Tree: Update documentation and configs

Signed-off-by: kingbri <bdashore3@proton.me>
This commit is contained in:
kingbri 2023-11-16 02:30:33 -05:00
parent 2248705c4a
commit 03f45cb0a3
5 changed files with 88 additions and 95 deletions

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.gitignore vendored
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@ -178,3 +178,7 @@ pyrightconfig.json
# User configuration
config.yml
api_tokens.yml
# Models folder
models/*
!models/place_your_models_here.txt

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README.md
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# TabbyAPI
# tabbyAPI
A FastAPI based application that allows for generating text using an LLM (large language model) using the [exllamav2 backend](https://github.com/turboderp/exllamav2).
tabbyAPI is a FastAPI-based application that provides an API for generating text using a language model. This README provides instructions on how to launch and use the tabbyAPI.
## Disclaimer
This API is still in the alpha phase. There may be bugs and changes down the line. Please be aware that you might need to reinstall dependencies if needed.
## Prerequisites
Before you get started, ensure you have the following prerequisites installed on your system:
To get started, make sure you have the following installed on your system:
- Python 3.x (with pip)
- Dependencies listed in `requirements.txt`
- Python 3.x (preferably 3.11) with pip
## Installation
- CUDA 12.1 or 11.8
1. Clone the repository to your local machine:
NOTE: For Flash Attention 2 to work on Windows, CUDA 12.1 **must** be installed!
git clone https://github.com/Splice86/tabbyAPI.git
## Installing
1. Clone this repository to your machine: `git clone https://github.com/theroyallab/tabbyAPI`
2. Navigate to the project directory:
2. Navigate to the project directory: `cd tabbyAPI`
cd tabbyAPI
3. Create a virtual environment:
1. `python -m venv venv`
2. On Windows: `.\venv\Scripts\activate`. On Linux: `source venv/bin/activate`
4. Install torch using the instructions found [here](https://pytorch.org/get-started/locally/)
3. Create a virtual environment (optional but recommended):
5. Install an exllamav2 wheel from [here](https://github.com/turboderp/exllamav2/releases):
1. Find the version that corresponds with your cuda and python version. For example, a wheel with `cu121` and `cp311` corresponds to CUDA 12.1 and python 3.11
python -m venv venv
source venv/bin/activate
6. Install the other requirements via: `pip install -r requirements.txt`
## Configuration
4. Install project dependencies using pip:
Copy over `config_sample.yml` to `config.yml`. All the fields are commented, so make sure to read the descriptions and comment out or remove fields that you don't need.
pip install -r requirements.txt
## Launching the Application
1. Make sure you are in the project directory and entered into the venv
5. Install exllamav2 to your venv
2. Run the tabbyAPI application: `python main.py`
git clone https://github.com/turboderp/exllamav2.git
## API Documentation
cd exllamav2
Docs can be accessed once you launch the API at `http://<your-IP>:<your-port>/docs`
pip install -r requirements.txt
If you use the default YAML config, it's accessible at `http://localhost:5000/docs`
python setup.py install
## Authentication
TabbyAPI uses an API key and admin key to authenticate a user's request. On first launch of the API, a file called `api_tokens.yml` will be generated with fields for the admin and API keys.
If you feel that the keys have been compromised, delete `api_tokens.yml` and the API will generate new keys for you.
## Launch the tabbyAPI Application
API keys and admin keys can be provided via:
To start the tabbyAPI application, follow these steps:
- `x-api-key` and `x-admin-key` respectively
1. Ensure you are in the project directory and the virtual environment is activated (if used).
- `Authorization` with the `Bearer ` prefix
2. Run the tabbyAPI application:
DO NOT share your admin key unless you want someone else to load/unload a model from your system!
#### Authentication Requrirements
python main.py
All routes require an API key except for the following which require an **admin** key
3. The tabbyAPI application should now be running. You can access it by opening a web browser and navigating to `http://localhost:8000` (if running locally).
- `/v1/model/load`
## Usage
- `/v1/model/unload`
The tabbyAPI application provides the following endpoint:
## Contributing
- '/v1/model' Retrieves information about the currently loaded model.
- '/v1/model/load' Loads a new model based on provided data and model configuration.
- '/v1/model/unload' Unloads the currently loaded model from the system.
- '/v1/completions' Use this endpoint to generate text based on the provided input data.
If you have issues with the project:
### Example Request (using `curl`)
- Describe the issues in detail
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer 2261702e8a220c6c4671a264cd1236ce" \
-d '{
"model": "airoboros-mistral2.2-7b-exl2",
"prompt": ["A tabby","is"],
"stream": true,
"top_p": 0.73,
"stop": "[",
"max_tokens": 360,
"temperature": 0.8,
"mirostat_mode": 2,
"mirostat_tau": 5,
"mirostat_eta": 0.1
}' \
http://127.0.0.1:8012/v1/completions
- If you have a feature request, please indicate it as such.
If you have a Pull Request
- Describe the pull request in detail, what, and why you are changing something
### Parameter Guide
## Developers and Permissions
*note* This stuff still needs to be expanded and updated
Creators/Developers:
{
"model": "airoboros-mistral2.2-7b-exl2",
"prompt": ["A tabby","is"],
"stream": true,
"top_p": 0.73,
"stop": "[",
"max_tokens": 360,
"temperature": 0.8,
"mirostat_mode": 2,
"mirostat_tau": 5,
"mirostat_eta": 0.1
}
- kingbri
Model: "airoboros-mistral2.2-7b-exl2"
This specifies the specific language model being used. It's essential for the API to know which model to employ for generating responses.
- Splice86
Prompt: ["Hello there! My name is", "Brian", "and I am", "an AI"]
The prompt *QUESTION* why is it a list of strings instead of a single string?
Stream: true
Whether the response should be streamed back or not.
Top_p: 0.73
cumulative probability threshold
Stop: "["
The stop parameter defines a string that stops the generation.
Max_tokens: 360
This parameter determines the maximum number of tokens.
Temperature: 0.8
Temperature controls the randomness of the generated text.
Mirostat_mode: 2
?
Mirostat_tau: 5
?
Mirostat_eta: 0.1
?
- Turboderp

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# Network options
# Options for networking
network:
host: "0.0.0.0"
port: 8012
# Only used if you want to initially load a model
# The IP to host on (default: 127.0.0.1).
# Use 0.0.0.0 to expose on all network adapters
host: "127.0.0.1"
# The port to host on (default: 5000)
port: 5000
# Options for model overrides and loading
model:
model_dir: "D:/models"
model_name: "airoboros-mistral2.2-7b-exl2"
# Overrides the directory to look for models (default: "models")
# Make sure to use forward slashes, even on Windows (or escape your backslashes).
# model_dir: "your model directory path"
# An initial model to load. Make sure the model is located in the model directory!
# A model can be loaded later via the API. This does not have to be specified
# model_name: "A model name"
# The below parameters apply only if model_name is set
# Maximum model context length (default: 4096)
max_seq_len: 4096
gpu_split: "auto"
# Automatically allocate resources to GPUs (default: True)
gpu_split_auto: True
# An integer array of GBs of vram to split between GPUs (default: [])
# gpu_split: [20.6, 24]
# Rope scaling parameters (default: 1.0)
rope_scale: 1.0
rope_alpha: 1.0
# Disable Flash-attention 2. Recommended for GPUs lower than Nvidia's 3000 series. (default: False)
no_flash_attention: False
# Enable low vram optimizations in exllamav2 (default: False)
low_mem: False

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