/models (1 model(s) visible)
+     Detected model: kimi-k2.5:cloud
+     Use this model? [Y/n]:
+   ```
+
+6. Leave context length blank to auto-detect:
+
+   ```
+   Context length in tokens [leave blank for auto-detect]:
+   ```
+
+### Connect messaging
+
+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'
+```
+
+### Launch
+
+```
+Launch hermes chat now? [Y/n]: Y
+```
+

docs/integrations/index.mdx

@@ -0,0 +1,58 @@
+---
+title: Overview
+---
+
+Ollama integrates with a wide range of tools.
+
+## Coding Agents
+
+Coding assistants that can read, modify, and execute code in your projects.
+
+- [Claude Code](/integrations/claude-code)
+- [Codex App](/integrations/codex-app)
+- [Codex CLI](/integrations/codex)
+- [Copilot CLI](/integrations/copilot-cli)
+- [Cline CLI](/integrations/cline-cli)
+- [OpenCode](/integrations/opencode)
+- [Droid](/integrations/droid)
+- [Goose](/integrations/goose)
+- [Oh My Pi](/integrations/oh-my-pi)
+- [Pi](/integrations/pi)
+- [Pool](/integrations/pool)
+
+## Assistants
+
+AI assistants that help with everyday tasks.
+
+- [OpenClaw](/integrations/openclaw)
+- [Hermes Agent](/integrations/hermes)
+- [Hermes Desktop](/integrations/hermes-desktop)
+
+## IDEs & Editors
+
+Native integrations for popular development environments.
+
+- [VS Code](/integrations/vscode)
+- [Cline](/integrations/cline)
+- [Roo Code](/integrations/roo-code)
+- [JetBrains](/integrations/jetbrains)
+- [Xcode](/integrations/xcode)
+- [Zed](/integrations/zed)
+
+## Chat & RAG
+
+Chat interfaces and retrieval-augmented generation platforms.
+
+- [Onyx](/integrations/onyx)
+
+## Automation
+
+Workflow automation platforms with AI integration.
+
+- [n8n](/integrations/n8n)
+
+## Notebooks
+
+Interactive computing environments with AI capabilities.
+
+- [marimo](/integrations/marimo)

docs/integrations/marimo.mdx

@@ -0,0 +1,73 @@
+---
+title: marimo
+---
+
+## Install
+
+Install [marimo](https://marimo.io). You can use `pip` or `uv` for this. You 
+can also use `uv` to create a sandboxed environment for marimo by running:
+
+```
+uvx marimo edit --sandbox notebook.py
+```
+
+## Usage with Ollama
+
+1. In marimo, go to the user settings and go to the AI tab. From here
+you can find and configure Ollama as an AI provider. For local use you
+would typically point the base url to `http://localhost:11434/v1`.
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/marimo-settings.png" 
+    alt="Ollama settings in marimo"
+    width="50%"
+  />
+</div>
+
+2. Once the AI provider is set up, you can turn on/off specific AI models you'd like to access. 
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/marimo-models.png" 
+    alt="Selecting an Ollama model"
+    width="50%"
+  />
+</div>
+
+3. You can also add a model to the list of available models by scrolling to the bottom and using the UI there. 
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/marimo-add-model.png" 
+    alt="Adding a new Ollama model"
+    width="50%"
+  />
+</div>
+
+4. Once configured, you can now use Ollama for AI chats in marimo.
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/marimo-chat.png" 
+    alt="Configure code completion"
+    width="50%"
+  />
+</div>
+
+4. Alternatively, you can now use Ollama for **inline code completion** in marimo. This can be configured in the "AI Features" tab. 
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/marimo-code-completion.png" 
+    alt="Configure code completion"
+    width="50%"
+  />
+</div>
+
+
+## Connecting to ollama.com
+
+1. Sign in to ollama cloud via `ollama signin` 
+2. In the ollama model settings add a model that ollama hosts, like `gpt-oss:120b`.
+3. You can now refer to this model in marimo!

docs/integrations/nemoclaw.mdx

@@ -0,0 +1,67 @@
+---
+title: NemoClaw
+---
+
+NemoClaw is NVIDIA's open source security stack for [OpenClaw](/integrations/openclaw). It wraps OpenClaw with the NVIDIA OpenShell runtime to provide kernel-level sandboxing, network policy controls, and audit trails for AI agents. 
+
+## Quick start
+
+Pull a model:
+
+```bash
+ollama pull nemotron-3-nano:30b
+```
+
+Run the installer:
+
+```bash
+curl -fsSL https://www.nvidia.com/nemoclaw.sh | \
+  NEMOCLAW_NON_INTERACTIVE=1 \
+  NEMOCLAW_PROVIDER=ollama \
+  NEMOCLAW_MODEL=nemotron-3-nano:30b \
+  bash
+```
+
+Connect to your sandbox:
+
+```bash
+nemoclaw my-assistant connect
+```
+
+Open the TUI:
+
+```bash
+openclaw tui
+```
+
+<Note>Ollama support in NemoClaw is still experimental.</Note>
+
+## Platform support
+
+| Platform | Runtime | Status |
+|----------|---------|--------|
+| Linux (Ubuntu 22.04+) | Docker | Primary |
+| macOS (Apple Silicon) | Colima or Docker Desktop | Supported |
+| Windows | WSL2 with Docker Desktop | Supported |
+
+CMD and PowerShell are not supported on Windows — WSL2 is required.
+
+<Note>Ollama must be installed and running before the installer runs. When running inside WSL2 or a container, ensure Ollama is reachable from the sandbox (e.g. `OLLAMA_HOST=0.0.0.0`).</Note>
+
+## System requirements
+
+- CPU: 4 vCPU minimum
+- RAM: 8 GB minimum (16 GB recommended)
+- Disk: 20 GB free (40 GB recommended for local models)
+- Node.js 20+ and npm 10+
+- Container runtime (Docker preferred)
+
+## Recommended models
+
+- `nemotron-3-super:cloud` — Strong reasoning and coding
+- `qwen3.5:cloud` — 397B; reasoning and code generation
+- `nemotron-3-nano:30b` — Recommended local model; fits in 24 GB VRAM
+- `qwen3.5:27b` — Fast local reasoning (~18 GB VRAM)
+- `glm-4.7-flash` — Reasoning and code generation (~25 GB VRAM)
+
+More models at [ollama.com/search](https://ollama.com/search).

docs/integrations/oh-my-pi.mdx

@@ -0,0 +1,35 @@
+---
+title: Oh My Pi
+---
+
+Oh My Pi (OMP) is a terminal coding agent with IDE-style tools built in. It combines chat, project context, structured code edits, language server support, debugging tools, browser access, plugins, and subagents in one terminal workflow.
+
+Ollama can configure OMP to use Ollama as its model provider and launch an interactive session.
+
+![Oh My Pi with Ollama](http://files.ollama.com/omp.png)
