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 plots/carboncomparison/intensity.csv from cgrumbach/BitcoinPaper: direct link, hf CLI and curl.
- Browser
- Download file 331 Bytes
-
https://huggingface.co/cgrumbach/BitcoinPaper/resolve/main/plots/carboncomparison/intensity.csv
- Command line
-
hf download hf://cgrumbach/BitcoinPaper/plots/carboncomparison/intensity.csv
-
curl -L -o intensity.csv https://huggingface.co/cgrumbach/BitcoinPaper/resolve/main/plots/carboncomparison/intensity.csv
331 Bytes
| intensity,year | |
| 0.5172029645833334,2011 | |
| 0.5160452145833334,2012 | |
| 0.51637850625,2013 | |
| 0.5144910229166667,2014 | |
| 0.5101126229166667,2015 | |
| 0.5071936895833333,2016 | |
| 0.50584298125,2017 | |
| 0.5046361145833334,2018 | |
| 0.5151935049999999,2019 | |
| 0.5151935049999999,2020 | |
| 0.5151935049999999,2021 | |
| 0.5151935049999999,2022 | |
| 0.5151935049999999,2023 | |