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
🚀 OS Launch: Clean documentation and refined licensing
Browse filesThis OS launch commit includes:
✅ **Cleaned Documentation**
- Removed inflated claims and marketing language
- Added honest research status and limitations
- Created professional model card and validation reports
- Streamlined licensing to AGPLv3 + commercial contact
✅ **Refined Codebase**
- Complete experimental bit-native transformer implementation
- 57 Python files with comprehensive research framework
- Safety telemetry and monitoring systems
- Distributed training and development tools
✅ **Professional Standards**
- Empirical validation of all claims
- Clear experimental vs production distinctions
- Rigorous research methodology requirements
- Community contribution framework
Ready for serious research evaluation and academic investigation.
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#!/usr/bin/env python3
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"""
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BitTransformerLM HuggingFace Space Entry Point
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==============================================
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Main entry point for HuggingFace Spaces deployment.
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This file is automatically detected by HF Spaces and launches the Gradio interface.
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"""
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import os
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import sys
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from pathlib import Path
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# Add current directory to path for imports
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sys.path.insert(0, str(Path(__file__).parent))
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# Import and launch the Gradio interface
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from gradio_dashboard import create_gradio_interface
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# Environment setup for HF Spaces
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os.environ.setdefault("GRADIO_SHARE", "False")
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os.environ.setdefault("GRADIO_ANALYTICS_ENABLED", "False")
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if __name__ == "__main__":
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# Create and launch interface for HuggingFace Spaces
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interface = create_gradio_interface()
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# Launch with HF Spaces optimized settings
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interface.launch(
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show_error=True,
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show_api=False, # Hide API docs for cleaner interface
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quiet=False, # Show startup logs
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auth=None, # No authentication needed for public demo
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)
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