在纯C/C++中推断Facebook的LLaMA模型
主要目标是在MacBook上使用4位量化运行模型
这是在一个晚上被黑客入侵的 - 我不知道它是否正常工作。请不要根据此实现的结果对模型做出结论。据我所知,这可能是完全错误的。该项目用于教育目的。新功能可能主要通过社区贡献来添加。
支持的平台:
以下是使用 LLaMA-7B 的典型运行:
make -j && ./main -m ./models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -t 8 -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
这是在单个M1 Pro MacBook上运行LLaMA-7B和whisper.cpp的另一个演示:
https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8b4f-add84093ffff.mp4
以下是LLaMA-7B型号的步骤:
# build this repo
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make
# 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 torch numpy sentencepiece
# convert the 7B model to ggml FP16 format
python3 convert-pth-to-ggml.py models/7B/ 1
# quantize the model to 4-bits
./quantize.sh 7B
# run the inference
./main -m ./models/7B/ggml-model-q4_0.bin -t 8 -n 128
运行较大的模型时,请确保有足够的磁盘空间来存储所有中间文件。
待办事项:添加模型磁盘/内存要求
如果你想要更像 ChatGPT 的体验,你可以通过作为参数传递在交互模式下运行。在此模式下,你始终可以通过按 Ctrl+C 并输入一行或多行文本来中断生成,这些文本将转换为标记并附加到当前上下文中。你还可以使用参数指定反向提示。这将导致每当在生成中遇到反向提示字符串的确切标记时,都会提示用户输入。一个典型的用途是使用提示,使LLaMa模拟多个用户之间的聊天,比如爱丽丝和鲍勃,并通过。
-i
-r "reverse prompt string"
-r "Alice:"
下面是一个示例 几个镜头交互,使用 命令调用
./main -m ./models/13B/ggml-model-q4_0.bin -t 8 -n 256 --repeat_penalty 1.0 --color -i -r "User:" \ -p \ "Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision. User: Hello, Bob. Bob: Hello. How may I help you today? User: Please tell me the largest city in Europe. Bob: Sure. The largest city in Europe is Moscow, the capital of Russia. User:"
请注意使用 来区分用户输入和生成的文本。
--color
LLAMA_NO_ACCELERATE=1 make
llama.cpp
int * var