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https://github.com/nickpotafiy/illama
A fast, lightweight, parallel inference server for Llama LLMs.
https://github.com/nickpotafiy/illama
exllama exllamav2 flash-attention-2 inference llama llama2 llama3 llm-inference paged-attention server
Last synced: 6 days ago
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A fast, lightweight, parallel inference server for Llama LLMs.
- Host: GitHub
- URL: https://github.com/nickpotafiy/illama
- Owner: nickpotafiy
- License: mit
- Created: 2024-05-23T05:37:08.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-07-30T14:33:37.000Z (2 months ago)
- Last Synced: 2024-09-18T18:25:12.388Z (15 days ago)
- Topics: exllama, exllamav2, flash-attention-2, inference, llama, llama2, llama3, llm-inference, paged-attention, server
- Language: Python
- Homepage:
- Size: 45.9 KB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# illama
**illama** is a lightweight, fast inference server for Llama and ExLlamav2 based large language models (LLMs).
## Features
- **Continuous batching** - Handles multiple requests simultaneously.
- **Open-AI compatible server** - Use official OpenAI API clients
- **Quantization Support** - Load any quantized ExLlamaV2 compatible models (GPTQ, EXL2, or SafeTensors).
- **GPU Focused** - Distribute model across any number of local GPUs.
- Uses [FlashAttention 2](https://github.com/Dao-AILab/flash-attention) with Paged Attention by default
## Getting Started
To get started, clone the repo.
```bash
git clone https://github.com/nickpotafiy/illama.git
cd illama
```
### With Conda
Optionally, create a new conda environment.
```bash
conda create -n illama python=3.10
conda activate illama
```
## Install PyTorch
Install [Nvidia Cuda Toolkit](https://developer.nvidia.com/cuda-toolkit-archive) and [PyTorch](https://pytorch.org/get-started/locally/). Ideally, both versions should match to minimize incompatibilities. PyTorch CUDA `12.1` is recommended with Nvidia CUDA Toolkit 12.1+.
### Install Torch w/ Pip
```bash
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
```
### Install Torch w/ Conda
```bash
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
```
Check Torch CUDA version with: `python -c "import torch; print(torch.version.cuda)"`
### Install Illama
First, install setup libraries:
```bash
pip install packaging ninja
```
Then, install the main package:
```bash
pip install .
```
If installation fails, you may need to `set MAX_JOBS=4` or `export MAX_JOBS=4` (or lower) depending on system memory. This is a known `flash-attn` problem.
## Running the Server
To start illama server, run this command:
```bash
python server.py --model-path "" --batch-size 10 --host "0.0.0.0" --port 5000 --verbose
```
Run `python server.py --help` to get a list of all available options.
## Troubleshooting
If you get an error saying `OSError: CUDA_HOME environment variable is not set. Please set it to your CUDA install root`, that typically means PyTorch was not installed correctly. You can verify PyTorch installation by activating your environment and executing `python`:
```python
import torch
torch.version.cuda
```
If you don't get your PyTorch CUDA version, then it was not installed correctly. You may have installed PyTorch without CUDA (like a Preview build).