llama-gpt README

The project's README from github.com/getumbrel/llama-gpt. As one markdown file: README.md.

LlamaGPT

LlamaGPT

A self-hosted, offline, ChatGPT-like chatbot, powered by Llama 2. 100% private, with no data leaving your device.
New: Support for Code Llama models and Nvidia GPUs.

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Contents

  1. Demo
  2. Supported Models
  3. How to install
  4. OpenAI-compatible API
  5. Benchmarks
  6. Roadmap and contributing
  7. Acknowledgements

Demo

https://github.com/getumbrel/llama-gpt/assets/10330103/5d1a76b8-ed03-4a51-90bd-12ebfaf1e6cd

Supported models

Currently, LlamaGPT supports the following models. Support for running custom models is on the roadmap.

Model name Model size Model download size Memory required
Nous Hermes Llama 2 7B Chat (GGML q4_0) 7B 3.79GB 6.29GB
Nous Hermes Llama 2 13B Chat (GGML q4_0) 13B 7.32GB 9.82GB
Nous Hermes Llama 2 70B Chat (GGML q4_0) 70B 38.87GB 41.37GB
Code Llama 7B Chat (GGUF Q4_K_M) 7B 4.24GB 6.74GB
Code Llama 13B Chat (GGUF Q4_K_M) 13B 8.06GB 10.56GB
Phind Code Llama 34B Chat (GGUF Q4_K_M) 34B 20.22GB 22.72GB

How to install

Install LlamaGPT on your umbrelOS home server

Running LlamaGPT on an umbrelOS home server is one click. Simply install it from the Umbrel App Store.

LlamaGPT on Umbrel App Store

Install LlamaGPT on M1/M2 Mac

Make sure your have Docker and Xcode installed.

Then, clone this repo and cd into it:

git clone https://github.com/getumbrel/llama-gpt.git
cd llama-gpt

Run LlamaGPT with the following command:

./run-mac.sh --model 7b

You can access LlamaGPT at http://localhost:3000.

To run 13B or 70B chat models, replace 7b with 13b or 70b respectively. To run 7B, 13B or 34B Code Llama models, replace 7b with code-7b, code-13b or code-34b respectively.

To stop LlamaGPT, do Ctrl + C in Terminal.

Install LlamaGPT anywhere else with Docker

You can run LlamaGPT on any x86 or arm64 system. Make sure you have Docker installed.

Then, clone this repo and cd into it:

git clone https://github.com/getumbrel/llama-gpt.git
cd llama-gpt

Run LlamaGPT with the following command:

./run.sh --model 7b

Or if you have an Nvidia GPU, you can run LlamaGPT with CUDA support using the --with-cuda flag, like:

./run.sh --model 7b --with-cuda

You can access LlamaGPT at http://localhost:3000.

To run 13B or 70B chat models, replace 7b with 13b or 70b respectively. To run Code Llama 7B, 13B or 34B models, replace 7b with code-7b, code-13b or code-34b respectively.

To stop LlamaGPT, do Ctrl + C in Terminal.

Note: On the first run, it may take a while for the model to be downloaded to the /models directory. You may also see lots of output like this for a few minutes, which is normal:

llama-gpt-llama-gpt-ui-1       | [INFO  wait] Host [llama-gpt-api-13b:8000] not yet available...

After the model has been automatically downloaded and loaded, and the API server is running, you'll see an output like:

llama-gpt-ui_1   | ready - started server on 0.0.0.0:3000, url: http://localhost:3000

You can then access LlamaGPT at http://localhost:3000.


Install LlamaGPT with Kubernetes

First, make sure you have a running Kubernetes cluster and kubectl is configured to interact with it.

Then, clone this repo and cd into it.

To deploy to Kubernetes first create a namespace:

kubectl create ns llama

Then apply the manifests under the /deploy/kubernetes directory with

kubectl apply -k deploy/kubernetes/. -n llama

Expose your service however you would normally do that.

OpenAI compatible API

Thanks to llama-cpp-python, a drop-in replacement for OpenAI API is available at http://localhost:3001. Open http://localhost:3001/docs to see the API documentation.

Benchmarks

We've tested LlamaGPT models on the following hardware with the default system prompt, and user prompt: "How does the universe expand?" at temperature 0 to guarantee deterministic results. Generation speed is averaged over the first 10 generations.

Feel free to add your own benchmarks to this table by opening a pull request.

