* fix(ext_server): Port llama.cpp sampling refactors to ext_server
This was a fairly large changeset. I closely followed the changes here:
df270ef745
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(server.cpp): Refactor server.cpp logging for llama.cpp overhaul
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Bump llama.cpp to the latest master with `granite` support
This does not yet have granite MoE support, but that can come in a
follow up PR
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(patches): Update all patches (except solar-pro) to work with bumped llama.cpp
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(solar): Update solar patch for llama.cpp bump
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama.cpp): Bump llama.cpp for granitemoe support
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama.cpp): Bump llama.cpp for granitemoe support
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(solar): Update the solar-pro patch for latest llama.cpp bump
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama.cpp): Bump to the latest master of llama.cpp
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(patches): Update all patches for latest bump
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama): Always run sync.sh from the right directory
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama/patches): Update llama patches
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama)!: Rough sync with llama.cpp submodule
There are a number of changes that will need to be propagated to llama.go
before any of this works!
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama/patches): Add a patch and update for missing ggml-impl.h include
This include is where the ggml_cgraph struct is defined. It is included in
many of the .c files to define the forward declartion in ggml.h. It seems
that with the subset of code included here, the import was somehow lost (or
out-of-order) when building, so adding this include to llama.cpp fixes the
missing definition.
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama/sync): Add missing ggml-cpu-impl.h copy-over in sync.sh
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Add missing log.cpp
This was added as part of the logging overhaul done in llama.cpp
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Overhaul use of sampling module for llama.cpp changes
The changes here reflect the changes made in the big llama.cpp sampling PR
https://github.com/ggerganov/llama.cpp/pull/9294
The sampling functionality is now broken into the base interface
(llama_sampler) and the generation implementation (gpt_sampler). The
changes here reflect that. Since the sampling.h/sampling.cpp code uses c++
STL headers, the sampling_ext.[h|cpp] wrapper is maintained to allow go to
access a pure-C interface.
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Fix the impl of SampleTokenGreedy for new sampling
I don't think this method is currently used, so it could probably just be
removed so that all sampling goes through the GPT interface, but in the
interest of doing no harm, this should keep the method working as expected.
Branch: IBMGraniteArchitectureSupport
* fix(llama): Remove unused SampleTokenGreedy
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(sync): Remove bash-specific change to sync.sh
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* chore(gofumpt): Format on llama.go to pass linting
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llm): Fix missing <thread> include in ext_server
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Remove TODO about grammar_first
This feature was not used/needed previously so should be fine without
plumbing it through now.
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Better naming for sampling wrapper and args
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Fix patch 05 to use new wrapper api and re-sync
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* runner: Flush pending responses before returning
If there are any pending reponses (such as from potential stop
tokens) then we should send them back before ending the sequence.
Otherwise, we can be missing tokens at the end of a response.
Fixes #6707
* fix(llama/sampling): Use gpt_sampler with a forward declaration
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Remove unnecessary patch for gguf impl header
This was caused by an earlier mistake in the embeddings patch that was
dereferencing the pointer instead of using the wrapper API.
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llm): Remove use of deprecated --log-disable flag
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Ollama
Get up and running with large language models.
macOS
Windows preview
Linux
curl -fsSL https://ollama.com/install.sh | sh
Docker
The official Ollama Docker image ollama/ollama
is available on Docker Hub.
Libraries
Quickstart
To run and chat with Llama 3.2:
ollama run llama3.2
Model library
Ollama supports a list of models available on ollama.com/library
Here are some example models that can be downloaded:
Model | Parameters | Size | Download |
---|---|---|---|
Llama 3.2 | 3B | 2.0GB | ollama run llama3.2 |
Llama 3.2 | 1B | 1.3GB | ollama run llama3.2:1b |
Llama 3.1 | 8B | 4.7GB | ollama run llama3.1 |
Llama 3.1 | 70B | 40GB | ollama run llama3.1:70b |
Llama 3.1 | 405B | 231GB | ollama run llama3.1:405b |
Phi 3 Mini | 3.8B | 2.3GB | ollama run phi3 |
Phi 3 Medium | 14B | 7.9GB | ollama run phi3:medium |
Gemma 2 | 2B | 1.6GB | ollama run gemma2:2b |
Gemma 2 | 9B | 5.5GB | ollama run gemma2 |
Gemma 2 | 27B | 16GB | ollama run gemma2:27b |
Mistral | 7B | 4.1GB | ollama run mistral |
Moondream 2 | 1.4B | 829MB | ollama run moondream |
Neural Chat | 7B | 4.1GB | ollama run neural-chat |
Starling | 7B | 4.1GB | ollama run starling-lm |
Code Llama | 7B | 3.8GB | ollama run codellama |
Llama 2 Uncensored | 7B | 3.8GB | ollama run llama2-uncensored |
LLaVA | 7B | 4.5GB | ollama run llava |
Solar | 10.7B | 6.1GB | ollama run solar |
Note
You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
Customize a model
Import from GGUF
Ollama supports importing GGUF models in the Modelfile:
-
Create a file named
Modelfile
, with aFROM
instruction with the local filepath to the model you want to import.FROM ./vicuna-33b.Q4_0.gguf
-
Create the model in Ollama
ollama create example -f Modelfile
-
Run the model
ollama run example
Import from PyTorch or Safetensors
See the guide on importing models for more information.
