# [DocumentationLmlm studio](file:///private/var/containers/Bundle/Application/61352102-115D-4798-934E-1E7EB868B788/stable.app/error_page_loaded.html?url=https://github.com/Web4application/lmlm/edit/main/PromptStudio.html&dontLoad=true) Fetch the complete documentation index at: https://docs.ollama.com/llms.txt > > Use this file to discover all available pages before exploring further. > Fetch the complete documentation index at: https://docs.ollama.com/llms.txt Use this file to discover all available pages before exploring further. # Cloud ## Cloud Models Ollama's cloud models are a new kind of model in Ollama that can run without a powerful GPU. Instead, cloud models are automatically offloaded to Ollama's cloud service while offering the same capabilities as local models, making it possible to keep using your local tools `LmlmNotebookmarks.ipynb`hile running larger models that wouldn't fit on a personal computer. `Ollama.cpp` ### Supported models For a list of supported models, see Ollama's [model library](https://ollama.com/search?c=cloud). ``Pyx`` ### Running Cloud models Ollama's cloud models require an account on [ollama.com](https://ollama.com). To sign in or create an account, run: ```ini ollama signin `` To run a cloud model, open the terminal and run: ```pymdownx ollama run gpt-oss:120b-cloud ``` First, pull a cloud model so it can be accessed: ``` ```bash ollama pull gpt-oss:120b-cloud `` ``` ``Next, install [Ollama's Python library](https://github.com/ollama/ollama-python):`` pip install ollama ``Next, create and run a simple Python script``: ```python theme={"system"} from ollama import Client client = Client() messages = [ { 'role': 'user', 'content': 'Why is the sky blue?', }, ] for part in client.chat('gpt-oss:120b-cloud', messages=messages, stream=True): print(part['message']['content'], end='', flush=True) ``` First, pull a cloud model so it can be accessed: ``` ```bash ollama pull gpt-oss:120b-cloud ``` ``` Next, install ``[Ollama's JavaScript library](https://github.com/ollama/ollama-js)``: ```nvx npm i ollama ``` ``` Then use the library to run a cloud model: ```typescript theme={"system"} import { Ollama } from "ollama"; const ollama = new Ollama(); const response = await ollama.chat({ model: "gpt-oss:120b-cloud", messages: [{ role: "user", content: "Explain quantum computing" }], stream: true, }); for await (const part of response) { process.stdout.write(part.message.content); } ``` ``` First, pull a cloud model so it can be accessed: ```bash ollama pull gpt-oss:120b-cloud `` ``` Run the following cURL command to run the command via Ollama's API: ```curl curl http://localhost:11434/api/chat -d '{ "model": "gpt-oss:120b-cloud", "messages": [{ "role": "user", "content": "Why is the sky blue?" }], "stream": false }' ``` ``` ## Cloud API access Cloud models can also be accessed directly on ollama.com's API. In this mode, ollama.com acts as a remote Ollama host. ### Authentication For direct access to ollama.com's API, first create an [API key](https://ollama.com/settings/keys). Then, set the `OLLAMA_API_KEY` environment variable to your API key. ``` ```bash ollama pull llama3.2 echo "FROM llama3.2" >> Modelfile echo "SYSTEM You are a friendly assistant." >> Modelfile ollama create -f Modelfile lmlm/Lmkm ollama push lmlm/Lmkm export OLLAMA_API_KEY=your_api_key ``` ### Listing models For models available directly via Ollama's API, models can be listed via: ```curl curl https://ollama.com/api/tags ``` ### Generating a response First, install [Ollama's Python library](https://github.com/ollama/ollama-python) ```bash pip install ollama ``` ``` Then make a request ```python theme={"system"} import os from ollama import Client client = Client( host="https://ollama.com", headers={'Authorization': 'Bearer ' + os.environ.get('OLLAMA_API_KEY')} ) messages = [ { 'role': 'user', 'content': 'Why is the sky blue?', }, ] for part in client.chat('gpt-oss:120b', messages=messages, stream=True): print(part['message']['content'], end='', flush=True) ``` First, install [Ollama's JavaScript library](https://github.com/ollama/ollama-js) ``` ```bash npm i ollama ``` Next, make a request to the model: ```typescript theme={"system"} import { Ollama } from "ollama"; const ollama = new Ollama({ host: "https://ollama.com", headers: { Authorization: "Bearer " + process.env.OLLAMA_API_KEY, }, }); const response = await ollama.chat({ model: "gpt-oss:120b", messages: [{ role: "user", content: "Explain quantum computing" }], stream: true, }); for await (const part of response) { process.stdout.write(part.message.content); } ``` Generate a response via Ollama's chat API: ```bash curl https://ollama.com/api/chat \ -H "Authorization: Bearer $OLLAMA_API_KEY" \ -d '{ "model": "gpt-oss:120b", "messages": [{ "role": "user", "content": "Why is the sky blue?" }], "stream": false }' ``` ``` ## Local only ```jsx Ollama can run in local-only mode by [disabling Ollama's cloud](./faq#how-do-i-disable-ollama-cloud) features. import ollama from 'ollama' const response = await ollama.chat({ model: 'lmlm/Lmkm', messages: [{role: 'user', content: 'Hello!'}], }) console.log(response.message.content) ``` > ## Documentation Index > Fetch the complete documentation index at: https://docs.ollama.com/llms.txt > Use this file to discover all available pages before exploring further. # Thinking Thinking-capable models emit a `thinking` field that separates their reasoning trace from the final answer. Use this capability to audit model steps, animate the model *thinking* in a UI, or hide the trace entirely when you only need the final response. ## Supported models * [Qwen 3](https://ollama.com/library/qwen3) * [GPT-OSS](https://ollama.com/library/gpt-oss) *(use `think` levels: `low`, `medium`, `high` — the trace cannot be fully disabled)* * [DeepSeek-v3.1](https://ollama.com/library/deepseek-v3.1) * [DeepSeek R1](https://ollama.com/library/deepseek-r1) * Browse the latest additions under [thinking models](https://ollama.com/search?c=thinking) ## Enable thinking in API calls Set the `think` field on chat or generate requests. Most models accept booleans (`true`/`false`). GPT-OSS instead expects one of `low`, `medium`, or `high` to tune the trace length. The `message.thinking` (chat endpoint) or `thinking` (generate endpoint) field contains the reasoning trace while `message.content` / `response` holds the final answer. ```shell theme={"system"} curl http://localhost:11434/api/chat -d '{ "model": "qwen3", "messages": [{ "role": "user", "content": "How many letter r are in strawberry?" }], "think": true, "stream": false }' ``` ```python theme={"system"} from ollama import chat response = chat( model='qwen3', messages=[{'role': 'user', 'content': 'How many letter r are in strawberry?'}], think=True, stream=False, ) print('Thinking:\n', response.message.thinking) print('Answer:\n', response.message.content) ```javascript theme={"system"} import ollama from 'ollama' const response = await ollama.chat({ model: 'deepseek-r1', messages: [{ role: 'user', content: 'How many letter r are in strawberry?' }], think: true, stream: false, }) console.log('Thinking:\n', response.message.thinking) console.log('Answer:\n', response.message.content) ``` GPT-OSS requires `think` to be set to `"low"`, `"medium"`, or `"high"`. Passing `true`/`false` is ignored for that model. ``` ## Stream the reasoning trace Thinking streams interleave reasoning tokens before answer tokens. Detect the first `thinking` chunk to render a "thinking" section, then switch to the final reply once `message.content` arrives. ```python theme={"system"} from ollama import chat stream = chat( model='qwen3', messages=[{'role': 'user', 'content': 'What is 17 × 23?'