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.

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:

代码语言：txt复制

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

Run LlamaGPT with the following command:

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./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:

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

Run LlamaGPT with the following command:

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./run.sh --model 7b

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

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./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:

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kubectl create ns llama

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

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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

人工智能自然语言处理llama 大语言模型

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