Ollama Knowledge Patch
Use this patch
- Identify whether the task concerns model creation, the native REST API, a compatibility API, integrations, cloud and web tools, image generation, or runtime scheduling.
- Read the matching reference before changing commands, environment variables, request shapes, response parsers, or deployment settings.
- Treat experimental image generation and MLX behavior as platform- and release-sensitive.
- Keep native Ollama endpoints separate from OpenAI- and Anthropic-compatible endpoints; their request fields and state behavior differ.
- Size context, parallelism, and the K/V cache together because each parallel request multiplies context memory.
Reference index
| Reference | Topics | | --- | --- | | releases-and-runtime.md | Changed defaults, withdrawn releases, accelerator support, runtime limits, exact-memory scheduling, streaming changes | | models-and-modelfiles.md | GGUF and Safetensors imports, adapters, quantization, model requirements, library tags, context sizing | | native-api.md | Thinking, tool results, blobs, model creation, embeddings, image generation, concurrency and cache controls | | compatibility-apis.md | Anthropic Messages, OpenAI Chat Completions, Completions, Embeddings, Responses, and Images compatibility | | cloud-integrations-and-web.md | Integration launcher, cloud models, hosted search and fetch, client helpers, MCP exposure | | image-generation.md | Experimental CLI image generation, interactive controls, native and compatibility endpoints, release availability |
Start with breaking changes and deprecations
Avoid the withdrawn release
Do not install or remain on Ollama 0.32.2. It was withdrawn; use 0.32.3 or newer.
Pin image-generation workflows
Ollama 0.32.6 temporarily removes experimental image generation. Workflows that require it must remain on 0.32.5 until a later release restores it.
Move native embeddings to /api/embed
Use POST /api/embed instead of the superseded /api/embeddings endpoint. The replacement accepts one string or a list in input, returns an embeddings matrix, supports dimensions, and truncates by default. Set truncate: false to reject overlong input.
curl http://localhost:11434/api/embed -d '{"model":"all-minilm","input":["first text","second text"],"truncate":false}'
Do not assume stateful Responses compatibility
The OpenAI-compatible /v1/responses endpoint does not support previous_response_id, conversation, or truncation. Applications must retain and resend their own conversation state.
Update streaming chunk parsers
OpenAI-compatible chat streams send role only in the first chunk, finish_reason in its own chunk, and requested usage in another chunk. A truncated response finishes with "length", not "tool_calls".
Expect launcher warnings for older model families
Launching CodeLlama, Qwen2.5 or Qwen2.5-coder, Llama 3.x, Mistral, StarCoder, or base DeepSeek-R1 tags emits a deprecation warning before continuing.
CLI and integration quick reference
Start the interactive agent
Running ollama with no subcommand opens the interactive agent and supplies the current working directory as project context. Sign in when web search or fetch needs authentication.
ollama
ollama signin
Configure without launching
ollama launch normally configures and starts an integration. Add --config to configure it only.
ollama launch opencode --config
Use the renamed ChatGPT launcher
Launch the former Codex App integration as chatgpt. --restore returns to the usual ChatGPT profile. Run ollama launch without an integration name to see the broader selection beyond the default popular menu.
ollama launch chatgpt
ollama launch chatgpt --restore
Allocate enough coding context
Give coding integrations at least 64,000 tokens. Suitable local tags include glm-4.7-flash, qwen3-coder, and gpt-oss:20b; full-context cloud options include glm-4.7:cloud, minimax-m2.1:cloud, gpt-oss:120b-cloud, and qwen3-coder:480b-cloud. At 64K, glm-4.7-flash needs about 23 GB of local VRAM.
Model creation quick reference
Import local weights
A Modelfile FROM can point to a GGUF file, a directory of GGUF files, or a supported Safetensors directory. When a Modelfile sits beside Safetensors weights, use FROM ..
FROM ./my-model.Q4_K_M.gguf
ollama create -f Modelfile my-model
ollama run my-model
Verify imported tool support
Tool calling remains available when the imported GGUF supports it. Confirm that ollama show my-model lists the tools capability before passing the model to an integration.
