Inference of LLaMA model in pure C/C++
Hot topics:
Q4
and Q8
have changed again (19 May) - (info)Q4
and Q5
have changed - requantize any old models (info)The main goal of llama.cpp
is to run the LLaMA model using 4-bit integer quantization on a MacBook
The original implementation of llama.cpp
was hacked in an evening.
Since then, the project has improved significantly thanks to many contributions. This project is for educational purposes and serves
as the main playground for developing new features for the ggml library.
Supported platforms:
Supported models:
Bindings:
UI:
Here is a typical run using LLaMA-7B:
make -j && ./main -m ./models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -n 512
I llama.cpp build info:
I UNAME_S: Darwin
I UNAME_P: arm
I UNAME_M: arm64
I CFLAGS: -I. -O3 -DNDEBUG -std=c11 -fPIC -pthread -DGGML_USE_ACCELERATE
I CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC -pthread
I LDFLAGS: -framework Accelerate
I CC: Apple clang version 14.0.0 (clang-1400.0.29.202)
I CXX: Apple clang version 14.0.0 (clang-1400.0.29.202)
make: Nothing to be done for `default'.
main: seed = 1678486056
llama_model_load: loading model from './models/7B/ggml-model-q4_0.bin' - please wait ...
llama_model_load: n_vocab = 32000
llama_model_load: n_ctx = 512
llama_model_load: n_embd = 4096
llama_model_load: n_mult = 256
llama_model_load: n_head = 32
llama_model_load: n_layer = 32
llama_model_load: n_rot = 128
llama_model_load: f16 = 2
llama_model_load: n_ff = 11008
llama_model_load: ggml ctx size = 4529.34 MB
llama_model_load: memory_size = 512.00 MB, n_mem = 16384
llama_model_load: .................................... done
llama_model_load: model size = 4017.27 MB / num tensors = 291
main: prompt: 'Building a website can be done in 10 simple steps:'
main: number of tokens in prompt = 15
1 -> ''
8893 -> 'Build'
292 -> 'ing'
263 -> ' a'
4700 -> ' website'
508 -> ' can'
367 -> ' be'
2309 -> ' done'
297 -> ' in'
29871 -> ' '
29896 -> '1'
29900 -> '0'
2560 -> ' simple'
6576 -> ' steps'
29901 -> ':'
sampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000
Building a website can be done in 10 simple steps:
1) Select a domain name and web hosting plan
2) Complete a sitemap
3) List your products
4) Write product descriptions
5) Create a user account
6) Build the template
7) Start building the website
8) Advertise the website
9) Provide email support
10) Submit the website to search engines
A website is a collection of web pages that are formatted with HTML. HTML is the code that defines what the website looks like and how it behaves.
The HTML code is formatted into a template or a format. Once this is done, it is displayed on the user's browser.
The web pages are stored in a web server. The web server is also called a host. When the website is accessed, it is retrieved from the server and displayed on the user's computer.
A website is known as a website when it is hosted. This means that it is displayed on a host. The host is usually a web server.
A website can be displayed on different browsers. The browsers are basically the software that renders the website on the user's screen.
A website can also be viewed on different devices such as desktops, tablets and smartphones.
Hence, to have a website displayed on a browser, the website must be hosted.
A domain name is an address of a website. It is the name of the website.
The website is known as a website when it is hosted. This means that it is displayed on a host. The host is usually a web server.
A website can be displayed on different browsers. The browsers are basically the software that renders the website on the user’s screen.
A website can also be viewed on different devices such as desktops, tablets and smartphones. Hence, to have a website displayed on a browser, the website must be hosted.
A domain name is an address of a website. It is the name of the website.
A website is an address of a website. It is a collection of web pages that are formatted with HTML. HTML is the code that defines what the website looks like and how it behaves.
The HTML code is formatted into a template or a format. Once this is done, it is displayed on the user’s browser.
A website is known as a website when it is hosted
main: mem per token = 14434244 bytes
main: load time = 1332.48 ms
main: sample time = 1081.40 ms
main: predict time = 31378.77 ms / 61.41 ms per token
main: total time = 34036.74 ms
And here is another demo of running both LLaMA-7B and whisper.cpp on a single M1 Pro MacBook:
https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8b4f-add84093ffff.mp4
Here are the steps for the LLaMA-7B model.
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
In order to build llama.cpp you have three different options.
Using make
:
On Linux or MacOS:
make
On Windows:
w64devkit
on your pc.w64devkit.exe
.cd
command to reach the llama.cpp
folder.make
Using CMake
:
mkdir build
cd build
cmake ..
cmake --build . --config Release
Using Zig
:
zig build -Drelease-fast
Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). BLAS doesn't affect the normal generation performance. There are currently three different implementations of it:
Accelerate Framework:
This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.
OpenBLAS:
This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.
Using make
:
On Linux:
make LLAMA_OPENBLAS=1
On Windows:
Download the latest fortran version of w64devkit.
Download the latest version of OpenBLAS for Windows.
Extract w64devkit
on your pc.
From the OpenBLAS zip that you just downloaded copy libopenblas.a
, located inside the lib
folder, inside w64devkit\x86_64-w64-mingw32\lib
.
