Instructions to use Open-Orca/OpenOrca-Platypus2-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Open-Orca/OpenOrca-Platypus2-13B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Open-Orca/OpenOrca-Platypus2-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Open-Orca/OpenOrca-Platypus2-13B")
model = AutoModelForCausalLM.from_pretrained("Open-Orca/OpenOrca-Platypus2-13B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Open-Orca/OpenOrca-Platypus2-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Open-Orca/OpenOrca-Platypus2-13B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Open-Orca/OpenOrca-Platypus2-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Open-Orca/OpenOrca-Platypus2-13B

SGLang

How to use Open-Orca/OpenOrca-Platypus2-13B 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 "Open-Orca/OpenOrca-Platypus2-13B" \
    --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": "Open-Orca/OpenOrca-Platypus2-13B",
		"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 "Open-Orca/OpenOrca-Platypus2-13B" \
        --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": "Open-Orca/OpenOrca-Platypus2-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Open-Orca/OpenOrca-Platypus2-13B with Docker Model Runner:
```
docker model run hf.co/Open-Orca/OpenOrca-Platypus2-13B
```

What is the prompt template for it in multi-turn conversation?

by tridungduong16 - opened Aug 13, 2023

Discussion

tridungduong16

Aug 13, 2023

•

edited Aug 13, 2023

Can you provide an example of prompt template of multi-turn conversation for this model?

tridungduong16 changed discussion title from What is the prompt template for it? to What is the prompt template for it in multi-turn conversation? Aug 13, 2023

bleysg

OpenOrca org Aug 18, 2023

We've only tested with the template from the OpenOrcaxOpenChat-Preview2-13B base model.

# Multi-turn V1 Llama 2
tokenize("User: Hello<|end_of_turn|>Assistant: Hi<|end_of_turn|>User: How are you today?<|end_of_turn|>Assistant:")
# Result: [1, 4911, 29901, 15043, 32000, 4007, 22137, 29901, 6324, 32000, 4911, 29901, 1128, 526, 366, 9826, 29973, 32000, 4007, 22137, 29901]

System prompt is just prepended to the first message with a space before "User: " ...

"You are OrcaPlaty, an LLM trained by Alignment Lab AI and garage-bAInd. Write out your thinking step by step before coming to a conclusion to be sure you get the right answer! User: Hello there<|end_of_turn|>Assistant: Hi, nice to meet you.<|end_of_turn|>User: What's new?<|end_of_turn|>Assistant: "

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