Instructions to use huggingtweets/st6cam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingtweets/st6cam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huggingtweets/st6cam")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huggingtweets/st6cam") model = AutoModelForCausalLM.from_pretrained("huggingtweets/st6cam", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use huggingtweets/st6cam with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huggingtweets/st6cam" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huggingtweets/st6cam", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/huggingtweets/st6cam
- SGLang
How to use huggingtweets/st6cam 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/st6cam" \ --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/st6cam", "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/st6cam" \ --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/st6cam", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use huggingtweets/st6cam with Docker Model Runner:
docker model run hf.co/huggingtweets/st6cam
Download pytorch_model.bin from huggingtweets/st6cam: direct link, hf CLI and curl.
- Browser
- Download file 510 MB
-
https://huggingface.co/huggingtweets/st6cam/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://huggingtweets/st6cam/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/huggingtweets/st6cam/resolve/main/pytorch_model.bin
510 MB
- Xet hash:
- 2aa338a832ea5b2cce31e12491f7f9dc1ab09013d473f0719698768909aff5cc
- Size of remote file:
- 510 MB
- SHA256:
- 9d8c9d6ba6e52453b1ec35078d12e1adcb3ec5ed734954dbd8e5adda1f4cf0e1
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