Instructions to use huggingtweets/spacex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingtweets/spacex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huggingtweets/spacex")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huggingtweets/spacex") model = AutoModelForCausalLM.from_pretrained("huggingtweets/spacex", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use huggingtweets/spacex with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huggingtweets/spacex" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huggingtweets/spacex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/huggingtweets/spacex
- SGLang
How to use huggingtweets/spacex 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 "huggingtweets/spacex" \ --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": "huggingtweets/spacex", "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 "huggingtweets/spacex" \ --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": "huggingtweets/spacex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use huggingtweets/spacex with Docker Model Runner:
docker model run hf.co/huggingtweets/spacex
Download pytorch_model.bin from huggingtweets/spacex: direct link, hf CLI and curl.
- Browser
- Download file 510 MB
-
https://huggingface.co/huggingtweets/spacex/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://huggingtweets/spacex/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/huggingtweets/spacex/resolve/main/pytorch_model.bin
510 MB
- Xet hash:
- b2a2e6e2ee146bbe366499e07b04eb440c855c06dcc4e100ab146f7471aaaaa7
- Size of remote file:
- 510 MB
- SHA256:
- 172cd98d90d238de80e4c2dae81f27e39a4364b30e2863b9ec38f972492f5ea2
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