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| Author | SHA1 | Date | |
|---|---|---|---|
 The Librarian
|
f10d4bfbcc | ||
|
The Librarian
|
1f556d946e |
+2
-2
@@ -4,8 +4,8 @@ This is a mirror of the Ollama repository.
|
||||
|
||||
**Synced from:** https://github.com/ollama/ollama.git
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**Branch:** main
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**Commit:** 82f905cd9c06c6f0254d74c5326aa2a7f2f07e1f
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**Sync Date:** 2026-07-12
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**Commit:** 53fed26112817f7c55f664efb9e3f65f06cab7db
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**Sync Date:** 2026-09-12
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**Content:** Paths: docs
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|
||||
---
|
||||
|
||||
+1
-1
@@ -19,5 +19,5 @@
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### Resources
|
||||
|
||||
* [Troubleshooting Guide](https://docs.ollama.com/troubleshooting)
|
||||
* [FAQ](https://docs.ollama.com/faq#faq)
|
||||
* [FAQ](https://docs.ollama.com/faq)
|
||||
* [Development guide](./development.md)
|
||||
|
||||
+5
-64
@@ -16,7 +16,6 @@
|
||||
- [Generate Embeddings](#generate-embeddings)
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- [List Running Models](#list-running-models)
|
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- [Version](#version)
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||||
- [Experimental: Image Generation](#image-generation-experimental)
|
||||
|
||||
## Conventions
|
||||
|
||||
@@ -59,15 +58,6 @@ Advanced parameters (optional):
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- `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):
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||||
|
||||
> [!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.
|
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@@ -110,7 +100,8 @@ The final response in the stream also includes additional data about the generat
|
||||
- `total_duration`: time spent generating the response
|
||||
- `load_duration`: time spent in nanoseconds loading the model
|
||||
- `prompt_eval_count`: number of tokens in the prompt
|
||||
- `prompt_eval_duration`: time spent in nanoseconds evaluating the prompt
|
||||
- `prompt_eval_cached_count`: number of prompt tokens read from the cache
|
||||
- `prompt_eval_duration`: time spent in nanoseconds evaluating uncached prompt tokens
|
||||
- `eval_count`: number of tokens in the response
|
||||
- `eval_duration`: time in nanoseconds spent generating the response
|
||||
- `context`: an encoding of the conversation used in this response, this can be sent in the next request to keep a conversational memory
|
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@@ -1189,7 +1180,7 @@ Create a model from:
|
||||
- a safetensors directory; or
|
||||
- a GGUF file.
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||||
|
||||
If you are creating a model from a safetensors directory or from a GGUF file, you must [create a blob](#create-a-blob) for each of the files and then use the file name and SHA256 digest associated with each blob in the `files` field.
|
||||
If you are creating a model from a safetensors directory or from a GGUF file, you must [push a blob](#push-a-blob) for each of the files and then use the file name and SHA256 digest associated with each blob in the `files` field.
|
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|
||||
### Parameters
|
||||
|
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@@ -1198,6 +1189,8 @@ If you are creating a model from a safetensors directory or from a GGUF file, yo
|
||||
- `files`: (optional) a dictionary of file names to SHA256 digests of blobs to create the model from
|
||||
- `adapters`: (optional) a dictionary of file names to SHA256 digests of blobs for LORA adapters
|
||||
- `template`: (optional) the prompt template for the model
|
||||
- `renderer`: (optional) the name of the renderer for the model
|
||||
- `parser`: (optional) the name of the parser for the model
|
||||
- `license`: (optional) a string or list of strings containing the license or licenses for the model
|
||||
- `system`: (optional) a string containing the system prompt for the model
|
||||
- `parameters`: (optional) a dictionary of parameters for the model (see [Modelfile](./modelfile.mdx#valid-parameters-and-values) for a list of parameters)
|
||||
@@ -1878,55 +1871,3 @@ curl http://localhost:11434/api/version
|
||||
"version": "0.5.1"
|
||||
}
|
||||
```
|
||||
|
||||
## Experimental Features
|
||||
|
||||
### Image Generation (Experimental)
|
||||
|
||||
> [!WARNING]
|
||||
> Image generation is experimental and may change in future versions.
|
||||
|
||||
Image generation is now supported through the standard `/api/generate` endpoint when using image generation models. The API automatically detects when an image generation model is being used.
|
||||
|
||||
See the [Generate a completion](#generate-a-completion) section for the full API documentation. The experimental image generation parameters (`width`, `height`, `steps`) are documented there.
|
||||
|
||||
#### Example
|
||||
|
||||
##### Request
|
||||
|
||||
```shell
|
||||
curl http://localhost:11434/api/generate -d '{
|
||||
"model": "x/z-image-turbo",
|
||||
"prompt": "a sunset over mountains",
|
||||
"width": 1024,
|
||||
"height": 768
|
||||
}'
|
||||
```
|
||||
|
||||
##### Response (streaming)
|
||||
|
||||
Progress updates during generation:
|
||||
|
||||
```json
|
||||
{
|
||||
"model": "x/z-image-turbo",
|
||||
"created_at": "2024-01-15T10:30:00.000000Z",
|
||||
"completed": 5,
|
||||
"total": 20,
|
||||
"done": false
|
||||
}
|
||||
```
|
||||
|
||||
##### Final Response
|
||||
|
||||
```json
|
||||
{
|
||||
"model": "x/z-image-turbo",
|
||||
"created_at": "2024-01-15T10:30:15.000000Z",
|
||||
"image": "iVBORw0KGgoAAAANSUhEUg...",
|
||||
"done": true,
|
||||
"done_reason": "stop",
|
||||
"total_duration": 15000000000,
|
||||
"load_duration": 2000000000
|
||||
}
|
||||
```
|
||||
|
||||
@@ -16,7 +16,7 @@ After installation, Ollama's API is served by default at:
|
||||
http://localhost:11434/api
|
||||
```
|
||||
|
||||
For running cloud models on **ollama.com**, the same API is available with the following base URL:
|
||||
For running cloud models on [ollama.com](https://ollama.com), the same API is available with the following base URL:
|
||||
|
||||
```
|
||||
https://ollama.com/api
|
||||
@@ -40,7 +40,7 @@ Ollama has official libraries for Python and JavaScript:
|
||||
- [Python](https://github.com/ollama/ollama-python)
|
||||
- [JavaScript](https://github.com/ollama/ollama-js)
|
||||
|
||||
Several community-maintained libraries are available for Ollama. For a full list, see the [Ollama GitHub repository](https://github.com/ollama/ollama?tab=readme-ov-file#libraries-1).