From the same OpenBLAS zip copy the content of the include
folder inside w64devkit\x86_64-w64-mingw32\include
.
Run w64devkit.exe
.
Use the cd
command to reach the llama.cpp
folder.
From here you can run:
make LLAMA_OPENBLAS=1
Using CMake
on Linux:
mkdir build
cd build
cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
cmake --build . --config Release
BLIS
Check BLIS.md for more information.
Intel MKL
By default, LLAMA_BLAS_VENDOR
is set to Generic
, so if you already sourced intel environment script and assign -DLLAMA_BLAS=ON
in cmake, the mkl version of Blas will automatically been selected. You may also specify it by:
mkdir build
cd build
cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
cmake --build . -config Release
cuBLAS
This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager or from here: CUDA Toolkit.
Using make
:
make LLAMA_CUBLAS=1
Using CMake
:
mkdir build
cd build
cmake .. -DLLAMA_CUBLAS=ON
cmake --build . --config Release
Note: Because llama.cpp uses multiple CUDA streams for matrix multiplication results are not guaranteed to be reproducible. If you need reproducibility, set GGML_CUDA_MAX_STREAMS
in the file ggml-cuda.cu
to 1.
CLBlast
OpenCL acceleration is provided by the matrix multiplication kernels from the CLBlast project and custom kernels for ggml that can generate tokens on the GPU.
You will need the OpenCL SDK.
For Ubuntu or Debian, the packages opencl-headers
, ocl-icd
may be needed.
git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git
mkdir OpenCL-SDK/build
cd OpenCL-SDK/build
cmake .. -DBUILD_DOCS=OFF \
-DBUILD_EXAMPLES=OFF \
-DBUILD_TESTING=OFF \
-DOPENCL_SDK_BUILD_SAMPLES=OFF \
-DOPENCL_SDK_TEST_SAMPLES=OFF
cmake --build . --config Release
cmake --install . --prefix /some/path
Installing CLBlast: it may be found in your operating system's packages.
git clone https://github.com/CNugteren/CLBlast.git
mkdir CLBlast/build
cd CLBLast/build
cmake .. -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
cmake --build . --config Release
cmake --install . --prefix /some/path
Where /some/path
is where the built library will be installed (default is /usr/loca
l`).
Building:
make LLAMA_CLBLAST=1
mkdir build
cd build
cmake .. -DLLAMA_CLBLAST=ON -DCLBlast_dir=/some/path
cmake --build . --config Release
Running:
The CLBlast build supports --gpu-layers|-ngl
like the CUDA version does.
To select the correct platform (driver) and device (GPU), you can use the environment variables GGML_OPENCL_PLATFORM
and GGML_OPENCL_DEVICE
.
The selection can be a number (starting from 0) or a text string to search:
GGML_OPENCL_PLATFORM=1 ./main ...
GGML_OPENCL_DEVICE=2 ./main ...
GGML_OPENCL_PLATFORM=Intel ./main ...
GGML_OPENCL_PLATFORM=AMD GGML_OPENCL_DEVICE=1 ./main ...
The default behavior is to find the first GPU device, but when it is an integrated GPU on a laptop, for instance, the selectors are useful. Using the variables it is possible to select a CPU-based driver as well, if so desired.
You can get a list of platforms and devices from the clinfo -l
command, etc.
# obtain the original LLaMA model weights and place them in ./models
ls ./models
65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model
# install Python dependencies
python3 -m pip install -r requirements.txt
# convert the 7B model to ggml FP16 format
python3 convert.py models/7B/
# quantize the model to 4-bits (using q4_0 method)
./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin q4_0
# run the inference
./main -m ./models/7B/ggml-model-q4_0.bin -n 128
When running the larger models, make sure you have enough disk space to store all the intermediate files.
As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same.
Model | Original size | Quantized size (4-bit) |
---|---|---|
7B | 13 GB | 3.9 GB |
13B | 24 GB | 7.8 GB |
30B | 60 GB | 19.5 GB |
65B | 120 GB | 38.5 GB |
Several quantization methods are supported. They differ in the resulting model disk size and inference speed.
Model | Measure | F16 | Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 |
---|---|---|---|---|---|---|---|
7B | perplexity | 5.9066 | 6.1565 | 6.0912 | 5.9862 | 5.9481 | 5.9070 |
7B | file size | 13.0G | 3.5G | 3.9G | 4.3G | 4.7G | 6.7G |
7B | ms/tok @ 4th | 127 | 55 | 54 | 76 | 83 | 72 |
7B | ms/tok @ 8th | 122 | 43 | 45 | 52 | 56 | 67 |
7B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 |
13B | perplexity | 5.2543 | 5.3860 | 5.3608 | 5.2856 | 5.2706 | 5.2548 |
13B | file size | 25.0G | 6.8G | 7.6G | 8.3G | 9.1G | 13G |
13B | ms/tok @ 4th | - | 103 | 105 | 148 | 160 | 131 |
13B | ms/tok @ 8th | - | 73 | 82 | 98 | 105 | 128 |
13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 |
You can use the perplexity
example to measure perplexity over a given prompt (lower perplexity is better).
