4 Steps in Running LLaMA-7B on a M1 MacBook with `llama.cpp`

llama-7b-m1.md

4 Steps in Running LLaMA-7B on a M1 MacBook

The large language models usability

The problem with large language models is that you can’t run these locally on your laptop. Thanks to Georgi Gerganov and his llama.cpp project, it is now possible to run Meta’s LLaMA on a single computer without a dedicated GPU.

Running LLaMA

There are multiple steps involved in running LLaMA locally on a M1 Mac after downloading the model weights.

After you downloaded the model weights, you should have something like this:

.
├── 7B
│  ├── checklist.chk
│  ├── consolidated.00.pth
│  └── params.json
├── 13B
│  ├── checklist.chk
│  ├── consolidated.00.pth
│  ├── consolidated.01.pth
│  └── params.json
├── 30B
│  ├── checklist.chk
│  ├── consolidated.00.pth
│  ├── consolidated.01.pth
│  ├── consolidated.02.pth
│  ├── consolidated.03.pth
│  └── params.json
├── 65B
│  ├── checklist.chk
│  ├── consolidated.00.pth
│  ├── consolidated.01.pth
│  ├── consolidated.02.pth
│  ├── consolidated.03.pth
│  ├── consolidated.04.pth
│  ├── consolidated.05.pth
│  ├── consolidated.06.pth
│  ├── consolidated.07.pth
│  └── params.json
├── tokenizer.model
└── tokenizer_checklist.chk

You can see the different models are in a different directories.

Step 1: Install dependencies

Xcode must be installed to compile the C++ project. If you don’t have it, please do the following:

$ xcode-select --install

Next, install dependencies for building the C++ project.

$ brew install pkgconfig cmake

Finally, we install Torch.

I assume you have Python 3 installed so you can create a virtual env like this:

$ /opt/homebrew/bin/python3.11 -m venv venv

Activate the venv. I am using bash.

$ source venv/bin/activate

Install PyTorch:

$ pip install --pre torch torchvision --extra-index-url https://download.pytorch.org/whl/nightly/cpu

Next, let's compile llama.cpp.

Step 2: Compile llama.cpp

Clone the repo:

$ git clone https://github.com/ggerganov/llama.cpp.git

Run make:

$ make

Output:

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)

cc  -I.              -O3 -DNDEBUG -std=c11   -fPIC -pthread -DGGML_USE_ACCELERATE   -c ggml.c -o ggml.o
c++ -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC -pthread -c utils.cpp -o utils.o
c++ -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC -pthread main.cpp ggml.o utils.o -o main  -framework Accelerate
./main -h
usage: ./main [options]

options:
  -h, --help            show this help message and exit
  -s SEED, --seed SEED  RNG seed (default: -1)
  -t N, --threads N     number of threads to use during computation (default: 4)
  -p PROMPT, --prompt PROMPT
                        prompt to start generation with (default: random)
  -n N, --n_predict N   number of tokens to predict (default: 128)
  --top_k N             top-k sampling (default: 40)
  --top_p N             top-p sampling (default: 0.9)
  --temp N              temperature (default: 0.8)
  -b N, --batch_size N  batch size for prompt processing (default: 8)
  -m FNAME, --model FNAME
                        model path (default: models/llama-7B/ggml-model.bin)

c++ -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC -pthread quantize.cpp ggml.o utils.o -o quantize  -framework Accelerate

Step 3: Convert the model to ggml format

Assuming you placed the models under models/ in the llama.cpp repo.

