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
Remove artifact: .github/workflows/ci.yml
Browse files- .github/workflows/ci.yml +0 -29
.github/workflows/ci.yml
DELETED
|
@@ -1,29 +0,0 @@
|
|
| 1 |
-
name: CI
|
| 2 |
-
|
| 3 |
-
on:
|
| 4 |
-
push:
|
| 5 |
-
branches: [ main ]
|
| 6 |
-
pull_request:
|
| 7 |
-
branches: [ main ]
|
| 8 |
-
|
| 9 |
-
jobs:
|
| 10 |
-
build:
|
| 11 |
-
runs-on: ubuntu-latest
|
| 12 |
-
steps:
|
| 13 |
-
- uses: actions/checkout@v4
|
| 14 |
-
- uses: actions/setup-python@v4
|
| 15 |
-
with:
|
| 16 |
-
python-version: '3.11'
|
| 17 |
-
- name: Install dependencies
|
| 18 |
-
run: |
|
| 19 |
-
pip install --upgrade pip
|
| 20 |
-
pip install --extra-index-url https://download.pytorch.org/whl/cpu -r requirements.txt
|
| 21 |
-
pip install build
|
| 22 |
-
- name: Run tests
|
| 23 |
-
run: pytest -q
|
| 24 |
-
- name: Build package
|
| 25 |
-
run: python -m build --sdist --wheel -o dist
|
| 26 |
-
- uses: actions/upload-artifact@v4
|
| 27 |
-
with:
|
| 28 |
-
name: dist
|
| 29 |
-
path: dist
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|