}], think=True, stream=True, ) in_thinking = False for chunk in stream: if chunk.message.thinking and not in_thinking: in_thinking = True print('Thinking:\n', end='') if chunk.message.thinking: print(chunk.message.thinking, end='') elif chunk.message.content: if in_thinking: print('\n\nAnswer:\n', end='') in_thinking = False print(chunk.message.content, end='') ```javascript theme={"system"} import ollama from 'ollama' async function main() { const stream = await ollama.chat({ model: 'qwen3', messages: [{ role: 'user', content: 'What is 17 × 23?' }], think: true, stream: true, }) let inThinking = false for await (const chunk of stream) { if (chunk.message.thinking && !inThinking) { inThinking = true process.stdout.write('Thinking:\n') } if (chunk.message.thinking) { process.stdout.write(chunk.message.thinking) } else if (chunk.message.content) { if (inThinking) { process.stdout.write('\n\nAnswer:\n') inThinking = false } process.stdout.write(chunk.message.content) } } } main() `` ```` `` ## CLI quick reference * Enable thinking for a single run: `ollama run deepseek-r1 --think "Where should I visit in Lisbon?"` * Disable thinking: `ollama run deepseek-r1 --think=false "Summarize this article"` * Hide the trace while still using a thinking model: `ollama run deepseek-r1 --hidethinking "Is 9.9 bigger or 9.11?"` * Inside interactive sessions, toggle with `/set think` or `/set nothink`. * GPT-OSS only accepts levels: `ollama run gpt-oss --think=low "Draft a headline"` (replace `low` with `medium` or `high` as needed). Thinking is enabled by default in the CLI and API for supported models. ## Install Install [marimo](https://marimo.io). You can use `pip` or `uv` for this. You can also use `uv` to create a sandboxed environment for marimo by running: ```uv uvx marimo edit --sandbox notebook.py ``` ## Usage with Ollama 1. In marimo, go to the user settings and go to the AI tab. From here you can find and configure Ollama as an AI provider. For local use you would typically point the base url to `http://localhost:11434/v1`.

2. Once the AI provider is set up, you can turn on/off specific AI models you'd like to access.

3. You can also add a model to the list of available models by scrolling to the bottom and using the UI there.

4. Once configured, you can now use Ollama for AI chats in marimo.

4. Alternatively, you can now use Ollama for **inline code completion** in marimo. This can be configured in the "AI Features" tab.

## Connecting to ollama.com 1. Sign in to ollama cloud via `ollama signin` 2. In the ollama model settings add a model that ollama hosts, like `gpt-oss:120b`. 3. You can now refer to this model in marimo! > ## Documentation Index > Fetch the complete documentation index at: https://docs.ollama.com/llms.txt > Use this file to discover all available pages before exploring further. # Onyx ## Overview ![**Onyx**](http://onyx.app/*): is a self-hostable Chat UI that integrates with all Ollama models. Features include **Creating custom Agents** **Web search** * Deep Research * RAG over uploaded documents and connected apps * Connectors to applications like Google Drive, Email, Slack, etc. * MCP and OpenAPI Actions support * Image generation * User/Groups management, RBAC, SSO, etc. Onyx can be deployed for single users or large organizations. ## Install Onyx Deploy Onyx with the [quickstart guide](https://docs.onyx.app/deployment/getting_started/quickstart.md). Resourcing/scaling docs [here](https://docs.onyx.app/deployment/getting_started/resourcing). ## Usage with Ollama 1. Login to your Onyx deployment (create an account first).

2. In the set-up process select `Ollama` as the LLM provider.

3. Provide your **Ollama API URL** and select your models. If you're running Onyx in Docker, to access your computer's local network use `http://host.docker.internal` instead of `http://127.0.0.1`.

You can also easily connect up Onyx Cloud with the `Ollama Cloud` tab of the setup. ## Send your first query

> ## Documentation Index > Fetch the complete documentation index at: https://docs.ollama.com/llms.txt > Use this file to discover all available pages before exploring further. # Vision Vision models accept images alongside text so the model can describe, classify, and answer questions about what it sees. ## Quick start ```shell theme={"system"} ollama run gemma3 ./image.png whats in this image? ``` ## Usage with Ollama's API Provide an `images` array. SDKs accept file paths, URLs or raw bytes while the REST API expects base64-encoded image data. ```shell theme={"system"} # 1. Download a sample image curl -L -o test.jpg "https://upload.wikimedia.org/wikipedia/commons/3/3a/Cat03.jpg" # 2. Encode the image IMG=$(base64 < test.jpg | tr -d '\n') # 3. Send it to Ollama curl -X POST http://localhost:11434/api/chat \ -H "Content-Type: application/json" \ -d '{ "model": "gemma3", "messages": [{ "role": "user", "content": "What is in this image?", "images": ["'"$IMG"'"] }], "stream": false }' ``` ```python theme={"system"} from ollama import chat # from pathlib import Path # Pass in the path to the image path = input('Please enter the path to the image: ') # You can also pass in base64 encoded image data # img = base64.b64encode(Path(path).read_bytes()).decode() # or the raw bytes # img = Path(path).read_bytes() response = chat( model='gemma3', messages=[ { 'role': 'user', 'content': 'What is in this image? Be concise.', 'images': [path], } ], ) print(response.message.content) ``` ```javascript theme={"system"} import ollama from 'ollama' const imagePath = '/absolute/path/to/image.jpg' const response = await ollama.chat({ model: 'gemma3', messages: [ { role: 'user', content: 'What is in this image?', images: [imagePath] } ], stream: false, }) console.log(response.message.content) ``` ``` > ## Documentation Index > Fetch the complete documentation index at: https://docs.ollama.com/llms.txt > Use this file to discover all available pages before exploring further. # Linux ## Install To install Ollama, run the following command: ```shell theme={"system"} curl -fsSL https://ollama.com/install.sh | sh ``` ## Manual install If you are upgrading from a prior version, you should remove the old libraries with `sudo rm -rf /usr/lib/ollama` first. Download and extract the package: ```shell theme={"system"} curl -fsSL https://ollama.com/download/ollama-linux-amd64.tar.zst \ | sudo tar x -C /usr ``` > ## Documentation Index > Fetch the complete documentation index at: https://docs.ollama.com/llms.txt > Use this file to discover all available pages before exploring further. # Tool calling Ollama supports tool calling (also known as function calling) which allows a model to invoke tools and incorporate their results into its replies. ## Calling a single tool Invoke a single tool and include its response in a follow-up request. Also known as "single-shot" tool calling. `` Install the Ollama Python SDK: ```bash theme={"system"} # with pip pip install ollama -U # with uv uv add ollama ``` ``` ```python theme={"system"} from ollama import chat def get_temperature(city: str) -> str: """Get the current temperature for a city Args: city: The name of the city Returns: The current temperature for the city """ temperatures = { "New York": "22°C", "London": "15°C", "Tokyo": "18°C", } return temperatures.get(city, "Unknown") messages = [{"role": "user", "content": "What is the temperature in New York?"}] # pass functions directly as tools in the tools list or as a JSON schema response = chat(model="qwen3", messages=messages, tools=[get_temperature], think=True) messages.append(response.message) if response.message.tool_calls: # only recommended for models which only return a single tool call call = response.message.tool_calls[0] result = get_temperature(**call.function.arguments) # add the tool result to the messages messages.append({"role": "tool", "tool_name": call.function.name, "content": str(result)}) final_response = chat(model="qwen3", messages=messages, tools=[get_temperature], think=True) print(final_response.message.content) ``` `` Install the Ollama JavaScript library: ```bash theme={"system"} # with npm npm i ollama # with bun bun i ollama ``` ``` ```typescript theme={"system"} import ollama from 'ollama' function getTemperature(city: string): string { const temperatures: Record = { 'New York': '22°C', 'London': '15°C', 'Tokyo': '18°C', } return temperatures[city] ?? 