Ollama Knowledge Patch

Ollama Knowledge Patch

Use this skill when implementing, reviewing, or troubleshooting Ollama model creation, native or compatibility APIs, integrations, cloud tools, acceleration, image generation, and server scheduling. Prefer the project's installed Ollama version and observed behavior when they differ from this guidance.

Reference index

Breaking changes and deprecations

Keep image workflows on 0.32.5

Ollama 0.32.6 temporarily removes experimental image generation. Pin 0.32.5 for workflows that require image output, and do not upgrade until a later release explicitly restores it. See acceleration, images, and scheduling.

Skip the withdrawn release

Do not deploy 0.32.2. Install 0.32.3 or newer instead. If image generation is also required, observe the separate 0.32.5 pin above.

Update streaming parsers

From 0.32.6, OpenAI-compatible chat streams emit role only in the first chunk, put finish_reason in its own chunk, and optionally put usage in a separate chunk. A truncated response ends with "length", not "tool_calls". Do not assume all chunk fields arrive together.

Use the current embeddings endpoint

Prefer native POST /api/embed over superseded /api/embeddings. The current endpoint accepts one string or a list, returns a matrix, supports output dimensions, and can reject rather than truncate overlong input.

Account for launcher renames and warnings

The former Codex App integration is exposed as ChatGPT through ollama launch chatgpt; --restore selects the usual ChatGPT profile. Older CodeLlama, Qwen2.5, Qwen2.5-coder, Llama 3.x, Mistral, StarCoder, and base DeepSeek-R1 tags produce a deprecation warning before continuing.

Model creation quick reference

Import GGUF artifacts

A Modelfile FROM may point to one GGUF file or a directory of GGUF files.

FROM ./my-model.Q4_K_M.gguf

ollama create -f Modelfile my-model
ollama run my-model

If the GGUF supports tools, verify that ollama show my-model reports the tools capability before selecting it in an integration.

Import Safetensors and adapters

Use FROM . when a Modelfile sits beside supported Safetensors weights. Use ADAPTER with a Safetensors directory or GGUF adapter file, and keep FROM matched to the exact fine-tuning base. Prefer non-quantized Safetensors adapters over QLoRA imports because framework quantization differs.

Quantize during creation

For an FP16 or FP32 source, pass -q or --quantize to ollama create. The native create API accepts q4_K_M, q4_K_S, and q8_0; q4_K_M and q8_0 are the recommended choices.

ollama create --quantize q4_K_M my-model

Use Modelfile REQUIRES when an artifact depends on a minimum Ollama version. See model creation for complete import and adapter rules.

Native API quick reference

Control reasoning and tool history

For /api/generate and /api/chat, think accepts a boolean or "low", "medium", "high", or "max". Chat returns reasoning separately in message.thinking. Append executed function results as role tool messages with tool_name so streamed calls can be matched to their results.

{"role":"tool","content":"11 degrees celsius","tool_name":"get_weather"}

Upload before creating

Upload GGUF or Safetensors content to POST /api/blobs/sha256:<digest>, optionally check it with HEAD, and map filenames to digests in /api/create under files or adapters.

Bound server work

Use OLLAMA_MAX_LOADED_MODELS, OLLAMA_NUM_PARALLEL, and OLLAMA_MAX_QUEUE to cap residency, per-model parallelism, and queued work. Expect HTTP 503 after the queue limit. Parallelism multiplies each model's context allocation and memory requirement.

With Flash Attention, OLLAMA_KV_CACHE_TYPE=q8_0 uses roughly half the default f16 cache memory, while q4_0 uses roughly one quarter with more quality loss. See native API and server operation.

Compatibility API quick reference

Point clients at the right base URL

Anthropic Messages clients connect at http://localhost:11434; client-required credentials are ignored locally. OpenAI clients use http://localhost:11434/v1/, also with a placeholder API key when required by the client.

Stay inside the supported subset

OpenAI-compatible Chat Completions supports streaming usage, JSON mode, seeded output, tools, vision with base64 images, and reasoning effort. It does not support tool_choice, log probabilities, logit_bias, user, or n.

Responses requests are stateless: carry conversation state in the application because previous_response_id, conversation, and truncation are not supported. The compatibility image endpoint requires b64_json and is experimental. See compatibility APIs for endpoint-specific limits.

Derive a larger-context alias

The compatibility API has no request field for context size. Create and call a derived model instead:

FROM llama3.2
PARAMETER num_ctx 65536

ollama create mymodel

CLI and integration quick reference

Running bare ollama starts the interactive agent and supplies the current working directory as project context. Use ollama signin for authenticated web search or fetch. ollama launch shows the wider integration list, and ollama launch <integration> --config configures without starting it.

Give coding integrations at least 64,000 tokens of context. A local glm-4.7-flash at that context needs about 23 GB VRAM; cloud tags provide full-context alternatives. Gemma 4 is available as gemma4.

See CLI and integrations for commands, recommended tags, and cloud-only tag resolution.

Cloud and web quick reference

Sign in before using cloud tags. They work with normal run, pull, ls, and cp commands and with the local API, while inference executes remotely. Set OLLAMA_NO_CLOUD=1 or disable_ollama_cloud in the server configuration to disable both cloud models and web search for local-only operation.

Hosted search and fetch require an account API key and expose /api/web_search and /api/web_fetch. Python and JavaScript clients expose matching helpers that can be passed directly as chat tools. Allocate roughly 32K context or more to a standalone search agent because results can be large.

See cloud and web tools for request shapes, integration behavior, and the stdio MCP configuration.

Acceleration and scheduling quick reference

Vulkan acceleration is enabled by default on supported AMD and Intel hardware. MLX text loading respects OLLAMA_LOAD_TIMEOUT; MLX-specific NVFP4 tags run on Apple Silicon. Windows ARM64 CUDA and B200 through CUDA 12 are supported in the documented accelerator path.

New-engine models measure exact memory before loading. This improves GPU placement and multi-GPU scheduling and makes ollama ps memory reporting agree more closely with system GPU tools. Consult acceleration, images, and scheduling for the affected model families and image controls.