Instructions to use huggingtweets/dabit3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingtweets/dabit3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huggingtweets/dabit3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huggingtweets/dabit3") model = AutoModelForCausalLM.from_pretrained("huggingtweets/dabit3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use huggingtweets/dabit3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huggingtweets/dabit3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huggingtweets/dabit3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/huggingtweets/dabit3
- SGLang
How to use huggingtweets/dabit3 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/dabit3" \ --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/dabit3", "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/dabit3" \ --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/dabit3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use huggingtweets/dabit3 with Docker Model Runner:
docker model run hf.co/huggingtweets/dabit3
Download training_args.bin from huggingtweets/dabit3: direct link, hf CLI and curl.
- Browser
- Download file 1.78 kB
-
https://huggingface.co/huggingtweets/dabit3/resolve/main/training_args.bin
- Command line
-
hf download hf://huggingtweets/dabit3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/huggingtweets/dabit3/resolve/main/training_args.bin
1.78 kB
- Xet hash:
- 2d7debd5d9a249f9208ecc4cb986659ccb6ce240a5b399643c1c49a5391b753f
- Size of remote file:
- 1.78 kB
- SHA256:
- 0f717639c7a17a193c8cf7eec86ca6f4b12d6614e6eea5dc31831ed3db225174
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