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Mélony QIN 3f71ba406a
Correct the kubernetes terminology (#3843)
* add details on kubernetes deployment and separate the testing process

* Update examples/kubernetes/README.md

thanks for suggesting this change, I agree with you and let's make this project better together !

Co-authored-by: JonZeolla <Zeolla@gmail.com>

---------

Co-authored-by: QIN Mélony <MQN1@dsone.3ds.com>
Co-authored-by: JonZeolla <Zeolla@gmail.com>
2024-05-07 09:53:08 -07:00
.github fix golangci workflow missing gofmt and goimports (#4190) 2024-05-07 09:49:40 -07:00
api Add MarshalJSON to Duration (#3284) 2024-05-06 15:59:18 -07:00
app Centralize server config handling 2024-05-05 16:49:50 -07:00
auth prompt to display and add local ollama keys to account (#3717) 2024-04-30 11:02:08 -07:00
cmd close server on receiving signal (#4213) 2024-05-06 16:01:37 -07:00
convert Fix lint warnings 2024-05-03 16:44:19 -07:00
docs note on naming restrictions (#2625) 2024-05-06 16:03:21 -07:00
examples Correct the kubernetes terminology (#3843) 2024-05-07 09:53:08 -07:00
format Request and model concurrency 2024-04-22 19:29:12 -07:00
gpu llm: add minimum based on layer size 2024-05-06 17:04:19 -07:00
integration Add integration test to push max queue limits 2024-05-05 10:46:25 -07:00
llm llm: add minimum based on layer size 2024-05-06 17:04:19 -07:00
macapp mac: update setup command to llama3 (#3986) 2024-04-27 22:52:10 -04:00
openai change github.com/jmorganca/ollama to github.com/ollama/ollama (#3347) 2024-03-26 13:04:17 -07:00
progress change github.com/jmorganca/ollama to github.com/ollama/ollama (#3347) 2024-03-26 13:04:17 -07:00
readline Add gemma safetensors conversion (#3250) 2024-03-28 18:54:01 -07:00
scripts Support Fedoras standard ROCm location 2024-05-01 15:47:12 -07:00
server close server on receiving signal (#4213) 2024-05-06 16:01:37 -07:00
types chore: format go code (#4149) 2024-05-05 16:08:09 -07:00
version add version 2023-08-22 09:40:58 -07:00
.dockerignore add macapp to .dockerignore 2024-03-09 16:07:06 -08:00
.gitattributes Update .gitattributes 2024-05-07 09:50:19 -07:00
.gitignore ignore debug bin files 2024-05-01 18:51:10 +00:00
.gitmodules Init submodule with new path 2024-01-04 13:00:13 -08:00
.golangci.yaml fix golangci workflow missing gofmt and goimports (#4190) 2024-05-07 09:49:40 -07:00
.prettierrc.json move .prettierrc.json to root 2023-07-02 17:34:46 -04:00
Dockerfile rearranged conditional logic for static build, dockerfile updated 2024-04-17 14:43:28 -04:00
go.mod Add llama2 / torch models for ollama create (#3607) 2024-04-15 11:26:42 -07:00
go.sum Add llama2 / torch models for ollama create (#3607) 2024-04-15 11:26:42 -07:00
LICENSE proto -> ollama 2023-06-26 15:57:13 -04:00
main.go change github.com/jmorganca/ollama to github.com/ollama/ollama (#3347) 2024-03-26 13:04:17 -07:00
README.md Update README.md to include ollama-r library (#4012) 2024-05-07 09:52:30 -07:00

 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 3:

ollama run llama3

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 8B 4.7GB ollama run llama3
Llama 3 70B 40GB ollama run llama3:70b
Phi-3 3.8B 2.3GB ollama run phi3
Mistral 7B 4.1GB ollama run mistral
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
Gemma 2B 1.4GB ollama run gemma:2b
Gemma 7B 4.8GB ollama run gemma:7b
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:

  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 llama3 model:

ollama pull llama3

Create a Modelfile:

FROM llama3

# 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

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

Remove a model

ollama rm llama3

Copy a model

ollama cp llama3 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

>>> 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 "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 llama3

REST API

Ollama has a REST API for running and managing models.

Generate a response

curl http://localhost:11434/api/generate -d '{
  "model": "llama3",
  "prompt":"Why is the sky blue?"
}'

Chat with a model

curl http://localhost:11434/api/chat -d '{
  "model": "llama3",
  "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

Supported backends

  • llama.cpp project founded by Georgi Gerganov.