$ python convert-pth-to-ggml.py models/7B 1

Output:

{'dim': 4096, 'multiple_of': 256, 'n_heads': 32, 'n_layers': 32, 'norm_eps': 1e-06, 'vocab_size': 32000}
n_parts =  1
Processing part  0
Processing variable: tok_embeddings.weight with shape:  torch.Size([32000, 4096])  and type:  torch.float16
Processing variable: norm.weight with shape:  torch.Size([4096])  and type:  torch.float16
  Converting to float32
Processing variable: output.weight with shape:  torch.Size([32000, 4096])  and type:  torch.float16
Processing variable: layers.0.attention.wq.weight with shape:  torch.Size([4096, 4096])  and type:  torch.f
loat16
Processing variable: layers.0.attention.wk.weight with shape:  torch.Size([4096, 4096])  and type:  torch.f
loat16
Processing variable: layers.0.attention.wv.weight with shape:  torch.Size([4096, 4096])  and type:  torch.f
loat16
Processing variable: layers.0.attention.wo.weight with shape:  torch.Size([4096, 4096])  and type:  torch.f
loat16
Processing variable: layers.0.feed_forward.w1.weight with shape:  torch.Size([11008, 4096])  and type:  tor
ch.float16
Processing variable: layers.0.feed_forward.w2.weight with shape:  torch.Size([4096, 11008])  and type:  tor
ch.float16
Processing variable: layers.0.feed_forward.w3.weight with shape:  torch.Size([11008, 4096])  and type:  tor
ch.float16
Processing variable: layers.0.attention_norm.weight with shape:  torch.Size([4096])  and type:  torch.float
16
...
Done. Output file: models/7B/ggml-model-f16.bin, (part  0 )

Next, quantize the model:

$ ./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin 2

You should see an output like this:

llama_model_quantize: loading model from './models/7B/ggml-model-f16.bin'
llama_model_quantize: n_vocab = 32000
llama_model_quantize: n_ctx   = 512
llama_model_quantize: n_embd  = 4096
llama_model_quantize: n_mult  = 256
llama_model_quantize: n_head  = 32
llama_model_quantize: n_layer = 32
llama_model_quantize: f16     = 1
...
layers.31.attention_norm.weight - [ 4096,     1], type =    f32 size =    0.016 MB
layers.31.ffn_norm.weight - [ 4096,     1], type =    f32 size =    0.016 MB
llama_model_quantize: model size  = 25705.02 MB
llama_model_quantize: quant size  =  4017.27 MB
llama_model_quantize: hist: 0.000 0.022 0.019 0.033 0.053 0.078 0.104 0.125 0.134 0.125 0.104 0.078 0.053 0.033 0.019 0.022

main: quantize time = 29389.45 ms
main:    total time = 29389.45 ms

Step 4: Run the model

$ ./main -m ./models/7B/ggml-model-q4_0.bin \
        -t 8 \
        -n 128 \
        -p 'The first man on the moon was '

main: seed = 1678615879
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: n_parts = 1
llama_model_load: ggml ctx size = 4529.34 MB
llama_model_load: memory_size =   512.00 MB, n_mem = 16384
llama_model_load: loading model part 1/1 from './models/7B/ggml-model-q4_0.bin'
llama_model_load: .................................... done
llama_model_load: model size =  4017.27 MB / num tensors = 291

main: prompt: 'The first man on the moon was '
main: number of tokens in prompt = 9
     1 -> ''
  1576 -> 'The'
   937 -> ' first'
   767 -> ' man'
   373 -> ' on'
   278 -> ' the'
 18786 -> ' moon'
   471 -> ' was'
 29871 -> ' '

sampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000


The first man on the moon was 38-year-old astronaut Neil A. Armstrong.
Apollo 11 landed on the moon on July 20, 1969.
Apollo 11 was the first manned mission to land on the Moon. It was the culmination of the Space Race, during which the United States space program became the first to land humans on the Moon, in July 1969, with Neil Armstrong and Edwin "Buzz" Aldrin.
Apollo 11 launched from the Kennedy Space Center on July 16, 1

main: mem per token = 14434244 bytes
main:     load time =  1309.32 ms
main:   sample time =   276.53 ms
main:  predict time =  7374.62 ms / 53.36 ms per token
main:    total time =  9013.06 ms

We're done. Have fun prompting the model!