|
||||
Several community-maintained libraries are available for Ollama. For a full list, see the [Ollama GitHub repository](https://github.com/ollama/ollama?tab=readme-ov-file#libraries--sdks).
|
||||
|
||||
## Versioning
|
||||
|
||||
|
||||
@@ -279,73 +279,6 @@ curl -X POST http://localhost:11434/v1/chat/completions \
|
||||
- [x] `dimensions`
|
||||
- [ ] `user`
|
||||
|
||||
### `/v1/images/generations` (experimental)
|
||||
|
||||
> Note: This endpoint is experimental and may change or be removed in future versions.
|
||||
|
||||
Generate images using image generation models.
|
||||
|
||||
<CodeGroup dropdown>
|
||||
|
||||
```python images.py
|
||||
from openai import OpenAI
|
||||
|
||||
client = OpenAI(
|
||||
base_url='http://localhost:11434/v1/',
|
||||
api_key='ollama', # required but ignored
|
||||
)
|
||||
|
||||
response = client.images.generate(
|
||||
model='x/z-image-turbo',
|
||||
prompt='A cute robot learning to paint',
|
||||
size='1024x1024',
|
||||
response_format='b64_json',
|
||||
)
|
||||
print(response.data[0].b64_json[:50] + '...')
|
||||
```
|
||||
|
||||
```javascript images.js
|
||||
import OpenAI from "openai";
|
||||
|
||||
const openai = new OpenAI({
|
||||
baseURL: "http://localhost:11434/v1/",
|
||||
apiKey: "ollama", // required but ignored
|
||||
});
|
||||
|
||||
const response = await openai.images.generate({
|
||||
model: "x/z-image-turbo",
|
||||
prompt: "A cute robot learning to paint",
|
||||
size: "1024x1024",
|
||||
response_format: "b64_json",
|
||||
});
|
||||
|
||||
console.log(response.data[0].b64_json.slice(0, 50) + "...");
|
||||
```
|
||||
|
||||
```shell images.sh
|
||||
curl -X POST http://localhost:11434/v1/images/generations \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"model": "x/z-image-turbo",
|
||||
"prompt": "A cute robot learning to paint",
|
||||
"size": "1024x1024",
|
||||
"response_format": "b64_json"
|
||||
}'
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
#### Supported request fields
|
||||
|
||||
- [x] `model`
|
||||
- [x] `prompt`
|
||||
- [x] `size` (e.g. "1024x1024")
|
||||
- [x] `response_format` (only `b64_json` supported)
|
||||
- [ ] `n`
|
||||
- [ ] `quality`
|
||||
- [ ] `style`
|
||||
- [ ] `user`
|
||||
|
||||
### `/v1/responses`
|
||||
|
||||
> Note: Added in Ollama v0.13.3
|
||||
|
||||
+4
-2
@@ -6,8 +6,9 @@ Ollama's API responses include metrics that can be used for measuring performanc
|
||||
|
||||
* `total_duration`: How long the response took to generate
|
||||
* `load_duration`: How long the model took to load
|
||||
* `prompt_eval_count`: How many input tokens were processed
|
||||
* `prompt_eval_duration`: How long it took to evaluate the prompt
|
||||
* `prompt_eval_count`: How many input tokens were in the prompt
|
||||
* `prompt_eval_cached_count`: How many prompt tokens were read from the cache
|
||||
* `prompt_eval_duration`: How long it took to evaluate the uncached prompt tokens
|
||||
* `eval_count`: How many output tokens were processes
|
||||
* `eval_duration`: How long it took to generate the output tokens
|
||||
|
||||
@@ -27,6 +28,7 @@ For endpoints that return usage metrics, the response body will include the usag
|
||||
"total_duration": 174560334,
|
||||
"load_duration": 101397084,
|
||||
"prompt_eval_count": 11,
|
||||
"prompt_eval_cached_count": 8,
|
||||
"prompt_eval_duration": 13074791,
|
||||
"eval_count": 18,
|
||||
"eval_duration": 52479709
|
||||
|
||||
@@ -7,7 +7,7 @@ Vision models accept images alongside text so the model can describe, classify,
|
||||
## Quick start
|
||||
|
||||
```shell
|
||||
ollama run gemma4 ./image.png whats in this image?
|
||||
ollama run gemma4 ./image.png what is in this image?
|
||||
```
|
||||
|
||||
|
||||
|
||||
+49
-28
@@ -229,9 +229,9 @@ curl https://ollama.com/api/chat \
|
||||
|
||||
## Local only
|
||||
|
||||
Ollama can run in local-only mode by [disabling Ollama's cloud](./faq#how-do-i-disable-ollama-cloud) features.
|
||||
Ollama can run in local-only mode by [disabling Ollama's cloud](./faq#how-do-i-disable-ollama-cloud-features) features.
|
||||
|
||||
## Deprecations
|
||||
## Retirements
|
||||
|
||||
Ollama will occasionally deprecate and retire older cloud models as newer and better open-source models are released.
|
||||
Tools and applications relying on Ollama Cloud models may need to be updated to keep working. Impacted users will be
|
||||
@@ -240,32 +240,53 @@ Ollama website.
