Instructions to use huggingtweets/dd0031 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingtweets/dd0031 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huggingtweets/dd0031")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huggingtweets/dd0031") model = AutoModelForCausalLM.from_pretrained("huggingtweets/dd0031", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use huggingtweets/dd0031 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huggingtweets/dd0031" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huggingtweets/dd0031", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/huggingtweets/dd0031
- SGLang
How to use huggingtweets/dd0031 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/dd0031" \ --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/dd0031", "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/dd0031" \ --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/dd0031", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use huggingtweets/dd0031 with Docker Model Runner:
docker model run hf.co/huggingtweets/dd0031
Download training_args.bin from huggingtweets/dd0031: direct link, hf CLI and curl.
- Browser
- Download file 2.29 kB
-
https://huggingface.co/huggingtweets/dd0031/resolve/main/training_args.bin
- Command line
-
hf download hf://huggingtweets/dd0031/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/huggingtweets/dd0031/resolve/main/training_args.bin
2.29 kB
- Xet hash:
- f07a5a2b10735633dd3565368cb6f60fc7eb1e195417c40a5ab37fa46acfb6bd
- Size of remote file:
- 2.29 kB
- SHA256:
- d98d07b242969821f1badeb9cdee2ae9d4cb0e90639938bdb96a23f7cd7fc87b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.