Instructions to use OrionStarAI/Orion-14B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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 Settings

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
```

renillhuang commited on Jan 25, 2024

Commit

054113c

verified ·

1 Parent(s): 52d8e6f

Update README_ja.md

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@@ -262,7 +262,7 @@ CUDA_VISIBLE_DEVICES=0 python demo/text_generation_base.py --model OrionStarAI/O
 # チャットモデル
 CUDA_VISIBLE_DEVICES=0 python demo/text_generation.py --model OrionStarAI/Orion-14B-Chat --tokenizer OrionStarAI/Orion-14B-Chat --prompt hi
 ```
 ## 4.4. vLLMを使用した推論
 - プロジェクトのアドレス<br>

 # チャットモデル
 CUDA_VISIBLE_DEVICES=0 python demo/text_generation.py --model OrionStarAI/Orion-14B-Chat --tokenizer OrionStarAI/Orion-14B-Chat --prompt hi
 ```
+<a name="vllm"></a><br>
 ## 4.4. vLLMを使用した推論
 - プロジェクトのアドレス<br>