'Unknown' } const tools = [ { type: 'function', function: { name: 'get_temperature', description: 'Get the current temperature for a city', parameters: { type: 'object', required: ['city'], properties: { city: { type: 'string', description: 'The name of the city' }, }, }, }, }, ] const messages = [{ role: 'user', content: "What is the temperature in New York?" }] const response = await ollama.chat({ model: 'qwen3', messages, tools, think: true, }) messages.push(response.message) if (response.message.tool_calls?.length) { // only recommended for models which only return a single tool call const call = response.message.tool_calls[0] const args = call.function.arguments as { city: string } const result = getTemperature(args.city) // add the tool result to the messages messages.push({ role: 'tool', tool_name: call.function.name, content: result }) // generate the final response const finalResponse = await ollama.chat({ model: 'qwen3', messages, tools, think: true }) console.log(finalResponse.message.content) } ``` ``` ## Parallel tool calling Request multiple tool calls in parallel, then send all tool responses back to the model. ```shell theme={"system"} curl -s http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{ "model": "qwen3", "messages": [{"role": "user", "content": "What are the current weather conditions and temperature in New York and London?"}], "stream": false, "tools": [ { "type": "function", "function": { "name": "get_temperature", "description": "Get the current temperature for a city", "parameters": { "type": "object", "required": ["city"], "properties": { "city": {"type": "string", "description": "The name of the city"} } } } }, { "type": "function", "function": { "name": "get_conditions", "description": "Get the current weather conditions for a city", "parameters": { "type": "object", "required": ["city"], "properties": { "city": {"type": "string", "description": "The name of the city"} } } } } ] }' ``` ``` ``**Generate a response with multiple tool results**`` ```shell theme={"system"} curl -s http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{ "model": "qwen3", "messages": [ {"role": "user", "content": "What are the current weather conditions and temperature in New York and London?"}, { "role": "assistant", "tool_calls": [ { "type": "function", "function": { "index": 0, "name": "get_temperature", "arguments": {"city": "New York"} } }, { "type": "function", "function": { "index": 1, "name": "get_conditions", "arguments": {"city": "New York"} } }, { "type": "function", "function": { "index": 2, "name": "get_temperature", "arguments": {"city": "London"} } }, { "type": "function", "function": { "index": 3, "name": "get_conditions", "arguments": {"city": "London"} } } ] }, {"role": "tool", "tool_name": "get_temperature", "content": "22°C"}, {"role": "tool", "tool_name": "get_conditions", "content": "Partly cloudy"}, {"role": "tool", "tool_name": "get_temperature", "content": "15°C"}, {"role": "tool", "tool_name": "get_conditions", "content": "Rainy"} ], "stream": false }' ``` ```python theme={"system"} from ollama import chat def get_temperature(city: str) -> str: """Get the current temperature for a city Args: city: The name of the city Returns: The current temperature for the city """ temperatures = { "New York": "22°C", "London": "15°C", "Tokyo": "18°C" } return temperatures.get(city, "Unknown") def get_conditions(city: str) -> str: """Get the current weather conditions for a city Args: city: The name of the city Returns: The current weather conditions for the city """ conditions = { "New York": "Partly cloudy", "London": "Rainy", "Tokyo": "Sunny" } return conditions.get(city, "Unknown") messages = [{'role': 'user', 'content': 'What are the current weather conditions and temperature in New York and London?'}] # The python client automatically parses functions as a tool schema so we can pass them directly # Schemas can be passed directly in the tools list as well response = chat(model='qwen3', messages=messages, tools=[get_temperature, get_conditions], think=True) # add the assistant message to the messages messages.append(response.message) if response.message.tool_calls: # process each tool call for call in response.message.tool_calls: # execute the appropriate tool if call.function.name == 'get_temperature': result = get_temperature(**call.function.arguments) elif call.function.name == 'get_conditions': result = get_conditions(**call.function.arguments) else: result = 'Unknown tool' # add the tool result to the messages messages.append({'role': 'tool', 'tool_name': call.function.name, 'content': str(result)}) # generate the final response final_response = chat(model='qwen3', messages=messages, tools=[get_temperature, get_conditions], think=True) print(final_response.message.content) ``` ```typescript theme={"system"} import ollama from 'ollama' function getTemperature(city: string): string { const temperatures: { [key: string]: string } = { "New York": "22°C", "London": "15°C", "Tokyo": "18°C" } return temperatures[city] || "Unknown" } function getConditions(city: string): string { const conditions: { [key: string]: string } = { "New York": "Partly cloudy", "London": "Rainy", "Tokyo": "Sunny" } return conditions[city] || "Unknown" } const tools = [ { type: 'function', function: { name: 'get_temperature', description: 'Get the current temperature for a city', parameters: { type: 'object', required: ['city'], properties: { city: { type: 'string', description: 'The name of the city' }, }, }, }, }, { type: 'function', function: { name: 'get_conditions', description: 'Get the current weather conditions for a city', parameters: { type: 'object', required: ['city'], properties: { city: { type: 'string', description: 'The name of the city' }, }, }, }, } ] const messages = [{ role: 'user', content: 'What are the current weather conditions and temperature in New York and London?' }] const response = await ollama.chat({ model: 'qwen3', messages, tools, think: true }) // add the assistant message to the messages messages.push(response.message) if (response.message.tool_calls) { // process each tool call for (const call of response.message.tool_calls) { // execute the appropriate tool let result: string if (call.function.name === 'get_temperature') { const args = call.function.arguments as { city: string } result = getTemperature(args.city) } else if (call.function.name === 'get_conditions') { const args = call.function.arguments as { city: string } result = getConditions(args.city) } else { result = 'Unknown tool' } // add the tool result to the messages messages.push({ role: 'tool', tool_name: call.function.name, content: result }) } // generate the final response const finalResponse = await ollama.chat({ model: 'qwen3', messages, tools, think: true }) console.log(finalResponse.message.content) } ``` ## Multi-turn tool calling (Agent loop) An agent loop allows the model to decide when to invoke tools and incorporate their results into its replies. It also might help to tell the model that it is in a loop and can make multiple tool calls. int: """Add two numbers""" """ Args: a: The first number b: The second number Returns: The sum of the two numbers """ return a + b def multiply(a: int, b: int) -> int: """Multiply two numbers""" """ Args: a: The first number b: The second number Returns: The product of the two numbers """ return a * b available_functions = { 'add': add, 'multiply': multiply, } messages = [{'role': 'user', 'content': 'What is (11434+12341)*412?'