+
+## Quick setup
+
+```bash
+ollama launch omp
+```
+
+This configures Ollama as a provider, sets up web search tools, and starts OMP.
+
+### Run directly with a model
+
+```shell
+ollama launch omp --model <model>
+```
+
+## Plugins
+
+OMP supports plugins for extra tools and capabilities. When launching OMP through Ollama, the Ollama web search plugin is managed automatically.
+
+## Manual setup
+
+Install OMP from [omp.sh](https://omp.sh), then run:
+
+```bash
+ollama launch omp --config
+```

docs/integrations/onyx.mdx

@@ -0,0 +1,63 @@
+---
+title: Onyx
+---
+
+## Overview
+[Onyx](http://onyx.app/) is a self-hostable Chat UI that integrates with all Ollama models. Features include:
+- Creating custom Agents
+- Web search
+- Deep Research
+- RAG over uploaded documents and connected apps
+- Connectors to applications like Google Drive, Email, Slack, etc.
+- MCP and OpenAPI Actions support
+- Image generation
+- User/Groups management, RBAC, SSO, etc.
+
+Onyx can be deployed for single users or large organizations.
+
+## Install Onyx
+
+Deploy Onyx with the [quickstart guide](https://docs.onyx.app/deployment/getting_started/quickstart).
+
+<Info>
+Resourcing/scaling docs [here](https://docs.onyx.app/deployment/getting_started/resourcing).
+</Info>
+
+## Usage with Ollama 
+
+1. Login to your Onyx deployment (create an account first).
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/onyx-login.png" 
+    alt="Onyx Login Page"
+    width="75%"
+  />
+</div>
+2. In the set-up process select `Ollama` as the LLM provider.
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/onyx-ollama-llm.png" 
+    alt="Onyx Set Up Form"
+    width="75%"
+  />
+</div>
+3. Provide your **Ollama API URL** and select your models.
+<Note>If you're running Onyx in Docker, to access your computer's local network use `http://host.docker.internal` instead of `http://127.0.0.1`.</Note>
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/onyx-ollama-form.png" 
+    alt="Selecting Ollama Models"
+    width="75%"
+  />
+</div>
+
+You can also easily connect up Onyx Cloud with the `Ollama Cloud` tab of the setup.
+
+## Send your first query
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/onyx-query.png" 
+    alt="Onyx Query Example"
+    width="75%"
+  />
+</div>

docs/integrations/openclaw.mdx

@@ -0,0 +1,95 @@
+---
+title: OpenClaw
+---
+
+OpenClaw is a personal AI assistant that runs on your own devices. It bridges messaging services (WhatsApp, Telegram, Slack, Discord, iMessage, and more) to AI coding agents through a centralized gateway.
+
+## Quick start
+
+```bash
+ollama launch openclaw
+```
+
+Ollama handles everything automatically:
+
+1. **Install** — If OpenClaw isn't installed, Ollama prompts to install it via npm
+2. **Security** — On the first launch, a security notice explains the risks of tool access
+3. **Model** — Pick a model from the selector (local or cloud)
+4. **Onboarding** — Ollama configures the provider, installs the gateway daemon, sets your model as the primary, and enables OpenClaw's bundled Ollama web search
+5. **Gateway** — Starts in the background and opens the OpenClaw TUI
+
+<Note>OpenClaw requires a larger context window. It is recommended to use a context window of at least 64k tokens if using local models. See [Context length](/context-length) for more information.</Note>
+
+<Note>Previously known as Clawdbot. `ollama launch clawdbot` still works as an alias.</Note>
+
+## Web search and fetch
+
+OpenClaw ships with a bundled Ollama `web_search` provider that lets local or cloud-backed Ollama setups search the web through the configured Ollama host.
+
+```bash
+ollama launch openclaw
+```
+
+Ollama web search is enabled automatically when launching OpenClaw through Ollama. To configure it manually:
+
+```bash
+openclaw configure --section web
+```
+
+<Note>Ollama web search for local models requires `ollama signin`.</Note>
+
+## Configure without launching
+
+To change the model without starting the gateway and TUI:
+
+```bash
+ollama launch openclaw --config
+```
+
+To use a specific model directly:
+
+```bash
+ollama launch openclaw --model kimi-k2.5:cloud
+```
+
+If the gateway is already running, it restarts automatically to pick up the new model.
+
+## Recommended models
+
+**Cloud models**:
+
+- `kimi-k2.5:cloud` — Multimodal reasoning with subagents
+- `qwen3.5:cloud` — Reasoning, coding, and agentic tool use with vision
+- `glm-5.1:cloud` — Reasoning and code generation
+- `minimax-m2.7:cloud` — Fast, efficient coding and real-world productivity
+
+**Local models:**
+
+- `gemma4` — Reasoning and code generation locally (~16 GB VRAM)
+- `qwen3.5` — Reasoning, coding, and visual understanding locally (~11 GB VRAM)
+
+More models at [ollama.com/search](https://ollama.com/search?c=cloud).
+
+## Non-interactive (headless) mode
+
+Run OpenClaw without interaction for use in Docker, CI/CD, or scripts:
+
+```bash
+ollama launch openclaw --model kimi-k2.5:cloud --yes
+```
+
+The `--yes` flag auto-pulls the model, skips selectors, and requires `--model` to be specified.
+
+## Connect messaging apps
+
+```bash
+openclaw configure --section channels
+```
+
+Link WhatsApp, Telegram, Slack, Discord, or iMessage to chat with your local models from anywhere.
+
+## Stopping the gateway
+
+```bash
+openclaw gateway stop
+```

docs/integrations/opencode.mdx

@@ -0,0 +1,31 @@
+---
+title: OpenCode
+---
+
+OpenCode is an open-source AI coding assistant that runs in your terminal.
+
+## Install
+
+Install the [OpenCode CLI](https://opencode.ai):
+
+```bash
+curl -fsSL https://opencode.ai/install | bash
+```
+
+<Note>OpenCode requires a larger context window. It is recommended to use a context window of at least 64k tokens. See [Context length](/context-length) for more information.</Note>
+
+## Usage with Ollama
+
+### Quick setup
+
+```bash
+ollama launch opencode
+```
+
+To configure without launching:
+
+```shell
+ollama launch opencode --config
+```
+
