Text Generation
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Instructions to use Wholesomeisland/autocomplete-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wholesomeisland/autocomplete-coder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Wholesomeisland/autocomplete-coder")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Wholesomeisland/autocomplete-coder") model = AutoModelForCausalLM.from_pretrained("Wholesomeisland/autocomplete-coder") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Wholesomeisland/autocomplete-coder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Wholesomeisland/autocomplete-coder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Wholesomeisland/autocomplete-coder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Wholesomeisland/autocomplete-coder
- SGLang
How to use Wholesomeisland/autocomplete-coder 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 "Wholesomeisland/autocomplete-coder" \ --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": "Wholesomeisland/autocomplete-coder", "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 "Wholesomeisland/autocomplete-coder" \ --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": "Wholesomeisland/autocomplete-coder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Wholesomeisland/autocomplete-coder with Docker Model Runner:
docker model run hf.co/Wholesomeisland/autocomplete-coder
| library_name: transformers | |
| license: mit | |
| base_model: gpt2 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: trainer_output | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # trainer_output | |
| This model is a training of a c/c++ autocomplete model on ~2GB of c/c++ files, the structure is based on gpt2 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 5e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 1 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.57.1 | |
| - Pytorch 2.9.0+cu126 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.1 | |