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
- 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
| [build-system] | |
| requires = ["setuptools>=67", "wheel"] | |
| build-backend = "setuptools.build_meta" | |
| [project] | |
| name = "bit-transformer-lm" | |
| version = "1.0.0-rc1" | |
| description = "Production-grade bit-native transformer with built-in safety telemetry and enterprise features" | |
| readme = "README.md" | |
| requires-python = ">=3.10" | |
| license = {text = "All Rights Reserved"} | |
| authors = [{name = "WCNegentropy", email = "research@wcnegentropy.com"}] | |
| keywords = ["transformer", "language-model", "safety", "telemetry", "distributed-training", "quantization"] | |
| classifiers = [ | |
| "Development Status :: 5 - Production/Stable", | |
| "Intended Audience :: Developers", | |
| "Intended Audience :: Science/Research", | |
| "Topic :: Scientific/Engineering :: Artificial Intelligence", | |
| "Topic :: Software Development :: Libraries :: Python Modules", | |
| "Programming Language :: Python :: 3", | |
| "Programming Language :: Python :: 3.10", | |
| "Programming Language :: Python :: 3.11", | |
| ] | |
| [project.urls] | |
| Homepage = "https://github.com/WCNegentropy/BitTransformerLM" | |
| Documentation = "https://github.com/WCNegentropy/BitTransformerLM/blob/main/README.md" | |
| Repository = "https://github.com/WCNegentropy/BitTransformerLM" | |
| Issues = "https://github.com/WCNegentropy/BitTransformerLM/issues" | |
| [tool.setuptools.packages.find] | |
| include = ["bit_transformer"] | |