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
File size: 3,310 Bytes
902f2d4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | # BitTransformerLM Scripts
This directory contains organized scripts for BitTransformerLM development, training, and evaluation.
## Directory Structure
```
scripts/
βββ training/ # Training scripts and experiments
βββ examples/ # Example usage and demonstrations
βββ testing/ # Test scripts and validation
βββ benchmarks/ # Performance benchmarks
βββ tools/ # Utility scripts and data processing
```
## Training Scripts (`training/`)
- **basic_training.py** - Simple training setup for small models
- **breakthrough_training.py** - Advanced training with breakthrough techniques
- **cpu_edge_training.py** - CPU-optimized training for edge deployment
- **final_breakthrough_training.py** - Production training pipeline
- **full_attention_training.py** - Full attention mechanism training
- **full_bits_train.py** - Complete bit-level training
- **production_training.py** - Production-ready training script
- **progressive_scaleup.py** - Progressive model scaling
- **quick_training_run.py** - Fast training for development
## Example Scripts (`examples/`)
- **example.py** - Basic usage example
- **better_sampling.py** - Advanced sampling techniques
- **debug_generation.py** - Generation debugging utilities
- **raw_generation.py** - Low-level generation examples
- **simple_test.py** - Simple model testing
## Testing Scripts (`testing/`)
- **code_test.py** - Code functionality testing
- **diffusion_tests.py** - Diffusion mode testing
- **enhanced_generation_test.py** - Advanced generation testing
- **full_attention_inference_test.py** - Attention mechanism tests
- **test_conversation.py** - Conversational AI testing
## Benchmark Scripts (`benchmarks/`)
- **wikitext_benchmark.py** - WikiText dataset benchmarking
- **wikitext_schedule.py** - WikiText training schedule
## Utility Tools (`tools/`)
- **build_full_bits.py** - Bit sequence construction
- **create_dataset.py** - Dataset creation utilities
- **enhanced_checkpoint_system.py** - Advanced checkpointing
- **integration_flow.py** - Integration workflow
- **integration_schedule.py** - Integration scheduling
- **sync_to_hf.py** - HuggingFace synchronization
- **unified_workflow.py** - Unified training workflow
- **watcher.py** - File system monitoring
## Usage
All scripts support the standardized CLI interface provided by `bit_transformer.cli_standards`. Use `--help` with any script to see available options.
### Quick Start
```bash
# Train a small model
python scripts/training/basic_training.py --model-size small --epochs 5
# Run a simple test
python scripts/examples/simple_test.py --d-model 64
# Benchmark on WikiText
python scripts/benchmarks/wikitext_benchmark.py --dataset-name wikitext-2
```
### Environment Variables
Scripts support configuration via environment variables with `BT_` prefix:
```bash
export BT_D_MODEL=128
export BT_NUM_LAYERS=4
export BT_BATCH_SIZE=16
python scripts/training/basic_training.py
```
## Development Guidelines
- All scripts should use `bit_transformer.cli_standards` for argument parsing
- Include proper logging and error handling
- Support both CPU and GPU execution
- Follow the naming conventions established in existing scripts
- Add documentation for any new hyperparameters or features |