Instructions to use huggingtweets/fallexcy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingtweets/fallexcy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huggingtweets/fallexcy")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huggingtweets/fallexcy") model = AutoModelForCausalLM.from_pretrained("huggingtweets/fallexcy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use huggingtweets/fallexcy with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huggingtweets/fallexcy" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huggingtweets/fallexcy", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/huggingtweets/fallexcy
- SGLang
How to use huggingtweets/fallexcy 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/fallexcy" \ --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/fallexcy", "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/fallexcy" \ --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/fallexcy", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use huggingtweets/fallexcy with Docker Model Runner:
docker model run hf.co/huggingtweets/fallexcy
Download pytorch_model.bin from huggingtweets/fallexcy: direct link, hf CLI and curl.
- Browser
- Download file 510 MB
-
https://huggingface.co/huggingtweets/fallexcy/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://huggingtweets/fallexcy/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/huggingtweets/fallexcy/resolve/main/pytorch_model.bin
510 MB
- Xet hash:
- 4d4b5e22fe9ed215f7b79a0f47c1ec7c094093403af3687fa9add2e320eb956d
- Size of remote file:
- 510 MB
- SHA256:
- a9d00fb13e20c20beb1b7bb8160c1335b0fcdc5384b133e332429fef23c858bd
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