amol-rainfall/Stratosphere

llama2-webui

Running Llama 2 with gradio web UI on GPU or CPU from anywhere (Linux/Windows/Mac).

• —Supporting all Llama 2 models (7B, 13B, 70B, GPTQ, GGML) with 8-bit, 4-bit mode.
• —Use llama2-wrapper as your local llama2 backend for Generative Agents/Apps; colab example.

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Features

• —Supporting models: Llama-2-7b/13b/70b, all Llama-2-GPTQ, all Llama-2-GGML ...
• —Supporting model backends: tranformers, bitsandbytes(8-bit inference), AutoGPTQ(4-bit inference), llama.cpp
• —Demos: Run Llama2 on MacBook Air; Run Llama2 on free Colab T4 GPU
• —Use llama2-wrapper as your local llama2 backend for Generative Agents/Apps; colab example.
• —News, Benchmark, Issue Solutions

Contents

• —Install
• —Usage
• —Start Web UI
• —Env Examples
• —Use llama2-wrapper for Your App
• —Benchmark
• —Download Llama-2 Models
• —Model List
• —Download Script
• —Tips
• —Run on Nvidia GPU
• —Run bitsandbytes 8 bit
• —Run GPTQ 4 bit
• —Run on CPU
• —Mac Metal Acceleration
• —AMD/Nvidia GPU Acceleration
• —License
• —Contributing

Install

Method 1: From PyPI

pip install llama2-wrapper

Method 2: From Source:

git clone https://github.com/liltom-eth/llama2-webui.git
cd llama2-webui
pip install -r requirements.txt

Install Issues:

bitsandbytes >= 0.39 may not work on older NVIDIA GPUs. In that case, to use LOAD_IN_8BIT, you may have to downgrade like this:

• —pip install bitsandbytes==0.38.1

bitsandbytes also need a special install for Windows:

pip uninstall bitsandbytes
pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.0-py3-none-win_amd64.whl

Usage

Start Web UI

Run chatbot simply with web UI:

python app.py

app.py will load the default config .env which uses llama.cpp as the backend to run llama-2-7b-chat.ggmlv3.q4_0.bin model for inference. The model llama-2-7b-chat.ggmlv3.q4_0.bin will be automatically downloaded.

Running on backend llama.cpp.
Use default model path: ./models/llama-2-7b-chat.ggmlv3.q4_0.bin
Start downloading model to: ./models/llama-2-7b-chat.ggmlv3.q4_0.bin

You can also customize your MODEL_PATH, BACKEND_TYPE, and model configs in .env file to run different llama2 models on different backends (llama.cpp, transformers, gptq).

Env Examples

There are some examples in ./env_examples/ folder.

Use llama2-wrapper for Your App

🔥 For developers, we released llama2-wrapper as a llama2 backend wrapper in PYPI.

Use llama2-wrapper as your local llama2 backend to answer questions and more, colab example:

# pip install llama2-wrapper
from llama2_wrapper import LLAMA2_WRAPPER, get_prompt 
llama2_wrapper = LLAMA2_WRAPPER()
# Default running on backend llama.cpp.
# Automatically downloading model to: ./models/llama-2-7b-chat.ggmlv3.q4_0.bin
prompt = "Do you know Pytorch"
answer = llama2_wrapper(get_prompt(prompt), temperature=0.9)

Run gptq llama2 model on Nvidia GPU, colab example:

from llama2_wrapper import LLAMA2_WRAPPER 
llama2_wrapper = LLAMA2_WRAPPER(backend_type="gptq")
# Automatically downloading model to: ./models/Llama-2-7b-Chat-GPTQ

Run llama2 7b with bitsandbytes 8 bit with a model_path:

from llama2_wrapper import LLAMA2_WRAPPER 
llama2_wrapper = LLAMA2_WRAPPER(
	model_path = "./models/Llama-2-7b-chat-hf",
  backend_type = "transformers",
  load_in_8bit = True
)

Benchmark

Run benchmark script to compute performance on your device, benchmark.py will load the same .env as app.py.:

python benchmark.py

You can also select the iter, backend_type and model_path the benchmark will be run (overwrite .env args) :

python benchmark.py --iter NB_OF_ITERATIONS --backend_type gptq

By default, the number of iterations is 5, but if you want a faster result or a more accurate one you can set it to whatever value you want, but please only report results with at least 5 iterations.

This colab example also show you how to benchmark gptq model on free Google Colab T4 GPU.

Some benchmark performance:

Check/contribute the performance of your device in the full performance doc.

Download Llama-2 Models

Llama 2 is a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters.

Llama-2-7b-Chat-GPTQ is the GPTQ model files for Meta's Llama 2 7b Chat. GPTQ 4-bit Llama-2 model require less GPU VRAM to run it.

Model List

Running 4-bit model Llama-2-7b-Chat-GPTQ needs GPU with 6GB VRAM.

Running 4-bit model llama-2-7b-chat.ggmlv3.q4_0.bin needs CPU with 6GB RAM. There is also a list of other 2, 3, 4, 5, 6, 8-bit GGML models that can be used from TheBloke/Llama-2-7B-Chat-GGML.

Download Script

These models can be downloaded from the link using CMD like:

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone git@hf.co:meta-llama/Llama-2-7b-chat-hf

To download Llama 2 models, you need to request access from https://ai.meta.com/llama/ and also enable access on repos like meta-llama/Llama-2-7b-chat-hf. Requests will be processed in hours.

For GPTQ models like TheBloke/Llama-2-7b-Chat-GPTQ, you can directly download without requesting access.

For GGML models like TheBloke/Llama-2-7B-Chat-GGML, you can directly download without requesting access.

Tips

Run on Nvidia GPU

The running requires around 14GB of GPU VRAM for Llama-2-7b and 28GB of GPU VRAM for Llama-2-13b.

If you are running on multiple GPUs, the model will be loaded automatically on GPUs and split the VRAM usage. That allows you to run Llama-2-7b (requires 14GB of GPU VRAM) on a setup like 2 GPUs (11GB VRAM each).

Run bitsandbytes 8 bit

If you do not have enough memory, you can set up your LOAD_IN_8BIT as True in .env. This can reduce memory usage by around half with slightly degraded model quality. It is compatible with the CPU, GPU, and Metal backend.

Llama-2-7b with 8-bit compression can run on a single GPU with 8 GB of VRAM, like an Nvidia RTX 2080Ti, RTX 4080, T4, V100 (16GB).

Run GPTQ 4 bit

If you want to run 4 bit Llama-2 model like Llama-2-7b-Chat-GPTQ, you can set up your BACKEND_TYPE as gptq in .env like example .env.7b_gptq_example.

Make sure you have downloaded the 4-bit model from Llama-2-7b-Chat-GPTQ and set the MODEL_PATH and arguments in .env file.

Llama-2-7b-Chat-GPTQ can run on a single GPU with 6 GB of VRAM.

If you encounter issue like NameError: name 'autogptq_cuda_256' is not defined, please refer to here

pip install https://github.com/PanQiWei/AutoGPTQ/releases/download/v0.3.0/autogptq-0.3.0+cu117-cp310-cp310-linuxx86_64.whl

Run on CPU

Run Llama-2 model on CPU requires llama.cpp dependency and llama.cpp Python Bindings, which are already installed.

Download GGML models like llama-2-7b-chat.ggmlv3.q4_0.bin following Download Llama-2 Models section. llama-2-7b-chat.ggmlv3.q4_0.bin model requires at least 6 GB RAM to run on CPU.

Set up configs like .env.7b_ggmlv3_q4_0_example from env_examples as .env.

Run web UI python app.py .

Mac Metal Acceleration

For Mac users, you can also set up Mac Metal for acceleration, try install this dependencies:

pip uninstall llama-cpp-python -y
CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install -U llama-cpp-python --no-cache-dir
pip install 'llama-cpp-python[server]'

or check details:

• —MacOS Install with Metal GPU

AMD/Nvidia GPU Acceleration

If you would like to use AMD/Nvidia GPU for acceleration, check this:

• —Installation with OpenBLAS / cuBLAS / CLBlast / Metal

License

MIT - see MIT License

This project enables users to adapt it freely for proprietary purposes without any restrictions.

Contributing

Kindly read our Contributing Guide to learn and understand our development process.

All Contributors

<a href="https://github.com/liltom-eth/llama2-webui/graphs/contributors"> <img src="https://contrib.rocks/image?repo=liltom-eth/llama2-webui" /> </a>

Review

<a href='https://github.com/repo-reviews/repo-reviews.github.io/blob/main/create.md' target="_blank"><img alt='Github' src='https://img.shields.io/badge/review-100000?style=flat&logo=Github&logoColor=white&labelColor=888888&color=555555'/></a>

Star History


Credits

• —https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
• —https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat
• —https://huggingface.co/TheBloke/Llama-2-7b-Chat-GPTQ
• —https://github.com/ggerganov/llama.cpp
• —https://github.com/TimDettmers/bitsandbytes
• —https://github.com/PanQiWei/AutoGPTQ