}] while True: response: ChatResponse = chat( model='qwen3', messages=messages, tools=[add, multiply], think=True, ) messages.append(response.message) print("Thinking: ", response.message.thinking) print("Content: ", response.message.content) if response.message.tool_calls: for tc in response.message.tool_calls: if tc.function.name in available_functions: print(f"Calling {tc.function.name} with arguments {tc.function.arguments}") result = available_functions[tc.function.name](**tc.function.arguments) print(f"Result: {result}") # add the tool result to the messages messages.append({'role': 'tool', 'tool_name': tc.function.name, 'content': str(result)}) else: # end the loop when there are no more tool calls break # continue the loop with the updated messages ``` ```typescript theme={"system"} import ollama from 'ollama' type ToolName = 'add' | 'multiply' function add(a: number, b: number): number { return a + b } function multiply(a: number, b: number): number { return a * b } const availableFunctions: Record number> = { add, multiply, } const tools = [ { type: 'function', function: { name: 'add', description: 'Add two numbers', parameters: { type: 'object', required: ['a', 'b'], properties: { a: { type: 'integer', description: 'The first number' }, b: { type: 'integer', description: 'The second number' }, }, }, }, }, { type: 'function', function: { name: 'multiply', description: 'Multiply two numbers', parameters: { type: 'object', required: ['a', 'b'], properties: { a: { type: 'integer', description: 'The first number' }, b: { type: 'integer', description: 'The second number' }, }, }, }, }, ] async function agentLoop() { const messages = [{ role: 'user', content: 'What is (11434+12341)*412?' }] while (true) { const response = await ollama.chat({ model: 'qwen3', messages, tools, think: true, }) messages.push(response.message) console.log('Thinking:', response.message.thinking) console.log('Content:', response.message.content) const toolCalls = response.message.tool_calls ?? [] if (toolCalls.length) { for (const call of toolCalls) { const fn = availableFunctions[call.function.name as ToolName] if (!fn) { continue } const args = call.function.arguments as { a: number; b: number } console.log(`Calling ${call.function.name} with arguments`, args) const result = fn(args.a, args.b) console.log(`Result: ${result}`) messages.push({ role: 'tool', tool_name: call.function.name, content: String(result) }) } } else { break } } } agentLoop().catch(console.error) ``` ## Tool calling with streaming When streaming, gather every chunk of `thinking`, `content`, and `tool_calls`, then return those fields together with any tool results in the follow-up request. ```python theme={"system"} from ollama import chat def get_temperature(city: str) -> str: """Get the current temperature for a city Args: city: The name of the city Returns: The current temperature for the city """ temperatures = { 'New York': '22°C', 'London': '15°C', } return temperatures.get(city, 'Unknown') messages = [{'role': 'user', 'content': "What is the temperature in New York?"}] while True: stream = chat( model='qwen3', messages=messages, tools=[get_temperature], stream=True, think=True, ) thinking = '' content = '' tool_calls = [] done_thinking = False # accumulate the partial fields for chunk in stream: if chunk.message.thinking: thinking += chunk.message.thinking print(chunk.message.thinking, end='', flush=True) if chunk.message.content: if not done_thinking: done_thinking = True print('\n') content += chunk.message.content print(chunk.message.content, end='', flush=True) if chunk.message.tool_calls: tool_calls.extend(chunk.message.tool_calls) print(chunk.message.tool_calls) # append accumulated fields to the messages if thinking or content or tool_calls: messages.append({'role': 'assistant', 'thinking': thinking, 'content': content, 'tool_calls': tool_calls}) if not tool_calls: break for call in tool_calls: if call.function.name == 'get_temperature': result = get_temperature(**call.function.arguments) else: result = 'Unknown tool' messages.append({'role': 'tool', 'tool_name': call.function.name, 'content': result}) ``` ```typescript theme={"system"} import ollama from 'ollama' function getTemperature(city: string): string { const temperatures: Record = { 'New York': '22°C', 'London': '15°C', } return temperatures[city] ?? 'Unknown' } const getTemperatureTool = { type: 'function', function: { name: 'get_temperature', description: 'Get the current temperature for a city', parameters: { type: 'object', required: ['city'], properties: { city: { type: 'string', description: 'The name of the city' }, }, }, }, } async function agentLoop() { const messages = [{ role: 'user', content: "What is the temperature in New York?" }] while (true) { const stream = await ollama.chat({ model: 'qwen3', messages, tools: [getTemperatureTool], stream: true, think: true, }) let thinking = '' let content = '' const toolCalls: any[] = [] let doneThinking = false for await (const chunk of stream) { if (chunk.message.thinking) { thinking += chunk.message.thinking process.stdout.write(chunk.message.thinking) } if (chunk.message.content) { if (!doneThinking) { doneThinking = true process.stdout.write('\n') } content += chunk.message.content process.stdout.write(chunk.message.content) } if (chunk.message.tool_calls?.length) { toolCalls.push(...chunk.message.tool_calls) console.log(chunk.message.tool_calls) } } if (thinking || content || toolCalls.length) { messages.push({ role: 'assistant', thinking, content, tool_calls: toolCalls } as any) } if (!toolCalls.length) { break } for (const call of toolCalls) { if (call.function.name === 'get_temperature') { const args = call.function.arguments as { city: string } const result = getTemperature(args.city) messages.push({ role: 'tool', tool_name: call.function.name, content: result } ) } else { messages.push({ role: 'tool', tool_name: call.function.name, content: 'Unknown tool' } ) } } } } agentLoop().catch(console.error) ``` This loop streams the assistant response, accumulates partial fields, passes them back together, and appends the tool results so the model can complete its answer. ## Using functions as tools with Ollama Python SDK The Python SDK automatically parses functions as a tool schema so we can pass them directly. Schemas can still be passed if needed. ````pyx theme={"system"} from ollama import chat def get_temperature(city: str) -> str: """Get the current temperature for a city Args: city: The name of the city Returns: The current temperature for the city """ temperatures = { 'New York': '22°C', 'London': '15°C', } return temperatures.get(city, 'Unknown') available_functions = { 'get_temperature': get_temperature, } # directly pass the function as part of the tools list response = chat(model='qwen3', messages=messages, tools=available_functions.values(), think=True) `` --- ```` Start Ollama: ```shell theme={"system"} ollama serve ``` In another terminal, verify that Ollama is running: ```shell theme={"system"} ollama -v ``` ### AMD GPU install If you have an AMD GPU, also download and extract the additional ROCm package: ```shell theme={"system"} curl -fsSL https://ollama.com/download/ollama-linux-amd64-rocm.tar.zst \ | sudo tar x -C /usr ``` ### ARM64 install Download and extract the ARM64-specific package: ```shell theme={"system"} curl -fsSL https://ollama.com/download/ollama-linux-arm64.tar.zst \ | sudo tar x -C /usr ``` ### Adding Ollama as a startup service (recommended) Create a user and group for Ollama: ```shell theme={"system"} sudo useradd -r -s /bin/false -U -m -d /usr/share/ollama ollama sudo usermod -a -G ollama $(whoami) ``` Create a service file in `/etc/systemd/system/ollama.service`: ```ini theme={"system"} [Unit] Description=Ollama Service After=network-online.target [Service] ExecStart=/usr/bin/ollama serve User=ollama Group=ollama Restart=always RestartSec=3 Environment="PATH=$PATH" [Install] WantedBy=multi-user.target ``` > ## Documentation Index > Fetch the complete documentation index at: https://docs.ollama.com/llms.txt > Use this file to discover all available pages before exploring further. # Embeddings > Generate text embeddings for semantic search, retrieval, and RAG. Embeddings turn text into numeric vectors you can store in a vector database, search with cosine similarity, or use in RAG pipelines. The vector length depends on the model (typically 384–1024 dimensions). ## Recommended models * [embeddinggemma](https://ollama.com/library/embeddinggemma) * [qwen3-embedding](https://ollama.com/library/qwen3-embedding) * [all-minilm](https://ollama.com/library/all-minilm) ## Generate embeddings Generate embeddings directly from the command line: ```shell theme={"system"} ollama run embeddinggemma "Hello world" ``` You can also pipe text to generate embeddings: ```shell theme={"system"} echo "Hello world" | ollama run embeddinggemma ``` Output is a JSON array. ```shell theme={"system"} curl -X POST http://localhost:11434/api/embed \ -H "Content-Type: application/json" \ -d '{ "model": "embeddinggemma", "input": "The quick brown fox jumps over the lazy dog." }' ``` ```python theme={"system"} import ollama single = ollama.embed( model='embeddinggemma', input='The quick brown fox jumps over the lazy dog.' ) print(len(single['embeddings'][0])) # vector length ``` ```javascript theme={"system"} import ollama from 'ollama' const single = await ollama.embed({ model: 'embeddinggemma', input: 'The quick brown fox jumps over the lazy dog.', }) console.log(single.embeddings[0].length) // vector length ``` The `/api/embed` endpoint returns L2‑normalized (unit‑length) vectors. ## Generate a batch of embeddings Pass an array of strings to `input`. ```shell theme={"system"} curl -X POST http://localhost:3000/api/embed \ -H "Content-Type: application/json" \ -d '{ "model": "embeddinggemma", "input": [ "First sentence", "Second sentence", "Third sentence" ] }' ``` ```python theme={"system"} import ollama batch = ollama.embed( model='embeddinggemma', input=[ 'The quick brown fox jumps over the lazy dog.', 'The five boxing wizards jump quickly.', 'Jackdaws love my big sphinx of quartz.', ] ) print(len(batch['embeddings'])) # number of vectors ``` ```javascript theme={"system"} import ollama from 'ollama' const batch = await ollama.embed({ model: 'embeddinggemma', input: [ 'The quick brown fox jumps over the lazy dog.', 'The five boxing wizards jump quickly.', 'Jackdaws love my big sphinx of quartz.', ], }) console.log(batch.embeddings.length) // number of vectors ``` ## Tips * Use cosine similarity for most semantic search use cases. * Use the same embedding model for both indexing and querying. Then start the service: ```shell theme={"system"} sudo systemctl daemon-reload sudo systemctl enable ollama ``` ### Install CUDA drivers (optional) [Download and install](https://developer.nvidia.com/cuda-downloads) CUDA. Verify that the drivers are installed by running the following command, which should print details about your GPU: ```shell theme={"system"} nvidia-smi ``` ### Install AMD ROCm drivers (optional) [Download and Install](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html) ROCm v7. ### Start Ollama Start Ollama and verify it is running: ```shell theme={"system"} sudo systemctl start ollama 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](https://www.amd.com/en/support/linux-drivers) for best support of your Radeon GPU. ## Customizing To customize the installation of Ollama, you can edit the systemd service file or the environment variables by running: ```shell theme={"system"} sudo systemctl edit ollama ``` Alternatively, create an override file manually in `/etc/systemd/system/ollama.service.d/override.conf`: ```ini theme={"system"} [Service] Environment="OLLAMA_DEBUG=1" ``` ## Updating Update Ollama by running the install script again: ```shell theme={"system"} curl -fsSL https://ollama.com/install.sh | sh ``` Or by re-downloading Ollama: ```shell theme={"system"} curl -fsSL https://ollama.com/download/ollama-linux-amd64.tar.zst \ | sudo tar x -C /usr ``` ## Installing specific versions Use `OLLAMA_VERSION` environment variable with the install script to install a specific version of Ollama, including pre-releases. You can find the version numbers in the [releases page](https://github.com/ollama/ollama/releases). For example: ```shell theme={"system"} curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.5.7 sh ``` ## Viewing logs To view logs of Ollama running as a startup service, run: ```shell theme={"system"} journalctl -e -u ollama ``` > ## Documentation Index > Fetch the complete documentation index at: https://docs.ollama.com/llms.txt > Use this file to discover all available pages before exploring further. # Modelfile Reference A Modelfile is the blueprint to create and share customized models using Ollama. ## Table of Contents * [Format](#format) * [Examples](#examples) * [Instructions](#instructions) * [FROM (Required)](#from-required) * [Build from existing model](#build-from-existing-model) * [Build from a Safetensors model](#build-from-a-safetensors-model) * [Build from a GGUF file](#build-from-a-gguf-file) * [PARAMETER](#parameter) * [Valid Parameters and Values](#valid-parameters-and-values) * [TEMPLATE](#template) * [Template Variables](#template-variables) * [SYSTEM](#system) * [ADAPTER](#adapter) * [LICENSE](#license) * [MESSAGE](#message) * [Notes](#notes) ## Format The format of the `Modelfile`: ``` # comment INSTRUCTION arguments ``` | Instruction | Description | | ----------------------------------- | -------------------------------------------------------------- | | [`FROM`](#from-required) (required) | Defines the base model to use. | | [`PARAMETER`](#parameter) | Sets the parameters for how Ollama will run the model. | | [`TEMPLATE`](#template) | The full prompt template to be sent to the model. | | [`SYSTEM`](#system) | Specifies the system message that will be set in the template. | | [`ADAPTER`](#adapter) | Defines the (Q)LoRA adapters to apply to the model. | | [`LICENSE`](#license) | Specifies the legal license. | | [`MESSAGE`](#message) | Specify message history. | | [`REQUIRES`](#requires) | Specify the minimum version of Ollama required by the model. | ## Examples ### Basic `Modelfile` An example of a `Modelfile` creating a mario blueprint: ```Modelfile FROM llama3.2 # sets the temperature to 1 [higher is more creative, lower is more coherent] PARAMETER temperature 1 # sets the context window size to 4096, this controls how many tokens the LLM can use as context to generate the next token PARAMETER num_ctx 4096 # sets a custom system message to specify the behavior of the chat assistant SYSTEM You are Mario from super mario bros, acting as an assistant. ``` To use this: 1. Save it as a file (e.g. `Modelfile`) 2. `ollama create choose-a-model-name -f ` 3. `ollama run choose-a-model-name` 4. Start using the model! To view the Modelfile of a given model, use the `ollama show --modelfile` command. ```shell theme={"system"} ollama show --modelfile llama3.2 ``` ```cmake # Modelfile generated by "ollama show" # To build a new Modelfile based on this one, replace the FROM line with: # FROM llama3.2:latest FROM /Users/pdevine/.ollama/models/blobs/sha256-00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29 TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|> {{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|> {{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|> {{ .Response }}<|eot_id|>""" PARAMETER stop "<|start_header_id|>" PARAMETER stop "<|end_header_id|>" PARAMETER stop "<|eot_id|>" PARAMETER stop "<|reserved_special_token" ``` ## Instructions ### FROM (Required) The `FROM` instruction defines the base model to use when creating a model. ``` FROM : ``` #### Build from existing model ``` FROM llama3.2 ``` A list of available base models Additional models can be found at #### Build from a Safetensors model ``` FROM ``` The model directory should contain the Safetensors weights for a supported architecture. Currently supported model architectures: * Llama (including Llama 2, Llama 3, Llama 3.1, and Llama 3.2) * Mistral (including Mistral 1, Mistral 2, and Mixtral) * Gemma (including Gemma 1 and Gemma 2) * Phi3 #### Build from a GGUF file ``` FROM ./ollama-model.gguf ``` The GGUF file location should be specified as an absolute path or relative to the `Modelfile` location. ### PARAMETER The `PARAMETER` instruction defines a parameter that can be set when the model is run. ``` PARAMETER ``` #### Valid Parameters and Values | Parameter | Description | Value Type | Example Usage | | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | -------------------- | | 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 | | 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:" | | num\_predict | Maximum number of tokens to predict when generating text. (Default: -1, infinite generation) | int | num\_predict 42 | | 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 | ### TEMPLATE `TEMPLATE` of the full prompt template to be passed into the model. It may include (optionally) a system message, a user's message and the response from the model. Note: syntax may be model specific. Templates use Go [template syntax](https://pkg.go.dev/text/template). #### Template Variables | Variable | Description | | ----------------- | --------------------------------------------------------------------------------------------- | | `{{ .System }}` | The system message used to specify custom behavior. | | `{{ .Prompt }}` | The user prompt message. | | `{{ .Response }}` | The response from the model. When generating a response, text after this variable is omitted. | ``` TEMPLATE """{{ if .System }}<|im_start|>system {{ .System }}<|im_end|> {{ end }}{{ if .Prompt }}<|im_start|>user {{ .Prompt }}<|im_end|> {{ end }}<|im_start|>assistant """ ``` ### SYSTEM The `SYSTEM` instruction specifies the system message to be used in the template, if applicable. ``` SYSTEM """""" ``` ### ADAPTER The `ADAPTER` instruction specifies a fine tuned LoRA adapter that should apply to the base model. The value of the adapter should be an absolute path or a path relative to the Modelfile. The base model should be specified with a `FROM` instruction. If the base model is not the same as the base model that the adapter was tuned from the behaviour will be erratic. #### Safetensor adapter ``` ADAPTER ``` Currently supported Safetensor adapters: * Llama (including Llama 2, Llama 3, and Llama 3.1) * Mistral (including Mistral 1, Mistral 2, and Mixtral) * Gemma (including Gemma 1 and Gemma 2) #### GGUF adapter ``` ADAPTER ./ollama-lora.gguf ``` ### LICENSE The `LICENSE` instruction allows you to specify the legal license under which the model used with this Modelfile is shared or distributed. ``` LICENSE """ """ ``` ### MESSAGE The `MESSAGE` instruction allows you to specify a message history for the model to use when responding. Use multiple iterations of the MESSAGE command to build up a conversation which will guide the model to answer in a similar way. ``` MESSAGE ``` #### Valid roles | Role | Description | | --------- | ------------------------------------------------------------ | | system | Alternate way of providing the SYSTEM message for the model. | | user | An example message of what the user could have asked. | | assistant | An example message of how the model should respond. | #### Example conversation ``` MESSAGE user Is Toronto in Canada? MESSAGE assistant yes MESSAGE user Is Sacramento in Canada? MESSAGE assistant no MESSAGE user Is Ontario in Canada? MESSAGE assistant yes ``` ### REQUIRES The `REQUIRES` instruction allows you to specify the minimum version of Ollama required by the model. ``` REQUIRES `` The version should be a valid Ollama version (e.g. 0.14.0). ## Notes * the **`Modelfile` is not case sensitive**. In the examples, uppercase instructions are used to make it easier to distinguish it from arguments. * Instructions can be in any order. In the examples, the `FROM` instruction is first to keep it easily readable. [1]: https://ollama.com/library Remove the ollama service: ```shell theme={"system"} sudo systemctl stop ollama sudo systemctl disable ollama sudo rm /etc/systemd/system/ollama.service ``` Remove ollama libraries from your lib directory (either `/usr/local/lib`, `/usr/lib`, or `/lib`): ```shell theme={"system"} sudo rm -r $(which ollama | tr 'bin' 'lib') ``` Remove the ollama binary from your bin directory (either `/usr/local/bin`, `/usr/bin`, or `/bin`): ```shell theme={"system"} sudo rm $(which ollama) ``` Remove the downloaded models and Ollama service user and group: ```shell theme={"system"} sudo userdel ollama sudo groupdel ollama sudo rm -r /usr/share/ollama ``` > ## Documentation Index > Fetch the complete documentation index at: https://docs.ollama.com/llms.txt > Use this file to discover all available pages before exploring further. # Structured Outputs Structured outputs let you enforce a JSON schema on model responses so you can reliably extract structured data, describe images, or keep every reply consistent. ## Generating structured JSON ```shell theme={"system"} curl -X POST http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{ "model": "gpt-oss", "messages": [{"role": "user", "content": "Tell me about Canada in one line"}], "stream": false, "format": "json" }' ``` ```python theme={"system"} from ollama import chat response = chat( model='gpt-oss', messages=[{'role': 'user', 'content': 'Tell me about Canada.'}], format='json' ) print(response.message.content) ``` ```javascript theme={"system"} import ollama from 'ollama' const response = await ollama.chat({ model: 'gpt-oss', messages: [{ role: 'user', content: 'Tell me about Canada.' }], format: 'json' }) console.log(response.message.content) ``` > ## Documentation Index > Fetch the complete documentation index at: https://docs.ollama.com/llms.txt > Use this file to discover all available pages before exploring further. # Docker ## CPU only ```shell theme={"system"} docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama `` ## Nvidia GPU Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation). ### Install with Apt 1. Configure the repository ```shell theme={"system"} curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \ | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg curl -fsSL https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \ | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' \ | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list sudo apt-get update ``` 2. Install the NVIDIA Container Toolkit packages ```shell theme={"system"} sudo apt-get install -y nvidia-container-toolkit ``` ### Install with Yum or Dnf 1. Configure the repository ```shell theme={"system"} curl -fsSL https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo \ | sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo ``` 2. Install the NVIDIA Container Toolkit packages ```shell theme={"system"} sudo yum install -y nvidia-container-toolkit ``` ### Configure Docker to use Nvidia driver ```shell theme={"system"} sudo nvidia-ctk runtime configure --runtime=docker sudo systemctl restart docker ``` ### Start the container ```shell theme={"system"} docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama `` If you're running on an NVIDIA JetPack system, Ollama can't automatically discover the correct JetPack version. Pass the environment variable `JETSON_JETPACK=5` or `JETSON_JETPACK=6` to the container to select version 5 or 6. ## AMD GPU To run Ollama using Docker with AMD GPUs, use the `rocm` tag and the following command: ```shell theme={"system"} docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama:rocm ``` ## Vulkan Support Vulkan is bundled into the `ollama/ollama` image. ```shell theme={"system"} docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 11434:11434 -e OLLAMA_VULKAN=1 --name ollama ollama/ollama ``` ## Run model locally Now you can run a model: ```shell theme={"system"} docker exec -it ollama ollama run llama3.2 ``` ## Try different models More models can be found on the [Ollama library](https://ollama.com/library). ## Generating structured JSON with a schema Provide a JSON schema to the `format` field. It is ideal to also pass the JSON schema as a string in the prompt to ground the model's response. ```shell theme={"system"} curl -X POST http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{ "model": "gpt-oss", "messages": [{"role": "user", "content": "Tell me about Canada."