+<Note>`ollama launch opencode` passes its configuration to OpenCode inline via the `OPENCODE_CONFIG_CONTENT` environment variable. OpenCode deep-merges its config sources on startup, so anything you declare in `~/.config/opencode/opencode.json` is still respected and available inside OpenCode. Models declared only in `opencode.json` won't appear in `ollama launch`'s model-selection menu.</Note>

docs/integrations/pi.mdx

@@ -0,0 +1,109 @@
+---
+title: Pi
+---
+
+Pi is a minimal and extensible coding agent.
+
+## Quick setup
+
+```bash
+ollama launch pi
+```
+
+This installs Pi if needed, configures Ollama as a provider including web tools, and drops you into an interactive session.
+
+To configure without launching:
+
+```shell
+ollama launch pi --config
+```
+
+### Run directly with a model
+
+```shell
+ollama launch pi --model qwen3.5:cloud
+```
+
+Cloud models are also available at [ollama.com](https://ollama.com/search?c=cloud).
+
+## Extensions 
+
+Pi ships with four core tools: `read`, `write`, `edit`, and `bash`. All other capabilities are added through its extension system.
+
+On-demand capability packages invoked via `/skill:name` commands.  
+
+Install from npm or git:
+
+```bash
+pi install npm:@foo/some-tools
+pi install git:github.com/user/repo@v1
+```
+
+See all packages at [pi.dev](https://pi.dev/packages) 
+
+### Web search
+
+Pi can use web search and fetch tools via the `@ollama/pi-web-search` package.
+
+When launching Pi through Ollama, package install/update is managed automatically.
+To install manually:
+
+```bash
+pi install npm:@ollama/pi-web-search
+```
+
+### Autoresearch with `pi-autoresearch`
+
+[pi-autoresearch](https://github.com/davebcn87/pi-autoresearch) brings autonomous experiment loops to Pi. Inspired by Karpathy's autoresearch, it turns any measurable metric into an optimization target: test speed, bundle size, build time, model training loss, Lighthouse scores.
+
+```bash
+pi install https://github.com/davebcn87/pi-autoresearch
+```
+
+Tell Pi what to optimize. It runs experiments, benchmarks each one, keeps improvements, reverts regressions, and repeats — all autonomously. A built-in dashboard tracks every run with confidence scoring to distinguish real gains from benchmark noise.
+
+```bash
+/autoresearch optimize unit test runtime
+```
+
+Each kept experiment is automatically committed. Each failed one is reverted. When you're done, Pi can group improvements into independent branches for clean review and merge.
+
+## Manual setup
+
+### Install
+
+Install [Pi](https://github.com/earendil-works/pi):
+
+```bash
+npm install -g @earendil-works/pi-coding-agent
+```
+
+
+
+Add a configuration block to `~/.pi/agent/models.json`:
+
+```json
+{
+  "providers": {
+    "ollama": {
+      "baseUrl": "http://localhost:11434/v1",
+      "api": "openai-completions",
+      "apiKey": "ollama",
+      "models": [
+        {
+          "id": "qwen3-coder"
+        }
+      ]
+    }
+  }
+}
+```
+
+Update `~/.pi/agent/settings.json` to set the default provider:
+
+```json
+{
+  "defaultProvider": "ollama",
+  "defaultModel": "qwen3-coder"
+}
+```

docs/integrations/pool.mdx

@@ -0,0 +1,54 @@
+---
+title: Pool
+---
+
+Pool is Poolside's software agent for the terminal, built for enterprise development workflows.
+
+## Install
+
+Install [Pool](https://github.com/poolsideai/pool):
+
+## Usage with Ollama
+
+### Quick setup
+
+```shell
+ollama launch pool
+```
+
+### Run directly with a model
+
+```shell
+ollama launch pool --model kimi-k2.6:cloud
+```
+
+### Pass arguments through to Pool
+
+Arguments after `--` are passed directly to Pool:
+
+```shell
+ollama launch pool -- --help
+```
+
+## Manual setup
+
+Pool connects to Ollama using the OpenAI-compatible API via environment variables.
+
+1. Set the environment variables:
+
+```shell
+export POOLSIDE_STANDALONE_BASE_URL=http://localhost:11434/v1
+export POOLSIDE_API_KEY=ollama
+```
+
+2. Run Pool with an Ollama model:
+
+```shell
+pool -m kimi-k2.6:cloud
+```
+
+Or run with environment variables inline:
+
+```shell
+POOLSIDE_STANDALONE_BASE_URL=http://localhost:11434/v1 POOLSIDE_API_KEY=ollama pool -m kimi-k2.6:cloud
+```

docs/integrations/vscode.mdx

@@ -2,33 +2,84 @@
 title: VS Code
 ---
 
-## Install
+VS Code includes built-in AI chat through GitHub Copilot Chat. Ollama models can be used directly in the Copilot Chat model picker.
 
-Install [VS Code](https://code.visualstudio.com/download).
 
-## Usage with Ollama
+![VS Code with Ollama](/images/vscode.png)
 
-1. Open Copilot side bar found in top right window
+
+## Prerequisites
+
+- 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)
+
+<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>
+
+## Quick setup
+
+```shell
+ollama launch vscode
+```
+
+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
+
+```shell
+ollama launch vscode --model qwen3.5:cloud
+```
+Cloud models are also available at [ollama.com](https://ollama.com/search?c=cloud).
+
+## Manual setup
+
+To configure Ollama manually without `ollama launch`:
+
+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. Select the model dropdown > **Manage models**
+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-models.png"
+       src="/images/vscode-other-models.png"
        alt="VS Code model picker"
        width="75%"
+       style={{ borderRadius: "4px" }}
      />
    </div>
-3. Enter **Ollama** under **Provider Dropdown** and select desired models (e.g `qwen3, qwen3-coder:480b-cloud`)
+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-model-options.png"
-       alt="VS Code model options dropdown"
+       src="/images/vscode-add-ollama.png"
+       alt="VS Code model options dropdown to add ollama models"
        width="75%"
+       style={{ borderRadius: "4px" }}
+     />
+   </div>
+
+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>

docs/linux.mdx

@@ -1,5 +1,5 @@
 ---
-title: "Linux"
+title: Linux
 ---
 
 ## Install
@@ -13,14 +13,15 @@ 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.
+  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
+curl -fsSL https://ollama.com/download/ollama-linux-amd64.tar.zst \
+    | sudo tar x -C /usr
 ```
 
