## New models - [Llama 3.3](https://ollama.com/library/llama3.3): a new state of the art 70B model. Llama 3.3 70B offers similar performance compared to Llama 3.1 405B model. - [Snowflake Arctic Embed 2](https://ollama.com/library/snowflake-arctic-embed2): Snowflake's frontier embedding model. Arctic Embed 2.0 adds multilingual support without sacrificing English performance or scalability. ## Structured outputs Ollama now supports structured outputs, making it possible to constrain a model's output to a specific format defined by a JSON schema. The Ollama Python and JavaScript libraries have been updated to support structured outputs, together with Ollama's OpenAI-compatible API endpoints. ### REST API To use structured outputs in Ollama's generate or chat APIs, provide a JSON schema object in the `format` parameter: ```shell curl -X POST http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{ "model": "llama3.1", "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" ] } }' ``` ### Python library Using the [Ollama Python library](https://github.com/ollama/ollama-python), pass in the schema as a JSON object to the `format` parameter as either `dict` or use Pydantic (recommended) to serialize the schema using `model_json_schema()`. ```py from ollama import chat from pydantic import BaseModel class Country(BaseModel): name: str capital: str languages: list[str] response = chat( messages=[ { 'role': 'user', 'content': 'Tell me about Canada.', } ], model='llama3.1', format=Country.model_json_schema(), ) country = Country.model_validate_json(response.message.content) print(country) ``` ### JavaScript library Using the [Ollama JavaScript library](https://github.com/ollama/ollama-js), pass in the schema as a JSON object to the `format` parameter as either `object` or use Zod (recommended) to serialize the schema using `zodToJsonSchema()`: ```js 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: 'llama3.1', messages: [{ role: 'user', content: 'Tell me about Canada.' }], format: zodToJsonSchema(Country), }); const country = Country.parse(JSON.parse(response.message.content)); console.log(country); ``` ## What's Changed * Fixed error importing model vocabulary files * Experimental: new flag to set KV cache quantization to 4-bit (`q4_0`), 8-bit (`q8_0`) or 16-bit (`f16`). This reduces VRAM requirements for longer context windows. * To enable for all models, use `OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q4_0 ollama serve` * Note: in the future flash attention will be enabled by default where available, with kv cache quantization available on a per-model basis * Thank you @sammcj for the contribution in in https://github.com/ollama/ollama/pull/7926 ## New Contributors * @dmayboroda made their first contribution in https://github.com/ollama/ollama/pull/7906 * @Geometrein made their first contribution in https://github.com/ollama/ollama/pull/7908 * @owboson made their first contribution in https://github.com/ollama/ollama/pull/7693 **Full Changelog**: https://github.com/ollama/ollama/compare/v0.4.7...v0.5.0

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