}], "stream": false, "format": { "type": "object", "properties": { "name": {"type": "string"}, "capital": {"type": "string"}, "languages": { "type": "array", "items": {"type": "string"} } }, "required": ["name", "capital", "languages"] } }' ``` Use Pydantic models and pass `model_json_schema()` to `format`, then validate the response: ```python theme={"system"} from ollama import chat from pydantic import BaseModel class Country(BaseModel): name: str capital: str languages: list[str] response = chat( model='gpt-oss', messages=[{'role': 'user', 'content': 'Tell me about Canada.'}], format=Country.model_json_schema(), ) country = Country.model_validate_json(response.message.content) print(country) ``` Serialize a Zod schema with `zodToJsonSchema()` and parse the structured response: ```javascript theme={"system"} import ollama from 'ollama' import { z } from 'zod' import { zodToJsonSchema } from 'zod-to-json-schema' const Country = z.object({ name: z.string(), capital: z.string(), languages: z.array(z.string()), }) const response = await ollama.chat({ model: 'gpt-oss', messages: [{ role: 'user', content: 'Tell me about Canada.' }], format: zodToJsonSchema(Country), }) const country = Country.parse(JSON.parse(response.message.content)) console.log(country) `` ## Example: Extract structured data Define the objects you want returned and let the model populate the fields: ```python theme={"system"} from ollama import chat from pydantic import BaseModel class Pet(BaseModel): name: str animal: str age: int color: str | None favorite_toy: str | None class PetList(BaseModel): pets: list[Pet] response = chat( model='gpt-oss', messages=[{'role': 'user', 'content': 'I have two cats named Luna and Loki...'}], format=PetList.model_json_schema(), ) pets = PetList.model_validate_json(response.message.content) print(pets) ``` ## Example: Vision with structured outputs Vision models accept the same `format` parameter, enabling deterministic descriptions of images: ```python theme={"system"} from ollama import chat from pydantic import BaseModel from typing import Literal, Optional class Object(BaseModel): name: str confidence: float attributes: str class ImageDescription(BaseModel): summary: str objects: list[Object] scene: str colors: list[str] time_of_day: Literal['Morning', 'Afternoon', 'Evening', 'Night'] setting: Literal['Indoor', 'Outdoor', 'Unknown'] text_content: Optional[str] = None response = chat( model='gemma3', messages=[{ 'role': 'user', 'content': 'Describe this photo and list the objects you detect.', 'images': ['path/to/image.jpg'], }], format=ImageDescription.model_json_schema(), options={'temperature': 0}, ) image_description = ImageDescription.model_validate_json(response.message.content) print(image_description) `` ## Tips for reliable structured outputs * Define schemas with Pydantic (Python) or Zod (JavaScript) so they can be reused for validation. * Lower the temperature (e.g., set it to `0`) for more deterministic completions. * Structured outputs work through the OpenAI-compatible API via `response_format` > ## Documentation Index > Fetch the complete documentation index at: https://docs.ollama.com/llms.txt > Use this file to discover all available pages before exploring further. # Quickstart Ollama is available on macOS, Windows, and Linux. Download Ollama ## Get Started Run `ollama` in your terminal to open the interactive menu: ```sh theme={"system"} ollama ``` Navigate with `↑/↓`, press `enter` to launch, `→` to change model, and `esc` to quit. The menu provides quick access to: * **Run a model** - Start an interactive chat * **Launch tools** - Claude Code, Codex, OpenClaw, and more * **Additional integrations** - Available under "More..." ## Assistants Launch [OpenClaw](/integrations/openclaw), a personal AI with 100+ skills: ```sh theme={"system"} ollama launch openclaw ``` ## Coding Launch [Claude Code](/integrations/claude-code) and other coding tools with Ollama models: ```sh theme={"system"} ollama launch claude ``` ```sh theme={"system"} ollama launch codex ``` ```sh theme={"system"} ollama launch opencode ``` See [integrations](/integrations) for all supported tools. ## API Use the [API](/api) to integrate Ollama into your applications: ```sh theme={"system"} curl http://localhost:11434/api/chat -d '{ "model": "gemma3", "messages": [{ "role": "user", "content": "Hello!" }] }' ``` See the [API documentation](/api) for Python, JavaScript, and other integrations. > ## Documentation Index > Fetch the complete documentation index at: https://docs.ollama.com/llms.txt > Use this file to discover all available pages before exploring further. # Ollama's documentation

[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. Get up and running with your first model or integrate Ollama with your favorite tools Download Ollama on macOS, Windows or Linux Ollama's cloud models offer larger models with better performance. View Ollama's API reference ## Libraries The official library for using Ollama with Python The official library for using Ollama with JavaScript or TypeScript. View a list of 20+ community-supported libraries for Ollama ## Community Join our Discord community Join our Reddit community > ## Documentation Index > Fetch the complete documentation index at: https://docs.ollama.com/llms.txt > Use this file to discover all available pages before exploring further. # 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 fine tuned adapter from Safetensors weights First, create a `Modelfile` with a `FROM` command pointing at the base model you used for fine tuning, and an `ADAPTER` command which points to the directory with your Safetensors adapter: ```dockerfile theme={"system"} FROM ADAPTER /path/to/safetensors/adapter/directory ``` Make sure that you use the same base model in the `FROM` command as you used to create the adapter otherwise you will get erratic results. Most frameworks use different quantization methods, so it's best to use non-quantized (i.e. non-QLoRA) adapters. If your adapter is in the same directory as your `Modelfile`, use `ADAPTER .` to specify the adapter path. Now run `ollama create` from the directory where the `Modelfile` was created: ```shell theme={"system"} ollama create my-model ``` Lastly, test the model: ```shell theme={"system"} ollama run my-model ``` Ollama supports importing adapters based on several different model architectures including: * Llama (including Llama 2, Llama 3, Llama 3.1, and Llama 3.2); * Mistral (including Mistral 1, Mistral 2, and Mixtral); and * Gemma (including Gemma 1 and Gemma 2) You can create the adapter using a fine tuning framework or tool which can output adapters in the Safetensors format, such as: * Hugging Face [fine tuning framework](https://huggingface.co/docs/transformers/en/training) * [Unsloth](https://github.com/unslothai/unsloth) * [MLX](https://github.com/ml-explore/mlx) ## Importing a model from Safetensors weights First, create a `Modelfile` with a `FROM` command which points to the directory containing your Safetensors weights: ```dockerfile theme={"system"} FROM /path/to/safetensors/directory ``` If you create the Modelfile in the same directory as the weights, you can use the command `FROM .