Compare commits
1
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 The Librarian
|
70feb5e845 |
+2
-2
@@ -4,8 +4,8 @@ This is a mirror of the Ollama repository.
|
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**Synced from:** https://github.com/ollama/ollama.git
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**Branch:** main
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**Commit:** 421faa0263f28499e2ff9076ffca240a8fc262ca
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**Sync Date:** 2026-05-12
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**Commit:** 12e04379cd5a9a2f150104a02c5761213f4be777
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**Sync Date:** 2026-06-12
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**Content:** Paths: docs
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---
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@@ -398,6 +398,7 @@ curl http://localhost:11434/api/generate -d '{
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"num_keep": 5,
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"seed": 42,
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"num_predict": 100,
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"draft_num_predict": 4,
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"top_k": 20,
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"top_p": 0.9,
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"min_p": 0.0,
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+4
-4
@@ -28,9 +28,9 @@ Errors are returned in the `application/json` format with the following structur
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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.
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```json
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{"model":"gemma3","created_at":"2025-10-26T17:21:21.196249Z","response":" Yes","done":false}
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{"model":"gemma3","created_at":"2025-10-26T17:21:21.207235Z","response":".","done":false}
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{"model":"gemma3","created_at":"2025-10-26T17:21:21.219166Z","response":"I","done":false}
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{"model":"gemma3","created_at":"2025-10-26T17:21:21.231094Z","response":"can","done":false}
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{"model":"gemma4","created_at":"2025-10-26T17:21:21.196249Z","response":" Yes","done":false}
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{"model":"gemma4","created_at":"2025-10-26T17:21:21.207235Z","response":".","done":false}
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{"model":"gemma4","created_at":"2025-10-26T17:21:21.219166Z","response":"I","done":false}
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{"model":"gemma4","created_at":"2025-10-26T17:21:21.231094Z","response":"can","done":false}
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{"error":"an error was encountered while running the model"}
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```
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@@ -28,7 +28,7 @@ Once Ollama is running, its API is automatically available and can be accessed v
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```shell
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curl http://localhost:11434/api/generate -d '{
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"model": "gemma3",
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"model": "gemma4",
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"prompt": "Why is the sky blue?"
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}'
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```
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@@ -5,13 +5,13 @@ title: Streaming
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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:
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```json
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{"model":"gemma3","created_at":"2025-10-26T17:15:24.097767Z","response":"That","done":false}
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{"model":"gemma3","created_at":"2025-10-26T17:15:24.109172Z","response":"'","done":false}
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{"model":"gemma3","created_at":"2025-10-26T17:15:24.121485Z","response":"s","done":false}
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{"model":"gemma3","created_at":"2025-10-26T17:15:24.132802Z","response":" a","done":false}
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{"model":"gemma3","created_at":"2025-10-26T17:15:24.143931Z","response":" fantastic","done":false}
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{"model":"gemma3","created_at":"2025-10-26T17:15:24.155176Z","response":" question","done":false}
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{"model":"gemma3","created_at":"2025-10-26T17:15:24.166576Z","response":"!","done":true, "done_reason": "stop"}
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{"model":"gemma4","created_at":"2025-10-26T17:15:24.097767Z","response":"That","done":false}
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{"model":"gemma4","created_at":"2025-10-26T17:15:24.109172Z","response":"'","done":false}
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{"model":"gemma4","created_at":"2025-10-26T17:15:24.121485Z","response":"s","done":false}
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{"model":"gemma4","created_at":"2025-10-26T17:15:24.132802Z","response":" a","done":false}
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{"model":"gemma4","created_at":"2025-10-26T17:15:24.143931Z","response":" fantastic","done":false}
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{"model":"gemma4","created_at":"2025-10-26T17:15:24.155176Z","response":" question","done":false}
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{"model":"gemma4","created_at":"2025-10-26T17:15:24.166576Z","response":"!","done":true, "done_reason": "stop"}
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```
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## Disabling streaming
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@@ -19,7 +19,7 @@ Certain API endpoints stream responses by default, such as `/api/generate`. Thes
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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:
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```json
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{"model":"gemma3","created_at":"2025-10-26T17:15:24.166576Z","response":"That's a fantastic question!","done":true}
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{"model":"gemma4","created_at":"2025-10-26T17:15:24.166576Z","response":"That's a fantastic question!","done":true}
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```
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## When to use streaming vs non-streaming
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+1
-1
@@ -19,7 +19,7 @@ For endpoints that return usage metrics, the response body will include the usag
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```json
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{
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"model": "gemma3",
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"model": "gemma4",
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"created_at": "2025-10-17T23:14:07.414671Z",
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"response": "Hello! How can I help you today?",
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"done": true,
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@@ -100,12 +100,11 @@ Provide a JSON schema to the `format` field.
