Text Generation
Transformers
PyTorch
English
experimental
research
bit-level
transformer
reversible
safety
telemetry
language-modeling
Instructions to use WCNegentropy/BitTransformerLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WCNegentropy/BitTransformerLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="WCNegentropy/BitTransformerLM")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("WCNegentropy/BitTransformerLM", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use WCNegentropy/BitTransformerLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "WCNegentropy/BitTransformerLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WCNegentropy/BitTransformerLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/WCNegentropy/BitTransformerLM
- SGLang
How to use WCNegentropy/BitTransformerLM 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 "WCNegentropy/BitTransformerLM" \ --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": "WCNegentropy/BitTransformerLM", "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 "WCNegentropy/BitTransformerLM" \ --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": "WCNegentropy/BitTransformerLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use WCNegentropy/BitTransformerLM with Docker Model Runner:
docker model run hf.co/WCNegentropy/BitTransformerLM
| # Byte-compiled / optimized / DLL files | |
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| *.so | |
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| MANIFEST | |
| # PyInstaller | |
| # Usually these files are written by a python script from a template | |
| # before PyInstaller builds the exe, so as to inject date/other infos into it. | |
| *.manifest | |
| *.spec | |
| # Installer logs | |
| pip-log.txt | |
| pip-delete-this-directory.txt | |
| # Unit test / coverage reports | |
| htmlcov/ | |
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| .coverage | |
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| .cache | |
| nosetests.xml | |
| coverage.xml | |
| *.cover | |
| *.py,cover | |
| .hypothesis/ | |
| .pytest_cache/ | |
| # Jupyter Notebook | |
| .ipynb_checkpoints | |
| # Pyre type checker | |
| .pyre/ | |
| # mypy | |
| .mypy_cache/ | |
| # Environments | |
| .env | |
| .venv | |
| env/ | |
| venv/ | |
| ENV/ | |
| # Spyder project settings | |
| .spyderproject | |
| .spyproject | |
| # Rope project settings | |
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| # IDEs | |
| .idea/ | |
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| # macOS | |
| .DS_Store | |
| # Logs | |
| *.log | |
| # Plot outputs | |
| *.png | |
| figures/ | |
| # Model artifacts | |
| *.pt | |
| *.pth | |
| *.bin | |
| candidates/ | |
| approved/ | |
| review_log.jsonl | |
| # Configurations | |
| *.ini | |
| # Local data | |
| *.sqlite3 | |
| *.pt.gz | |