Text Generation
Transformers
PyTorch
Safetensors
gpt_bigcode
code
Eval Results (legacy)
text-generation-inference
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 Settings
- 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
Using starcoder in question answering pipeline
#42
by liukuo99 - opened
Is it possible to use starcoder for question and answering tasks? I'd like to use starcoder in a question-answering pipeline, but it seems it's not supported. I got the following error when running
question = "some specific question"
pipe(question=question, context=contents)```
```The model 'GPTBigCodeForCausalLM' is not supported for question-answering. Supported models are ['AlbertForQuestionAnswering', 'BartForQuestionAnswering', 'BertForQuestionAnswering', 'BigBirdForQuestionAnswering', 'BigBirdPegasusForQuestionAnswering', 'BloomForQuestionAnswering', 'CamembertForQuestionAnswering', 'CanineForQuestionAnswering', 'ConvBertForQuestionAnswering', 'Data2VecTextForQuestionAnswering', 'DebertaForQuestionAnswering', 'DebertaV2ForQuestionAnswering', 'DistilBertForQuestionAnswering', 'ElectraForQuestionAnswering', 'ErnieForQuestionAnswering', 'ErnieMForQuestionAnswering', 'FlaubertForQuestionAnsweringSimple', 'FNetForQuestionAnswering', 'FunnelForQuestionAnswering', 'GPT2ForQuestionAnswering', 'GPTNeoForQuestionAnswering', 'GPTNeoXForQuestionAnswering', 'GPTJForQuestionAnswering', 'IBertForQuestionAnswering', 'LayoutLMv2ForQuestionAnswering', 'LayoutLMv3ForQuestionAnswering', 'LEDForQuestionAnswering', 'LiltForQuestionAnswering', 'LongformerForQuestionAnswering', 'LukeForQuestionAnswering', 'LxmertForQuestionAnswering', 'MarkupLMForQuestionAnswering', 'MBartForQuestionAnswering', 'MegaForQuestionAnswering', 'MegatronBertForQuestionAnswering', 'MobileBertForQuestionAnswering', 'MPNetForQuestionAnswering', 'MvpForQuestionAnswering', 'NezhaForQuestionAnswering', 'NystromformerForQuestionAnswering', 'OPTForQuestionAnswering', 'QDQBertForQuestionAnswering', 'ReformerForQuestionAnswering', 'RemBertForQuestionAnswering', 'RobertaForQuestionAnswering', 'RobertaPreLayerNormForQuestionAnswering', 'RoCBertForQuestionAnswering', 'RoFormerForQuestionAnswering', 'SplinterForQuestionAnswering', 'SqueezeBertForQuestionAnswering', 'XLMForQuestionAnsweringSimple', 'XLMRobertaForQuestionAnswering', 'XLMRobertaXLForQuestionAnswering', 'XLNetForQuestionAnsweringSimple', 'XmodForQuestionAnswering', 'YosoForQuestionAnswering'].```
Its most likely:
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
On huggingface on top just below the reponame it says this model is a text generation, so that should be in the pipeline, not Q&A