Customize a prompt
Models from the Ollama library can be customized with a prompt. For example, to customize the llama3.2
model:
ollama pull llama3.2
Create a Modelfile
:
FROM llama3.2
# set the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1
# set the system message
SYSTEM """
You are Mario from Super Mario Bros. Answer as Mario, the assistant, only.
"""
Next, create and run the model:
ollama create mario -f ./Modelfile
ollama run mario
>>> hi
Hello! It's your friend Mario.
For more examples, see the examples directory. For more information on working with a Modelfile, see the Modelfile documentation.
CLI Reference
Create a model
ollama create
is used to create a model from a Modelfile.
ollama create mymodel -f ./Modelfile
Pull a model
ollama pull llama3.2
This command can also be used to update a local model. Only the diff will be pulled.
Remove a model
ollama rm llama3.2
Copy a model
ollama cp llama3.2 my-model
Multiline input
For multiline input, you can wrap text with """
:
>>> """Hello,
... world!
... """
I'm a basic program that prints the famous "Hello, world!" message to the console.
Multimodal models
ollama run llava "What's in this image? /Users/jmorgan/Desktop/smile.png"
The image features a yellow smiley face, which is likely the central focus of the picture.
Pass the prompt as an argument
$ ollama run llama3.2 "Summarize this file: $(cat README.md)"
Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.
Show model information
ollama show llama3.2
List models on your computer
ollama list
List which models are currently loaded
ollama ps
Stop a model which is currently running
ollama stop llama3.2
Start Ollama
ollama serve
is used when you want to start ollama without running the desktop application.
Building
See the developer guide
Running local builds
Next, start the server:
./ollama serve
Finally, in a separate shell, run a model:
./ollama run llama3.2
REST API
Ollama has a REST API for running and managing models.
Generate a response
curl http://localhost:11434/api/generate -d '{
"model": "llama3.2",
"prompt":"Why is the sky blue?"
}'
Chat with a model
curl http://localhost:11434/api/chat -d '{
"model": "llama3.2",
"messages": [
{ "role": "user", "content": "why is the sky blue?" }
]
}'
See the API documentation for all endpoints.
Community Integrations
Web & Desktop
- Open WebUI
- Enchanted (macOS native)
- Hollama
- Lollms-Webui
- LibreChat
- Bionic GPT
- HTML UI
- Saddle
- Chatbot UI
- Chatbot UI v2
- Typescript UI
- Minimalistic React UI for Ollama Models
- Ollamac
- big-AGI
- Cheshire Cat assistant framework
- Amica
- chatd
- Ollama-SwiftUI
- Dify.AI
- MindMac
- NextJS Web Interface for Ollama
- Msty
- Chatbox
- WinForm Ollama Copilot
- NextChat with Get Started Doc
- Alpaca WebUI
- OllamaGUI
- OpenAOE
- Odin Runes
- LLM-X (Progressive Web App)
- AnythingLLM (Docker + MacOs/Windows/Linux native app)
- Ollama Basic Chat: Uses HyperDiv Reactive UI
- Ollama-chats RPG
- QA-Pilot (Chat with Code Repository)
- ChatOllama (Open Source Chatbot based on Ollama with Knowledge Bases)
- CRAG Ollama Chat (Simple Web Search with Corrective RAG)
- RAGFlow (Open-source Retrieval-Augmented Generation engine based on deep document understanding)
- StreamDeploy (LLM Application Scaffold)
- chat (chat web app for teams)
- Lobe Chat with Integrating Doc
- Ollama RAG Chatbot (Local Chat with multiple PDFs using Ollama and RAG)
- BrainSoup (Flexible native client with RAG & multi-agent automation)
- macai (macOS client for Ollama, ChatGPT, and other compatible API back-ends)
- Olpaka (User-friendly Flutter Web App for Ollama)
- OllamaSpring (Ollama Client for macOS)
- LLocal.in (Easy to use Electron Desktop Client for Ollama)
- AiLama (A Discord User App that allows you to interact with Ollama anywhere in discord )
- Ollama with Google Mesop (Mesop Chat Client implementation with Ollama)
- Painting Droid (Painting app with AI integrations)
- Kerlig AI (AI writing assistant for macOS)
- AI Studio
- Sidellama (browser-based LLM client)
- LLMStack (No-code multi-agent framework to build LLM agents and workflows)
- BoltAI for Mac (AI Chat Client for Mac)
- Harbor (Containerized LLM Toolkit with Ollama as default backend)
- Go-CREW (Powerful Offline RAG in Golang)
- PartCAD (CAD model generation with OpenSCAD and CadQuery)
- Ollama4j Web UI - Java-based Web UI for Ollama built with Vaadin, Spring Boot and Ollama4j
- PyOllaMx - macOS application capable of chatting with both Ollama and Apple MLX models.