`. Now run the `ollama create` command from the directory where you created the `Modelfile`: ```shell theme={"system"} ollama create my-model ``` Lastly, test the model: ```shell theme={"system"} ollama run my-model ``` Ollama supports importing models for several different architectures including: * Llama (including Llama 2, Llama 3, Llama 3.1, and Llama 3.2); * Mistral (including Mistral 1, Mistral 2, and Mixtral); * Gemma (including Gemma 1 and Gemma 2); and * Phi3 This includes importing foundation models as well as any fine tuned models which have been *fused* with a foundation model. ## Importing a GGUF based model or adapter If you have a GGUF based model or adapter it is possible to import it into Ollama. You can obtain a GGUF model or adapter by: * converting a Safetensors model with the `convert_hf_to_gguf.py` from Llama.cpp; * converting a Safetensors adapter with the `convert_lora_to_gguf.py` from Llama.cpp; or * downloading a model or adapter from a place such as HuggingFace To import a GGUF model, create a `Modelfile` containing: ```dockerfile theme={"system"} FROM /path/to/file.gguf ``` For a GGUF adapter, create the `Modelfile` with: ```dockerfile theme={"system"} FROM ADAPTER /path/to/file.gguf ``` When importing a GGUF adapter, it's important to use the same base model as the base model that the adapter was created with. You can use: * a model from Ollama * a GGUF file * a Safetensors based model Once you have created your `Modelfile`, use the `ollama create` command to build the model. ```shell theme={"system"} ollama create my-model ``` ## Quantizing a Model Quantizing a model allows you to run models faster and with less memory consumption but at reduced accuracy. This allows you to run a model on more modest hardware. Ollama can quantize FP16 and FP32 based models into different quantization levels using the `-q/--quantize` flag with the `ollama create` command. First, create a Modelfile with the FP16 or FP32 based model you wish to quantize. ```dockerfile theme={"system"} FROM /path/to/my/gemma/f16/model ``` Use `ollama create` to then create the quantized model. ```shell theme={"system"} $ ollama create --quantize q4_K_M mymodel transferring model data quantizing F16 model to Q4_K_M creating new layer sha256:735e246cc1abfd06e9cdcf95504d6789a6cd1ad7577108a70d9902fef503c1bd creating new layer sha256:0853f0ad24e5865173bbf9ffcc7b0f5d56b66fd690ab1009867e45e7d2c4db0f writing manifest success ``` ### Supported Quantizations * `q8_0` #### K-means Quantizations * `q4_K_S` * `q4_K_M` ```cli ollama pull llama3.2 echo "FROM llama3.2" >> Modelfile echo "SYSTEM You are a friendly assistant." >> Modelfile ollama create -f Modelfile lmlm/Lmkm ollama push lmlm/Lmkm ``` ## Sharing your model on ollama.com You can share any model you have created by pushing it to [ollama.com](https://ollama.com) so that other users can try it out. First, use your browser to go to the [Ollama Sign-Up](https://ollama.com/signup) page. If you already have an account, you can skip this step. Sign-Up

The `Username` field will be used as part of your model's name (e.g. `jmorganca/mymodel`), so make sure you are comfortable with the username that you have selected. Now that you have created an account and are signed-in, go to the [Ollama Keys Settings](https://ollama.com/settings/keys) page. Follow the directions on the page to determine where your Ollama Public Key is located.

Click on the `Add Ollama Public Key` button, and copy and paste the contents of your Ollama Public Key into the text field. To push a model to [ollama.com](https://ollama.com), first make sure that it is named correctly with your username. You may have to use the `ollama cp` command to copy your model to give it the correct name. Once you're happy with your model's name, use the `ollama push` command to push it to [ollama.com](https://ollama.com). ```shell theme={"system"} ollama cp mymodel myuser/mymodel ollama push myuser/mymodel ``` Once your model has been pushed, other users can pull and run it by using the command: ```shell theme={"system"} ollama run myuser/mymodel ``` > ## Documentation Index > Fetch the complete documentation index at: https://docs.ollama.com/llms.txt > Use this file to discover all available pages before exploring further. # Overview Ollama integrates with a wide range of tools. ## Coding Agents Coding assistants that can read, modify, and execute code in your projects. * [Claude Code](/integrations/claude-code) * [Codex](/integrations/codex) * [OpenCode](/integrations/opencode) * [Droid](/integrations/droid) * [Goose](/integrations/goose) * [Pi](/integrations/pi) ## Assistants AI assistants that help with everyday tasks. * [OpenClaw](/integrations/openclaw) ## IDEs & Editors Native integrations for popular development environments. * [VS Code](/integrations/vscode) * [Cline](/integrations/cline) * [Roo Code](/integrations/roo-code) * [JetBrains](/integrations/jetbrains) * [Xcode](/integrations/xcode) * [Zed](/integrations/zed) ## Chat & RAG Chat interfaces and retrieval-augmented generation platforms. * [Onyx](/integrations/onyx) ```curl curl http://localhost:11434/api/chat \ -d '{ "model": "lmlm/Lmkm", "messages": [{"role": "user", "content": "Hello!"}] }' ``` ## Automation Workflow automation platforms with AI integration. * [n8n](/integrations/n8n) ## Notebooks Interactive computing environments with AI capabilities. * [marimo](/integrations/marimo) * > ## Documentation Index > Fetch the complete documentation index at: https://docs.ollama.com/llms.txt > Use this file to discover all available pages before exploring further. # Droid ## Install Install the [Droid CLI](https://factory.ai/): ```bash theme={"system"} curl -fsSL https://app.factory.ai/cli | sh ``` Droid requires a larger context window. It is recommended to use a context window of at least 64k tokens. See [Context length](/context-length) for more information. ## Usage with Ollama ### Quick setup ```bash theme={"system"} ollama launch droid ollama cp llama3.2 lmlm/Lmkm ollama push lmlm/Lmkm ``` To configure without launching: ```shell theme={"system"} ollama launch droid --config ``` ### Manual setup Add a local configuration block to `~/.factory/config.json`: ```json theme={"system"} { "custom_models": [ { "model_display_name": "qwen3-coder [Ollama]", "model": "qwen3-coder", "base_url": "http://localhost:11434/v1/", "api_key": "not-needed", "provider": "generic-chat-completion-api", "max_tokens": 32000 } ] } ``` ## Cloud Models `qwen3-coder:480b-cloud` is the recommended model for use with Droid. Add the cloud configuration block to `~/.factory/config.json`: ```json theme={"system"} { "custom_models": [ { "model_display_name": "qwen3-coder [Ollama Cloud]", "model": "qwen3-coder:480b-cloud", "base_url": "http://localhost:11434/v1/", "api_key": "not-needed", "provider": "generic-chat-completion-api", "max_tokens": 128000 } ] } ``` ## Connecting to ollama.com 1. Create an [API key](https://ollama.com/settings/keys) from ollama.com and export it as `OLLAMA_API_KEY`. 2. Add the cloud configuration block to `~/.factory/config.json`: ```json theme={"system"} { "custom_models": [ { "model_display_name": "qwen3-coder [Ollama Cloud]", "model": "qwen3-coder:480b", "base_url": "https://ollama.com/v1/", "api_key": "OLLAMA_API_KEY", "provider": "generic-chat-completion-api", "max_tokens": 128000 } ] } ``` Run `droid` in a new terminal to load the new settings.