Nous Hermes Llama 2 7B Chat (GGML q4_0)

Device Generation speed
M1 Max MacBook Pro (64GB RAM) 54 tokens/sec
GCP c2-standard-16 vCPU (64 GB RAM) 16.7 tokens/sec
Ryzen 5700G 4.4GHz 4c (16 GB RAM) 11.50 tokens/sec
GCP c2-standard-4 vCPU (16 GB RAM) 4.3 tokens/sec
Umbrel Home (16GB RAM) 2.7 tokens/sec
Raspberry Pi 4 (8GB RAM) 0.9 tokens/sec

Nous Hermes Llama 2 13B Chat (GGML q4_0)

Device Generation speed
M1 Max MacBook Pro (64GB RAM) 20 tokens/sec
GCP c2-standard-16 vCPU (64 GB RAM) 8.6 tokens/sec
GCP c2-standard-4 vCPU (16 GB RAM) 2.2 tokens/sec
Umbrel Home (16GB RAM) 1.5 tokens/sec

Nous Hermes Llama 2 70B Chat (GGML q4_0)

Device Generation speed
M1 Max MacBook Pro (64GB RAM) 4.8 tokens/sec
GCP e2-standard-16 vCPU (64 GB RAM) 1.75 tokens/sec
GCP c2-standard-16 vCPU (64 GB RAM) 1.62 tokens/sec

Code Llama 7B Chat (GGUF Q4_K_M)

Device Generation speed
M1 Max MacBook Pro (64GB RAM) 41 tokens/sec

Code Llama 13B Chat (GGUF Q4_K_M)

Device Generation speed
M1 Max MacBook Pro (64GB RAM) 25 tokens/sec

Phind Code Llama 34B Chat (GGUF Q4_K_M)

Device Generation speed
M1 Max MacBook Pro (64GB RAM) 10.26 tokens/sec

Roadmap and contributing

We're looking to add more features to LlamaGPT. You can see the roadmap here. The highest priorities are:

If you're a developer who'd like to help with any of these, please open an issue to discuss the best way to tackle the challenge. If you're looking to help but not sure where to begin, check out these issues that have specifically been marked as being friendly to new contributors.

Acknowledgements

A massive thank you to the following developers and teams for making LlamaGPT possible:


License

umbrel.com

llama-gpt (getumbrel/llama-gpt)

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10.9K ·GitHub
What is this?LlamaGPT is a self-hosted, offline, ChatGPT-like chatbot powered by Llama 2 (and now Code Llama), so you get a familiar chat UI without sending any data to a third-party cloud.
Is this a ready-to-run app, or just a model I load into something else?It is a packaged application: a Docker-based service that runs the Llama 2/Code Llama model through llama.cpp, exposes it behind a ChatGPT-style web UI (Chatbot UI), and also serves an OpenAI-compatible API.
How do I get LlamaGPT running?On an umbrelOS home server it's a one-click install from the Umbrel App Store; everywhere else (Mac, Linux, x86/arm64) you clone the repo and run it with Docker, or apply the provided manifests to a Kubernetes cluster.
What do I need installed before running it?Docker is required on every platform; on M1/M2 Macs you also need Xcode; an Nvidia GPU is optional and enabled with the --with-cuda flag for faster inference.
Can I load my own custom model instead of the presets?Not yet — LlamaGPT only ships with a fixed set of Llama 2 and Code Llama checkpoints; loading arbitrary custom models is still on the roadmap.
Does any of my chat data leave my device?No — the whole point of LlamaGPT is that the model and all inference run locally, so nothing is sent to an external server.
Do I need an Nvidia GPU for this to be usable?No, it runs on CPU out of the box via llama.cpp (even a Raspberry Pi 4 works, just slowly at ~0.9 tokens/sec for the 7B model); if you have an Nvidia GPU you can pass --with-cuda to speed generation up significantly.
Which models can I actually run with LlamaGPT?It currently supports Nous Hermes Llama 2 Chat in 7B, 13B and 70B sizes, plus Code Llama Chat in 7B and 13B, and the Phind Code Llama 34B Chat model, all as pre-quantized GGML/GGUF downloads.
How fast is generation in practice?It varies a lot by hardware: the 7B model hits about 54 tokens/sec on an M1 Max MacBook Pro but only ~0.9 tokens/sec on a Raspberry Pi 4, while the 70B model runs at under 5 tokens/sec even on high-end machines.
Can I point my existing OpenAI-based app at LlamaGPT?Yes, it exposes an OpenAI-compatible API via llama-cpp-python, so you can often swap it in as a drop-in replacement for the OpenAI API.
What's the simplest way to try it without touching Docker commands?If you already have an Umbrel home server, you can install LlamaGPT with a single click straight from the Umbrel App Store.
What are similar projects?h2ogpt, jan, textgen, petals, gpt4free, OpenLLM, project-maya, Janus
Creator
getumbrel
Size
1.7 MB
Stars
10.9K
Updated
CommitOct 09
Age
3y
Commits · 90 Days