Instructions to use OrionStarAI/Orion-14B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OrionStarAI/Orion-14B-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OrionStarAI/Orion-14B-Base", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("OrionStarAI/Orion-14B-Base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use OrionStarAI/Orion-14B-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OrionStarAI/Orion-14B-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OrionStarAI/Orion-14B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/OrionStarAI/Orion-14B-Base
- SGLang
How to use OrionStarAI/Orion-14B-Base 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 "OrionStarAI/Orion-14B-Base" \ --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": "OrionStarAI/Orion-14B-Base", "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 "OrionStarAI/Orion-14B-Base" \ --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": "OrionStarAI/Orion-14B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use OrionStarAI/Orion-14B-Base with Docker Model Runner:
docker model run hf.co/OrionStarAI/Orion-14B-Base
Change to base & fix duplicate flag - JA
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README_ja.md
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- GGUF形式に変換する方法
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```shell
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python convert-hf-to-gguf.py path/to/Orion-14B-
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```
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- モデル推論方法
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```shell
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./main --frequency-penalty 0.5 --
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```
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## 4.6 例の出力
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```shell
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python convert-hf-to-gguf.py path/to/Orion-14B-Base --outfile base.gguf
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```
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- モデル推論方法
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```shell
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./main --frequency-penalty 0.5 --top-k 5 --top-p 0.9 -m base.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e
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```
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## 4.6 例の出力
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