 Start Ollama:
@@ -40,8 +41,8 @@ ollama -v
 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
+curl -fsSL https://ollama.com/download/ollama-linux-amd64-rocm.tar.zst \
+    | sudo tar x -C /usr
 ```
 
 ### ARM64 install
@@ -49,8 +50,8 @@ curl -fsSL https://ollama.com/download/ollama-linux-amd64-rocm.tgz \
 Download and extract the ARM64-specific package:
 
 ```shell
-curl -fsSL https://ollama.com/download/ollama-linux-arm64.tgz \
-    | sudo tar zx -C /usr
+curl -fsSL https://ollama.com/download/ollama-linux-arm64.tar.zst \
+    | sudo tar x -C /usr
 ```
 
 ### Adding Ollama as a startup service (recommended)
@@ -100,7 +101,7 @@ 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.
+[Download and Install](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html) ROCm v7.
 
 ### Start Ollama
 
@@ -112,7 +113,11 @@ 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.
+  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
@@ -141,8 +146,8 @@ 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
+curl -fsSL https://ollama.com/download/ollama-linux-amd64.tar.zst \
+    | sudo tar x -C /usr
 ```
 
 ## Installing specific versions
@@ -191,4 +196,4 @@ Remove the downloaded models and Ollama service user and group:
 sudo userdel ollama
 sudo groupdel ollama
 sudo rm -r /usr/share/ollama
-```
+```

docs/modelfile.mdx

@@ -157,6 +157,7 @@ PARAMETER <parameter> <parametervalue>
 | 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       |
+| draft_num_predict | Maximum number of speculative draft tokens to predict per step when a draft model is available. Separate draft models default to 4; embedded MTP tensors require setting this parameter. Set to 0 to disable speculative drafting.                                                                                                                                               | int        | draft_num_predict 4  |
 | 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           |