|
||||
|
||||
Ollama Cloud model retirement does not affect local models.
|
||||
|
||||
### Upcoming deprecations
|
||||
### Upcoming retirements
|
||||
|
||||
| Retirement date | Model | Recommended alternative |
|
||||
| --- | --- | --- |
|
||||
| June 16, 2026 | `kimi-k2-thinking` | `kimi-k2.6` |
|
||||
| June 16, 2026 | `kimi-k2:1t` | `kimi-k2.6` |
|
||||
| June 16, 2026 | `minimax-m2` | `minimax-m3` |
|
||||
| June 16, 2026 | `glm-4.6` | `glm-5.1` |
|
||||
| June 16, 2026 | `qwen3-next:80b` | `qwen3.5` |
|
||||
| June 16, 2026 | `qwen3-vl:235b` | `qwen3.5` |
|
||||
| June 16, 2026 | `qwen3-vl:235b-instruct` | `qwen3.5` |
|
||||
| June 16, 2026 | `cogito-2.1:671b` | `deepseek-v4-flash` |
|
||||
| June 30, 2026 | `rnj-1:8b` | |
|
||||
| July 15, 2026 | `deepseek-v3.1:671b` | `deepseek-v4-flash` |
|
||||
| July 15, 2026 | `deepseek-v3.2` | `deepseek-v4-flash` |
|
||||
| July 15, 2026 | `devstral-2:123b` | `mistral-large-3:675b` |
|
||||
| July 15, 2026 | `devstral-small-2:24b` | |
|
||||
| July 15, 2026 | `ministral-3:14b` | |
|
||||
| July 15, 2026 | `ministral-3:3b` | |
|
||||
| July 15, 2026 | `ministral-3:8b` | |
|
||||
| July 15, 2026 | `gemini-3-flash-preview` | `minimax-m3` |
|
||||
| July 15, 2026 | `gemma3:12b` | `gemma4:31b` |
|
||||
| July 15, 2026 | `gemma3:27b` | `gemma4:31b` |
|
||||
| July 15, 2026 | `gemma3:4b` | `gemma4:31b` |
|
||||
| July 15, 2026 | `glm-4.7` | `glm-5.2` |
|
||||
| July 15, 2026 | `glm-5` | `glm-5.2` |
|
||||
| July 15, 2026 | `minimax-m2.1` | `minimax-m3` |
|
||||
| July 15, 2026 | `qwen3-coder-next` | `qwen3.5:397b` |
|
||||
| July 15, 2026 | `qwen3-coder:480b` | `qwen3.5:397b` |
|
||||
| July 31, 2026 | `minimax-m2.5` | `minimax-m2.7` |
|
||||
| July 31, 2026 | `kimi-k2.5` | `kimi-k2.6` |
|
||||
|
||||
### Past retirements
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="July 15, 2026">
|
||||
| Model | Recommended alternative |
|
||||
| --- | --- |
|
||||
| `deepseek-v3.1:671b` | `deepseek-v4-flash` |
|
||||
| `deepseek-v3.2` | `deepseek-v4-flash` |
|
||||
| `devstral-2:123b` | `mistral-large-3:675b` |
|
||||
| `devstral-small-2:24b` | |
|
||||
| `ministral-3:14b` | |
|
||||
| `ministral-3:3b` | |
|
||||
| `ministral-3:8b` | |
|
||||
| `gemini-3-flash-preview` | `minimax-m3` |
|
||||
| `gemma3:12b` | `gemma4:31b` |
|
||||
| `gemma3:27b` | `gemma4:31b` |
|
||||
| `gemma3:4b` | `gemma4:31b` |
|
||||
| `glm-4.7` | `glm-5.2` |
|
||||
| `glm-5` | `glm-5.2` |
|
||||
| `minimax-m2.1` | `minimax-m3` |
|
||||
| `qwen3-coder-next` | `qwen3.5:397b` |
|
||||
| `qwen3-coder:480b` | `qwen3.5:397b` |
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="June 30, 2026">
|
||||
| Model | Recommended alternative |
|
||||
| --- | --- |
|
||||
| `rnj-1:8b` | |
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="June 16, 2026">
|
||||
| Model | Recommended alternative |
|
||||
| --- | --- |
|
||||
| `kimi-k2-thinking` | `kimi-k2.6` |
|
||||
| `kimi-k2:1t` | `kimi-k2.6` |
|
||||
| `minimax-m2` | `minimax-m3` |
|
||||
| `glm-4.6` | `glm-5.1` |
|
||||
| `qwen3-next:80b` | `qwen3.5` |
|
||||
| `qwen3-vl:235b` | `qwen3.5` |
|
||||
| `qwen3-vl:235b-instruct` | `qwen3.5` |
|
||||
| `cogito-2.1:671b` | `deepseek-v4-flash` |
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
@@ -60,6 +60,26 @@
|
||||
}
|
||||
},
|
||||
"redirects": [
|
||||
{
|
||||
"source": "/development.md",
|
||||
"destination": "/development"
|
||||
},
|
||||
{
|
||||
"source": "/api/openai-compatibility.mdx",
|
||||
"destination": "/api/openai-compatibility"
|
||||
},
|
||||
{
|
||||
"source": "/gpu.mdx",
|
||||
"destination": "/gpu"
|
||||
},
|
||||
{
|
||||
"source": "/integrations/vscode.mdx",
|
||||
"destination": "/integrations/vscode"
|
||||
},
|
||||
{
|
||||
"source": "/troubleshooting.mdx",
|
||||
"destination": "/troubleshooting"
|
||||
},
|
||||
{
|
||||
"source": "/openai",
|
||||
"destination": "/api/openai-compatibility"
|
||||
@@ -137,6 +157,7 @@
|
||||
"group": "Assistants",
|
||||
"expanded": true,
|
||||
"pages": [
|
||||
"/integrations/claude-desktop",
|
||||
"/integrations/openclaw",
|
||||
"/integrations/hermes",
|
||||
"/integrations/hermes-desktop"
|
||||
@@ -148,6 +169,7 @@
|
||||
"pages": [
|
||||
"/integrations/claude-code",
|
||||
"/integrations/opencode",
|
||||
"/integrations/deepseek-harness",
|
||||
"/integrations/cline-cli",
|
||||
"/integrations/codex-app",
|
||||
"/integrations/codex",
|
||||
|
||||
+1
-1
@@ -11,4 +11,4 @@ Ollama JavaScript examples at [ollama-js/examples](https://github.com/ollama/oll
|
||||
|
||||
|
||||
## OpenAI compatibility examples
|
||||
Ollama OpenAI compatibility examples at [ollama/examples/openai](../docs/openai.md)
|
||||
Ollama OpenAI compatibility examples at [ollama/examples/openai](./api/openai-compatibility.mdx)
|
||||
|
||||
+2
-2
@@ -160,7 +160,7 @@ docker run -d -e HTTPS_PROXY=https://my.proxy.example.com -p 11434:11434 ollama-
|
||||
|
||||
Ollama runs locally. We don't see your prompts or data when you run locally. When using cloud-hosted models, we process your prompts and responses to provide the service but do not store or log that content and never train on it. We collect basic account info and limited usage metadata to provide the service that does not include prompt or response content. We don't sell your data. You can delete your account anytime.
