Instructions to use twdooley/breitbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use twdooley/breitbot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="twdooley/breitbot")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("twdooley/breitbot") model = AutoModelForCausalLM.from_pretrained("twdooley/breitbot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use twdooley/breitbot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "twdooley/breitbot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "twdooley/breitbot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/twdooley/breitbot
- SGLang
How to use twdooley/breitbot 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 "twdooley/breitbot" \ --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": "twdooley/breitbot", "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 "twdooley/breitbot" \ --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": "twdooley/breitbot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use twdooley/breitbot with Docker Model Runner:
docker model run hf.co/twdooley/breitbot
Download pytorch_model.bin from twdooley/breitbot: direct link, hf CLI and curl.
- Browser
- Download file 510 MB
-
https://huggingface.co/twdooley/breitbot/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://twdooley/breitbot/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/twdooley/breitbot/resolve/main/pytorch_model.bin
510 MB
- Xet hash:
- 2b1023d875456abfd2d3fea13efe140dbfe2e3b9cca6e9bf995112da491796d8
- Size of remote file:
- 510 MB
- SHA256:
- b9a8a38aa75d314f4f6dd673cfd8373585c279d3efd8fc348f3ef7e9f99f39ab
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