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Get up and running with Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. https://ollama.com
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jmorganca 36049361cd llama/compat: simplify shim (gemma3-tested) 
Round of post-first-arch cleanup. No behavioral change — text + vision
still pass end-to-end on Ollama gemma3:latest (4B), 1B, and 270m.

Removes:
  - apply_tensor_transforms() and the TransformSpec registry. Registered
    the +1 RMSNorm shift at one point but empirically Ollama's blobs
    already have it baked in. YAGNI until an arch actually needs a
    tensor-data transform; the hook can come back data-driven.
  - Corresponding llama-model.cpp patch hunk (include + post-load call).
  - copy_kv<> template plumbing. Direct copy_u32_kv / copy_f32_kv are
    shorter and more readable.
  - set_str wrapper (one-liner around gguf_set_val_str).
  - find_tensor helper (only used once; inlined as a loop).
  - convert_f16_to_f32 helper (one-line inner loop, inlined).
  - set_f32_if_missing wrapper (inlined at 3 call sites).

Tightens:
  - Clip tensor renames are now a table (kGemma3ClipRenames) iterated by
    handle_gemma3_clip. Adding a rename is one row.
  - translate_clip_metadata now reuses detect_ollama_gemma3 for its
    "is this an Ollama blob?" check instead of a separate ad-hoc check.

Net:
  llama-ollama-compat.cpp   488 -> 343 lines (-30%)
  llama-ollama-compat.h      83 ->  63 lines (-24%)
  upstream-edits.patch       20 ->  16 lines of real edits, 3 -> 2 files
2026-04-20 09:29:34 -07:00
.github runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
anthropic
api
app runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
auth
cmd runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
convert runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
discover runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
docs docs: update hermes (#15655) 2026-04-17 14:20:59 -07:00
envconfig runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
format
fs
harmony
integration runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
internal
kvcache
llama llama/compat: simplify shim (gemma3-tested) 2026-04-20 09:29:34 -07:00
llm llm,server: route Ollama-format gemma3 blobs through llama/compat 2026-04-20 09:29:34 -07:00
logutil
manifest create: avoid gc race with create (#15628) 2026-04-16 13:29:16 -07:00
middleware
ml runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
model runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
openai
parser
progress
readline
runner runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
scripts runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
server llm,server: route Ollama-format gemma3 blobs through llama/compat 2026-04-20 09:29:34 -07:00
template runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
thinking
tokenizer
tools runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
types
version
x mlx: apply repeat penalties in sampler (#15631) 2026-04-18 07:49:38 -07:00
.dockerignore
.gitattributes
.gitignore
.golangci.yaml
CMakeLists.txt runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
CMakePresets.json runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
CONTRIBUTING.md
Dockerfile runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
go.mod runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
go.sum runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
LICENSE
LLAMA_CPP_VERSION runner: Remove CGO engines, use llama-server exclusively for GGML models 2026-04-20 08:44:02 -07:00
main.go
MLX_C_VERSION
MLX_VERSION
README.md cmd/launch: add Copilot CLI integration (#15583) 2026-04-15 17:22:53 -07:00
SECURITY.md

README.md

ollama

Ollama

Start building with open models.

Download

macOS

curl -fsSL https://ollama.com/install.sh | sh

or download manually

Windows

irm https://ollama.com/install.ps1 | iex

or download manually

Linux

curl -fsSL https://ollama.com/install.sh | sh

Manual install instructions

Docker

The official Ollama Docker image ollama/ollama is available on Docker Hub.

Libraries

Community

Get started

ollama

You'll be prompted to run a model or connect Ollama to your existing agents or applications such as Claude Code, OpenClaw, OpenCode , Codex, Copilot, and more.

Coding

To launch a specific integration:

ollama launch claude

Supported integrations include Claude Code, Codex, Copilot CLI, Droid, and OpenCode.

AI assistant

Use OpenClaw to turn Ollama into a personal AI assistant across WhatsApp, Telegram, Slack, Discord, and more:

ollama launch openclaw

Chat with a model

Run and chat with Gemma 3:

ollama run gemma3

See ollama.com/library for the full list.

See the quickstart guide for more details.

REST API

Ollama has a REST API for running and managing models.

curl http://localhost:11434/api/chat -d '{
  "model": "gemma3",
  "messages": [{
    "role": "user",
    "content": "Why is the sky blue?"
  }],
  "stream": false
}'

See the API documentation for all endpoints.

Python

pip install ollama

from ollama import chat

response = chat(model='gemma3', messages=[
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
])
print(response.message.content)

JavaScript

npm i ollama

import ollama from "ollama";

const response = await ollama.chat({
  model: "gemma3",
  messages: [{ role: "user", content: "Why is the sky blue?" }],
});
console.log(response.message.content);

Supported backends

  • llama.cpp project founded by Georgi Gerganov.

Documentation

Community Integrations

Want to add your project? Open a pull request.

Chat Interfaces

Web

Desktop

  • Dify.AI - LLM app development platform
  • AnythingLLM - All-in-one AI app for Mac, Windows, and Linux
  • Maid - Cross-platform mobile and desktop client
  • Witsy - AI desktop app for Mac, Windows, and Linux
  • Cherry Studio - Multi-provider desktop client
  • Ollama App - Multi-platform client for desktop and mobile
  • PyGPT - AI desktop assistant for Linux, Windows, and Mac
  • Alpaca - GTK4 client for Linux and macOS
  • SwiftChat - Cross-platform including iOS, Android, and Apple Vision Pro
  • Enchanted - Native macOS and iOS client
  • RWKV-Runner - Multi-model desktop runner
  • Ollama Grid Search - Evaluate and compare models
  • macai - macOS client for Ollama and ChatGPT
  • AI Studio - Multi-provider desktop IDE
  • Reins - Parameter tuning and reasoning model support
  • ConfiChat - Privacy-focused with optional encryption
  • LLocal.in - Electron desktop client
  • MindMac - AI chat client for Mac
  • Msty - Multi-model desktop client
  • BoltAI for Mac - AI chat client for Mac
  • IntelliBar - AI-powered assistant for macOS
  • Kerlig AI - AI writing assistant for macOS
  • Hillnote - Markdown-first AI workspace
  • Perfect Memory AI - Productivity AI personalized by screen and meeting history

Mobile

SwiftChat, Enchanted, Maid, Ollama App, Reins, and ConfiChat listed above also support mobile platforms.

Code Editors & Development

Libraries & SDKs

Frameworks & Agents

RAG & Knowledge Bases

  • RAGFlow - RAG engine based on deep document understanding
  • R2R - Open-source RAG engine
  • MaxKB - Ready-to-use RAG chatbot
  • Minima - On-premises or fully local RAG
  • Chipper - AI interface with Haystack RAG
  • ARGO - RAG and deep research on Mac/Windows/Linux
  • Archyve - RAG-enabling document library
  • Casibase - AI knowledge base with RAG and SSO
  • BrainSoup - Native client with RAG and multi-agent automation

Bots & Messaging

Terminal & CLI

Productivity & Apps

Observability & Monitoring

  • Opik - Debug, evaluate, and monitor LLM applications
  • OpenLIT - OpenTelemetry-native monitoring for Ollama and GPUs
  • Lunary - LLM observability with analytics and PII masking
  • Langfuse - Open source LLM observability
  • HoneyHive - AI observability and evaluation for agents
  • MLflow Tracing - Open source LLM observability

Database & Embeddings

Infrastructure & Deployment

Cloud

Package Managers