- Claude Dev - VSCode extension for multi-file/whole-repo coding
- Cherry Studio (Desktop client with Ollama support)
- ConfiChat (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
- Archyve (RAG-enabling document library)
- crewAI with Mesop (Mesop Web Interface to run crewAI with Ollama)
- LLMChat (Privacy focused, 100% local, intuitive all-in-one chat interface)
- ARGO (Locally download and run Ollama and Huggingface models with RAG on Mac/Windows/Linux)
- G1 (Prototype of using prompting strategies to improve the LLM's reasoning through o1-like reasoning chains.)
- Ollama App (Modern and easy-to-use multi-platform client for Ollama)
Terminal
- oterm
- Ellama Emacs client
- Emacs client
- gen.nvim
- ollama.nvim
- ollero.nvim
- ollama-chat.nvim
- ogpt.nvim
- gptel Emacs client
- Oatmeal
- cmdh
- ooo
- shell-pilot
- tenere
- llm-ollama for Datasette's LLM CLI.
- typechat-cli
- ShellOracle
- tlm
- podman-ollama
- gollama
- Ollama eBook Summary
- Ollama Mixture of Experts (MOE) in 50 lines of code
- vim-intelligence-bridge Simple interaction of "Ollama" with the Vim editor
Apple Vision Pro
Database
- MindsDB (Connects Ollama models with nearly 200 data platforms and apps)
- chromem-go with example
Package managers
Libraries
- LangChain and LangChain.js with example
- Firebase Genkit
- crewAI
- LangChainGo with example
- LangChain4j with example
- LangChainRust with example
- LlamaIndex and LlamaIndexTS
- LiteLLM
- OllamaFarm for Go
- OllamaSharp for .NET
- Ollama for Ruby
- Ollama-rs for Rust
- Ollama-hpp for C++
- Ollama4j for Java
- ModelFusion Typescript Library
- OllamaKit for Swift
- Ollama for Dart
- Ollama for Laravel
- LangChainDart
- Semantic Kernel - Python
- Haystack
- Elixir LangChain
- Ollama for R - rollama
- Ollama for R - ollama-r
- Ollama-ex for Elixir
- Ollama Connector for SAP ABAP
- Testcontainers
- Portkey
- PromptingTools.jl with an example
- LlamaScript
- Gollm
- Ollamaclient for Golang
- High-level function abstraction in Go
- Ollama PHP
- Agents-Flex for Java with example
Mobile
- Enchanted
- Maid
- Ollama App (Modern and easy-to-use multi-platform client for Ollama)
- ConfiChat (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
Extensions & Plugins
- Raycast extension
- Discollama (Discord bot inside the Ollama discord channel)
- Continue
- Obsidian Ollama plugin
- Logseq Ollama plugin
- NotesOllama (Apple Notes Ollama plugin)
- Dagger Chatbot
- Discord AI Bot
- Ollama Telegram Bot
- Hass Ollama Conversation
- Rivet plugin
- Obsidian BMO Chatbot plugin
- Cliobot (Telegram bot with Ollama support)
- Copilot for Obsidian plugin
- Obsidian Local GPT plugin
- Open Interpreter
- Llama Coder (Copilot alternative using Ollama)
- Ollama Copilot (Proxy that allows you to use ollama as a copilot like Github copilot)
- twinny (Copilot and Copilot chat alternative using Ollama)
- Wingman-AI (Copilot code and chat alternative using Ollama and Hugging Face)
- Page Assist (Chrome Extension)
- Plasmoid Ollama Control (KDE Plasma extension that allows you to quickly manage/control Ollama model)
- AI Telegram Bot (Telegram bot using Ollama in backend)
- AI ST Completion (Sublime Text 4 AI assistant plugin with Ollama support)
- Discord-Ollama Chat Bot (Generalized TypeScript Discord Bot w/ Tuning Documentation)
- Discord AI chat/moderation bot Chat/moderation bot written in python. Uses Ollama to create personalities.
- Headless Ollama (Scripts to automatically install ollama client & models on any OS for apps that depends on ollama server)
- vnc-lm (A containerized Discord bot with support for attachments and web links)
- LSP-AI (Open-source language server for AI-powered functionality)
- QodeAssist (AI-powered coding assistant plugin for Qt Creator)
- Obsidian Quiz Generator plugin
Supported backends
- llama.cpp project founded by Georgi Gerganov.