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```
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</Tab>
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<Tab title="JavaScript">
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Serialize a Zod schema with `zodToJsonSchema()` and parse the structured response:
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Serialize a Zod schema with `z.toJSONSchema()` and parse the structured response:
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```javascript
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import ollama from 'ollama'
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import { z } from 'zod'
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import { zodToJsonSchema } from 'zod-to-json-schema'
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import * as z from 'zod'
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const Country = z.object({
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name: z.string(),
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@@ -116,7 +115,7 @@ Provide a JSON schema to the `format` field.
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const response = await ollama.chat({
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model: 'gpt-oss',
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messages: [{ role: 'user', content: 'Tell me about Canada.' }],
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format: zodToJsonSchema(Country),
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format: z.toJSONSchema(Country),
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})
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const country = Country.parse(JSON.parse(response.message.content))
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@@ -177,7 +176,7 @@ class ImageDescription(BaseModel):
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text_content: Optional[str] = None
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response = chat(
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model='gemma3',
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model='gemma4',
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messages=[{
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'role': 'user',
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'content': 'Describe this photo and list the objects you detect.',
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@@ -7,7 +7,7 @@ Vision models accept images alongside text so the model can describe, classify,
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## Quick start
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```shell
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ollama run gemma3 ./image.png whats in this image?
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ollama run gemma4 ./image.png whats in this image?
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```
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@@ -28,7 +28,7 @@ Provide an `images` array. SDKs accept file paths, URLs or raw bytes while the R
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curl -X POST http://localhost:11434/api/chat \
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-H "Content-Type: application/json" \
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-d '{
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"model": "gemma3",
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"model": "gemma4",
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"messages": [{
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"role": "user",
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"content": "What is in this image?",
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@@ -52,7 +52,7 @@ Provide an `images` array. SDKs accept file paths, URLs or raw bytes while the R
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# img = Path(path).read_bytes()
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response = chat(
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model='gemma3',
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model='gemma4',
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messages=[
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{
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'role': 'user',
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@@ -71,7 +71,7 @@ Provide an `images` array. SDKs accept file paths, URLs or raw bytes while the R
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const imagePath = '/absolute/path/to/image.jpg'
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const response = await ollama.chat({
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model: 'gemma3',
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model: 'gemma4',
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messages: [
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{ role: 'user', content: 'What is in this image?', images: [imagePath] }
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],
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+6
-6
@@ -5,7 +5,7 @@ title: CLI Reference
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### Run a model
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```
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ollama run gemma3
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ollama run gemma4
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```
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### Launch integrations
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@@ -64,7 +64,7 @@ I'm a basic program that prints the famous "Hello, world!" message to the consol
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#### Multimodal models
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```
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ollama run gemma3 "What's in this image? /Users/jmorgan/Desktop/smile.png"
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ollama run gemma4 "What's in this image? /Users/jmorgan/Desktop/smile.png"
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```
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### Generate embeddings
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@@ -82,13 +82,13 @@ echo "Hello world" | ollama run nomic-embed-text
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### Download a model
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```
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ollama pull gemma3
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ollama pull gemma4
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```
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### Remove a model
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```
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ollama rm gemma3
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ollama rm gemma4
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```
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### List models
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@@ -114,7 +114,7 @@ ollama signout
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First, create a `Modelfile`
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```
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FROM gemma3
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FROM gemma4
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SYSTEM """You are a happy cat."""
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```
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@@ -133,7 +133,7 @@ ollama ps
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### Stop a running model
|
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```
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ollama stop gemma3
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ollama stop gemma4
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```
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### Start Ollama
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+23
-1
@@ -229,4 +229,26 @@ curl https://ollama.com/api/chat \
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## Local only
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Ollama can run in local-only mode by [disabling Ollama's cloud](./faq#how-do-i-disable-ollama-cloud) features.
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Ollama can run in local-only mode by [disabling Ollama's cloud](./faq#how-do-i-disable-ollama-cloud) features.
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## Deprecations
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|
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Ollama will occasionally deprecate and retire older cloud models as newer and better open-source models are released.
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Tools and applications relying on Ollama Cloud models may need to be updated to keep working. Impacted users will be
|
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notified in advance of model deprecation and retirement. Deprecations will be communicated through email and on the
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Ollama website.
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|
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Ollama Cloud model retirement does not affect local models.
