私密离线聊天新体验!llama-gpt聊天机器人:极速、安全、搭载Llama 2,尽享Code Llama支持!

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汀丶 发表于 2023/10/11 10:58:02 2023/10/11
【摘要】 私密离线聊天新体验!llama-gpt聊天机器人:极速、安全、搭载Llama 2,尽享Code Llama支持!

“私密离线聊天新体验!llama-gpt聊天机器人:极速、安全、搭载Llama 2,尽享Code Llama支持!”

一个自托管的、离线的、类似chatgpt的聊天机器人。由美洲驼提供动力。100%私密,没有数据离开您的设备。

Demo

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

1.支持模型

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

1.1 安装LlamaGPT 在 umbrelOS

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

LlamaGPT on Umbrel App Store

1.2 安装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.

1.3 在 Docker上安装

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.


1.4 在Kubernetes安装

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.

2.OpenAI兼容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.

  • 基线

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.

2.1 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

2.2 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

2.3 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

2.4 Code Llama 7B Chat (GGUF Q4_K_M)

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

2.5 Code Llama 13B Chat (GGUF Q4_K_M)

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

2.6 Phind Code Llama 34B Chat (GGUF Q4_K_M)

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

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