Instructions to use hongzoh/Yi-6B_Open-Platypus-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hongzoh/Yi-6B_Open-Platypus-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="hongzoh/Yi-6B_Open-Platypus-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("hongzoh/Yi-6B_Open-Platypus-v2") model = AutoModelForCausalLM.from_pretrained("hongzoh/Yi-6B_Open-Platypus-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use hongzoh/Yi-6B_Open-Platypus-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hongzoh/Yi-6B_Open-Platypus-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hongzoh/Yi-6B_Open-Platypus-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/hongzoh/Yi-6B_Open-Platypus-v2
- SGLang
How to use hongzoh/Yi-6B_Open-Platypus-v2 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 "hongzoh/Yi-6B_Open-Platypus-v2" \ --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": "hongzoh/Yi-6B_Open-Platypus-v2", "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 "hongzoh/Yi-6B_Open-Platypus-v2" \ --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": "hongzoh/Yi-6B_Open-Platypus-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use hongzoh/Yi-6B_Open-Platypus-v2 with Docker Model Runner:
docker model run hf.co/hongzoh/Yi-6B_Open-Platypus-v2
Download pytorch_model-00001-of-00002.bin from hongzoh/Yi-6B_Open-Platypus-v2: direct link, hf CLI and curl.
- Browser
- Download file 9.94 GB
-
https://huggingface.co/hongzoh/Yi-6B_Open-Platypus-v2/resolve/0bea025be691d1c27b76a8cb0d563c5d3c09703e/pytorch_model-00001-of-00002.bin
- Command line
-
hf download hf://hongzoh/Yi-6B_Open-Platypus-v2@0bea025be691d1c27b76a8cb0d563c5d3c09703e/pytorch_model-00001-of-00002.bin
-
curl -L -o pytorch_model-00001-of-00002.bin https://huggingface.co/hongzoh/Yi-6B_Open-Platypus-v2/resolve/0bea025be691d1c27b76a8cb0d563c5d3c09703e/pytorch_model-00001-of-00002.bin
9.94 GB
- Xet hash:
- ebd53b1718f967f0a55ebf5e47d52f95eeb8e7854c7570796f728b83b08f9b15
- Size of remote file:
- 9.94 GB
- SHA256:
- 72219f9c5b0c19fddec18fb16b72279686c2521f600dcbe8aba44475ef5c4859
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