|
||||
|
||||
## How do I disable Ollama's cloud features?
|
||||
## How do I disable Ollama Cloud features?
|
||||
|
||||
Ollama can run in local only mode by disabling Ollama's cloud features. By turning off Ollama's cloud features, you will lose the ability to use Ollama's cloud models and web search.
|
||||
|
||||
@@ -248,7 +248,7 @@ Refer to the section [above](#how-do-i-configure-ollama-server) for how to set e
|
||||
|
||||
## How can I use Ollama in Visual Studio Code?
|
||||
|
||||
There is already a large collection of plugins available for VS Code as well as other editors that leverage Ollama. See the list of [extensions & plugins](https://github.com/ollama/ollama#extensions--plugins) at the bottom of the main repository readme.
|
||||
Install the [Ollama extension](https://marketplace.visualstudio.com/items?itemName=Ollama.ollama) to use Ollama models in VS Code Chat. See the [VS Code integration guide](./integrations/vscode.mdx) for setup and troubleshooting.
|
||||
|
||||
## How do I use Ollama with GPU acceleration in Docker?
|
||||
|
||||
|
||||
+1
-1
@@ -35,7 +35,7 @@ Check your compute compatibility to see if your card is supported:
|
||||
| 5.0 | GeForce GTX | `GTX 750 Ti` `GTX 750` `NVS 810` |
|
||||
| | Quadro | `K2200` `K1200` `K620` `M1200` `M520` `M5000M` `M4000M` `M3000M` `M2000M` `M1000M` `K620M` `M600M` `M500M` |
|
||||
|
||||
For building locally to support older GPUs, see [developer](./development#linux-cuda-nvidia)
|
||||
For building locally to support older GPUs, see [development](./development.md).
|
||||
|
||||
### GPU Selection
|
||||
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" width="50" height="50" viewBox="0 0 50 50" fill="none">
|
||||
<style>@media (prefers-color-scheme: dark) { path { fill: #fff; } }</style>
|
||||
<path d="M48.8354 10.0479C48.3232 9.79199 48.1025 10.2798 47.8032 10.5278C46.7793 11.624 45.9048 12.1597 44.7622 12.0957C43.0923 12 41.666 12.5356 40.4058 13.8398C40.1377 12.2319 39.2476 11.272 37.8926 10.6558C36.4668 10.0156 35.9702 9.31982 35.356 7.72754C35.2456 7.3999 35.1353 7.06396 34.7651 7.00781C34.3633 6.94385 34.2056 7.2876 34.0479 7.57568C33.418 8.75195 33.1733 10.0479 33.1973 11.3599C33.2524 14.312 34.4736 16.6641 36.8999 18.3359C37.1758 18.5278 37.2466 18.7197 37.1597 19C36.9946 19.5757 36.7974 20.1357 36.624 20.7119C36.5137 21.0801 36.3486 21.1597 35.9624 21C32.4092 19.4878 30.0381 16.2319 27.2334 13.52C26.7764 13.1758 26.3193 12.856 25.8467 12.5518C23.8618 10.584 26.1069 8.96777 26.627 8.77588C27.1704 8.57568 26.8159 7.8877 25.0591 7.896C22.8691 7.90381 20.4507 9.06396 18.7095 9.58398C16.8501 9.22363 14.9199 9.14355 12.9033 9.37598C5.30859 10.2397 1.15674 16.4717 1.30664 27.2559C2.11768 31.9521 4.46582 35.8398 8.07373 38.8799C11.8159 42.0322 16.1255 43.5762 21.041 43.2803C24.0269 43.104 27.3516 42.6963 31.1016 39.4561C33.0396 40.1279 37.1758 40.208 38.1211 40.0078C39.6021 39.688 39.4995 38.2881 38.9639 38.0322C34.623 35.9678 35.5762 36.8081 34.71 36.1279C36.9155 33.4639 40.2402 30.6958 41.54 21.728C41.6426 21.0161 41.5557 20.5679 41.54 19.9917C41.5322 19.6396 41.6108 19.5039 42.0049 19.4639C46.6924 18.9116 49.064 15.9038 49.3315 11.2559C49.3711 10.7837 49.3237 10.2959 48.8354 10.0479ZM24.3262 37.8398C20.1196 34.4639 18.0791 33.3521 17.2358 33.3999C16.4482 33.4482 16.5898 34.3682 16.7632 34.9678C16.9443 35.5601 17.1812 35.9683 17.5117 36.4878C17.7402 36.832 17.8979 37.3442 17.2832 37.728C15.9282 38.584 13.5728 37.4399 13.4624 37.3838C7.97949 34.0879 4.48926 28.9282 4.19775 21.3677C4.1582 20.5757 4.38672 20.2959 5.15869 20.1519C11.8945 18.8799 17.165 22.0879 19.2529 25.7759C23.5381 30.104 25.335 35.1523 30.479 39.104C28.8643 39.2881 26.1699 39.3281 24.3262 37.8398ZM26.3433 24.6001C26.3433 24.248 26.6191 23.9678 26.9658 23.9678C27.3042 23.9678 27.5801 24.248 27.5801 24.6001C27.5801 24.9521 27.3042 25.2319 26.9575 25.2319C26.6108 25.2319 26.3433 24.9521 26.3433 24.6001ZM32.6064 27.8799C31.6372 28.2881 30.6289 28.3042 29.8096 27.688C28.6987 26.8555 28.6279 25.7759 28.7305 24.9199C28.8721 24.248 28.7144 23.8159 28.2495 23.4238C27.8716 23.104 27.3911 23.0161 26.8633 23.0161C26.666 23.0161 26.4849 22.9277 26.3511 22.856C25.8467 22.5762 25.9805 22.1758 26.5088 21.688C28.0996 20.7598 29.6362 21.9917 30.834 23.3281C31.6216 24.2559 32.8901 26.312 33.1104 26.9521C33.2446 27.3521 33.0713 27.6802 32.6064 27.8799Z" fill="#000"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 2.7 KiB |
