Instructions to use Litzy619/V0224O2_merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Litzy619/V0224O2_merged with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Litzy619/V0224O2_merged")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Litzy619/V0224O2_merged") model = AutoModelForCausalLM.from_pretrained("Litzy619/V0224O2_merged", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Litzy619/V0224O2_merged with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Litzy619/V0224O2_merged" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Litzy619/V0224O2_merged", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Litzy619/V0224O2_merged
- SGLang
How to use Litzy619/V0224O2_merged 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 "Litzy619/V0224O2_merged" \ --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": "Litzy619/V0224O2_merged", "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 "Litzy619/V0224O2_merged" \ --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": "Litzy619/V0224O2_merged", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Litzy619/V0224O2_merged with Docker Model Runner:
docker model run hf.co/Litzy619/V0224O2_merged
Download adapter_model.bin from Litzy619/V0224O2_merged: direct link, hf CLI and curl.
- Browser
- Download file 529 MB
-
https://huggingface.co/Litzy619/V0224O2_merged/resolve/main/adapter_model.bin
- Command line
-
hf download hf://Litzy619/V0224O2_merged/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/Litzy619/V0224O2_merged/resolve/main/adapter_model.bin
529 MB
- Xet hash:
- 334dbcdeb50fc8037901d78c46f498d427d8fe306bde593f34ec1d64fba523be
- Size of remote file:
- 529 MB
- SHA256:
- d19551dbd6bb7795418d56df1dd5ef7ddde746c67932c100b44f963117574edc
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