docs/openapi.yaml

@@ -596,6 +596,15 @@ components:
         name:
           type: string
           description: Model name
+        model:
+          type: string
+          description: Model name
+        remote_model:
+          type: string
+          description: Name of the upstream model, if the model is remote
+        remote_host:
+          type: string
+          description: URL of the upstream Ollama host, if the model is remote
         modified_at:
           type: string
           description: Last modified timestamp in ISO 8601 format
@@ -636,6 +645,9 @@ components:
     Ps:
       type: object
       properties:
+        name:
+          type: string
+          description: Name of the running model
         model:
           type: string
           description: Name of the running model
@@ -782,14 +794,14 @@ paths:
           label: Default
           source: |
             curl http://localhost:11434/api/generate -d '{
-              "model": "gemma3",
+              "model": "gemma4",
               "prompt": "Why is the sky blue?"
             }'
         - lang: bash
           label: Non-streaming
           source: |
             curl http://localhost:11434/api/generate -d '{
-              "model": "gemma3",
+              "model": "gemma4",
               "prompt": "Why is the sky blue?",
               "stream": false
             }'
@@ -797,7 +809,7 @@ paths:
           label: With options
           source: |
             curl http://localhost:11434/api/generate -d '{
-              "model": "gemma3",
+              "model": "gemma4",
               "prompt": "Why is the sky blue?",
               "options": {
                 "temperature": 0.8,
@@ -809,7 +821,7 @@ paths:
           label: Structured outputs
           source: |
             curl http://localhost:11434/api/generate -d '{
-              "model": "gemma3",
+              "model": "gemma4",
               "prompt": "What are the populations of the United States and Canada?",
               "stream": false,
               "format": {
@@ -834,7 +846,7 @@ paths:
           label: With images
           source: |
             curl http://localhost:11434/api/generate -d '{
-              "model": "gemma3",
+              "model": "gemma4",
               "prompt": "What is in this picture?",
               "images": ["iVBORw0KGgoAAAANSUhEUgAAAG0AAABmCAYAAADBPx+VAAAACXBIWXMAAAsTAAALEwEAmpwYAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAA3VSURBVHgB7Z27r0zdG8fX743i1bi1ikMoFMQloXRpKFFIqI7LH4BEQ+NWIkjQuSWCRIEoULk0gsK1kCBI0IhrQVT7tz/7zZo888yz1r7MnDl7z5xvsjkzs2fP3uu71nNfa7lkAsm7d++Sffv2JbNmzUqcc8m0adOSzZs3Z+/XES4ZckAWJEGWPiCxjsQNLWmQsWjRIpMseaxcuTKpG/7HP27I8P79e7dq1ars/yL4/v27S0ejqwv+cUOGEGGpKHR37tzJCEpHV9tnT58+dXXCJDdECBE2Ojrqjh071hpNECjx4cMHVycM1Uhbv359B2F79+51586daxN/+pyRkRFXKyRDAqxEp4yMlDDzXG1NPnnyJKkThoK0VFd1ELZu3TrzXKxKfW7dMBQ6bcuWLW2v0VlHjx41z717927ba22U9APcw7Nnz1oGEPeL3m3p2mTAYYnFmMOMXybPPXv2bNIPpFZr1NHn4HMw0KRBjg9NuRw95s8PEcz/6DZELQd/09C9QGq5RsmSRybqkwHGjh07OsJSsYYm3ijPpyHzoiacg35MLdDSIS/O1yM778jOTwYUkKNHWUzUWaOsylE00MyI0fcnOwIdjvtNdW/HZwNLGg+sR1kMepSNJXmIwxBZiG8tDTpEZzKg0GItNsosY8USkxDhD0Rinuiko2gfL/RbiD2LZAjU9zKQJj8RDR0vJBR1/Phx9+PHj9Z7REF4nTZkxzX4LCXHrV271qXkBAPGfP/atWvu/PnzHe4C97F48eIsRLZ9+3a3f/9+87dwP1JxaF7/3r17ba+5l4EcaVo0lj3SBq5kGTJSQmLWMjgYNei2GPT1MuMqGTDEFHzeQSP2wi/jGnkmPJ/nhccs44jvDAxpVcxnq0F6eT8h4ni/iIWpR5lPyA6ETkNXoSukvpJAD3AsXLiwpZs49+fPn5ke4j10TqYvegSfn0OnafC+Tv9ooA/JPkgQysqQNBzagXY55nO/oa1F7qvIPWkRL12WRpMWUvpVDYmxAPehxWSe8ZEXL20sadYIozfmNch4QJPAfeJgW3rNsnzphBKNJM2KKODo1rVOMRYik5ETy3ix4qWNI81qAAirizgMIc+yhTytx0JWZuNI03qsrgWlGtwjoS9XwgUhWGyhUaRZZQNNIEwCiXD16tXcAHUs79co0vSD8rrJCIW98pzvxpAWyyo3HYwqS0+H0BjStClcZJT5coMm6D2LOF8TolGJtK9fvyZpyiC5ePFi9nc/oJU4eiEP0jVoAnHa9wyJycITMP78+eMeP37sXrx44d6+fdt6f82aNdkx1pg9e3Zb5W+RSRE+n+VjksQWifvVaTKFhn5O8my63K8Qabdv33b379/PiAP//vuvW7BggZszZ072/+TJk91YgkafPn166zXB1rQHFvouAWHq9z3SEevSUerqCn2/dDCeta2jxYbr69evk4MHDyY7d+7MjhMnTiTPnz9Pfv/+nfQT2ggpO2dMF8cghuoM7Ygj5iWCqRlGFml0QC/ftGmTmzt3rmsaKDsgBSPh0/8yPeLLBihLkOKJc0jp8H8vUzcxIA1k6QJ/c78tWEyj5P3o4u9+jywNPdJi5rAH9x0KHcl4Hg570eQp3+vHXGyrmEeigzQsQsjavXt38ujRo44LQuDDhw+TW7duRS1HGgMxhNXHgflaNTOsHyKvHK5Ijo2jbFjJBQK9YwFd6RVMzfgRBmEfP37suBBm/p49e1qjEP2mwTViNRo0VJWH1deMXcNK08uUjVUu7s/zRaL+oLNxz1bpANco4npUgX4G2eFbpDFyQoQxojBCpEGSytmOH8qrH5Q9vuzD6ofQylkCUmh8DBAr+q8JCyVNtWQIidKQE9wNtLSQnS4jDSsxNHogzFuQBw4cyM61UKVsjfr3ooBkPSqqQHesUPWVtzi9/vQi1T+rJj7WiTz4Pt/l3LxUkr5P2VYZaZ4URpsE+st/dujQoaBBYokbrz/8TJNQYLSonrPS9kUaSkPeZyj1AWSj+d+VBoy1pIWVNed8P0Ll/ee5HdGRhrHhR5GGN0r4LGZBaj8oFDJitBTJzIZgFcmU0Y8ytWMZMzJOaXUSrUs5RxKnrxmbb5YXO9VGUhtpXldhEUogFr3IzIsvlpmdosVcGVGXFWp2oU9kLFL3dEkSz6NHEY1sjSRdIuDFWEhd8KxFqsRi1uM/nz9/zpxnwlESONdg6dKlbsaMGS4EHFHtjFIDHwKOo46l4TxSuxgDzi+rE2jg+BaFruOX4HXa0Nnf1lwAPufZeF8/r6zD97WK2qFnGjBxTw5qNGPxT+5T/r7/7RawFC3j4vTp09koCxkeHjqbHJqArmH5UrFKKksnxrK7FuRIs8STfBZv+luugXZ2pR/pP9Ois4z+TiMzUUkUjD0iEi1fzX8GmXyuxUBRcaUfykV0YZnlJGKQpOiGB76x5GeWkWWJc3mOrK6S7xdND+W5N6XyaRgtWJFe13GkaZnKOsYqGdOVVVbGupsyA/l7emTLHi7vwTdirNEt0qxnzAvBFcnQF16xh/TMpUuXHDowhlA9vQVraQhkudRdzOnK+04ZSP3DUhVSP61YsaLtd/ks7ZgtPcXqPqEafHkdqa84X6aCeL7YWlv6edGFHb+ZFICPlljHhg0bKuk0CSvVznWsotRu433alNdFrqG45ejoaPCaUkWERpLXjzFL2Rpllp7PJU2a/v7Ab8N05/9t27Z16KUqoFGsxnI9EosS2niSYg9SpU6B4JgTrvVW1flt1sT+0ADIJU2maXzcUTraGCRaL1Wp9rUMk16PMom8QhruxzvZIegJjFU7LLCePfS8uaQdPny4jTTL0dbee5mYokQsXTIWNY46kuMbnt8Kmec+LGWtOVIl9cT1rCB0V8WqkjAsRwta93TbwNYoGKsUSChN44lgBNCoHLHzquYKrU6qZ8lolCIN0Rh6cP0Q3U6I6IXILYOQI513hJaSKAorFpuHXJNfVlpRtmYBk1Su1obZr5dnKAO+L10Hrj3WZW+E3qh6IszE37F6EB+68mGpvKm4eb9bFrlzrok7fvr0Kfv727dvWRmdVTJHw0qiiCUSZ6wCK+7XL/AcsgNyL74DQQ730sv78Su7+t/A36MdY0sW5o40ahslXr58aZ5HtZB8GH64m9EmMZ7FpYw4T6QnrZfgenrhFxaSiSGXtPnz57e9TkNZLvTjeqhr734CNtrK41L40sUQckmj1lGKQ0rC37x544r8eNXRpnVE3ZZY7zXo8NomiO0ZUCj2uHz58rbXoZ6gc0uA+F6ZeKS/jhRDUq8MKrTho9fEkihMmhxtBI1DxKFY9XLpVcSkfoi8JGnToZO5sU5aiDQIW716ddt7ZLYtMQlhECdBGXZZMWldY5BHm5xgAroWj4C0hbYkSc/jBmggIrXJWlZM6pSETsEPGqZOndr2uuuR5rF169a2HoHPdurUKZM4CO1WTPqaDaAd+GFGKdIQkxAn9RuEWcTRyN2KSUgiSgF5aWzPTeA/lN5rZubMmR2bE4SIC4nJoltgAV/dVefZm72AtctUCJU2CMJ327hxY9t7EHbkyJFseq+EJSY16RPo3Dkq1kkr7+q0bNmyDuLQcZBEPYmHVdOBiJyIlrRDq41YPWfXOxUysi5fvtyaj+2BpcnsUV/oSoEMOk2CQGlr4ckhBwaetBhjCwH0ZHtJROPJkyc7UjcYLDjmrH7ADTEBXFfOYmB0k9oYBOjJ8b4aOYSe7QkKcYhFlq3QYLQhSidNmtS2RATwy8YOM3EQJsUjKiaWZ+vZToUQgzhkHXudb/PW5YMHD9yZM2faPsMwoc7RciYJXbGuBqJ1UIGKKLv915jsvgtJxCZDubdXr165mzdvtr1Hz5LONA8jrUwKPqsmVesKa49S3Q4WxmRPUEYdTjgiUcfUwLx589ySJUva3oMkP6IYddq6HMS4o55xBJBUeRjzfa4Zdeg56QZ43LhxoyPo7Lf1kNt7oO8wWAbNwaYjIv5lhyS7kRf96dvm5Jah8vfvX3flyhX35cuX6HfzFHOToS1H4BenCaHvO8pr8iDuwoUL7tevX+b5ZdbBair0xkFIlFDlW4ZknEClsp/TzXyAKVOmmHWFVSbDNw1l1+4f90U6IY/q4V27dpnE9bJ+v87QEydjqx/UamVVPRG+mwkNTYN+9tjkwzEx+atCm/X9WvWtDtAb68Wy9LXa1UmvCDDIpPkyOQ5ZwSzJ4jMrvFcr0rSjOUh+GcT4LSg5ugkW1Io0/SCDQBojh0hPlaJdah+tkVYrnTZowP8iq1F1TgMBBauufyB33x1v+NWFYmT5KmppgHC+NkAgbmRkpD3yn9QIseXymoTQFGQmIOKTxiZIWpvAatenVqRVXf2nTrAWMsPnKrMZHz6bJq5jvce6QK8J1cQNgKxlJapMPdZSR64/UivS9NztpkVEdKcrs5alhhWP9NeqlfWopzhZScI6QxseegZRGeg5a8C3Re1Mfl1ScP36ddcUaMuv24iOJtz7sbUjTS4qBvKmstYJoUauiuD3k5qhyr7QdUHMeCgLa1Ear9NquemdXgmum4fvJ6w1lqsuDhNrg1qSpleJK7K3TF0Q2jSd94uSZ60kK1e3qyVpQK6PVWXp2/FC3mp6jBhKKOiY2h3gtUV64TWM6wDETRPLDfSakXmH3w8g9Jlug8ZtTt4kVF0kLUYYmCCtD/DrQ5YhMGbA9L3ucdjh0y8kOHW5gU/VEEmJTcL4Pz/f7mgoAbYkAAAAAElFTkSuQmCC"]