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### Upcoming deprecations
|
||||
|
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| Retirement date | Model | Recommended alternative |
|
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| --- | --- | --- |
|
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| 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` |
|
||||
|
||||
@@ -37,5 +37,5 @@ ollama ps
|
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```
|
||||
```
|
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NAME ID SIZE PROCESSOR CONTEXT UNTIL
|
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gemma3:latest a2af6cc3eb7f 6.6 GB 100% GPU 65536 2 minutes from now
|
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gemma4:latest c6eb396dbd59 9.6 GB 100% GPU 131072 2 minutes from now
|
||||
```
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|
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+73
-104
@@ -3,9 +3,11 @@
|
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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
|
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|
||||
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:
|
||||
|
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```shell
|
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go run . serve
|
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@@ -14,53 +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
|
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./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:
|
||||
>
|
||||
@@ -73,36 +95,20 @@ 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)
|
||||
@@ -111,57 +117,17 @@ Install prerequisites:
|
||||
> [!IMPORTANT]
|
||||
> Ensure prerequisites are in `PATH` before running CMake.
|
||||
|
||||
|
||||
Then, configure and build the project:
|
||||
|
||||
```shell
|
||||
cmake -B build
|
||||
cmake --build build
|
||||
```
|
||||
|
||||
Lastly, run Ollama:
|
||||
|
||||
```shell
|
||||
go run . serve
|
||||
```
|
||||
|
||||
## MLX Engine (Optional)
|
||||
|
||||
The MLX engine enables running safetensor based models. It requires building the [MLX](https://github.com/ml-explore/mlx) and [MLX-C](https://github.com/ml-explore/mlx-c) shared libraries separately via CMake. On MacOS, MLX leverages the Metal library to run on the GPU, and on Windows and Linux, runs on NVIDIA GPUs via CUDA v13.
|
||||
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`.
|
||||
|
||||
### macOS (Apple Silicon)
|
||||
|
||||
Requires the Metal toolchain. Install [Xcode](https://developer.apple.com/xcode/) first, then:
|
||||
|
||||
```shell
|
||||
xcodebuild -downloadComponent MetalToolchain
|
||||
```
|
||||
|
||||
Verify it's installed correctly (should print "no input files"):
|
||||
|
||||
```shell
|
||||
xcrun metal
|
||||
```
|
||||
|
||||
Then build:
|
||||
|
||||
```shell
|
||||
cmake -B build --preset MLX
|
||||
cmake --build build --preset MLX --parallel
|
||||
cmake --install build --component MLX
|
||||
```
|
||||
|
||||
> [!NOTE]
|
||||
> Without the Metal toolchain, cmake will silently complete with Metal disabled. Check the cmake output for `Setting MLX_BUILD_METAL=OFF` which indicates the toolchain is missing.
|
||||
|
||||
### Windows / Linux (CUDA)
|
||||
### CUDA
|
||||
|
||||
Requires CUDA 13+ and [cuDNN](https://developer.nvidia.com/cudnn) 9+.
|
||||
|
||||
```shell
|
||||
cmake -B build --preset "MLX CUDA 13"
|
||||
cmake --build build --target mlx --target mlxc --config Release --parallel
|
||||
cmake --install build --component MLX --strip
|
||||
cmake -B build . -DOLLAMA_MLX_BACKENDS=cuda_v13
|
||||
cmake --build build --parallel 8
|
||||
```
|
||||
|
||||
### Local MLX source overrides
|
||||
@@ -173,17 +139,20 @@ export OLLAMA_MLX_SOURCE=/path/to/mlx
|
||||
export OLLAMA_MLX_C_SOURCE=/path/to/mlx-c
|
||||
```
|
||||
|
||||
For example, using the helper scripts with local mlx and mlx-c repos:
|
||||
```shell
|
||||
OLLAMA_MLX_SOURCE=../mlx OLLAMA_MLX_C_SOURCE=../mlx-c ./scripts/build_linux.sh
|
||||
On macOS arm64:
|
||||
|
||||
OLLAMA_MLX_SOURCE=../mlx OLLAMA_MLX_C_SOURCE=../mlx-c ./scripts/build_darwin.sh
|
||||
```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"
|
||||
./scripts/build_darwin.ps1
|
||||
cmake -B build . -DOLLAMA_MLX_BACKENDS=cuda_v13
|
||||
cmake --build build --parallel 8
|
||||
```
|
||||
|
||||
## Docker
|
||||
@@ -208,11 +177,11 @@ go test ./...
|
||||
|
||||
## 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.
|
||||
|
||||
+6
-3
@@ -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).