Apply adapters against the exact base
ADAPTER accepts a Safetensors directory or GGUF adapter file. FROM must name the exact fine-tuning base or output can be erratic. Prefer non-quantized Safetensors adapters over QLoRA adapters because framework quantization methods differ.
Quantize while creating
Use ollama create -q or --quantize with FP16 or FP32 source weights.
ollama create --quantize q4_K_M my-model
Declare model requirements
Use REQUIRES to set a model's minimum Ollama version.
FROM llama3.2
REQUIRES 0.14.0
Native API quick reference
Control thinking
/api/generate and /api/chat accept think as a boolean or "low", "medium", "high", or "max". Chat responses place reasoning separately in message.thinking.
Return named tool results
Append an executed function result as a tool message with tool_name, including when tool calls were streamed.
{"role":"tool","content":"11 degrees celsius","tool_name":"get_weather"}
Upload and create by digest
Upload GGUF or Safetensors content with POST /api/blobs/sha256:<digest>, check it with HEAD, then map filenames to digests in /api/create under files or adapters.
Quantize through /api/create
Set quantize to q4_K_M, q4_K_S, or q8_0 when creating from a non-quantized model. Prefer q4_K_M or q8_0.
Runtime quick reference
Disable cloud features
Set disable_ollama_cloud in ~/.ollama/server.json or start with OLLAMA_NO_CLOUD=1, then restart. This disables both cloud models and web search.
Bound parallel work
OLLAMA_MAX_LOADED_MODELS defaults to three times the GPU count, or three on CPU. OLLAMA_NUM_PARALLEL defaults to one per model, and OLLAMA_MAX_QUEUE defaults to 512 before excess work receives HTTP 503.
OLLAMA_MAX_LOADED_MODELS=2 OLLAMA_NUM_PARALLEL=4 OLLAMA_MAX_QUEUE=128 ollama serve
Reduce K/V cache memory
Flash Attention is selected automatically when supported and can be forced with OLLAMA_FLASH_ATTENTION=1 or disabled with 0. With it enabled, set OLLAMA_KV_CACHE_TYPE=q8_0 for roughly half the default f16 memory or q4_0 for roughly one quarter, accepting greater quality loss.
Trust measured new-engine memory
New-engine models measure exact memory before loading. This improves placement across multiple or mismatched GPUs and makes ollama ps memory agree more closely with device tools.
Compatibility API quick reference
Set client base URLs
Anthropic Messages clients connect at http://localhost:11434; OpenAI clients use http://localhost:11434/v1/. Clients require a token or API key, but Ollama ignores its value.
Respect unsupported fields
The Chat Completions subset does not support tool_choice, log probabilities, logit_bias, user, or n. Remote image URLs are unsupported; send base64 image data. Check the compatibility reference for endpoint-specific limits.
Set context through a derived model
The OpenAI-compatible API has no per-request context-size field. Put PARAMETER num_ctx in a Modelfile, create a derived model, and call that model name.
FROM llama3.2
PARAMETER num_ctx 65536
Cloud, web, image, and MLX quick reference
Use cloud tags locally
Sign in, pull a cloud tag, and use it through normal CLI, native API, and library calls; inference runs on ollama.com.
Authenticate hosted web tools
The hosted web_search and web_fetch endpoints require an ollama.com account API key as a bearer token. Python and JavaScript clients expose corresponding helpers that can be passed directly as chat tools.
Treat image generation as experimental
Image generation uses /api/generate or /v1/images/generations, and CLI prompting is available on supported macOS releases. Platform support, request fields, and release availability are narrower than text generation; read the image reference before depending on it.
Use MLX tags on Apple Silicon
MLX supports imported NVFP4 weights and dedicated tags such as qwen3.5:35b-a3b-coding-nvfp4 and gemma4:12b-mlx. The initial Qwen coding preview needs more than 32 GB of unified memory.