             }'
@@ -842,13 +854,13 @@ paths:
           label: Load model
           source: |
             curl http://localhost:11434/api/generate -d '{
-              "model": "gemma3"
+              "model": "gemma4"
             }'
         - lang: bash
           label: Unload model
           source: |
             curl http://localhost:11434/api/generate -d '{
-              "model": "gemma3",
+              "model": "gemma4",
               "keep_alive": 0
             }'
       requestBody:
@@ -858,7 +870,7 @@ paths:
             schema:
               $ref: "#/components/schemas/GenerateRequest"
             example:
-              model: gemma3
+              model: gemma4
               prompt: Why is the sky blue?
       responses:
         "200":
@@ -868,7 +880,7 @@ paths:
               schema:
                 $ref: "#/components/schemas/GenerateResponse"
               example:
-                model: "gemma3"
+                model: "gemma4"
                 created_at: "2025-10-17T23:14:07.414671Z"
                 response: "Hello! How can I help you today?"
                 done: true
@@ -894,7 +906,7 @@ paths:
           label: Default
           source: |
             curl http://localhost:11434/api/chat -d '{
-              "model": "gemma3",
+              "model": "gemma4",
               "messages": [
                 {
                   "role": "user",
@@ -906,7 +918,7 @@ paths:
           label: Non-streaming
           source: |
             curl http://localhost:11434/api/chat -d '{
-              "model": "gemma3",
+              "model": "gemma4",
               "messages": [
                 {
                   "role": "user",
@@ -919,7 +931,7 @@ paths:
           label: Structured outputs
           source: |
             curl -X POST http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
-              "model": "gemma3",
+              "model": "gemma4",
               "messages": [
                 {
                   "role": "user",
@@ -999,7 +1011,7 @@ paths:
           label: Images
           source: |
             curl http://localhost:11434/api/chat -d '{
-              "model": "gemma3",
+              "model": "gemma4",
               "messages": [
                 {
                   "role": "user",
@@ -1024,7 +1036,7 @@ paths:
               schema:
                 $ref: "#/components/schemas/ChatResponse"
               example:
-                model: "gemma3"
+                model: "gemma4"
                 created_at: "2025-10-17T23:14:07.414671Z"
                 message:
                   role: "assistant"
@@ -1136,16 +1148,17 @@ paths:
                 $ref: "#/components/schemas/ListResponse"
               example:
                 models:
-                  - name: "gemma3"
+                  - name: "gemma4"
+                    model: "gemma4"
                     modified_at: "2025-10-03T23:34:03.409490317-07:00"
-                    size: 3338801804
-                    digest: "a2af6cc3eb7fa8be8504abaf9b04e88f17a119ec3f04a3addf55f92841195f5a"
+                    size: 9608350245
+                    digest: "c6eb396dbd5992bbe3f5cdb947e8bbc0ee413d7c17e2beaae69f5d569cf982eb"
                     details:
                       format: "gguf"
-                      family: "gemma"
+                      family: "gemma4"
                       families:
-                        - "gemma"
-                      parameter_size: "4.3B"
+                        - "gemma4"
+                      parameter_size: "8.0B"
                       quantization_level: "Q4_K_M"
   /api/ps:
     get:
@@ -1168,16 +1181,17 @@ paths:
                 $ref: "#/components/schemas/PsResponse"
               example:
                 models:
-                  - model: "gemma3"
+                  - name: "gemma4"
+                    model: "gemma4"
                     size: 6591830464
-                    digest: "a2af6cc3eb7fa8be8504abaf9b04e88f17a119ec3f04a3addf55f92841195f5a"
+                    digest: "c6eb396dbd5992bbe3f5cdb947e8bbc0ee413d7c17e2beaae69f5d569cf982eb"
                     details:
                       parent_model: ""
                       format: "gguf"
-                      family: "gemma3"
+                      family: "gemma4"
                       families:
-                        - "gemma3"
-                      parameter_size: "4.3B"
+                        - "gemma4"
+                      parameter_size: "8.0B"
                       quantization_level: "Q4_K_M"
                     expires_at: "2025-10-17T16:47:07.93355-07:00"
                     size_vram: 5333539264
@@ -1191,13 +1205,13 @@ paths:
           label: Default
           source: |
             curl http://localhost:11434/api/show -d '{
-              "model": "gemma3"
+              "model": "gemma4"
             }'
         - lang: bash
           label: Verbose
           source: |
             curl http://localhost:11434/api/show -d '{
-              "model": "gemma3",
+              "model": "gemma4",
               "verbose": true
             }'
       requestBody:
@@ -1207,7 +1221,7 @@ paths:
             schema:
               $ref: "#/components/schemas/ShowRequest"
             example:
-              model: gemma3
+              model: gemma4
       responses:
         "200":
           description: Model information
@@ -1225,44 +1239,64 @@ paths:
                 details:
                   parent_model: ""
                   format: "gguf"
-                  family: "gemma3"
+                  family: "gemma4"
                   families:
-                    - "gemma3"
-                  parameter_size: "4.3B"
+                    - "gemma4"
+                  parameter_size: "8.0B"
                   quantization_level: "Q4_K_M"
                 model_info:
-                  gemma3.attention.head_count: 8
-                  gemma3.attention.head_count_kv: 4
-                  gemma3.attention.key_length: 256
-                  gemma3.attention.sliding_window: 1024
-                  gemma3.attention.value_length: 256
-                  gemma3.block_count: 34
