Instructions to use loubnabnl/santacoder-code-to-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use loubnabnl/santacoder-code-to-text with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="loubnabnl/santacoder-code-to-text", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("loubnabnl/santacoder-code-to-text", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("loubnabnl/santacoder-code-to-text", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use loubnabnl/santacoder-code-to-text with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "loubnabnl/santacoder-code-to-text" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "loubnabnl/santacoder-code-to-text", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/loubnabnl/santacoder-code-to-text
- SGLang
How to use loubnabnl/santacoder-code-to-text 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 "loubnabnl/santacoder-code-to-text" \ --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": "loubnabnl/santacoder-code-to-text", "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 "loubnabnl/santacoder-code-to-text" \ --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": "loubnabnl/santacoder-code-to-text", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use loubnabnl/santacoder-code-to-text with Docker Model Runner:
docker model run hf.co/loubnabnl/santacoder-code-to-text
| license: openrail | |
| datasets: | |
| - codeparrot/github-jupyter-code-to-text | |
| library_name: transformers | |
| tags: | |
| - code | |
| # Santacoder code-to-text | |
| This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on | |
| [copdeparrot/gitub-jupyter-code-to-text](https://huggingface.co/datasets/codeparrot/github-jupyter-code-to-text). | |
| ## Training procedure | |
| The model was trained on 4 A100 for 3h with the following hyperparameters were used during training on 4 A100: | |
| - learning_rate: 5e-05 | |
| - train_batch_size: 2 | |
| - eval_batch_size: 2 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 4 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 100 | |
| - training_steps: 800 | |