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.
• Supporting GPU inference with at least 6 GB VRAM, and CPU inference.

• Supporting models: Llama-2-7b/13b/70b, all Llama-2-GPTQ, all Llama-2-GGML ...

• Supporting model backends

  • Nvidia GPU: tranformers, bitsandbytes(8-bit inference), AutoGPTQ(4-bit inference)

    • GPU inference with at least 6 GB VRAM

  • CPU, Mac/AMD GPU: llama.cpp

    • CPU inference Demo on Macbook Air.

• Web UI interface: gradio

• News, Benchmark, Issue Solutions

• Install
• Download Llama-2 Models
  • Model List
  • Download Script
• Usage
  • Config Examples
  • Start Web UI
  • Run on Nvidia GPU
    • Run on Low Memory GPU with 8 bit
    • Run on Low Memory GPU with 4 bit
  • Run on CPU
    • Mac Metal Acceleration
    • AMD/Nvidia GPU Acceleration
  • Benchmark
• License
• Contributing

pip install llama2-wrapper

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

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

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.

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.

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 [email protected]: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.

Setup your MODEL_PATH and model configs in .env file.

There are some examples in ./env_examples/ folder.

Run chatbot with web UI:

python app.py

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).

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).

If you want to run 4 bit Llama-2 model like Llama-2-7b-Chat-GPTQ, you can set up your LOAD_IN_4BIT as True 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.

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 .

If you would like to use Mac Metal for acceleration,

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

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

• Installation with OpenBLAS / cuBLAS / CLBlast / Metal

Run benchmark script to compute performance on your device:

python benchmark.py

benchmark.py will load the same .env as app.py.

Some benchmark performance:

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

MIT - see MIT License

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

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

• 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