-                  gemma3.context_length: 131072
-                  gemma3.embedding_length: 2560
-                  gemma3.feed_forward_length: 10240
-                  gemma3.mm.tokens_per_image: 256
-                  gemma3.vision.attention.head_count: 16
-                  gemma3.vision.attention.layer_norm_epsilon: 0.000001
-                  gemma3.vision.block_count: 27
-                  gemma3.vision.embedding_length: 1152
-                  gemma3.vision.feed_forward_length: 4304
-                  gemma3.vision.image_size: 896
-                  gemma3.vision.num_channels: 3
-                  gemma3.vision.patch_size: 14
-                  general.architecture: "gemma3"
+                  gemma4.attention.head_count: 8
+                  gemma4.attention.head_count_kv: 2
+                  gemma4.attention.key_length: 512
+                  gemma4.attention.key_length_swa: 256
+                  gemma4.attention.layer_norm_rms_epsilon: 0.000001
+                  gemma4.attention.shared_kv_layers: 18
+                  gemma4.attention.sliding_window: 512
+                  gemma4.attention.value_length: 512
+                  gemma4.attention.value_length_swa: 256
+                  gemma4.audio.attention.head_count: 8
+                  gemma4.audio.attention.layer_norm_epsilon: 0.000001
+                  gemma4.audio.block_count: 12
+                  gemma4.audio.conv_kernel_size: 5
+                  gemma4.audio.embedding_length: 1024
+                  gemma4.audio.feed_forward_length: 4096
+                  gemma4.block_count: 42
+                  gemma4.context_length: 131072
+                  gemma4.embedding_length: 2560
+                  gemma4.embedding_length_per_layer_input: 256
+                  gemma4.feed_forward_length: 10240
+                  gemma4.final_logit_softcapping: 30
+                  gemma4.rope.dimension_count: 512
+                  gemma4.rope.dimension_count_swa: 256
+                  gemma4.rope.freq_base: 1000000
+                  gemma4.rope.freq_base_swa: 10000
+                  gemma4.vision.attention.head_count: 12
+                  gemma4.vision.attention.layer_norm_epsilon: 0.000001
+                  gemma4.vision.block_count: 16
+                  gemma4.vision.embedding_length: 768
+                  gemma4.vision.feed_forward_length: 3072
+                  gemma4.vision.num_channels: 3
+                  gemma4.vision.patch_size: 16
+                  gemma4.vision.projector.scale_factor: 3
+                  general.architecture: "gemma4"
                   general.file_type: 15
-                  general.parameter_count: 4299915632
                   general.quantization_version: 2
-                  tokenizer.ggml.add_bos_token: true
+                  tokenizer.ggml.add_bos_token: false
                   tokenizer.ggml.add_eos_token: false
+                  tokenizer.ggml.add_mask_token: false
                   tokenizer.ggml.add_padding_token: false
                   tokenizer.ggml.add_unknown_token: false
                   tokenizer.ggml.bos_token_id: 2
                   tokenizer.ggml.eos_token_id: 1
+                  tokenizer.ggml.eos_token_ids:
+                    - 1
+                    - 106
+                    - 50
+                  tokenizer.ggml.mask_token_id: 4
                   tokenizer.ggml.merges: null
                   tokenizer.ggml.model: "llama"
                   tokenizer.ggml.padding_token_id: 0
-                  tokenizer.ggml.pre: "default"
+                  tokenizer.ggml.pre: "gemma4"
                   tokenizer.ggml.scores: null
                   tokenizer.ggml.token_type: null
                   tokenizer.ggml.tokens: null
@@ -1278,7 +1312,7 @@ paths:
           label: Default
           source: |
             curl http://localhost:11434/api/create -d '{
-              "from": "gemma3",
+              "from": "gemma4",
               "model": "alpaca",
               "system": "You are Alpaca, a helpful AI assistant. You only answer with Emojis."
             }'
@@ -1287,7 +1321,7 @@ paths:
           source: |
             curl http://localhost:11434/api/create -d '{
               "model": "ollama",
-              "from": "gemma3",
+              "from": "gemma4",
               "system": "You are Ollama the llama."
             }'
         - lang: bash
@@ -1306,7 +1340,7 @@ paths:
               $ref: "#/components/schemas/CreateRequest"
             example:
               model: mario
-              from: gemma3
+              from: gemma4
               system: "You are Mario from Super Mario Bros."
       responses:
         "200":
@@ -1333,8 +1367,8 @@ paths:
           label: Copy a model to a new name
           source: |
             curl http://localhost:11434/api/copy -d '{
-              "source": "gemma3",
-              "destination": "gemma3-backup"
+              "source": "gemma4",
+              "destination": "gemma4-backup"
             }'
       requestBody:
         required: true
@@ -1343,8 +1377,8 @@ paths:
             schema:
               $ref: "#/components/schemas/CopyRequest"
             example:
-              source: gemma3
-              destination: gemma3-backup
+              source: gemma4
+              destination: gemma4-backup
       responses:
         "200":
           description: Model successfully copied
@@ -1359,13 +1393,13 @@ paths:
           label: Default
           source: |
             curl http://localhost:11434/api/pull -d '{
-              "model": "gemma3"
+              "model": "gemma4"
             }'
         - lang: bash
           label: Non-streaming
           source: |
             curl http://localhost:11434/api/pull -d '{
-              "model": "gemma3",
+              "model": "gemma4",
               "stream": false
             }'
       requestBody:
@@ -1375,7 +1409,7 @@ paths:
             schema:
               $ref: "#/components/schemas/PullRequest"
             example:
-              model: gemma3
+              model: gemma4
       responses:
         "200":
           description: Pull status updates.
@@ -1443,7 +1477,7 @@ paths:
           label: Delete model
           source: |
             curl -X DELETE http://localhost:11434/api/delete -d '{
-              "model": "gemma3"
+              "model": "gemma4"
             }'
       requestBody:
         required: true
@@ -1452,7 +1486,7 @@ paths:
             schema:
               $ref: "#/components/schemas/DeleteRequest"
             example:
-              model: gemma3
+              model: gemma4
       responses:
         "200":
           description: Model successfully deleted

