This package provides containers for both ExLlama and ExLlamaV2:
exllamacontainer uses the https://github.com/jllllll/exllama fork of https://github.com/turboderp/exllama (installed under/opt/exllama)exllama:v2container uses https://github.com/turboderp/exllamav2 (installed under/opt/exllamav2)
Both loaders are also supported in the oobabooga text-generation-webui container.
Substitute the GPTQ model from HuggingFace Hub that you want to run (see exllama compatible models)
./run.sh --workdir=/opt/exllama $(./autotag exllama) /bin/bash -c \
'python3 test_benchmark_inference.py --perf --validate -d $(huggingface-downloader TheBloke/Llama-2-7B-GPTQ)'If the model repository is private or requires authentication, add
--env HUGGINGFACE_TOKEN=<YOUR-ACCESS-TOKEN>
| Model | Memory (MB) |
|---|---|
TheBloke/Llama-2-7B-GPTQ |
5,200 |
TheBloke/Llama-2-13B-GPTQ |
9,135 |
TheBloke/LLaMA-30b-GPTQ |
20,206 |
TheBloke/Llama-2-70B-GPTQ |
35,462 |
CONTAINERS
exllama:0.0.15 |
|
|---|---|
| Aliases | exllama |
| Requires | L4T ['>=36'] |
| Dependencies | build-essential cuda cudnn python numpy cmake onnx pytorch huggingface_hub |
| Dependants | text-generation-webui:1.7 text-generation-webui:6a7cd01 text-generation-webui:main |
| Dockerfile | Dockerfile |
exllama:0.0.14 |
|
|---|---|
| Aliases | exllama |
| Requires | L4T ['==35.*'] |
| Dependencies | build-essential cuda cudnn python numpy cmake onnx pytorch huggingface_hub |
| Dockerfile | Dockerfile |
CONTAINER IMAGES
| Repository/Tag | Date | Arch | Size |
|---|---|---|---|
dustynv/exllama:r35.2.1 |
2023-12-15 |
arm64 |
5.5GB |
dustynv/exllama:r35.3.1 |
2023-12-11 |
arm64 |
5.5GB |
dustynv/exllama:r35.4.1 |
2023-12-14 |
arm64 |
5.4GB |
dustynv/exllama:v1-r36.2.0 |
2023-12-15 |
arm64 |
7.2GB |
dustynv/exllama:v2-r35.2.1 |
2023-12-15 |
arm64 |
5.5GB |
dustynv/exllama:v2-r35.3.1 |
2023-12-14 |
arm64 |
5.5GB |
dustynv/exllama:v2-r35.4.1 |
2023-12-12 |
arm64 |
5.5GB |
dustynv/exllama:v2-r36.2.0 |
2023-12-15 |
arm64 |
7.2GB |
Container images are compatible with other minor versions of JetPack/L4T:
• L4T R32.7 containers can run on other versions of L4T R32.7 (JetPack 4.6+)
• L4T R35.x containers can run on other versions of L4T R35.x (JetPack 5.1+)
RUN CONTAINER
To start the container, you can use jetson-containers run and autotag, or manually put together a docker run command:
# automatically pull or build a compatible container image
jetson-containers run $(autotag exllama)
# or explicitly specify one of the container images above
jetson-containers run dustynv/exllama:v1-r36.2.0
# or if using 'docker run' (specify image and mounts/ect)
sudo docker run --runtime nvidia -it --rm --network=host dustynv/exllama:v1-r36.2.0
jetson-containers runforwards arguments todocker runwith some defaults added (like--runtime nvidia, mounts a/datacache, and detects devices)
autotagfinds a container image that's compatible with your version of JetPack/L4T - either locally, pulled from a registry, or by building it.
To mount your own directories into the container, use the -v or --volume flags:
jetson-containers run -v /path/on/host:/path/in/container $(autotag exllama)To launch the container running a command, as opposed to an interactive shell:
jetson-containers run $(autotag exllama) my_app --abc xyzYou can pass any options to it that you would to docker run, and it'll print out the full command that it constructs before executing it.
BUILD CONTAINER
If you use autotag as shown above, it'll ask to build the container for you if needed. To manually build it, first do the system setup, then run:
jetson-containers build exllamaThe dependencies from above will be built into the container, and it'll be tested during. Run it with --help for build options.