+4
-4
@@ -4,10 +4,10 @@ title: Importing a Model
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [Importing a Safetensors adapter](#Importing-a-fine-tuned-adapter-from-Safetensors-weights)
|
||||
- [Importing a Safetensors model](#Importing-a-model-from-Safetensors-weights)
|
||||
- [Importing a GGUF file](#Importing-a-GGUF-based-model-or-adapter)
|
||||
- [Sharing models on ollama.com](#Sharing-your-model-on-ollamacom)
|
||||
- [Importing a Safetensors adapter](#importing-a-fine-tuned-adapter-from-safetensors-weights)
|
||||
- [Importing a Safetensors model](#importing-a-model-from-safetensors-weights)
|
||||
- [Importing a GGUF file](#importing-a-gguf-based-model-or-adapter)
|
||||
- [Sharing models on ollama.com](#sharing-your-model-on-ollama-com)
|
||||
|
||||
## Importing a fine tuned adapter from Safetensors weights
|
||||
|
||||
|
||||
@@ -1,13 +1,42 @@
|
||||
---
|
||||
title: Claude Desktop
|
||||
description: Use Ollama models in Claude on macOS.
|
||||
---
|
||||
|
||||
Claude Desktop is no longer supported by `ollama launch`.
|
||||
|
||||
Existing installations can be restored to the usual Claude profile:
|
||||
## Prerequisites
|
||||
|
||||
- [Ollama](https://ollama.com/download/). Windows support coming soon.
|
||||
- To use a local model, [download it](/quickstart#3-start-a-chat) before setup.
|
||||
- To use a cloud model, sign in to Ollama and enable cloud models. Some models require a [paid plan](https://ollama.com/pricing).
|
||||
|
||||
If Claude is not installed, Ollama offers to download it during setup.
|
||||
|
||||
## Set up Claude
|
||||
|
||||
1. Open Ollama and select **Apps**.
|
||||
2. Toggle Claude to On.
|
||||
3. If Claude is not installed, Ollama will download and finish the installation.
|
||||
|
||||
## Switch models
|
||||
|
||||
Open Ollama **Settings**. Under **Apps**, choose a model you want to use, then select **Restart Claude**. You can assign the same Ollama model to more than one option.
|
||||
|
||||
## Supported features
|
||||
|
||||
- **Subagents** - Split larger tasks across agents
|
||||
- **Web search** - Supported by default through [Ollama's web search](/capabilities/web-search)
|
||||
- **Cowork** - Complete difficult tasks with Claude Cowork
|
||||
- **Auto mode** - Let the agent decide when to ask before making changes
|
||||
|
||||
## Disconnect Claude
|
||||
|
||||
Open Ollama **Apps** and turn Claude off. Ollama restores Claude's previous configuration. If Claude is open, Ollama asks to restart it so the change takes effect.
|
||||
|
||||
You can also restore Claude from the terminal:
|
||||
|
||||
```shell
|
||||
ollama launch claude-desktop --restore
|
||||
```
|
||||
|
||||
Use [Claude Code](/integrations/claude-code) for Anthropic-compatible coding workflows with Ollama.
|
||||
Quitting Ollama while Claude is connected also restores Claude's usual configuration.
|
||||
|
||||
@@ -76,3 +76,15 @@ Then run:
|
||||
```
|
||||
codex --profile ollama-launch
|
||||
```
|
||||
|
||||
## Web search
|
||||
|
||||
Codex web-search requests sent through the Ollama profile are executed by
|
||||
Ollama for both local and cloud models. Sign in with `ollama signin` to use the
|
||||
web-search service.
|
||||
|
||||
To disable web search for a Codex session:
|
||||
|
||||
```shell
|
||||
codex --profile ollama-launch -c 'web_search="disabled"'
|
||||
```
|
||||
|
||||
@@ -0,0 +1,50 @@
|
||||
---
|
||||
title: DeepSeek Harness
|
||||
---
|
||||
|
||||
[DeepSeek Harness](https://github.com/deepseek-ai/deepseek-harness) is an open-source coding agent.
