Instructions to use huggingtweets/annasvirtual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingtweets/annasvirtual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huggingtweets/annasvirtual")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huggingtweets/annasvirtual") model = AutoModelForCausalLM.from_pretrained("huggingtweets/annasvirtual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use huggingtweets/annasvirtual with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huggingtweets/annasvirtual" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huggingtweets/annasvirtual", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/huggingtweets/annasvirtual
- SGLang
How to use huggingtweets/annasvirtual 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/annasvirtual" \ --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/annasvirtual", "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/annasvirtual" \ --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/annasvirtual", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use huggingtweets/annasvirtual with Docker Model Runner:
docker model run hf.co/huggingtweets/annasvirtual
Download pytorch_model.bin from huggingtweets/annasvirtual: direct link, hf CLI and curl.
- Browser
- Download file 510 MB
-
https://huggingface.co/huggingtweets/annasvirtual/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://huggingtweets/annasvirtual/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/huggingtweets/annasvirtual/resolve/main/pytorch_model.bin
510 MB
- Xet hash:
- bc7b91bb0f2541e9aebca86b2f60b64dad517a39058d3e79f3f0ebff965e18db
- Size of remote file:
- 510 MB
- SHA256:
- 3d11cd6e9ed47009177509f1cd8fdc0088683a1db43ad2534c9e3f8aad5982bf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.