Instructions to use Defetya/orca-sharded with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Defetya/orca-sharded with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Defetya/orca-sharded")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Defetya/orca-sharded") model = AutoModelForCausalLM.from_pretrained("Defetya/orca-sharded", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Defetya/orca-sharded with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Defetya/orca-sharded" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Defetya/orca-sharded", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Defetya/orca-sharded
- SGLang
How to use Defetya/orca-sharded 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 "Defetya/orca-sharded" \ --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": "Defetya/orca-sharded", "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 "Defetya/orca-sharded" \ --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": "Defetya/orca-sharded", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Defetya/orca-sharded with Docker Model Runner:
docker model run hf.co/Defetya/orca-sharded
Download pytorch_model-00006-of-00013.bin from Defetya/orca-sharded: direct link, hf CLI and curl.
- Browser
- Download file 4.19 GB
-
https://huggingface.co/Defetya/orca-sharded/resolve/main/pytorch_model-00006-of-00013.bin
- Command line
-
hf download hf://Defetya/orca-sharded/pytorch_model-00006-of-00013.bin
-
curl -L -o pytorch_model-00006-of-00013.bin https://huggingface.co/Defetya/orca-sharded/resolve/main/pytorch_model-00006-of-00013.bin
4.19 GB
- Xet hash:
- 181c6bb84dd0ed46b5eb92488a5b9394bbe276f1cb1da1421b00077cd4f85c0f
- Size of remote file:
- 4.19 GB
- SHA256:
- 306c38cee37629ea91dd44a49b4a408747d15dfc32fe63d163cd347b1637d5a7
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