From ollama
Provides Ollama REST API reference for running local LLMs: text generation, chat, embeddings, tool calling, structured output, and model management.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ollama:ollamaThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Ollama runs large language models locally. It exposes a REST API on `http://localhost:11434` for text generation, chat, embeddings, model management, and more.
Ollama runs large language models locally. It exposes a REST API on http://localhost:11434 for text generation, chat, embeddings, model management, and more.
model:tag format (e.g., llama3.2:latest, orca-mini:3b-q8_0). Tag defaults to latest."stream": false.eval_count / eval_duration * 10^9.5m). Set to 0 to unload immediately, -1 to keep loaded indefinitely."think": true (or "high", "medium", "low", "max") to enable chain-of-thought reasoning. Reasoning text is returned separately in the thinking field."format" parameter: set to "json" for JSON mode, or pass a JSON Schema object./api/chat by providing a tools array (and in /api/generate; tool calls come back in tool_calls)./api/tags and /api/show return a capabilities array (e.g. completion, tools, vision, thinking, insert, embedding).draft_num_predict option controls draft tokens per step; /api/create accepts draft_files/draft_quantize for draft models."logprobs": true (and optional "top_logprobs", 0-20) on generate/chat to return token log probabilities./v1/chat/completions, /v1/completions, /v1/embeddings, /v1/models, /v1/responses and Anthropic-compatible /v1/messages are also served (native /api/* is preferred and documented here).| Method | Path | Description |
|---|---|---|
POST | /api/generate | Generate text completion (streaming) |
POST | /api/chat | Chat completion with message history (streaming) |
POST | /api/embed | Generate embeddings (single or batch) |
POST | /api/embeddings | Generate embeddings (legacy, deprecated) |
GET | /api/tags | List locally available models |
POST | /api/show | Show model details and metadata |
POST | /api/create | Create a model from Modelfile, GGUF, or safetensors |
POST | /api/pull | Pull/download a model from registry |
POST | /api/push | Push a model to registry |
POST | /api/copy | Copy/clone a model locally |
DELETE | /api/delete | Delete a model |
GET | /api/ps | List currently loaded/running models |
HEAD | /api/blobs/:digest | Check if a blob exists |
POST | /api/blobs/:digest | Upload a blob (for GGUF/safetensors creation) |
GET | /api/version | Get Ollama server version |
GET | /api/experimental/model-recommendations | Recommended models for this host (experimental) |
POST | /api/web_search | Web search via Ollama cloud (experimental, needs OLLAMA_API_KEY) |
POST | /api/web_fetch | Fetch a web page via Ollama cloud (experimental, needs OLLAMA_API_KEY) |
Cloud-account endpoints (GET /api/status, POST /api/me, POST /api/signout) exist for Ollama cloud sign-in but are not needed for local inference.
# Pull a model
curl http://localhost:11434/api/pull -d '{"model": "llama3.2"}'
# Generate text
curl http://localhost:11434/api/generate -d '{
"model": "llama3.2",
"prompt": "Why is the sky blue?",
"stream": false
}'
# Chat
curl http://localhost:11434/api/chat -d '{
"model": "llama3.2",
"messages": [{"role": "user", "content": "Hello!"}],
"stream": false
}'
# Embeddings
curl http://localhost:11434/api/embed -d '{
"model": "all-minilm",
"input": "Why is the sky blue?"
}'
options field)These runtime parameters can be passed in the options object of generate/chat/embed requests:
| Parameter | Type | Default | Description |
|---|---|---|---|
num_ctx | int | auto | Context window size (default auto: 4K/32K/256K by VRAM) |
num_batch | int | 512 | Batch size for prompt processing |
num_gpu | int | -1 (auto) | Number of layers to offload to GPU |
main_gpu | int | 0 (null) | Main GPU index (nullable) |
use_mmap | bool | (auto) | Use memory-mapped files |
num_thread | int | 0 (auto) | Number of threads |
draft_num_predict | int | 4 | Speculative draft tokens per step (0=disable; for draft/MTP models) |
num_keep | int | 4 | Number of tokens to keep from initial prompt |
seed | int | -1 | Random seed (-1 = random) |
num_predict | int | -1 | Max tokens to generate (-1 = infinite) |
top_k | int | 40 | Top-K sampling |
top_p | float | 0.9 | Top-P (nucleus) sampling |
min_p | float | 0.0 | Min-P sampling |
typical_p | float | 1.0 | Typical-P sampling |
repeat_last_n | int | 64 | Lookback for repeat penalty (0=disabled, -1=num_ctx) |
temperature | float | 0.8 | Sampling temperature |
repeat_penalty | float | 1.1 | Repetition penalty |
presence_penalty | float | 0.0 | Presence penalty |
frequency_penalty | float | 0.0 | Frequency penalty |
stop | string[] | [] | Stop sequences |
/api/generate and /api/chat with all fields, streaming, tool calling, structured output, thinking, images/api/tags, /api/show, /api/create, /api/pull, /api/push, /api/copy, /api/delete, /api/ps, blobs, version/api/embed and legacy /api/embeddingsnpx claudepluginhub datathings/marketplace --plugin ollamaAutomates Ollama installation, hardware-based model selection, GPU setup, and client integration (Python/Node.js/REST) for local LLM inference on macOS/Linux/Docker.
Guides local LLM inference, VRAM optimization, model selection, and quantization using Ollama, llama.cpp, vLLM, and LM Studio. Covers GGUF, EXL2 formats and privacy-first deployment.
Offloads summarization, extraction, classification, and other high-volume tasks to local LM Studio models to reduce paid API token usage. Requires LM Studio 0.4+ server running on localhost:1234.