💫StarCoder in C++

This is a C++ example running 💫 StarCoder inference using the ggml library.

The program can run on the CPU - no video card is required.

The example supports the following 💫 StarCoder models:

• bigcode/starcoder
• bigcode/gpt_bigcode-santacoder aka the smol StarCoder
• HuggingFaceH4/starchat-beta - the coding assistants based on StarCoderPlus

Sample performance on MacBook M1 Pro:

TODO

Sample output:

$ ./bin/starcoder -h
usage: ./bin/starcoder [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: 8)
  -p PROMPT, --prompt PROMPT
                        prompt to start generation with (default: random)
  -n N, --n_predict N   number of tokens to predict (default: 200)
  --top_k N             top-k sampling (default: 40)
  --top_p N             top-p sampling (default: 0.9)
  --temp N              temperature (default: 1.0)
  -b N, --batch_size N  batch size for prompt processing (default: 8)
  -m FNAME, --model FNAME
                        model path (default: models/starcoder-117M/ggml-model.bin)

$ ./bin/starcoder -m ../models/bigcode/gpt_bigcode-santacoder-ggml-q4_1.bin -p "def fibonnaci(" -t 4 --top_k 0 --top_p 0.95 --temp 0.2      
main: seed = 1683881276
starcoder_model_load: loading model from '../models/bigcode/gpt_bigcode-santacoder-ggml-q4_1.bin'
starcoder_model_load: n_vocab = 49280
starcoder_model_load: n_ctx   = 2048
starcoder_model_load: n_embd  = 2048
starcoder_model_load: n_head  = 16
starcoder_model_load: n_layer = 24
starcoder_model_load: ftype   = 3
starcoder_model_load: ggml ctx size = 1794.90 MB
starcoder_model_load: memory size =   768.00 MB, n_mem = 49152
starcoder_model_load: model size  =  1026.83 MB
main: prompt: 'def fibonnaci('
main: number of tokens in prompt = 7, first 8 tokens: 563 24240 78 2658 64 2819 7 

def fibonnaci(n):
    if n == 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fibonacci(n-1) + fibonacci(n-2)

print(fibo(10))

main: mem per token =  9597928 bytes
main:     load time =   480.43 ms
main:   sample time =    26.21 ms
main:  predict time =  3987.95 ms / 19.36 ms per token
main:    total time =  4580.56 ms

Quick start

git clone https://github.com/bigcode-project/starcoder.cpp
cd starcoder.cpp

# Convert HF model to ggml
python convert-hf-to-ggml.py bigcode/gpt_bigcode-santacoder

# Build ggml libraries
make

# quantize the model
./quantize models/bigcode/gpt_bigcode-santacoder-ggml.bin models/bigcode/gpt_bigcode-santacoder-ggml-q4_1.bin 3

# run inference
./main -m models/bigcode/gpt_bigcode-santacoder-ggml-q4_1.bin -p "def fibonnaci(" --top_k 0 --top_p 0.95 --temp 0.2

Downloading and converting the original models (💫 StarCoder)

You can download the original model and convert it to ggml format using the script convert-hf-to-ggml.py:

# Convert HF model to ggml
python convert-hf-to-ggml.py bigcode/gpt_bigcode-santacoder

This conversion requires that you have python and Transformers installed on your computer.

Quantizing the models

You can also try to quantize the ggml models via 4-bit integer quantization.

# quantize the model
./quantize models/bigcode/gpt_bigcode-santacoder-ggml.bin models/bigcode/gpt_bigcode-santacoder-ggml-q4_1.bin 3

Model	Original size	Quantized size	Quantization type
bigcode/gpt_bigcode-santacoder	5396.45 MB	1026.83 MB	4-bit integer (q4_1)
bigcode/starcoder	71628.23 MB	13596.23 MB	4-bit integer (q4_1)

iOS App

The repo includes a proof-of-concept iOS app in the StarCoderApp directory. You need to provide the converted (and possibly quantized) model weights, placing a file called bigcode_ggml_model.bin.bin inside that folder. This is what it looks like on an iPhone: