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
| FROM ubuntu:22.04 | |
| ENV DEBIAN_FRONTEND=noninteractive | |
| RUN apt-get update && \ | |
| apt-get install -y python3.11 python3-pip python3.11-venv curl && \ | |
| apt-get clean && rm -rf /var/lib/apt/lists/* | |
| WORKDIR /opt/bit_transformer | |
| COPY . . | |
| ARG TORCH_CUDA=cpu | |
| RUN pip3 install --no-cache-dir --upgrade pip && \ | |
| if [ "$TORCH_CUDA" = "cu118" ]; then \ | |
| pip3 install torch==2.7.1+cu118 --extra-index-url https://download.pytorch.org/whl/cu118; \ | |
| else \ | |
| pip3 install torch==2.7.1+cpu --extra-index-url https://download.pytorch.org/whl/cpu; \ | |
| fi && \ | |
| pip3 install -r requirements.txt | |
| ENV MCP_SERVER_ADDR=http://127.0.0.1:7000 | |
| EXPOSE 5000 7000 | |
| RUN chmod +x start.sh | |
| HEALTHCHECK CMD curl -f http://localhost:7000/health || exit 1 | |
| CMD ["/opt/bit_transformer/start.sh"] | |