Instructions to use liezeleinstein/erikatest2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liezeleinstein/erikatest2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="liezeleinstein/erikatest2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("liezeleinstein/erikatest2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use liezeleinstein/erikatest2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "liezeleinstein/erikatest2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "liezeleinstein/erikatest2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/liezeleinstein/erikatest2
- SGLang
How to use liezeleinstein/erikatest2 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 "liezeleinstein/erikatest2" \ --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": "liezeleinstein/erikatest2", "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 "liezeleinstein/erikatest2" \ --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": "liezeleinstein/erikatest2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use liezeleinstein/erikatest2 with Docker Model Runner:
docker model run hf.co/liezeleinstein/erikatest2
Download pytorch_model-00001-of-00002.bin from liezeleinstein/erikatest2: direct link, hf CLI and curl.
- Browser
- Download file 9.98 GB
-
https://huggingface.co/liezeleinstein/erikatest2/resolve/main/pytorch_model-00001-of-00002.bin
- Command line
-
hf download hf://liezeleinstein/erikatest2/pytorch_model-00001-of-00002.bin
-
curl -L -o pytorch_model-00001-of-00002.bin https://huggingface.co/liezeleinstein/erikatest2/resolve/main/pytorch_model-00001-of-00002.bin
9.98 GB
- Xet hash:
- 9da57c3e4c4c25a3097fb225623ba09162c997848aa9569e360fe817fedbbbe8
- Size of remote file:
- 9.98 GB
- SHA256:
- c9f7ef69f3ba95f2650b4434dff72c063e93821e08e9d8e1989569245aa6226a
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