Instructions to use codeparrot/starcoder-conala with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codeparrot/starcoder-conala with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="codeparrot/starcoder-conala")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("codeparrot/starcoder-conala") model = AutoModelForCausalLM.from_pretrained("codeparrot/starcoder-conala") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use codeparrot/starcoder-conala with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "codeparrot/starcoder-conala" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codeparrot/starcoder-conala", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/codeparrot/starcoder-conala
- SGLang
How to use codeparrot/starcoder-conala 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 "codeparrot/starcoder-conala" \ --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": "codeparrot/starcoder-conala", "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 "codeparrot/starcoder-conala" \ --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": "codeparrot/starcoder-conala", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use codeparrot/starcoder-conala with Docker Model Runner:
docker model run hf.co/codeparrot/starcoder-conala
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This model is an instruction-tuned version of ⭐️ StarCoder. The instruction dataset involved is [Conala-mined-curated](https://huggingface.co/datasets/codeparrot/conala-mined-curated)
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which was built by boostrapping by predicting the column *rewritten_intent* of the mined subset of the [CoNaLa corpus](https://huggingface.co/datasets/neulab/conala).
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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The model was fine-tuned with the following template
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## More information
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For additional information, check
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- [Conala-mined-curated](https://huggingface.co/datasets/codeparrot/conala-mined-curated)
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This model is an instruction-tuned version of ⭐️ StarCoder. The instruction dataset involved is [Conala-mined-curated](https://huggingface.co/datasets/codeparrot/conala-mined-curated)
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which was built by boostrapping by predicting the column *rewritten_intent* of the mined subset of the [CoNaLa corpus](https://huggingface.co/datasets/neulab/conala).
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## Usage
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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The model was fine-tuned with the following template
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## More information
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For additional information, check
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- [Conala-mined-curated](https://huggingface.co/datasets/codeparrot/conala-mined-curated)
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- [Starcoder](https://huggingface.co/bigcode/starcoder)
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