Georgi Gerganov
2 years ago
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# llama.cpp
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Inference of [Facebook's LLaMA](https://github.com/facebookresearch/llama) model in pure C/C++
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## Description
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The main goal is to run the model using 4-bit quantization on a MacBook.
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- Plain C/C++ implementation without dependencies
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- Apple silicon first-class citizen - optimized via Arm Neon and Accelerate framework
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- Mixed F16 / F32 precision
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- 4-bit quantization support
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- Runs on the CPU
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This was hacked in an evening - I have no idea if it works correctly.
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So far, I've tested just the 7B model and the generated text starts coherently, but typically degrades significanlty after ~30-40 tokens.
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Here is a "typicaly" run:
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```java
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make -j && ./main -m ./models/7B/ggml-model-q4_0.bin -t 8 -n 128
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I llama.cpp build info:
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I UNAME_S: Darwin
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I UNAME_P: arm
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I UNAME_M: arm64
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I CFLAGS: -I. -O3 -DNDEBUG -std=c11 -fPIC -pthread -DGGML_USE_ACCELERATE
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I CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC -pthread
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I LDFLAGS: -framework Accelerate
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I CC: Apple clang version 14.0.0 (clang-1400.0.29.202)
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I CXX: Apple clang version 14.0.0 (clang-1400.0.29.202)
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c++ -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC -pthread main.cpp ggml.o utils.o -o main -framework Accelerate
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./main -h
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usage: ./main [options]
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options:
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-h, --help show this help message and exit
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-s SEED, --seed SEED RNG seed (default: -1)
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-t N, --threads N number of threads to use during computation (default: 4)
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-p PROMPT, --prompt PROMPT
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prompt to start generation with (default: random)
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-n N, --n_predict N number of tokens to predict (default: 128)
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--top_k N top-k sampling (default: 40)
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--top_p N top-p sampling (default: 0.9)
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--temp N temperature (default: 0.8)
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-b N, --batch_size N batch size for prompt processing (default: 8)
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-m FNAME, --model FNAME
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model path (default: models/llama-7B/ggml-model.bin)
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main: seed = 1678476633
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llama_model_load: loading model from './models/7B/ggml-model-q4_0.bin' - please wait ...
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llama_model_load: n_vocab = 32000
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llama_model_load: n_ctx = 512
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llama_model_load: n_embd = 4096
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llama_model_load: n_mult = 256
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llama_model_load: n_head = 32
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llama_model_load: n_layer = 32
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llama_model_load: n_rot = 64
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llama_model_load: f16 = 2
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llama_model_load: n_ff = 11008
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llama_model_load: ggml ctx size = 4529.34 MB
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llama_model_load: memory_size = 512.00 MB, n_mem = 16384
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llama_model_load: .................................... done
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llama_model_load: model size = 4017.27 MB / num tensors = 291
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main: prompt: 'If'
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main: number of tokens in prompt = 2
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1 -> ''
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3644 -> 'If'
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sampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000
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If you are a fan of the original Star Wars trilogy, then you'll want to see this.
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If you don't know your Star Wars lore, this will be a huge eye-opening and you will be a little confusing.
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Awesome movie.(end of text)
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main: mem per token = 14434244 bytes
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main: load time = 1313.77 ms
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main: sample time = 6.17 ms
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main: predict time = 3271.53 ms / 54.53 ms per token
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main: total time = 4797.98 ms
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```
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## Usage
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```bash
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# build this repo
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git clone https://github.com/ggerganov/llama.cpp
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cd llama.cpp
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make
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# obtain the original LLaMA model weights and place them in ./models
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ls ./models
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65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model
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# convert the 7B model to ggml FP16 format
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python3 convert-pth-to-ggml.py models/7B/ 1
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# quantize the model to 4-bits
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./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin 2
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# run the inference
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./main -m ./models/7B/ggml-model-q4_0.bin -t 8 -n 128
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```
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## Limitations
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- Currently, only LLaMA-7B is supported since I haven't figured out how to merge the tensors of the bigger models. However, in theory, you should be able to run 65B on a 64GB MacBook
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- Not sure if my tokenizer is correct. There are a few places where we might have a mistake:
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- https://github.com/ggerganov/llama.cpp/blob/26c084662903ddaca19bef982831bfb0856e8257/convert-pth-to-ggml.py#L79-L87
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- https://github.com/ggerganov/llama.cpp/blob/26c084662903ddaca19bef982831bfb0856e8257/utils.h#L65-L69
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In general, it seems to work, but I think it fails for unicode character support. Hopefully, someone can help with that
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- I don't know yet how much the quantization affects the quality of the generated text
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- Probably the token sampling can be improved
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- No Windows support
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- x86 quantization support [not yet ready](https://github.com/ggerganov/ggml/pull/27). Basically, you want to run this on Apple Silicon
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