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Daniel Hiltgen 6c5ccb11f9 Revamp ROCm support
This refines where we extract the LLM libraries to by adding a new
OLLAMA_HOME env var, that defaults to `~/.ollama` The logic was already
idempotenent, so this should speed up startups after the first time a
new release is deployed.  It also cleans up after itself.

We now build only a single ROCm version (latest major) on both windows
and linux.  Given the large size of ROCms tensor files, we split the
dependency out.  It's bundled into the installer on windows, and a
separate download on windows.  The linux install script is now smart and
detects the presence of AMD GPUs and looks to see if rocm v6 is already
present, and if not, then downloads our dependency tar file.

For Linux discovery, we now use sysfs and check each GPU against what
ROCm supports so we can degrade to CPU gracefully instead of having
llama.cpp+rocm assert/crash on us.  For Windows, we now use go's windows
dynamic library loading logic to access the amdhip64.dll APIs to query
the GPU information.
2024-03-07 10:36:50 -08:00
.github/workflows Revamp ROCm support 2024-03-07 10:36:50 -08:00
api Fix embeddings load model behavior (#2848) 2024-02-29 17:40:56 -08:00
app Revamp ROCm support 2024-03-07 10:36:50 -08:00
auth rerefactor 2024-02-15 05:56:45 +00:00
cmd Convert Safetensors to an Ollama model (#2824) 2024-03-06 21:01:51 -08:00
convert Convert Safetensors to an Ollama model (#2824) 2024-03-06 21:01:51 -08:00
docs Revamp ROCm support 2024-03-07 10:36:50 -08:00
examples Add README.md (#2249) 2024-02-22 14:03:44 -05:00
format remove format/openssh.go 2024-02-23 16:52:23 -08:00
gpu Revamp ROCm support 2024-03-07 10:36:50 -08:00
llm Revamp ROCm support 2024-03-07 10:36:50 -08:00
macapp Move Mac App to a new dir 2024-02-15 05:56:45 +00:00
openai Initial OpenAI /v1/chat/completions API compatibility (#2376) 2024-02-07 17:24:29 -05:00
parser Save and load sessions (#2063) 2024-01-25 12:12:36 -08:00
progress fix lint 2024-01-09 09:36:58 -08:00
readline handle race condition while setting raw mode in windows (#2509) 2024-02-14 21:28:35 -08:00
scripts Revamp ROCm support 2024-03-07 10:36:50 -08:00
server Revamp ROCm support 2024-03-07 10:36:50 -08:00
version add version 2023-08-22 09:40:58 -07:00
.dockerignore Code shuffle to clean up the llm dir 2024-01-04 12:12:05 -08:00
.gitignore Implement new Go based Desktop app 2024-02-15 05:56:45 +00:00
.gitmodules Init submodule with new path 2024-01-04 13:00:13 -08:00
.golangci.yaml add .golangci.yaml 2024-01-09 09:36:58 -08:00
.prettierrc.json move .prettierrc.json to root 2023-07-02 17:34:46 -04:00
Dockerfile Revamp ROCm support 2024-03-07 10:36:50 -08:00
go.mod Convert Safetensors to an Ollama model (#2824) 2024-03-06 21:01:51 -08:00
go.sum Convert Safetensors to an Ollama model (#2824) 2024-03-06 21:01:51 -08:00
LICENSE proto -> ollama 2023-06-26 15:57:13 -04:00
main.go set non-zero error code on error 2023-08-14 14:09:58 -07:00
README.md docs: Add LLM-X to Web Integration section (#2759) 2024-03-07 10:11:53 -05:00

ollama

Ollama

Discord

Get up and running with large language models locally.

macOS

Download

Windows preview

Download

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

Quickstart

To run and chat with Llama 2:

ollama run llama2

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 2 7B 3.8GB ollama run llama2
Mistral 7B 4.1GB ollama run mistral
Dolphin Phi 2.7B 1.6GB ollama run dolphin-phi
Phi-2 2.7B 1.7GB ollama run phi
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
Llama 2 13B 13B 7.3GB ollama run llama2:13b
Llama 2 70B 70B 39GB ollama run llama2:70b
Orca Mini 3B 1.9GB ollama run orca-mini
Vicuna 7B 3.8GB ollama run vicuna
LLaVA 7B 4.5GB ollama run llava
Gemma 2B 1.4GB ollama run gemma:2b
Gemma 7B 4.8GB ollama run gemma:7b

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:

  1. Create a file named Modelfile, with a FROM instruction with the local filepath to the model you want to import.

    FROM ./vicuna-33b.Q4_0.gguf
    
  2. Create the model in Ollama

    ollama create example -f Modelfile
    
  3. 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 llama2 model:

ollama pull llama2

Create a Modelfile:

FROM llama2

# 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 llama2

This command can also be used to update a local model. Only the diff will be pulled.

Remove a model

ollama rm llama2

Copy a model

ollama cp llama2 my-llama2

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

>>> 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 in prompt as arguments

$ ollama run llama2 "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.

List models on your computer

ollama list

Start Ollama

ollama serve is used when you want to start ollama without running the desktop application.

Building

Install cmake and go:

brew install cmake go

Then generate dependencies:

go generate ./...

Then build the binary:

go build .

More detailed instructions can be found in the developer guide

Running local builds

Next, start the server:

./ollama serve

Finally, in a separate shell, run a model:

./ollama run llama2

REST API

Ollama has a REST API for running and managing models.

Generate a response

curl http://localhost:11434/api/generate -d '{
  "model": "llama2",
  "prompt":"Why is the sky blue?"
}'

Chat with a model

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

See the API documentation for all endpoints.

Community Integrations

Web & Desktop

Terminal

Database

Package managers

Libraries

Mobile

Extensions & Plugins