|
||||
|
||||
|
||||
+5
-1
@@ -115,7 +115,8 @@
|
||||
"expanded": true,
|
||||
"pages": [
|
||||
"/integrations/openclaw",
|
||||
"/integrations/hermes"
|
||||
"/integrations/hermes",
|
||||
"/integrations/hermes-desktop"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -123,11 +124,14 @@
|
||||
"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"
|
||||
]
|
||||
|
||||
+10
-7
@@ -4,6 +4,7 @@ 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)
|
||||
@@ -75,7 +76,7 @@ using the `amdgpu-install` utility from
|
||||
|
||||
### 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 |
|
||||
| -------------- | -------------------------------------------------------------------------------------------------------------------- |
|
||||
@@ -142,12 +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
|
||||
@@ -173,4 +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.
|
||||
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 484 KiB |
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|
After Width: | Height: | Size: 615 KiB |
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|
After Width: | Height: | Size: 491 KiB |
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|
After Width: | Height: | Size: 610 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 593 KiB |
+1
-1
@@ -5,7 +5,7 @@ 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">
|
||||
|
||||
@@ -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
|
||||
```
|
||||
@@ -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.
|
||||
+17
-12
@@ -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
|
||||
@@ -21,12 +21,21 @@ npm install -g @openai/codex
|
||||
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:
|
||||
@@ -49,25 +58,21 @@ codex --oss -m gpt-oss:120b-cloud
|
||||
|
||||
### Profile-based setup
|
||||
|
||||
For a persistent configuration, add an Ollama provider and profiles to `~/.codex/config.toml`:
|
||||
For a persistent Codex CLI configuration, create `~/.codex/ollama-launch.config.toml`:
|
||||
|
||||
```toml
|
||||
[model_providers.ollama-launch]
|
||||
name = "Ollama"
|
||||
base_url = "http://localhost:11434/v1"
|
||||
|
||||
[profiles.ollama-launch]
|
||||
model = "gpt-oss:120b"
|
||||
model_provider = "ollama-launch"
|
||||
model_catalog_json = "/Users/you/.codex/model.json"
|
||||
|
||||
[profiles.ollama-cloud]
|
||||
model = "gpt-oss:120b-cloud"
|
||||
model_provider = "ollama-launch"
|
||||
[model_providers.ollama-launch]
|
||||
name = "Ollama"
|
||||
base_url = "http://localhost:11434/v1/"
|
||||
wire_api = "responses"
|
||||
```
|
||||
|
||||
Then run:
|
||||
|
||||
```
|
||||
codex --profile ollama-launch
|
||||
codex --profile ollama-cloud
|
||||
```
|
||||
|
||||
@@ -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.
|
||||
|
||||
|
||||
|
||||
## 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.
|
||||
@@ -19,8 +19,6 @@ Ollama handles everything automatically:
|
||||
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
|
||||
|
||||
<Note>Hermes on Windows requires WSL2. Install it with `wsl --install` and re-run from inside the WSL shell.</Note>
|
||||
|
||||
## Recommended models
|
||||
|
||||
**Cloud models**:
|
||||
|
||||
@@ -9,11 +9,14 @@ Ollama integrates with a wide range of tools.
|
||||
Coding assistants that can read, modify, and execute code in your projects.
|
||||
|
||||
- [Claude Code](/integrations/claude-code)
|
||||
- [Codex](/integrations/codex)
|
||||
- [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)
|
||||
|
||||
@@ -23,6 +26,7 @@ AI assistants that help with everyday tasks.
|
||||
|
||||
- [OpenClaw](/integrations/openclaw)
|
||||
- [Hermes Agent](/integrations/hermes)
|
||||
- [Hermes Desktop](/integrations/hermes-desktop)
|
||||
|
||||
## IDEs & Editors
|
||||
|
||||
|
||||
@@ -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.
|
||||
|
||||
|
||||
|
||||
## 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
|
||||
```
|
||||
+12
-12
@@ -4,23 +4,13 @@ title: Pi
|
||||
|
||||
Pi is a minimal and extensible coding agent.
|
||||
|
||||
## Install
|
||||
|
||||
Install [Pi](https://github.com/badlogic/pi-mono):
|
||||
|
||||
```bash
|
||||
npm install -g @mariozechner/pi-coding-agent
|
||||
```
|
||||
|
||||
## Usage with Ollama
|
||||
|
||||
### Quick setup
|
||||
## Quick setup
|
||||
|
||||
```bash
|
||||
ollama launch pi
|
||||
```
|
||||
|
||||
This installs Pi, configures Ollama as a provider including web tools, and drops you into an interactive session.
|
||||
This installs Pi if needed, configures Ollama as a provider including web tools, and drops you into an interactive session.
