Instructions to use huggingtweets/fishbeelamp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingtweets/fishbeelamp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huggingtweets/fishbeelamp")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("huggingtweets/fishbeelamp") model = AutoModelForMultimodalLM.from_pretrained("huggingtweets/fishbeelamp") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use huggingtweets/fishbeelamp with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huggingtweets/fishbeelamp" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huggingtweets/fishbeelamp", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/huggingtweets/fishbeelamp
- SGLang
How to use huggingtweets/fishbeelamp 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/fishbeelamp" \ --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/fishbeelamp", "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/fishbeelamp" \ --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/fishbeelamp", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use huggingtweets/fishbeelamp with Docker Model Runner:
docker model run hf.co/huggingtweets/fishbeelamp
- Xet hash:
- 864b53aedf69e48ac553880fff472b5be478ef9203364d154c49baa1ca3be789
- Size of remote file:
- 498 MB
- SHA256:
- 0bd3b7863211bcb385c3029c54d4a796256c005b499c7f628f7da5ab68e2f36c
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