Sync ollama docs from 9330bb91 on 2026-04-12

2026-04-12 04:00:07 +00:00
parent 1c2aec910d
commit 5fb5e0e5b8
22 changed files with 418 additions and 39 deletions
@@ -4,8 +4,8 @@ This is a mirror of the Ollama repository.

 **Synced from:** https://github.com/ollama/ollama.git
 **Branch:** main
-**Commit:** 8f45236d09332949aa91774dc9eb46caf2abbbc1
-**Sync Date:** 2026-03-12
+**Commit:** 9330bb912079ed1ba3c384cc762728700c9e3691
+**Sync Date:** 2026-04-12
 **Content:** Paths: docs

 ---
@@ -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`
@@ -21,6 +21,7 @@ Configure and launch external applications to use Ollama models. This provides a
 - **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
@@ -110,7 +110,8 @@
                "group": "Assistants",
                "expanded": true,
                "pages": [
-                  "/integrations/openclaw"
+                  "/integrations/openclaw",
+                  "/integrations/hermes"
                ]
              },
              {
@@ -127,6 +128,7 @@
              },
              {
                "group": "IDEs & Editors",
+                "expanded": true,
                "pages": [
                  "/integrations/cline",
                  "/integrations/jetbrains",
@@ -160,6 +162,12 @@
            "group": "More information",
            "pages": [
              "/cli",
+              {
+                "group": "Assistant Sandboxing",
+                "pages": [
+                  "/integrations/nemoclaw"
+                ]
+              },
              "/modelfile",
              "/context-length",
              "/linux",
@@ -61,6 +61,10 @@ Ollama supports the following AMD GPUs via the ROCm library:

 ### Linux Support

+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` |
@@ -41,13 +41,27 @@ ollama launch claude --model kimi-k2.5:cloud

 - `kimi-k2.5:cloud`
 - `glm-5:cloud`
- `minimax-m2.5: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.
@@ -82,6 +96,18 @@ The `/loop` command runs a prompt or slash command on a recurring schedule insid
 /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.
@@ -35,36 +35,39 @@ 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 configuration, add an Ollama provider and profiles to `~/.codex/config.toml`:

 ```toml
-model = "gpt-oss:120b"
-model_provider = "ollama"
-
-[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"
+
+[profiles.ollama-launch]
+model = "gpt-oss:120b"
+model_provider = "ollama-launch"
+
+[profiles.ollama-cloud]
+model = "gpt-oss:120b-cloud"
+model_provider = "ollama-launch"
 ```

-Run `codex` in a new terminal to load the new settings.
+Then run:
+
+```
+codex --profile ollama-launch
+codex --profile ollama-cloud
+```
@@ -0,0 +1,111 @@
+---
+title: Hermes Agent
+---
+
+Hermes Agent is a self-improving AI agent built by Nous Research. It features automatic skill creation, cross-session memory, and connects messaging platforms (Telegram, Discord, Slack, WhatsApp, Signal, Email) to models through a unified gateway.
+
+## Quick start
+
+### Pull a model
+
+Before running the setup wizard, make sure you have a model available. Hermes will auto-detect models downloaded through Ollama.
+
+```bash
+ollama pull kimi-k2.5:cloud
+```
+
+See [Recommended models](#recommended-models) for more options.
+
+### Install
+
+```bash
+curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
+```
+
+### Set up
+
+After installation, 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
+```
+
+## 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/models).
+
+## Configure later
+
+Re-run the setup wizard at any time:
+
+```bash
+hermes setup
+```
+
+To configure just messaging:
+
+```bash
+hermes setup gateway
+```
@@ -20,6 +20,7 @@ Coding assistants that can read, modify, and execute code in your projects.
 AI assistants that help with everyday tasks.

 - [OpenClaw](/integrations/openclaw)
+- [Hermes Agent](/integrations/hermes)

 ## IDEs & Editors

@@ -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).
@@ -15,13 +15,29 @@ 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, and sets your model as the primary
+4. **Onboarding** — Ollama configures the provider, installs the gateway daemon, sets your model as the primary, and installs the web search and fetch plugin
 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 web search and fetch plugin that gives local or cloud models the ability to search the web and extract readable page content.
+
+```bash
+ollama launch openclaw
+```
+
+Web search and fetch is enabled automatically when launching OpenClaw through Ollama. To install the plugin directly:
+
+```bash
+openclaw plugins install @ollama/openclaw-web-search
+```
+
+<Note>Web search for local models requires `ollama signin`.</Note>
+
 ## Configure without launching

 To change the model without starting the gateway and TUI:
@@ -43,15 +59,27 @@ If the gateway is already running, it restarts automatically to pick up the new
 **Cloud models**:

 - `kimi-k2.5:cloud` — Multimodal reasoning with subagents
- `minimax-m2.5:cloud` — Fast, efficient coding and real-world productivity
- `glm-5:cloud` — Reasoning and code generation
+- `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:**

- `glm-4.7-flash` — Reasoning and code generation locally (~25 GB VRAM)
+- `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
@@ -2,7 +2,7 @@
 title: Pi
 ---

-Pi is a minimal AI agent toolkit with plugin support.
+Pi is a minimal and extensible coding agent.

 ## Install

@@ -20,13 +20,65 @@ npm install -g @mariozechner/pi-coding-agent
 ollama launch pi
 ```

+This installs Pi, configures Ollama as a provider including web tools, and drops you into an interactive session.
+
 To configure without launching:

 ```shell
 ollama launch pi --config
 ```

-### Manual setup
+### 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

 Add a configuration block to `~/.pi/agent/models.json`:

@@ -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>
@@ -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.
@@ -80,9 +80,13 @@ 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/).