Instructions to use Open-Orca/OpenOrcaxOpenChat-Preview2-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Open-Orca/OpenOrcaxOpenChat-Preview2-13B with Transformers:

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

pipe = pipeline("text-generation", model="Open-Orca/OpenOrcaxOpenChat-Preview2-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Open-Orca/OpenOrcaxOpenChat-Preview2-13B")
model = AutoModelForCausalLM.from_pretrained("Open-Orca/OpenOrcaxOpenChat-Preview2-13B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Open-Orca/OpenOrcaxOpenChat-Preview2-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Open-Orca/OpenOrcaxOpenChat-Preview2-13B"
# 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/OpenOrcaxOpenChat-Preview2-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B

SGLang

How to use Open-Orca/OpenOrcaxOpenChat-Preview2-13B 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/OpenOrcaxOpenChat-Preview2-13B" \
    --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/OpenOrcaxOpenChat-Preview2-13B",
		"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/OpenOrcaxOpenChat-Preview2-13B" \
        --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/OpenOrcaxOpenChat-Preview2-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Open-Orca/OpenOrcaxOpenChat-Preview2-13B with Docker Model Runner:
```
docker model run hf.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B
```

bleysg commited on Aug 4, 2023

Commit

26d1bc5

1 Parent(s): fc8b1b0

Update README.md

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@@ -88,8 +88,7 @@ Our average performance for BigBench-Hard: 0.488
 Average for AGIEval: 0.447
-In the Orca paper, they measured their score relative to Vicuna on these evals.
-We have done the same and have found our score averages to **~103%** of the total performance that was shown in the Orca paper, using the same evaluation methods as outlined in the paper.
 So we are surpassing Orca performance with <20% of the dataset size and <1/10th the training budget!

 Average for AGIEval: 0.447
+We find our score averages to **~103%** of the total performance that was shown in the Orca paper, using the same evaluation methods as outlined in the paper.
 So we are surpassing Orca performance with <20% of the dataset size and <1/10th the training budget!