Thanks for the nice instruction.
What are the meanings of the parameters? ./models/7B/ggml-model-q4_0.bin 2 and ./main -m ./models/7B/ggml-model-q4_0.bin \ -t 8 \ -n 128 \

Now you can use metal:
https://github.com/ggerganov/llama.cpp/tree/master#metal-build

LLAMA_METAL=1 make

GPU

./main -m ./models/ggml-model-q4_0.bin -t 8 -n 128 -ngl 1 -p 'The first man on the moon was '

--gpu-layers|-ngl 1

It's faster on an M1 Pro 16GB - 15% on my on test

CPU

llama_print_timings:        load time =  3796.38 ms
llama_print_timings:      sample time =    93.10 ms /   128 runs   (    0.73 ms per token)
llama_print_timings: prompt eval time =   313.91 ms /     9 tokens (   34.88 ms per token)
llama_print_timings:        eval time =  7539.37 ms /   127 runs   (   59.37 ms per token)
llama_print_timings:       total time =  7959.69 ms

GPU

llama_print_timings:        load time =  6475.77 ms
llama_print_timings:      sample time =    90.81 ms /   128 runs   (    0.71 ms per token)
llama_print_timings: prompt eval time =   500.01 ms /     9 tokens (   55.56 ms per token)
llama_print_timings:        eval time =  5661.41 ms /   127 runs   (   44.58 ms per token)
llama_print_timings:       total time =  6265.56 ms

hi i tried to follow your guide, when running make in llama.cpp git i get

rosetta error: Attachment of code signature supplement failed: 1 
 /var/db/oah/26ca8d9acfae67956160c9bf78b89a6dfd32e5c6281abcfa97476815e1766e6a/2453f289ab97914095ba641c2021e9ca52e9ba43b145d2ea01bf302c7f5fccb1/libxcrun.dylib.aotrosetta error: Attachment of code signature supplement failed: 1 
 /var/db/oah/26ca8d9acfae67956160c9bf78b89a6dfd32e5c6281abcfa97476815e1766e6a/2453f289ab97914095ba641c2021e9ca52e9ba43b145d2ea01bf302c7f5fccb1/libxcrun.dylib.aotI llama.cpp build info: 

you need to disable rosetta: rosetta is an intel interpreter. It runs natively in the background is try to install software that was written for x86

How do we execute the python convert-pth-to-ggml.py models/7B 1 and ./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin 2 for the 13B and 70B models?

I used thia for mac M1: step 3 answers your question:
https://gist.github.com/cedrickchee/e8d4cb0c4b1df6cc47ce8b18457ebde0 :

$ python convert-pth-to-ggml.py models/7B 1

$ ./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin 2

I tried to run the 7b-chat model on M2 Macbook Air with "-ngl 1", it is actually slower than using CPU a little bit. Is it normal?

convert-pth-to-ggml.py is no longer in the repo for some reason. Looks like you can use "python convert.py" instead

How to connect it using llamaindex /langchain ?

When i run the convert.py i get an error saying config.json file not found. where can i find this file?

When i run the convert.py i get an error saying config.json file not found. where can i find this file?

Same :(

When i run the convert.py i get an error saying config.json file not found. where can i find this file?

Same :(
Here is the config file i am using:

{
  "_name_or_path": "meta-llama/Llama-2-7b-chat-hf",
  "architectures": [
    "LlamaForCausalLM"
  ],
  "bos_token_id": 1,
  "eos_token_id": 2,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 11008,
  "max_position_embeddings": 4096,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 32,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-06,
  "rope_scaling": null,
  "tie_word_embeddings": false,
  "torch_dtype": "float16",
  "transformers_version": "4.32.0.dev0",
  "use_cache": true,
  "vocab_size": 32000,
  "pad_token_id": 0,
  "quantization_config": {
    "quant_method": "awq",
    "zero_point": true,
    "group_size": 128,
    "bits": 4,
    "version": "gemm"
  }
}

This gist was great but now the ggml is outdated. Could we rewrite this to use the current gguf format?

I am getting an error after running python convert.py

line 1451, in main
if np.uint32(1) == np.uint32(1).newbyteorder("<"):
^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: newbyteorder was removed from scalar types in NumPy 2.0. Use sc.view(sc.dtype.newbyteorder(order)) instead.

Downgrade NumPy to the last 1.x version, this fixed it for me.