Instructions to use huggingtweets/harmchair with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingtweets/harmchair with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huggingtweets/harmchair")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huggingtweets/harmchair") model = AutoModelForCausalLM.from_pretrained("huggingtweets/harmchair", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use huggingtweets/harmchair with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huggingtweets/harmchair" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huggingtweets/harmchair", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/huggingtweets/harmchair
- SGLang
How to use huggingtweets/harmchair 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/harmchair" \ --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/harmchair", "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/harmchair" \ --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/harmchair", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use huggingtweets/harmchair with Docker Model Runner:
docker model run hf.co/huggingtweets/harmchair
Commit History
-
upload flax model
f910818 -
allow flax
624d1be
-
Update model preview
2e1f3a6 -
Update model preview
19beb95 -
Update model preview
f83cdc3 -
Update model preview
32d981e -
New model from https://wandb.ai/wandb/huggingtweets/runs/2maud2dh
58ee0a8 -
Update model preview
fd905a0 -
Update model preview
e4891cd -
Update model preview
08c1381 -
Update model preview
889d1a4 -
Update model preview
062fe99 -
New model from https://wandb.ai/wandb/huggingtweets/runs/3vvzp5pa
aa53663 -
initial commit
78e9915