|
||||
|
||||
<Note>DeepSeek Harness is currently a developer preview. Its upstream configuration may change between releases.</Note>
|
||||
|
||||
## Setup
|
||||
|
||||
```shell
|
||||
ollama launch dsh
|
||||
```
|
||||
|
||||
Ollama installs `@deepseek-ai/dsh` if needed. To choose a model:
|
||||
|
||||
```shell
|
||||
ollama launch dsh --model qwen3.8
|
||||
ollama launch dsh --model deepseek-v4-flash:cloud
|
||||
```
|
||||
|
||||
To configure without starting:
|
||||
|
||||
```shell
|
||||
ollama launch dsh --config
|
||||
```
|
||||
|
||||
## Web search
|
||||
|
||||
Web search is enabled automatically. It requires Ollama cloud access and a model that supports tools. Run `ollama signin` if needed.
|
||||
|
||||
## Configuration
|
||||
|
||||
Ollama stores its settings in `~/.ollama/launch/dsh/settings.yaml`. These settings load last and set the model, provider, and web search connection. Repeated launches preserve other settings in this file. Ollama does not change `~/.dsh/settings.yaml`, profiles, sessions, or credentials.
|
||||
|
||||
Launch rejects additional `--patch` arguments. Pass other Harness arguments after `--`:
|
||||
|
||||
```shell
|
||||
ollama launch dsh -- --port 3081
|
||||
```
|
||||
|
||||
## Manual install
|
||||
|
||||
DeepSeek Harness requires Node.js. To install it manually:
|
||||
|
||||
```shell
|
||||
npm install -g @deepseek-ai/dsh@latest
|
||||
```
|
||||
|
||||
Then run `ollama launch dsh`. On Windows, install Node.js for Windows.
|
||||
@@ -4,7 +4,7 @@ title: Goose
|
||||
|
||||
## Goose Desktop
|
||||
|
||||
Install [Goose](https://block.github.io/goose/docs/getting-started/installation/) Desktop.
|
||||
Install [Goose](https://goose-docs.ai/docs/getting-started/installation/) Desktop.
|
||||
|
||||
### Usage with Ollama
|
||||
1. In Goose, open **Settings** → **Configure Provider**.
|
||||
@@ -27,7 +27,7 @@ Install [Goose](https://block.github.io/goose/docs/getting-started/installation/
|
||||
|
||||
## Goose CLI
|
||||
|
||||
Install [Goose](https://block.github.io/goose/docs/getting-started/installation/) CLI
|
||||
Install [Goose](https://goose-docs.ai/docs/getting-started/installation/) CLI
|
||||
|
||||
### Usage with Ollama
|
||||
1. Run `goose configure`
|
||||
|
||||
@@ -14,7 +14,7 @@ ollama launch hermes-desktop
|
||||
|
||||
Ollama handles the setup flow automatically:
|
||||
|
||||
1. **Install** - If Hermes Desktop isn't installed, Ollama prompts to install it
|
||||
1. **Install** - If Hermes isn't installed, Ollama prompts to install the Hermes command-line agent. On first desktop launch, Hermes builds its packaged desktop app.
|
||||
2. **Model** - Pick a model from the selector
|
||||
3. **Configure** - Ollama configures Hermes Desktop to use your selected Ollama model
|
||||
4. **Launch** - Ollama opens Hermes Desktop
|
||||
@@ -26,3 +26,13 @@ ollama launch hermes-desktop --model <model>
|
||||
```
|
||||
|
||||
Run `ollama launch hermes-desktop` again to switch models later.
|
||||
|
||||
## Install Hermes Desktop directly
|
||||
|
||||
On macOS and Windows, the Hermes Desktop installer is the recommended upstream installation path. It installs the desktop app and Hermes Agent together. If you prefer the command line, `ollama launch hermes-desktop` remains the explicit Ollama-managed path and uses the same Hermes configuration, sessions, skills, and memory as the CLI.
|
||||
|
||||
To force Hermes to rebuild its packaged desktop app:
|
||||
|
||||
```bash
|
||||
ollama launch hermes-desktop -- --force-build
|
||||
```
|
||||
|
||||
@@ -14,7 +14,7 @@ ollama launch hermes
|
||||
|
||||
Ollama handles everything automatically:
|
||||
|
||||
1. **Install** — If Hermes isn't installed, Ollama prompts to install it via the Nous Research install script
|
||||
1. **Install** — If Hermes isn't installed, Ollama prompts to install the Hermes command-line agent
|
||||
2. **Model** — Pick a model from the selector (local or cloud)
|
||||
3. **Onboarding** — Ollama configures the Ollama provider, points Hermes at `http://127.0.0.1:11434/v1`, and sets your model as the primary
|
||||
4. **Gateway** — Optionally connects a messaging platform (Telegram, Discord, Slack, WhatsApp, Signal, Email) and launches the Hermes chat
|
||||
@@ -45,10 +45,10 @@ hermes gateway setup
|
||||
|
||||
## Reconfigure
|
||||
|
||||
Re-run the full setup wizard at any time:
|
||||
Use Hermes's model picker to change providers or models later:
|
||||
|
||||
```bash
|
||||
hermes setup
|
||||
hermes model
|
||||
```
|
||||
|
||||
## Manual setup
|
||||
@@ -106,7 +106,7 @@ Optionally connect a messaging platform during setup:
|
||||
Connect a messaging platform? (Telegram, Discord, etc.)
|
||||
|
||||
→ Set up messaging now (recommended)
|
||||
Skip — set up later with 'hermes setup gateway'
|
||||
Skip — set up later with 'hermes gateway setup'
|
||||
```
|
||||
|
||||
### Launch
|
||||
@@ -114,4 +114,3 @@ Connect a messaging platform? (Telegram, Discord, etc.)
|
||||
```
|
||||
Launch hermes chat now? [Y/n]: Y
|
||||
```
|
||||
|
||||
|
||||
@@ -17,13 +17,21 @@ Run `ollama launch` to see the latest integrations you can run from the terminal
|
||||
<Card title="OpenCode" icon="/images/launch-icons/opencode.svg" href="/integrations/opencode">
|
||||
Open-source coding agent that edits, runs, and iterates on code.
