Instructions to use cgrumbach/BitcoinPaper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cgrumbach/BitcoinPaper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cgrumbach/BitcoinPaper")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cgrumbach/BitcoinPaper") model = AutoModelForCausalLM.from_pretrained("cgrumbach/BitcoinPaper", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cgrumbach/BitcoinPaper with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cgrumbach/BitcoinPaper" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cgrumbach/BitcoinPaper", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cgrumbach/BitcoinPaper
- SGLang
How to use cgrumbach/BitcoinPaper 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 "cgrumbach/BitcoinPaper" \ --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": "cgrumbach/BitcoinPaper", "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 "cgrumbach/BitcoinPaper" \ --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": "cgrumbach/BitcoinPaper", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use cgrumbach/BitcoinPaper with Docker Model Runner:
docker model run hf.co/cgrumbach/BitcoinPaper
Download bitcoinforum/2_train_set_creation/inputs.csv from cgrumbach/BitcoinPaper: direct link, hf CLI and curl.
- Browser
- Download file 71.4 MB
-
https://huggingface.co/cgrumbach/BitcoinPaper/resolve/main/bitcoinforum/2_train_set_creation/inputs.csv
- Command line
-
hf download hf://cgrumbach/BitcoinPaper/bitcoinforum/2_train_set_creation/inputs.csv
-
curl -L -o inputs.csv https://huggingface.co/cgrumbach/BitcoinPaper/resolve/main/bitcoinforum/2_train_set_creation/inputs.csv
71.4 MB
- Xet hash:
- 3911f532332828a080397484dde5db8fddc32e0bed48f5eca00903da00758242
- Size of remote file:
- 71.4 MB
- SHA256:
- 4b1a1985edebcc58cf9ab32b72b135b3df8a76a176447ca83e472c69a24025e8
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