Instructions to use bigcode/starcoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigcode/starcoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigcode/starcoder")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bigcode/starcoder") model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use bigcode/starcoder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigcode/starcoder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bigcode/starcoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigcode/starcoder
- SGLang
How to use bigcode/starcoder with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "bigcode/starcoder" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bigcode/starcoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "bigcode/starcoder" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bigcode/starcoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigcode/starcoder with Docker Model Runner:
docker model run hf.co/bigcode/starcoder
How to speed up inferring?
Apart from int8, is there any plan to speed up inferring, such as fastertransformer?
I dont think fastertransformer is an easy way... may torchscript and pytorch2.0 work
The easiest way to do this may be to use the inference server:
https://github.com/bigcode-project/starcoder#text-generation-inference
You could try: https://huggingface.co/michaelfeil/ct2fast-starcoder/blob/main/README.md
Amazing, how much speed could ct2fast-starcoder bring compared with the oringinal starcoder?
You could try: https://huggingface.co/michaelfeil/ct2fast-starcoder/blob/main/README.md
Amazing, how much speed could ct2fast-starcoder bring compared with the oringinal starcoder?
Did not have time to check for starcoder. For santacoder:
Task: "def hello" -> generate 30 tokens
-> transformers pipeline in float 16, cuda: ~1300ms per inference
-> ctranslate2 in int8, cuda -> 315ms per inference
I assume for starcoder, weights are bigger, hence maybe 1.5-2.5x speedup.
You could try: https://huggingface.co/michaelfeil/ct2fast-starcoder/blob/main/README.md
This works like a charm, 100 times faster than the starchat and starcoder. I tried with 8, 12, 16G but failed, at least 24G RAM GPU will work.