|
||||
</Card>
|
||||
|
||||
<Card title="DeepSeek Harness" icon="/images/launch-icons/deepseek-harness.svg" href="/integrations/deepseek-harness">
|
||||
DeepSeek's open-source agent harness with subagents and web search.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Connect an assistant
|
||||
|
||||
Assistants with memory, skills, and messaging app access.
|
||||
Use open models in assistant apps.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Claude" href="/integrations/claude-desktop">
|
||||
Desktop assistant with local and cloud Ollama models.
|
||||
</Card>
|
||||
|
||||
<Card title="OpenClaw" icon="/images/launch-icons/openclaw.svg" href="/integrations/openclaw">
|
||||
Personal assistant for messaging apps and everyday tasks.
|
||||
</Card>
|
||||
@@ -39,6 +47,6 @@ Use Ollama models inside your editor.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="VS Code" icon="/images/launch-icons/vscode.svg" href="/integrations/vscode">
|
||||
Select Ollama models from the Copilot Chat model picker in VS Code.
|
||||
Use Ollama models in VS Code Chat.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
@@ -2,84 +2,57 @@
|
||||
title: VS Code
|
||||
---
|
||||
|
||||
VS Code includes built-in AI chat through GitHub Copilot Chat. Ollama models can be used directly in the Copilot Chat model picker.
|
||||
Use Ollama models in VS Code Chat with the [Ollama extension](https://marketplace.visualstudio.com/items?itemName=Ollama.ollama).
|
||||
|
||||
## Requirements
|
||||
|
||||
|
||||
- [Visual Studio Code 1.127 or newer](https://code.visualstudio.com/download)
|
||||
- Ollama installed and running
|
||||
- At least one local or cloud model available in Ollama
|
||||
|
||||
Earlier VS Code versions do not reliably cancel requests from language model providers.
|
||||
|
||||
## Prerequisites
|
||||
Ollama 0.17.6 or newer is recommended for cloud model sign-in and richer model metadata. Older versions may still work with local models.
|
||||
|
||||
- Ollama v0.18.3+
|
||||
- [VS Code 1.113+](https://code.visualstudio.com/download)
|
||||
- [GitHub Copilot Chat extension 0.41.0+](https://marketplace.visualstudio.com/items?itemName=GitHub.copilot-chat)
|
||||
## Install the extension
|
||||
|
||||
<Note> VS Code requires you to be logged in to use its model selector, even for custom models. This doesn't require a paid GitHub Copilot account; GitHub Copilot Free will enable model selection for custom models.</Note>
|
||||
1. Install the [Ollama extension](https://marketplace.visualstudio.com/items?itemName=Ollama.ollama) from the VS Code Marketplace.
|
||||
2. Open Chat in VS Code.
|
||||
3. Open the model picker at the bottom of the chat input.
|
||||
4. Choose a model from the **Ollama** section.
|
||||
|
||||
## Quick setup
|
||||
The extension discovers models from `http://127.0.0.1:11434` by default.
|
||||
|
||||
## Add a model
|
||||
|
||||
Pull a local model:
|
||||
|
||||
```shell
|
||||
ollama launch vscode
|
||||
ollama pull qwen3.6
|
||||
```
|
||||
|
||||
Recommended models will be shown after running the command. See the latest models at [ollama.com](https://ollama.com/search?c=tools).
|
||||
|
||||
Make sure **Local** is selected at the bottom of the Copilot Chat panel to use your Ollama models.
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/local.png"
|
||||
alt="Ollama Local Models"
|
||||
width="60%"
|
||||
style={{ borderRadius: "4px", marginTop: "10px", marginBottom: "10px" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
|
||||
## Run directly with a model
|
||||
To use a cloud model, pull it and sign in:
|
||||
|
||||
```shell
|
||||
ollama launch vscode --model qwen3.5:cloud
|
||||
ollama pull kimi-k2.6:cloud
|
||||
ollama signin
|
||||
```
|
||||
Cloud models are also available at [ollama.com](https://ollama.com/search?c=cloud).
|
||||
|
||||
## Manual setup
|
||||
Local models do not require sign-in.
|
||||
|
||||
To configure Ollama manually without `ollama launch`:
|
||||
## Context length
|
||||
|
||||
1. Open the **Copilot Chat** side bar from the top right corner
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/vscode-sidebar.png"
|
||||
alt="VS Code chat Sidebar"
|
||||
width="75%"
|
||||
style={{ borderRadius: "4px" }}
|
||||
/>
|
||||
</div>
|
||||
2. Click the **settings gear icon** (<Icon icon="gear" />) to bring up the Language Models window
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/vscode-other-models.png"
|
||||
alt="VS Code model picker"
|
||||
width="75%"
|
||||
style={{ borderRadius: "4px" }}
|
||||
/>
|
||||
</div>
|
||||
3. Click **Add Models** and select **Ollama** to load all your Ollama models into VS Code
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/vscode-add-ollama.png"
|
||||
alt="VS Code model options dropdown to add ollama models"
|
||||
width="75%"
|
||||
style={{ borderRadius: "4px" }}
|
||||
/>
|
||||
</div>
|
||||
VS Code may show a model's maximum supported context length even when Ollama allocates a smaller context at runtime.
|
||||
|
||||
4. Click the **Unhide** button in the model picker to show your Ollama models
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/vscode-unhide.png"
|
||||
alt="VS Code unhide models button"
|
||||
width="75%"
|
||||
style={{ borderRadius: "4px" }}
|
||||
/>
|
||||
</div>
|
||||
For local models, open Ollama **Settings**, set the context length to at least 64k, reload the VS Code window, and resend your prompt. See [Context length](/context-length) for more information.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
If Ollama models do not appear in the model picker:
|
||||
|
||||
1. Make sure Ollama is running.
|
||||
2. Run `ollama list` and confirm that models are available.
|
||||
3. Run **Ollama: Refresh Models** from the Command Palette.
|
||||
4. Run **Ollama: Diagnose Models** and check the **Ollama** output channel.
|
||||
|
||||
If a cloud model asks you to sign in, run `ollama signin`.
|
||||
|
||||
+1
-1
@@ -116,7 +116,7 @@ sudo systemctl status ollama
|
||||
While AMD has contributed the `amdgpu` driver upstream to the official linux
|
||||
kernel source, the version is older and may not support all ROCm features. We
|
||||
recommend you install the latest driver from
|
||||
https://www.amd.com/en/support/linux-drivers for best support of your Radeon
|
||||
https://www.amd.com/en/support/download/linux-drivers.html for best support of your Radeon
|
||||
GPU.
|
||||
</Note>
|
||||
|
||||
|
||||
+3
-7
@@ -107,12 +107,8 @@ FROM <model name>:<tag>
|
||||
FROM llama3.2
|
||||
```
|
||||
|
||||
<Card title="Base Models" href="https://github.com/ollama/ollama#model-library">
|
||||
A list of available base models
|
||||
</Card>
|
||||
|
||||
<Card title="Base Models" href="https://ollama.com/library">
|
||||
Additional models can be found at
|
||||
<Card title="Model library" href="https://ollama.com/library">
|
||||
Browse available models
|
||||
</Card>
|
||||
|
||||
#### Build from a Safetensors model
|
||||
@@ -152,7 +148,7 @@ PARAMETER <parameter> <parametervalue>
|
||||
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | -------------------- |
|
||||
| num_ctx | Sets the size of the context window used to generate the next token. (Default: 2048) | int | num_ctx 4096 |
|
||||
| repeat_last_n | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx) | int | repeat_last_n 64 |
|
||||
| repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1) | float | repeat_penalty 1.1 |
|
||||
| repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.0, disabled) | float | repeat_penalty 1.1 |
|
||||
| temperature | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8) | float | temperature 0.7 |
|
||||
| seed | Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. (Default: 0) | int | seed 42 |
|
||||
| stop | Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Multiple stop patterns may be set by specifying multiple separate `stop` parameters in a modelfile. | string | stop "AI assistant:" |
|
||||
|
||||
+18
-3
@@ -147,9 +147,12 @@ components:
|
||||
prompt_eval_count:
|
||||
type: integer
|
||||
description: Number of input tokens in the prompt
|
||||
prompt_eval_cached_count:
|
||||
type: integer
|
||||
description: Number of prompt tokens read from the cache
|
||||
prompt_eval_duration:
|
||||
type: integer
|
||||
description: Time spent evaluating the prompt in nanoseconds
|
||||
description: Time spent evaluating uncached prompt tokens in nanoseconds
|
||||
eval_count:
|
||||
type: integer
|
||||
description: Number of output tokens generated in the response
|
||||
@@ -191,9 +194,12 @@ components:
|
||||
prompt_eval_count:
|
||||
type: integer
|
||||
description: Number of input tokens in the prompt
|
||||
prompt_eval_cached_count:
|
||||
type: integer
|
||||
description: Number of prompt tokens read from the cache
|
||||
prompt_eval_duration:
|
||||
type: integer
|
||||
description: Time spent evaluating the prompt in nanoseconds
|
||||
description: Time spent evaluating uncached prompt tokens in nanoseconds
|
||||
eval_count:
|
||||
type: integer
|
||||
description: Number of output tokens generated in the response
|
||||
@@ -352,9 +358,12 @@ components:
|
||||
prompt_eval_count:
|
||||
type: integer
|
||||
description: Number of tokens in the prompt
|
||||
prompt_eval_cached_count:
|
||||
type: integer
|
||||
description: Number of prompt tokens read from the cache
|
||||
prompt_eval_duration:
|
||||
type: integer
|
||||
description: Time spent evaluating the prompt in nanoseconds
|
||||
description: Time spent evaluating uncached prompt tokens in nanoseconds
|
||||
eval_count:
|
||||
type: integer
|
||||
description: Number of tokens generated in the response
|
||||
@@ -483,6 +492,12 @@ components:
|
||||
template:
|
||||
type: string
|
||||
description: Prompt template to use for the model
|
||||
renderer:
|
||||
type: string
|
||||
description: Name of the renderer for the model
|
||||
parser:
|
||||
type: string
|
||||
description: Name of the parser for the model
|
||||
license:
|
||||
oneOf:
|
||||
- type: string
|
||||
|
||||
@@ -115,3 +115,16 @@ pre, code, .font-mono {
|
||||
grid-template-columns: 1fr;
|
||||
}
|
||||
}
|
||||
|
||||
.accordion-group {
|
||||
border-style: none;
|
||||
border-radius: 0;
|
||||
}
|
||||
|
||||
.accordion-group div[id*="-accordion-children"] {
|
||||
margin-inline: calc(var(--spacing) * 1);
|
||||
}
|
||||
|
||||
.accordion-group details summary {
|
||||
padding-inline: 0;
|
||||
}
|
||||
|
||||
@@ -137,7 +137,7 @@ After upgrading, reboot and restart Ollama.
|
||||
|
||||
If you experience gibberish responses when models load across multiple AMD GPUs on Linux, see the following guide.
|
||||
|
||||
- https://rocm.docs.amd.com/projects/radeon/en/latest/docs/install/native_linux/mgpu.html#mgpu-known-issues-and-limitations
|
||||
- https://rocm.docs.amd.com/projects/radeon-ryzen/en/latest/docs/install/installrad/native_linux/mgpu.html#mgpu-known-issues-and-limitations
|
||||
|
||||
## Windows Terminal Errors
|
||||
|
||||
|
||||
Reference in New Issue
Block a user