For more information, see https://huggingface.co/docs/transformers/perplexity.
The perplexity measurements in table above are done against the wikitext2
test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512.
The time per token is measured on a MacBook M1 Pro 32GB RAM using 4 and 8 threads.
If you want a more ChatGPT-like experience, you can run in interactive mode by passing -i
as a parameter.
In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a reverse prompt with the parameter -r "reverse prompt string"
. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMa emulate a chat between multiple users, say Alice and Bob, and pass -r "Alice:"
.
Here is an example of a few-shot interaction, invoked with the command
# default arguments using a 7B model
./examples/chat.sh
# advanced chat with a 13B model
./examples/chat-13B.sh
# custom arguments using a 13B model
./main -m ./models/13B/ggml-model-q4_0.bin -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt
Note the use of --color
to distinguish between user input and generated text. Other parameters are explained in more detail in the README for the main
example program.
The prompt, user inputs, and model generations can be saved and resumed across calls to ./main
by leveraging --prompt-cache
and --prompt-cache-all
. The ./examples/chat-persistent.sh
script demonstrates this with support for long-running, resumable chat sessions. To use this example, you must provide a file to cache the initial chat prompt and a directory to save the chat session, and may optionally provide the same variables as chat-13B.sh
. The same prompt cache can be reused for new chat sessions. Note that both prompt cache and chat directory are tied to the initial prompt (PROMPT_TEMPLATE
) and the model file.
# Start a new chat
PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh
# Resume that chat
PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh
# Start a different chat with the same prompt/model
PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/another ./examples/chat-persistent.sh
# Different prompt cache for different prompt/model
PROMPT_TEMPLATE=./prompts/chat-with-bob.txt PROMPT_CACHE_FILE=bob.prompt.bin \
CHAT_SAVE_DIR=./chat/bob ./examples/chat-persistent.sh
ggml
Alpaca model into the ./models
foldermain
tool like this:./examples/alpaca.sh
Sample run:
== Running in interactive mode. ==
- Press Ctrl+C to interject at any time.
- Press Return to return control to LLaMa.
- If you want to submit another line, end your input in '\'.
Below is an instruction that describes a task. Write a response that appropriately completes the request.
> How many letters are there in the English alphabet?
There 26 letters in the English Alphabet
> What is the most common way of transportation in Amsterdam?
The majority (54%) are using public transit. This includes buses, trams and metros with over 100 lines throughout the city which make it very accessible for tourists to navigate around town as well as locals who commute by tram or metro on a daily basis
> List 5 words that start with "ca".
cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach.
>
tokenizer.model
file from LLaMA model and put it to models
added_tokens.json
file from Alpaca model and put it to models
gpt4all-lora-quantized.bin
file from GPT4All model and put it to models/gpt4all-7B
ggml
format which is now obsoletedconvert.py
:python3 convert.py models/gpt4all-7B/gpt4all-lora-quantized.bin
You can now use the newly generated models/gpt4all-7B/ggml-model-q4_0.bin
model in exactly the same way as all other models
The newer GPT4All-J model is not yet supported!
ggml
format using the convert.py
script in this repo:python3 convert.py pygmalion-7b/ --outtype q4_1
The Pygmalion 7B & Metharme 7B weights are saved in bfloat16 precision. If you wish to convert to
ggml
without quantizating, please specify the--outtype
asf32
instead off16
.
Please verify the sha256 checksums of all downloaded model files to confirm that you have the correct model data files before creating an issue relating to your model files.
./models
subdirectory:# run the verification script
python3 .\scripts\verify-checksum-models.py
./models
subdirectory:
sha256sum --ignore-missing -c SHA256SUMS
shasum -a 256 --ignore-missing -c SHA256SUMS
If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw
perplexity : calculating perplexity over 655 chunks
24.43 seconds per pass - ETA 4.45 hours
[1]4.5970,[2]5.1807,[3]6.0382,...
And after 4.45 hours, you will have the final perplexity.
You can easily run llama.cpp
on Android device with termux.
First, obtain the Android NDK and then build with CMake:
$ mkdir build-android
$ cd build-android
$ export NDK=<your_ndk_directory>
$ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
$ make
Install termux on your device and run termux-setup-storage
to get access to your SD card.
Finally, copy the llama
binary and the model files to your device storage. Here is a demo of an interactive session running on Pixel 5 phone:
https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
We have two Docker images available for this project:
ghcr.io/ggerganov/llama.cpp:full
: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.ghcr.io/ggerganov/llama.cpp:light
: This image only includes the main executable file.The easiest way to download the models, convert them to ggml and optimize them is with the --all-in-one command which includes the full docker image.
Replace /path/to/models
below with the actual path where you downloaded the models.
docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-one "/models/" 7B
On completion, you are ready to play!
docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -n 512
or with a light image:
docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -n 512
llama.cpp
repo and merge PRs into the master
branchfor
loops, avoid templates, keep it simplevoid * ptr
, int & a
link |
Stars: 28793 |
Last commit: 4 hours ago |
Swiftpack is being maintained by Petr Pavlik | @ptrpavlik | @swiftpackco | API | Analytics