|
||||
|
||||
To configure without launching:
|
||||
|
||||
@@ -80,6 +70,16 @@ Each kept experiment is automatically committed. Each failed one is reverted. Wh
|
||||
|
||||
## 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
|
||||
|
||||
@@ -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 |
|
||||
|
||||
+87
-67
@@ -794,14 +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
|
||||
}'
|
||||
@@ -809,7 +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,
|
||||
@@ -821,7 +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": {
|
||||
@@ -846,7 +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": ["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"]
|
||||
}'
|
||||
@@ -854,13 +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:
|
||||
@@ -870,7 +870,7 @@ paths:
|
||||
schema:
|
||||
$ref: "#/components/schemas/GenerateRequest"
|
||||
example:
|
||||
model: gemma3
|
||||
model: gemma4
|
||||
prompt: Why is the sky blue?
|
||||
responses:
|
||||
"200":
|
||||
@@ -880,7 +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
|
||||
@@ -906,7 +906,7 @@ paths:
|
||||
label: Default
|
||||
source: |
|
||||
curl http://localhost:11434/api/chat -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
@@ -918,7 +918,7 @@ paths:
|
||||
label: Non-streaming
|
||||
source: |
|
||||
curl http://localhost:11434/api/chat -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
@@ -931,7 +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",
|
||||
@@ -1011,7 +1011,7 @@ paths:
|
||||
label: Images
|
||||
source: |
|
||||
curl http://localhost:11434/api/chat -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
@@ -1036,7 +1036,7 @@ paths:
|
||||
schema:
|
||||
$ref: "#/components/schemas/ChatResponse"
|
||||
example:
|
||||
model: "gemma3"
|
||||
model: "gemma4"
|
||||
created_at: "2025-10-17T23:14:07.414671Z"
|
||||
message:
|
||||
role: "assistant"
|
||||
@@ -1148,17 +1148,17 @@ paths:
|
||||
$ref: "#/components/schemas/ListResponse"
|
||||
example:
|
||||
models:
|
||||
- name: "gemma3"
|
||||
model: "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:
|
||||
@@ -1181,17 +1181,17 @@ paths:
|
||||
$ref: "#/components/schemas/PsResponse"
|
||||
example:
|
||||
models:
|
||||
- name: "gemma3"
|
||||
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
|
||||
@@ -1205,13 +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:
|
||||
@@ -1221,7 +1221,7 @@ paths:
|
||||
schema:
|
||||
$ref: "#/components/schemas/ShowRequest"
|
||||
example:
|
||||
model: gemma3
|
||||
model: gemma4
|
||||
responses:
|
||||
"200":
|
||||
description: Model information
|
||||
@@ -1239,44 +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
|
||||
@@ -1292,7 +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."
|
||||
}'
|
||||
@@ -1301,7 +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
|
||||
@@ -1320,7 +1340,7 @@ paths:
|
||||
$ref: "#/components/schemas/CreateRequest"
|
||||
example:
|
||||
model: mario
|
||||
from: gemma3
|
||||
from: gemma4
|
||||
system: "You are Mario from Super Mario Bros."
|
||||
responses:
|
||||
"200":
|
||||
@@ -1347,8 +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
|
||||
@@ -1357,8 +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
|
||||
@@ -1373,13 +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:
|
||||
@@ -1389,7 +1409,7 @@ paths:
|
||||
schema:
|
||||
$ref: "#/components/schemas/PullRequest"
|
||||
example:
|
||||
model: gemma3
|
||||
model: gemma4
|
||||
responses:
|
||||
"200":
|
||||
description: Pull status updates.
|
||||
@@ -1457,7 +1477,7 @@ paths:
|
||||
label: Delete model
|
||||
source: |
|
||||
curl -X DELETE http://localhost:11434/api/delete -d '{
|
||||
"model": "gemma3"
|
||||
"model": "gemma4"
|
||||
}'
|
||||
requestBody:
|
||||
required: true
|
||||
@@ -1466,7 +1486,7 @@ paths:
|
||||
schema:
|
||||
$ref: "#/components/schemas/DeleteRequest"
|
||||
example:
|
||||
model: gemma3
|
||||
model: gemma4
|
||||
responses:
|
||||
"200":
|
||||
description: Model successfully deleted
|
||||
|
||||
+1
-1
@@ -59,7 +59,7 @@ Use the [API](/api) to integrate Ollama into your applications:
|
||||
|
||||
```sh
|
||||
curl http://localhost:11434/api/chat -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"messages": [{ "role": "user", "content": "Hello!" }]
|
||||
}'
|
||||
```
|
||||
|
||||
+9
-1
@@ -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.
|
||||
|
||||
Reference in New Issue
Block a user