docs/quickstart.mdx

@@ -2,7 +2,7 @@
 title: Quickstart
 ---
 
-This quickstart will walk your through running your first model with Ollama. To get started, download Ollama on macOS, Windows or Linux.
+Ollama is available on macOS, Windows, and Linux.
 
 <a
   href="https://ollama.com/download"
@@ -12,92 +12,56 @@ This quickstart will walk your through running your first model with Ollama. To
   Download Ollama
 </a>
 
-## Run a model
+## Get Started
 
-<Tabs>
-  <Tab title="CLI">
-    Open a terminal and run the command:
+Run `ollama` in your terminal to open the interactive menu:
 
-    ```
-    ollama run gemma3
-    ```
+```sh
+ollama
+```
 
-  </Tab>
-  <Tab title="cURL">
-    ```
-    ollama pull gemma3
-    ```
+Navigate with `↑/↓`, press `enter` to launch, `→` to change model, and `esc` to quit.
 
-    Lastly, chat with the model:
+The menu provides quick access to:
+- **Run a model** - Start an interactive chat
+- **Launch tools** - Claude Code, Codex, OpenClaw, and more
+- **Additional integrations** - Available under "More..."
 
-    ```shell
-    curl http://localhost:11434/api/chat -d '{
-      "model": "gemma3",
-      "messages": [{
-        "role": "user",
-        "content": "Hello there!"
-      }],
-      "stream": false
-    }'
-    ```
+## Assistants
 
-  </Tab>
-  <Tab title="Python">
-    Start by downloading a model:
+Launch [OpenClaw](/integrations/openclaw), a personal AI with 100+ skills:
 
-    ```
-    ollama pull gemma3
-    ```
+```sh
+ollama launch openclaw
+```
 
-    Then install Ollama's Python library:
+## Coding
 
-    ```
-    pip install ollama
-    ```
+Launch [Claude Code](/integrations/claude-code) and other coding tools with Ollama models:
 
-    Lastly, chat with the model:
+```sh
+ollama launch claude
+```
 
-    ```python
-    from ollama import chat
-    from ollama import ChatResponse
+```sh
+ollama launch codex
+```
 
-    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)
-    ```
+```sh
+ollama launch opencode
+```
 
-  </Tab>
-  <Tab title="JavaScript">
-    Start by downloading a model:
+See [integrations](/integrations) for all supported tools.
 
-    ```
-    ollama pull gemma3
-    ```
+## API
 
-    Then install the Ollama JavaScript library:
-    ```
-    npm i ollama
-    ```
+Use the [API](/api) to integrate Ollama into your applications:
 
-    Lastly, chat with the model:
+```sh
+curl http://localhost:11434/api/chat -d '{
+  "model": "gemma4",
+  "messages": [{ "role": "user", "content": "Hello!" }]
+}'
+```
 
-    ```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).
+See the [API documentation](/api) for Python, JavaScript, and other integrations.

docs/troubleshooting.mdx

@@ -114,6 +114,25 @@ If you are experiencing problems getting Ollama to correctly discover or use you
 - `OLLAMA_DEBUG=1` During GPU discovery additional information will be reported
 - Check dmesg for any errors from amdgpu or kfd drivers `sudo dmesg | grep -i amdgpu` and `sudo dmesg | grep -i kfd`
 
+### AMD Driver Version Mismatch
+
+If your AMD GPU is not detected on Linux and the server logs contain messages like:
+
+```
+msg="failure during GPU discovery" ... error="failed to finish discovery before timeout"
+msg="bootstrap discovery took" duration=30s ...
+```
+
+This typically means the system's AMD GPU driver is too old. Ollama bundles
+ROCm 7 linux libraries which require a compatible ROCm 7 kernel driver. If the
+system is running an older driver (ROCm 6.x or earlier), GPU initialization
+will hang during device discovery and eventually time out, causing Ollama to
+fall back to CPU.
+
+To resolve this, upgrade to the ROCm v7 driver using the `amdgpu-install`
+utility from [AMD's ROCm documentation](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/).
+After upgrading, reboot and restart Ollama.
+
 ## Multiple AMD GPUs
 
 If you experience gibberish responses when models load across multiple AMD GPUs on Linux, see the following guide.

docs/windows.mdx

@@ -12,10 +12,18 @@ terminal application. As usual the Ollama [API](/api) will be served on
 
 - Windows 10 22H2 or newer, Home or Pro
 - NVIDIA 452.39 or newer Drivers if you have an NVIDIA card
-- AMD Radeon Driver https://www.amd.com/en/support if you have a Radeon card
+- AMD ROCm v7 / HIP7-capable driver stack for ROCm acceleration, or a Vulkan-capable AMD Radeon driver for Vulkan acceleration
 
 Ollama uses unicode characters for progress indication, which may render as unknown squares in some older terminal fonts in Windows 10. If you see this, try changing your terminal font settings.
 
+<Note>
+  Some RDNA2 / Radeon RX 6000 systems, including RX 6800-class cards, may not
+  expose ROCm v7 on current Windows AMD drivers. Vulkan is enabled by default
+  and is the recommended fallback for those systems. If a mixed iGPU/dGPU
+  system selects an unstable Vulkan iGPU, set `GGML_VK_VISIBLE_DEVICES` to the
+  discrete GPU index.
+</Note>
+
 ## Filesystem Requirements
 
 The Ollama install does not require Administrator, and installs in your home directory by default. You'll need at least 4GB of space for the binary install. Once you've installed Ollama, you'll need additional space for storing the Large Language models, which can be tens to hundreds of GB in size. If your home directory doesn't have enough space, you can change where the binaries are installed, and where the models are stored.
@@ -80,9 +88,13 @@ help you keep up to date.
 
 If you'd like to install or integrate Ollama as a service, a standalone
 `ollama-windows-amd64.zip` zip file is available containing only the Ollama CLI
-and GPU library dependencies for Nvidia. If you have an AMD GPU, also download
-and extract the additional ROCm package `ollama-windows-amd64-rocm.zip` into the
-same directory. This allows for embedding Ollama in existing applications, or
+and GPU library dependencies for Nvidia. Depending on your hardware, you may also
+need to download and extract additional packages into the same directory:
+
+- **AMD GPU**: `ollama-windows-amd64-rocm.zip`
+- **MLX (CUDA)**: `ollama-windows-amd64-mlx.zip`
+
+This allows for embedding Ollama in existing applications, or
 running it as a system service via `ollama serve` with tools such as
 [NSSM](https://nssm.cc/).